Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

10,000 validators. 5 entities controlling consensus. Justin Bons, which number matters? Garand Tyson explains why transparent trust topology > inflated validator counts.

StellarOrg's profile picture

Stellar

844,502 subscribers

33,144 Aufrufe • vor 9 Monaten •via X (Twitter)

Wissenschaft & TechnologieBildungFinanzen
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Most $TAO holders staking right now are trusting the wrong validators. Not because they are careless. Because nobody explained what the numbers on the Validators page actually mean. There is a tool inside Taostats that shows you exactly which validators are genuinely working and which ones are collecting your emissions without contributing anything to the network. It is free. It is live. And almost nobody is using it correctly. Here is exactly how to read it. Step 1: Understand what Dominance actually measures. Dominance is not popularity. It is not a ranking of which validator is best. It describes a validator's Stake Weight as a percentage of all validator stake weights combined across the network. Stake Weight is calculated as: root stake multiplied by 0.18, plus all alpha staked across subnets converted into TAO. Root stake is deliberately discounted at 18 percent of its face value. Alpha stake carries the full weight. This means a validator with deep subnet-level staking is structurally more powerful than one sitting purely on root, even if their raw TAO numbers look similar on the surface. When you see a validator with rising Dominance over time, it is not just getting more popular. It is getting more alpha stake directed toward it across active subnets. That is a meaningful signal about where serious capital is moving inside the network. Step 2: Check the Take percentage before you delegate anything. Take is the percentage of emissions the validator keeps for itself. Everything above that number flows to you as a nominator. A validator with a 18 percent Take keeps 18 percent of the emissions their position generates and distributes the remainder to stakeholders. A validator with a 50 percent Take is keeping half of what your stake earns. Most people never look at this number before delegating. It is the first number you should check. A high Take is not automatically a red flag if the validator is genuinely performing well and contributing to the network. But a high Take combined with low VTrust in their subnet performance page is the exact combination that should make you move your stake immediately. Step 3: Open the Validator Performance page and find the VTrust score. This is the number most holders never see. VTrust measures how closely a validator's weight assignments align with the honest stake-weighted majority across the network inside each subnet they operate in. Validators are responsible for evaluating miner output and assigning scores. Those scores go into Yuma Consensus and determine which miners earn emissions. A validator doing genuine evaluation work will have weights that align closely with the honest consensus. High VTrust. Consistent emissions. Reliable nominator returns. A validator that is weight copying, meaning they are simply copying the Yuma consensus scores back onto themselves rather than doing real evaluation, will show a flagged return on Taostats. Their nom/24hr/1k TAO score appears in red. This is Taostats telling you directly: this validator is extracting value from the network without contributing to it. When you stake to a weight copying validator, you are funding a free rider. Step 4: Watch the 24hr Nominator Change column. This number moves fast and it tells you something before any other signal does. A validator losing nominators over consecutive days is a validator that informed stakers are quietly leaving. A validator gaining nominators rapidly while their VTrust is healthy is a validator attracting attention for the right reasons. The 24hr column is the on-chain version of sentiment before sentiment becomes a narrative on social media. Step 5: Check Active subnets alongside Total Weight. Active tells you the number of subnets where the validator has a parent or child hotkey running. A validator with high Total Weight but low Active subnets is concentrated. They are running a specific strategy in specific markets. A validator with broad Active coverage across many subnets is building a wider surface area for emissions and is more exposed to the overall network performance rather than any single subnet cycle. Neither is inherently better. But knowing which type of validator you are delegating to tells you what you are actually betting on when you stake. Step 6: Check the Weight Change column over time. Total Weight is a snapshot. Weight Change is momentum. A validator with stable or growing Total Weight over consecutive days is attracting net new stake consistently. A validator with declining Weight Change is losing stake faster than it is gaining it. Most people look at the current number. The people positioning correctly are watching which direction the number is moving and how fast. The difference between a good validator and a dangerous one is not obvious from the outside. It is not the name. It is not the size. It is the VTrust score, the Take percentage, the nominator trend, and whether Taostats is showing their return in red or not. Every one of those signals is sitting on the Validators page right now. Free. Live. Updated every block. The investors who read the data layer before the narrative layer will not need to explain their staking decisions later. Open Taostats tonight. You will want to find this post when you do.
3:44

Most $TAO holders staking right now are trusting the wrong validators. Not because they are careless. Because nobody explained what the numbers on the Validators page actually mean. There is a tool inside Taostats that shows you exactly which validators are genuinely working and which ones are collecting your emissions without contributing anything to the network. It is free. It is live. And almost nobody is using it correctly. Here is exactly how to read it. Step 1: Understand what Dominance actually measures. Dominance is not popularity. It is not a ranking of which validator is best. It describes a validator's Stake Weight as a percentage of all validator stake weights combined across the network. Stake Weight is calculated as: root stake multiplied by 0.18, plus all alpha staked across subnets converted into TAO. Root stake is deliberately discounted at 18 percent of its face value. Alpha stake carries the full weight. This means a validator with deep subnet-level staking is structurally more powerful than one sitting purely on root, even if their raw TAO numbers look similar on the surface. When you see a validator with rising Dominance over time, it is not just getting more popular. It is getting more alpha stake directed toward it across active subnets. That is a meaningful signal about where serious capital is moving inside the network. Step 2: Check the Take percentage before you delegate anything. Take is the percentage of emissions the validator keeps for itself. Everything above that number flows to you as a nominator. A validator with a 18 percent Take keeps 18 percent of the emissions their position generates and distributes the remainder to stakeholders. A validator with a 50 percent Take is keeping half of what your stake earns. Most people never look at this number before delegating. It is the first number you should check. A high Take is not automatically a red flag if the validator is genuinely performing well and contributing to the network. But a high Take combined with low VTrust in their subnet performance page is the exact combination that should make you move your stake immediately. Step 3: Open the Validator Performance page and find the VTrust score. This is the number most holders never see. VTrust measures how closely a validator's weight assignments align with the honest stake-weighted majority across the network inside each subnet they operate in. Validators are responsible for evaluating miner output and assigning scores. Those scores go into Yuma Consensus and determine which miners earn emissions. A validator doing genuine evaluation work will have weights that align closely with the honest consensus. High VTrust. Consistent emissions. Reliable nominator returns. A validator that is weight copying, meaning they are simply copying the Yuma consensus scores back onto themselves rather than doing real evaluation, will show a flagged return on Taostats. Their nom/24hr/1k TAO score appears in red. This is Taostats telling you directly: this validator is extracting value from the network without contributing to it. When you stake to a weight copying validator, you are funding a free rider. Step 4: Watch the 24hr Nominator Change column. This number moves fast and it tells you something before any other signal does. A validator losing nominators over consecutive days is a validator that informed stakers are quietly leaving. A validator gaining nominators rapidly while their VTrust is healthy is a validator attracting attention for the right reasons. The 24hr column is the on-chain version of sentiment before sentiment becomes a narrative on social media. Step 5: Check Active subnets alongside Total Weight. Active tells you the number of subnets where the validator has a parent or child hotkey running. A validator with high Total Weight but low Active subnets is concentrated. They are running a specific strategy in specific markets. A validator with broad Active coverage across many subnets is building a wider surface area for emissions and is more exposed to the overall network performance rather than any single subnet cycle. Neither is inherently better. But knowing which type of validator you are delegating to tells you what you are actually betting on when you stake. Step 6: Check the Weight Change column over time. Total Weight is a snapshot. Weight Change is momentum. A validator with stable or growing Total Weight over consecutive days is attracting net new stake consistently. A validator with declining Weight Change is losing stake faster than it is gaining it. Most people look at the current number. The people positioning correctly are watching which direction the number is moving and how fast. The difference between a good validator and a dangerous one is not obvious from the outside. It is not the name. It is not the size. It is the VTrust score, the Take percentage, the nominator trend, and whether Taostats is showing their return in red or not. Every one of those signals is sitting on the Validators page right now. Free. Live. Updated every block. The investors who read the data layer before the narrative layer will not need to explain their staking decisions later. Open Taostats tonight. You will want to find this post when you do.

2xnmore

11,771 Aufrufe • vor 26 Tagen

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.
1:52

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.

2xnmore

136,230 Aufrufe • vor 2 Monaten

Ethereum is starting from the endgame. Episode 4 of TheCoordinate is a deep dive into Lean Ethereum: a clean-slate rethink of consensus, execution, and data availability. I sat down with Justin Drake from Ethereum Foundation to unpack: > need for the rewrite, > rewrite items: post-quantum security + fast finality, > endgame finality (3-slot -> 2-slot -> maybe 1-slot), > slot anatomy, networking constraints, and the "SOL slots" meme, > real-time ZK proving changing the execution roadmap, > censorship resistance with FOSSIL, > role of L2s in the world of Lean Ethereum, > incentives across proposer, builder, prover, includer, attester. If you’re building on Ethereum or trying to understand where the base layer is headed, this one is for you. This is Episode 4 of TheCoordinate. Hope you enjoy it! ------------------------------- Timestamps: 0:00 Intro: digital intelligence needs digital institutions 0:30 The big questions: Lean Ethereum, consensus/execution, post-quantum 1:25 Why Ethereum needs an endgame mindset (and a clean-slate approach) 3:30 The two “rewrite-class” items: post-quantum security + fast finality 5:52 Beamchain → Lean Consensus → Lean Ethereum (expands beyond consensus) 6:34 ZK EVM + real-time proving within a slot → “10,000 TPS” target 10:10 “SOL slots”: pushing slot duration toward speed-of-light constraints 11:09 3-slot finality (3SF) → endgame finality (2-slot / 1-slot paths) 18:19 eFP2P: erasure-coded gossip, bandwidth efficiency, scaling blobs 26:21 FOSSIL today: inclusion lists + opening includers beyond validators 39:09 Lean VM: minimal ZKVM 51:04 XMSS explained: Merkle signatures, 2^32 leaves, statefulness tradeoff 1:00:36 Rollups: 99.9% throughput on L2s + “native rollups” 1:06:53 Economics: roles (builder/prover/includer/attester), proving costs, stake capping
1:16:31

Ethereum is starting from the endgame. Episode 4 of TheCoordinate is a deep dive into Lean Ethereum: a clean-slate rethink of consensus, execution, and data availability. I sat down with Justin Drake from Ethereum Foundation to unpack: > need for the rewrite, > rewrite items: post-quantum security + fast finality, > endgame finality (3-slot -> 2-slot -> maybe 1-slot), > slot anatomy, networking constraints, and the "SOL slots" meme, > real-time ZK proving changing the execution roadmap, > censorship resistance with FOSSIL, > role of L2s in the world of Lean Ethereum, > incentives across proposer, builder, prover, includer, attester. If you’re building on Ethereum or trying to understand where the base layer is headed, this one is for you. This is Episode 4 of TheCoordinate. Hope you enjoy it! ------------------------------- Timestamps: 0:00 Intro: digital intelligence needs digital institutions 0:30 The big questions: Lean Ethereum, consensus/execution, post-quantum 1:25 Why Ethereum needs an endgame mindset (and a clean-slate approach) 3:30 The two “rewrite-class” items: post-quantum security + fast finality 5:52 Beamchain → Lean Consensus → Lean Ethereum (expands beyond consensus) 6:34 ZK EVM + real-time proving within a slot → “10,000 TPS” target 10:10 “SOL slots”: pushing slot duration toward speed-of-light constraints 11:09 3-slot finality (3SF) → endgame finality (2-slot / 1-slot paths) 18:19 eFP2P: erasure-coded gossip, bandwidth efficiency, scaling blobs 26:21 FOSSIL today: inclusion lists + opening includers beyond validators 39:09 Lean VM: minimal ZKVM 51:04 XMSS explained: Merkle signatures, 2^32 leaves, statefulness tradeoff 1:00:36 Rollups: 99.9% throughput on L2s + “native rollups” 1:06:53 Economics: roles (builder/prover/includer/attester), proving costs, stake capping

Soubhik Deb

86,471 Aufrufe • vor 4 Monaten

been diving deep into Solstice lately...and honestly, this feels like the next stage of defi evolution on solana !! i actually spent 6+ hours researching and breaking it all down in a full detailed video > from how usx works to the ai-powered yieldvault, staking architecture, and why solstice might change the solana defi landscape. here’s what makes it stand out 👇 ================================ The Core Idea: solstice is building a self sustaining defi economy on solana, blending tradfi reliability with defi creativity. the focus? real yield, transparent onchain activity, and long term wealth creation, not hype cycles. ================================ 3 Pillars driving it all: 1. usx > solana native stablecoin fully collateralized, programmable, and scalable. more than a stablecoin, it fuels the entire solstice ecosystem. think of it as the liquidity backbone for all yield and staking strategies. 2. yieldvault > automated compounding engine ai-powered, delta-neutral strategies that generate yield in any market. capital protected, transparent, and designed for passive income. no inflated apy gimmicks. just sustainable, on-chain compounding. 3. solstice staking > institutional grade non-custodial validator network, 100% renewable energy, 99.99% uptime. over $1b in staked assets already powering solana. built for both individual stakers and large protocols. ================================ Why it matters: while most defi projects chase trends, solstice is building infrastructure that scales - for users, investors, and institutions. it’s aiming to onboard the next billion into defi by keeping things fast, clean, and reliable. ================================ How your capital grows here: • mint or hold $USX - always fully backed • deposit into yieldvault - earn stable returns through delta-neutral ai strategies • stake in the validator network - earn yield while securing solana • reinvest and compound - every token works, every second counts ================================ Key advantages: ✅ built on solana - instant txs + ultra-low fees ✅ yield from real onchain activity, not emissions ✅ cross-integrated products that reinforce each other ✅ powered by renewable energy ✅ transparent analytics + verifiable smart contracts ================================ this isn’t “degen defi” it’s infrastructure for the next wave of sustainable onchain finance. if you’re into yield, stability, and scalable defi... keep an eye on Solstice. the future of defi might just start here with xeet .
2:16

been diving deep into Solstice lately...and honestly, this feels like the next stage of defi evolution on solana !! i actually spent 6+ hours researching and breaking it all down in a full detailed video > from how usx works to the ai-powered yieldvault, staking architecture, and why solstice might change the solana defi landscape. here’s what makes it stand out 👇 ================================ The Core Idea: solstice is building a self sustaining defi economy on solana, blending tradfi reliability with defi creativity. the focus? real yield, transparent onchain activity, and long term wealth creation, not hype cycles. ================================ 3 Pillars driving it all: 1. usx > solana native stablecoin fully collateralized, programmable, and scalable. more than a stablecoin, it fuels the entire solstice ecosystem. think of it as the liquidity backbone for all yield and staking strategies. 2. yieldvault > automated compounding engine ai-powered, delta-neutral strategies that generate yield in any market. capital protected, transparent, and designed for passive income. no inflated apy gimmicks. just sustainable, on-chain compounding. 3. solstice staking > institutional grade non-custodial validator network, 100% renewable energy, 99.99% uptime. over $1b in staked assets already powering solana. built for both individual stakers and large protocols. ================================ Why it matters: while most defi projects chase trends, solstice is building infrastructure that scales - for users, investors, and institutions. it’s aiming to onboard the next billion into defi by keeping things fast, clean, and reliable. ================================ How your capital grows here: • mint or hold $USX - always fully backed • deposit into yieldvault - earn stable returns through delta-neutral ai strategies • stake in the validator network - earn yield while securing solana • reinvest and compound - every token works, every second counts ================================ Key advantages: ✅ built on solana - instant txs + ultra-low fees ✅ yield from real onchain activity, not emissions ✅ cross-integrated products that reinforce each other ✅ powered by renewable energy ✅ transparent analytics + verifiable smart contracts ================================ this isn’t “degen defi” it’s infrastructure for the next wave of sustainable onchain finance. if you’re into yield, stability, and scalable defi... keep an eye on Solstice. the future of defi might just start here with xeet .

ANNABEL❤️

14,216 Aufrufe • vor 8 Monaten

Eric Schmidt, former CEO of Google, offers a sobering view: The biggest technological shift in human history is happening, and almost no one is talking about it. Schmidt opens with a startling industry prediction: "We believe as an industry that in the next one year the vast majority of programmers will be replaced by AI programmers. We also believe that within one year you will have graduate level mathematicians that are at the tippy top of graduate math programs." He explains why this matters so much. Programming and math aren't just two fields among many: "Programming plus math are the basis of sort of our whole digital world." And the AI labs are already using AI to build better AI: "The research groups in OpenAI and anthropic and so forth… around 10 or 20% of the code that they're developing in their research programs is being generated by the computer. That's called recursive self-improvement." Eric Schmidt then lays out the timeline most people haven't grasped: "Within 3 to 5 years we'll have what is called general intelligence AGI which can be defined as a system that is as smart as the smartest mathematician physicist artist writer thinker politician." He gives this belief system a name: "I call this by the way the San Francisco consensus because everyone who believes this is in San Francisco it may be the water." But the truly unsettling part comes next. Once AI starts improving itself, humans become optional to the process: "The computers are now doing self-improvement… they don't have to listen to us anymore. We call that super intelligence or ASI… computers that are smarter than the sum of humans. The San Francisco consensus is this occurs within six years." And here's where Schmidt sounds the alarm. The conversation isn't keeping pace with the technology: "This path is not understood in our society. There's no language for what happens with the arrival of this. This is happening faster than our human that our society, our democracy, our laws will address." His closing thought captures why this matters: "That's why it's underhyped. People do not understand what happens when you have intelligence at this level which is largely free."
2:24

Eric Schmidt, former CEO of Google, offers a sobering view: The biggest technological shift in human history is happening, and almost no one is talking about it. Schmidt opens with a startling industry prediction: "We believe as an industry that in the next one year the vast majority of programmers will be replaced by AI programmers. We also believe that within one year you will have graduate level mathematicians that are at the tippy top of graduate math programs." He explains why this matters so much. Programming and math aren't just two fields among many: "Programming plus math are the basis of sort of our whole digital world." And the AI labs are already using AI to build better AI: "The research groups in OpenAI and anthropic and so forth… around 10 or 20% of the code that they're developing in their research programs is being generated by the computer. That's called recursive self-improvement." Eric Schmidt then lays out the timeline most people haven't grasped: "Within 3 to 5 years we'll have what is called general intelligence AGI which can be defined as a system that is as smart as the smartest mathematician physicist artist writer thinker politician." He gives this belief system a name: "I call this by the way the San Francisco consensus because everyone who believes this is in San Francisco it may be the water." But the truly unsettling part comes next. Once AI starts improving itself, humans become optional to the process: "The computers are now doing self-improvement… they don't have to listen to us anymore. We call that super intelligence or ASI… computers that are smarter than the sum of humans. The San Francisco consensus is this occurs within six years." And here's where Schmidt sounds the alarm. The conversation isn't keeping pace with the technology: "This path is not understood in our society. There's no language for what happens with the arrival of this. This is happening faster than our human that our society, our democracy, our laws will address." His closing thought captures why this matters: "That's why it's underhyped. People do not understand what happens when you have intelligence at this level which is largely free."

Big Brain AI

633,645 Aufrufe • vor 2 Monaten

Vitalik Buterin explains why proof-of-stake is more secure than proof-of-work “I think proof of stake is very secure because to attack the system, you need to have basically as much stake as the rest of the network. Right now, for example, we have 5 million ETH staking, which means you have to come up with 5 million ETH and then join the network.” At the time of this writing, more than 37 million ETH are being staked, with 3 million ETH waiting to join via the validator queue. At today’s prices, that’s more than $80 billion of ETH someone would have to acquire to attack the network and revert finalized blocks, which is more than the cost of attacking even the Bitcoin network by some estimates. The other defense mechanism that proof-of-stake has that proof-of-work doesn’t is slashing, which makes Ethereum antifragile. Vitalik explains: “Recovering from attacks is much easier in proof-of-stake than proof-of-work. For many kinds of attacks you do against [the Ethereum] network, we have this concept of automatic slashing. In order to revert a finalized block, you basically have to have a big portion of your validators sign two conflicting messages. This is something where once these messages are on the network, you can go and prove ‘these people did it.’ So we have this feature in the protocol where you basically take all these people who provably misbehaved and you burn their coins.” Vitalik also acknowledges the possibility of censoring attacks, where if 1/3rd of validators refuse to attest, the chain can’t finalize. But, as he explains, Ethereum has a contingency plan for this as well: “Everyone who got censored would create a minority chain, and the community would have to do a soft fork. The would have to say, ‘this chain is clearly attacking us and this one is not attacking us, so we’re going to join this chain.’ Then what happens is, on that new chain, the attackers also lose a lot of coins. The difference between proof-of-stake and proof-of-work is that in a proof-of-stake system, you can identify specific participants — and this isn’t a human going in and saying ‘I don’t like you’. It’s all automated.” One last benefit of proof-of-stake is that security scales with the value of the network. As Vitalik put it five years ago, it is really relative security, and not absolute security, that matters: “The security needs of a thing have to be proportional to the size of that thing, because as a thing gets bigger, its enemies become bigger and more well-motivated. If BTC were 100x as big as it is today, the value from destroying it would be 100x higher, and the kinds of actors that would want to care about destroying it would be much bigger and scarier. This is also why countries of all sizes have roughly similarly sized militaries as a percentage of GDP. Hence, cost of attack divided by market cap really is the correct statistic to measure, and in the long run issuance-free PoW really does look not that good." Source: Lex Fridman (Jun 2021)
2:04

Vitalik Buterin explains why proof-of-stake is more secure than proof-of-work “I think proof of stake is very secure because to attack the system, you need to have basically as much stake as the rest of the network. Right now, for example, we have 5 million ETH staking, which means you have to come up with 5 million ETH and then join the network.” At the time of this writing, more than 37 million ETH are being staked, with 3 million ETH waiting to join via the validator queue. At today’s prices, that’s more than $80 billion of ETH someone would have to acquire to attack the network and revert finalized blocks, which is more than the cost of attacking even the Bitcoin network by some estimates. The other defense mechanism that proof-of-stake has that proof-of-work doesn’t is slashing, which makes Ethereum antifragile. Vitalik explains: “Recovering from attacks is much easier in proof-of-stake than proof-of-work. For many kinds of attacks you do against [the Ethereum] network, we have this concept of automatic slashing. In order to revert a finalized block, you basically have to have a big portion of your validators sign two conflicting messages. This is something where once these messages are on the network, you can go and prove ‘these people did it.’ So we have this feature in the protocol where you basically take all these people who provably misbehaved and you burn their coins.” Vitalik also acknowledges the possibility of censoring attacks, where if 1/3rd of validators refuse to attest, the chain can’t finalize. But, as he explains, Ethereum has a contingency plan for this as well: “Everyone who got censored would create a minority chain, and the community would have to do a soft fork. The would have to say, ‘this chain is clearly attacking us and this one is not attacking us, so we’re going to join this chain.’ Then what happens is, on that new chain, the attackers also lose a lot of coins. The difference between proof-of-stake and proof-of-work is that in a proof-of-stake system, you can identify specific participants — and this isn’t a human going in and saying ‘I don’t like you’. It’s all automated.” One last benefit of proof-of-stake is that security scales with the value of the network. As Vitalik put it five years ago, it is really relative security, and not absolute security, that matters: “The security needs of a thing have to be proportional to the size of that thing, because as a thing gets bigger, its enemies become bigger and more well-motivated. If BTC were 100x as big as it is today, the value from destroying it would be 100x higher, and the kinds of actors that would want to care about destroying it would be much bigger and scarier. This is also why countries of all sizes have roughly similarly sized militaries as a percentage of GDP. Hence, cost of attack divided by market cap really is the correct statistic to measure, and in the long run issuance-free PoW really does look not that good." Source: Lex Fridman (Jun 2021)

Etherealize

102,010 Aufrufe • vor 4 Monaten

I had a voice chat with founder of Humanity this morning. I want to be very transparent and keep everything up to you, that is why I am publishing this video recording even though he asked me not to. I want to let all VCs and Web3 people know the founder's mentality about their users. TLDR : 1) They reserved some percentage of allocation from Community Incentive (which was supposed to be distribute among users) for CEX listing 2) If you published your referral link publicly and any bad actor joined through it, your allocation was significantly penalized. You were expected to share it only with friends you trust. 3) They distributed the airdrop based on the value of your community. For example, if a project founder got 100 referrals and an airdrop influencer got the same number, the founder’s community was considered more valuable, so the founder received a larger allocation than a regular airdrop influencer. 4) If a bad actor joined through your referral link, or if you joined through a bad actor’s referral link, both of you were penalized or deemed ineligible for the airdrop. 5) A few minutes ago, I checked that they updated my allocation to 20,565 $H, while other users’ allocations increased by only 100 $H.
10:00

I had a voice chat with founder of Humanity this morning. I want to be very transparent and keep everything up to you, that is why I am publishing this video recording even though he asked me not to. I want to let all VCs and Web3 people know the founder's mentality about their users. TLDR : 1) They reserved some percentage of allocation from Community Incentive (which was supposed to be distribute among users) for CEX listing 2) If you published your referral link publicly and any bad actor joined through it, your allocation was significantly penalized. You were expected to share it only with friends you trust. 3) They distributed the airdrop based on the value of your community. For example, if a project founder got 100 referrals and an airdrop influencer got the same number, the founder’s community was considered more valuable, so the founder received a larger allocation than a regular airdrop influencer. 4) If a bad actor joined through your referral link, or if you joined through a bad actor’s referral link, both of you were penalized or deemed ineligible for the airdrop. 5) A few minutes ago, I checked that they updated my allocation to 20,565 $H, while other users’ allocations increased by only 100 $H.

Zun

133,500 Aufrufe • vor 1 Jahr

Glamsterdam is the performance upgrade, I've already talked about it. But Hegotá is something different: scheduled for H2 2026, it's Ethereum's "cleanup and hardening" fork. 3 problems. 3 technical solutions. All shipping in one fork 👇 1️⃣ The problem Hegotá is actually solving Glamsterdam targets throughput: 10,000 TPS, 200M gas limit, parallel execution. The performance gap with Solana narrows materially. Hegotá targets something harder to quantify but more fundamental. After Glamsterdam, Ethereum will be fast. The question Hegotá answers is: fast and controlled by whom? The Tornado Cash sanctions in 2022 exposed the vulnerability. OFAC-compliant block builders (the entities that construct Ethereum blocks under MEV-Boost) began filtering Tornado Cash transactions entirely. Legitimate users with sanctioned addresses couldn't get transactions included. The block builders, sitting between validators and the mempool, had the practical ability to censor at will. ePBS (shipping in Glamsterdam) brings block building onchain and removes the external relay dependency. But it doesn't solve the censorship problem at the transaction inclusion level. A block builder onchain can still refuse to include specific transactions. FOCIL solves that. --------------------------------------------------------------------------------------- 2️⃣ FOCIL: anti-censorship mechanism EIP-7805. Fork-Choice Enforced Inclusion Lists. The mechanism: every block slot, 17 participants are randomly selected from the validator set. Each one can submit a short list of transactions they want included in the next block. The block builder, even the onchain builder introduced by ePBS, must include those transactions or the block is invalid. 17 random validators per slot. Statistically, any attempt to censor a transaction requires controlling enough of the validator set to dominate every random selection simultaneously. At Ethereum's current validator count accounting for over 1 million, that requires controlling a supermajority of stake. In practice, FOCIL makes censorship at the block production level computationally and economically prohibitive for any entity that doesn't control an implausible share of staked ETH. → Block builders can no longer selectively exclude transactions → OFAC-compliant relays lose their censorship leverage at the inclusion layer → The Tornado Cash scenario becomes structurally impossible at protocol level → FOCIL prototype has a runnable implementation, entering multi-client devnet validation now This is the most significant censorship-resistance improvement in Ethereum's history. It's also the least discussed upgrade in CT because censorship resistance doesn't generate price speculation the way throughput numbers do. --------------------------------------------------------------------------------------- 3️⃣ Verkle Trees: the node operator revolution Currently, Ethereum nodes use Merkle Patricia Trees to store and verify state. To verify any piece of state, a node needs a "witness": a proof that includes all the intermediate hashes along the path from the root to the target data. For Ethereum's current state size, witnesses are large, bandwidth-heavy, and require the node to store significant local data. Verkle Trees replace this with a cryptographic structure that produces dramatically smaller witnesses. The same proof that requires kilobytes under the Merkle Patricia Tree model requires only hundreds of bytes under Verkle Trees. The consequence: → Node storage requirements drop by approximately 90% → Witnesses become small enough to transmit in real time during block propagation → "Stateless clients" become possible thanks to nodes that can verify the chain without storing full state locally → The hardware and bandwidth requirements to run a full Ethereum node drop to consumer levels permanently The long-term threat to Ethereum's decentralisation is not a 51% attack but the quiet centralization of the validator set as node hardware requirements creep upward with state growth. Verkle Trees break that trend structurally: → Anyone with a laptop and residential internet can run a full node post-Hegotá → The validator set becomes more accessible, not less, as Ethereum scales → Home stakers that represents the most decentralisation-aligned validator category stop being priced out by state growth The transition requires migrating every account and contract on the network from the Merkle Patricia Tree structure to Verkle Trees. --------------------------------------------------------------------------------------- 4️⃣ Account Abstraction ERC-4337 account abstraction has existed as an application-layer standard since 2023. Hegotá brings native protocol-level account abstraction: the scope has been defined and the multi-client devnet validation phase is beginning now. What protocol-level AA enables that ERC-4337 doesn't: → Any Ethereum account can have programmable spending rules without deploying a separate smart contract → Social recovery becomes a native feature → Gasless transactions, batched operations, and custom signature schemes work at the protocol level rather than requiring wrapper contracts → The UX gap between crypto wallets and traditional financial apps narrows at the infrastructure level For DeFi specifically, account abstraction at the protocol level means liquidation bots, yield automation, and portfolio management strategies can be encoded directly into wallet logic. The current pattern of deploying separate smart contract accounts for every user who wants programmable behaviour disappears. --------------------------------------------------------------------------------------- 5️⃣ The thesis The Glamsterdam piece ended with a thesis about performance re-rating and monetary premium recovery. Hegotá's thesis is different and in some ways more durable. Ethereum in early 2027 (post both upgrades) will be a structurally different network from the one that exists today. Not only faster. Harder to censor. Cheaper to secure. More accessible to home validators. Native smart account functionality for every user. The market prices upgrades for what they do to throughput and fees because those metrics are immediately visible. Censorship resistance, node decentralisation, and wallet programmability compound over years rather than showing up in 30-day fee data. Hegotá is the upgrade that determines whether Ethereum is still genuinely decentralised and censorship-resistant five years from now... ...or whether it quietly became something controlled by a small set of sophisticated block builders and large node operators. That question doesn't generate CT threads. But if think about it is the one that actually matters!
0:24

Glamsterdam is the performance upgrade, I've already talked about it. But Hegotá is something different: scheduled for H2 2026, it's Ethereum's "cleanup and hardening" fork. 3 problems. 3 technical solutions. All shipping in one fork 👇 1️⃣ The problem Hegotá is actually solving Glamsterdam targets throughput: 10,000 TPS, 200M gas limit, parallel execution. The performance gap with Solana narrows materially. Hegotá targets something harder to quantify but more fundamental. After Glamsterdam, Ethereum will be fast. The question Hegotá answers is: fast and controlled by whom? The Tornado Cash sanctions in 2022 exposed the vulnerability. OFAC-compliant block builders (the entities that construct Ethereum blocks under MEV-Boost) began filtering Tornado Cash transactions entirely. Legitimate users with sanctioned addresses couldn't get transactions included. The block builders, sitting between validators and the mempool, had the practical ability to censor at will. ePBS (shipping in Glamsterdam) brings block building onchain and removes the external relay dependency. But it doesn't solve the censorship problem at the transaction inclusion level. A block builder onchain can still refuse to include specific transactions. FOCIL solves that. --------------------------------------------------------------------------------------- 2️⃣ FOCIL: anti-censorship mechanism EIP-7805. Fork-Choice Enforced Inclusion Lists. The mechanism: every block slot, 17 participants are randomly selected from the validator set. Each one can submit a short list of transactions they want included in the next block. The block builder, even the onchain builder introduced by ePBS, must include those transactions or the block is invalid. 17 random validators per slot. Statistically, any attempt to censor a transaction requires controlling enough of the validator set to dominate every random selection simultaneously. At Ethereum's current validator count accounting for over 1 million, that requires controlling a supermajority of stake. In practice, FOCIL makes censorship at the block production level computationally and economically prohibitive for any entity that doesn't control an implausible share of staked ETH. → Block builders can no longer selectively exclude transactions → OFAC-compliant relays lose their censorship leverage at the inclusion layer → The Tornado Cash scenario becomes structurally impossible at protocol level → FOCIL prototype has a runnable implementation, entering multi-client devnet validation now This is the most significant censorship-resistance improvement in Ethereum's history. It's also the least discussed upgrade in CT because censorship resistance doesn't generate price speculation the way throughput numbers do. --------------------------------------------------------------------------------------- 3️⃣ Verkle Trees: the node operator revolution Currently, Ethereum nodes use Merkle Patricia Trees to store and verify state. To verify any piece of state, a node needs a "witness": a proof that includes all the intermediate hashes along the path from the root to the target data. For Ethereum's current state size, witnesses are large, bandwidth-heavy, and require the node to store significant local data. Verkle Trees replace this with a cryptographic structure that produces dramatically smaller witnesses. The same proof that requires kilobytes under the Merkle Patricia Tree model requires only hundreds of bytes under Verkle Trees. The consequence: → Node storage requirements drop by approximately 90% → Witnesses become small enough to transmit in real time during block propagation → "Stateless clients" become possible thanks to nodes that can verify the chain without storing full state locally → The hardware and bandwidth requirements to run a full Ethereum node drop to consumer levels permanently The long-term threat to Ethereum's decentralisation is not a 51% attack but the quiet centralization of the validator set as node hardware requirements creep upward with state growth. Verkle Trees break that trend structurally: → Anyone with a laptop and residential internet can run a full node post-Hegotá → The validator set becomes more accessible, not less, as Ethereum scales → Home stakers that represents the most decentralisation-aligned validator category stop being priced out by state growth The transition requires migrating every account and contract on the network from the Merkle Patricia Tree structure to Verkle Trees. --------------------------------------------------------------------------------------- 4️⃣ Account Abstraction ERC-4337 account abstraction has existed as an application-layer standard since 2023. Hegotá brings native protocol-level account abstraction: the scope has been defined and the multi-client devnet validation phase is beginning now. What protocol-level AA enables that ERC-4337 doesn't: → Any Ethereum account can have programmable spending rules without deploying a separate smart contract → Social recovery becomes a native feature → Gasless transactions, batched operations, and custom signature schemes work at the protocol level rather than requiring wrapper contracts → The UX gap between crypto wallets and traditional financial apps narrows at the infrastructure level For DeFi specifically, account abstraction at the protocol level means liquidation bots, yield automation, and portfolio management strategies can be encoded directly into wallet logic. The current pattern of deploying separate smart contract accounts for every user who wants programmable behaviour disappears. --------------------------------------------------------------------------------------- 5️⃣ The thesis The Glamsterdam piece ended with a thesis about performance re-rating and monetary premium recovery. Hegotá's thesis is different and in some ways more durable. Ethereum in early 2027 (post both upgrades) will be a structurally different network from the one that exists today. Not only faster. Harder to censor. Cheaper to secure. More accessible to home validators. Native smart account functionality for every user. The market prices upgrades for what they do to throughput and fees because those metrics are immediately visible. Censorship resistance, node decentralisation, and wallet programmability compound over years rather than showing up in 30-day fee data. Hegotá is the upgrade that determines whether Ethereum is still genuinely decentralised and censorship-resistant five years from now... ...or whether it quietly became something controlled by a small set of sophisticated block builders and large node operators. That question doesn't generate CT threads. But if think about it is the one that actually matters!

Mercek

18,052 Aufrufe • vor 1 Monat

OSTRICH FARM UPDATE - Counting, Transparency, Welfare and Due Process Response to CFIA Statement – October 22, 2025 We appreciate that the Canadian Food Inspection Agency (CFIA) has chosen to communicate publicly about the ongoing situation at Universal Ostrich Farm. However, several points in their statement misrepresent the truth and must be respectfully corrected for the public record. You state that 330 Ostrich are out there that's your number, lets use that. WHY cant we get to that number with clear images from the mountains around? 1. Court Orders and Custody The Supreme Court of Canada’s stay of execution was meant to protect life and maintain the status quo while the Court considers our appeal not to enable indefinite seizure and isolation of our animals. The CFIA continues to restrict our access and oversight, despite these being our animals and our property. Custody requires care, transparency, and accountability not exclusion and secrecy. 2. Counting and Transparency We have repeatedly requested an updated count of our birds, and each request has been denied. We are the lawful owners, yet we have been told we cannot physically verify or count our own animals. Nor have a independent count by a vet This is unreasonable and deeply concerning. We simply wish to confirm their welfare. Furthermore, we have informed CFIA that our detailed manifest and lineage records will be provided only to our lawyer, who we trust to hold them securely. This decision was made after numerous breaches of trust by the agency. We must ask why does CFIA insist on obtaining the full genetic manifest of our flock? What use is this information to them while refusing transparency to us? 3. Samples and Intellectual Property Concerns CFIA’s ongoing presence, coupled with their refusal to allow independent oversight, raises legitimate concerns about unauthorized sample collection. We have no verification of what materials may have been taken since the occupation began. Given the value of ostrich antibodies proven in our past research to neutralize multiple viral strains, we have reason to fear these antibodies could be exploited for research or profit without our consent or acknowledgment. If CFIA has taken blood or tissue samples, we call for immediate disclosure of what was collected, where it was sent, and for what purpose. 4. Welfare and Care Oversight CFIA claims to provide feed, water, and bedding. Yet the family and ongoing local veterinarians have been barred from conducting welfare checks besides the one time. If CFIA truly has nothing to hide, why are they preventing independent welfare verification? The public deserves to know that these animals are being cared for humanely. We see they are not 5. Record Keeping and Accountability The CFIA states we have not provided flock records. That is false. Our affidavits, court filings, and veterinary documents have been provided under oath. The only reason additional data is withheld is the total loss of trust in an agency that has repeatedly acted outside the spirit of the law and without transparency. 6. Why This Matters If this policy continues unchecked, it sets a dangerous precedent: Farmers can lose their life’s work without due process or testing. Agencies can claim control of healthy animals while denying owners even basic oversight. Decades of biodiversity, genetic resilience, and natural immunity painstakingly cultivated can be wiped out overnight. When CFIA destroys healthy flocks under the guise of “biosecurity,” Canada loses more than animals we lose knowledge, immunity, and our agricultural future. 7. Our Call to Canadians We ask Canadians to stand with us peacefully and respectfully: Email your MP, MLA, and the Minister of Agriculture demanding an inquiry into CFIA’s conduct. Request transparent, science-based testing before any depopulation or seizure. Demand independent animal-welfare oversight whenever the CFIA exercises custody. Support biodiversity and generational farming the foundation of real food security and scientific innovation We remain calm, peaceful, and steadfast in truth. This is not just about ostriches it’s about trust in the institutions meant to serve Canadians. No agency should operate with absolute power and zero accountability. Blessings Katie Pasitney & the Universal Ostrich Farm Family “Unity for Immunity Standing Tall for All” Source Credit Katie Pasitney Oct 23, 2025
1:17

OSTRICH FARM UPDATE - Counting, Transparency, Welfare and Due Process Response to CFIA Statement – October 22, 2025 We appreciate that the Canadian Food Inspection Agency (CFIA) has chosen to communicate publicly about the ongoing situation at Universal Ostrich Farm. However, several points in their statement misrepresent the truth and must be respectfully corrected for the public record. You state that 330 Ostrich are out there that's your number, lets use that. WHY cant we get to that number with clear images from the mountains around? 1. Court Orders and Custody The Supreme Court of Canada’s stay of execution was meant to protect life and maintain the status quo while the Court considers our appeal not to enable indefinite seizure and isolation of our animals. The CFIA continues to restrict our access and oversight, despite these being our animals and our property. Custody requires care, transparency, and accountability not exclusion and secrecy. 2. Counting and Transparency We have repeatedly requested an updated count of our birds, and each request has been denied. We are the lawful owners, yet we have been told we cannot physically verify or count our own animals. Nor have a independent count by a vet This is unreasonable and deeply concerning. We simply wish to confirm their welfare. Furthermore, we have informed CFIA that our detailed manifest and lineage records will be provided only to our lawyer, who we trust to hold them securely. This decision was made after numerous breaches of trust by the agency. We must ask why does CFIA insist on obtaining the full genetic manifest of our flock? What use is this information to them while refusing transparency to us? 3. Samples and Intellectual Property Concerns CFIA’s ongoing presence, coupled with their refusal to allow independent oversight, raises legitimate concerns about unauthorized sample collection. We have no verification of what materials may have been taken since the occupation began. Given the value of ostrich antibodies proven in our past research to neutralize multiple viral strains, we have reason to fear these antibodies could be exploited for research or profit without our consent or acknowledgment. If CFIA has taken blood or tissue samples, we call for immediate disclosure of what was collected, where it was sent, and for what purpose. 4. Welfare and Care Oversight CFIA claims to provide feed, water, and bedding. Yet the family and ongoing local veterinarians have been barred from conducting welfare checks besides the one time. If CFIA truly has nothing to hide, why are they preventing independent welfare verification? The public deserves to know that these animals are being cared for humanely. We see they are not 5. Record Keeping and Accountability The CFIA states we have not provided flock records. That is false. Our affidavits, court filings, and veterinary documents have been provided under oath. The only reason additional data is withheld is the total loss of trust in an agency that has repeatedly acted outside the spirit of the law and without transparency. 6. Why This Matters If this policy continues unchecked, it sets a dangerous precedent: Farmers can lose their life’s work without due process or testing. Agencies can claim control of healthy animals while denying owners even basic oversight. Decades of biodiversity, genetic resilience, and natural immunity painstakingly cultivated can be wiped out overnight. When CFIA destroys healthy flocks under the guise of “biosecurity,” Canada loses more than animals we lose knowledge, immunity, and our agricultural future. 7. Our Call to Canadians We ask Canadians to stand with us peacefully and respectfully: Email your MP, MLA, and the Minister of Agriculture demanding an inquiry into CFIA’s conduct. Request transparent, science-based testing before any depopulation or seizure. Demand independent animal-welfare oversight whenever the CFIA exercises custody. Support biodiversity and generational farming the foundation of real food security and scientific innovation We remain calm, peaceful, and steadfast in truth. This is not just about ostriches it’s about trust in the institutions meant to serve Canadians. No agency should operate with absolute power and zero accountability. Blessings Katie Pasitney & the Universal Ostrich Farm Family “Unity for Immunity Standing Tall for All” Source Credit Katie Pasitney Oct 23, 2025

Shareaware Canada

13,607 Aufrufe • vor 8 Monaten

DeepFreeze on the XRP Ledger – A Comprehensive Examination We need to discuss an amendment that went unnoticed for a long time: DeepFreeze. If you are to lazy to read, just watch the video. Eminence is already voting for its activation, and I urge my fellow node operators and the community to support it. Let’s look at why. Welcome to a detailed examination of DeepFreeze, a transformative feature introduced to the XRP Ledger. This amendment is critical for institutional asset management within the ledger ecosystem. In this analysis, we’ll explore the full scope of DeepFreeze—its definition, technical architecture, institutional significance, community development, and long-term implications for XRPL’s role in financial systems. This is a deep dive into a feature that could redefine blockchain compliance and adoption. What exactly is DeepFreeze? DeepFreeze is an advanced asset-freezing mechanism integrated into the XRPL, tailored explicitly for fungible tokens issued on the ledger, such as stablecoins and tokenised real-world assets. Unlike XRP, which remains unaffected due to its native status, issued tokens fall under the control of their issuers, who can now leverage DeepFreeze for unprecedented oversight. The standard freeze, a pre-existing feature, restricts an account to only receiving tokens, preventing outward transfers. DeepFreeze, however, escalates this control by prohibiting both sending and receiving, effectively isolating the account from all token-related activities except direct transactions with the issuer. According to the XRPL documentation, available at DeepFreeze requires the activation of the DeepFreeze amendment—a network-wide upgrade voted on by XRPL validators. It cannot be applied if the issuer has set the NoFreeze flag on their account, a safeguard that permanently disables freezing capabilities for that issuer’s tokens. This layered design ensures flexibility while prioritising compliance, making DeepFreeze a powerful tool for managing token ecosystems in regulated environments. The significance for Institutions. The significance of DeepFreeze becomes evident when viewed through an institutional lens. For financial entities—such as central banks issuing central bank digital currencies (CBDCs), or stablecoin providers like Ripple’s RLUSD, Societe Generale Group Forge’s EURCV, and Braza Bank’s BBRL—this feature offers a robust mechanism to enforce regulatory compliance. Consider a scenario where an account is identified on an international sanctions list, such as those maintained by the U.S. Office of Foreign Assets Control (OFAC Treasury Department). DeepFreeze allows the issuer to immediately halt all token activity for that account, preventing inflows or outflows that could violate anti-money laundering (AML) or know-your-customer (KYC) regulations. Beyond sanctions, DeepFreeze addresses fraud mitigation. If a stablecoin issuer detects suspicious activity—a hacked account attempting to siphon funds—they can deep-freeze it, stopping the damage while investigations unfold. A article underscores this utility, noting that the standard freeze’s limitation—allowing incoming transfers—falls short for high-stakes compliance needs. DeepFreeze’s total lockdown fills this gap, enhancing security and trust. This capability could attract major regulated entities like Circle, issuer of USDC, to deploy stablecoins on the XRPL, drawn by its compliance-ready infrastructure. Such adoption would increase token volume, liquidity, and the ledger’s utility for real-world asset tokenization—think real estate or commodities—positioning the XRPL as a leader in institutional blockchain applications. The Technical Mechanics. (This is a bit technical) Let’s examine the technical architecture underpinning DeepFreeze, which introduces specific flags to the XRPL’s ledger structure. These flags, detailed in the XRPL documentation, govern trust lines—the bilateral agreements between accounts that enable token holding—and enforce the freeze’s effects. Here’s how they work: The lsfLowDeepFreeze flag is set on the RippleState object to indicate that the low account in a trust line is deep-frozen. This prevents the high account from sending or receiving the token along that trust line, effectively severing its transactional capability. Conversely, the lsfHighDeepFreeze flag marks the high account as deep-frozen, blocking the low account from similar activities. This bidirectional control ensures symmetry in enforcement. In TrustSet transactions, issuers use the tfSetDeepFreeze flag, to apply the DeepFreeze to a specific trust line, activating the lockdown. To reverse this, the tfClearDeepFreeze flag is invoked in a TrustSet transaction, restoring normal functionality to the trust line. These flags have sweeping effects across XRPL operations. Payments to a deep-frozen account fail outright, with the transaction engine returning a tecDSTfrozen error if the destination is locked. Rippling—where tokens pass through intermediary accounts—ceases for deep-frozen trust lines, halting multi-hop transfers. On the decentralized exchange (DEX) and automated market maker (AMM) systems, OfferCreate transactions involving a deep-frozen TakerPays token fail with a tecFROZEN error, and existing offers tied to frozen accounts are implicitly canceled when crossed by new offers, rendering them unfunded. The GitHub discussion at XRPLF/XRPL-Standards #220 adds further nuance, noting impacts on Check transactions—a feature for deferred payments. CheckCash fails if the recipient’s trust line is deep-frozen, protecting against unauthorized redemption, though CheckCreate and CheckCancel remain unaffected, preserving issuer flexibility. This granular control reflects DeepFreeze’s design for precision in compliance-driven scenarios. Community Development. The development of DeepFreeze highlights the XRPL community’s collaborative strength. On August 26, 2024, Shawn Xie of Ripple initiated the XLS-77d proposal in a GitHub discussion, accessible at XRPLF/XRPL-Standards #220. Spanning six comments and seven replies, the thread reveals active engagement. One participant (Wietse Wind - 🪝☝️🛠 Xaman® + XRPL + Xahau) suggested renaming ‘blackholing’—disabling an account permanently—to ‘permafrosting,’ arguing it better conveys the frozen state’s permanence and aligns with DeepFreeze’s theme. This linguistic refinement, while minor, exemplifies community influence on usability. Technical clarifications also emerged. The discussion distinguishes DeepFreeze from GlobalFreeze, which freezes all trust lines for an issuer’s tokens, noting that DeepFreeze targets specific trust lines for finer control. A question arose about rare cases where the standard tfSetFreeze might suffice—such as temporary holds—but the consensus favored DeepFreeze’s comprehensive approach for most compliance needs. The proposal, now in draft status, was merged into the rippled software codebase via pull request XRPLF/rippled #5187, confirming its deployment readiness as of March 19, 2025. This milestone underscores XRPL’s commitment to evolving through community-driven innovation. The Institutional Impact. From an institutional standpoint, DeepFreeze addresses critical gaps in the standard freeze’s functionality. The article explains that the older mechanism, while useful, permitted incoming transfers and balance adjustments, rendering it inadequate for scenarios requiring total isolation—such as sanctions enforcement or fraud containment. DeepFreeze’s ability to block all activity offers a superior solution, tailored to the demands of regulated finance. Consider its applications: a stablecoin issuer like Ripple could deep-freeze an account suspected of laundering funds, halting its operations pending review. A tokenized real estate platform could use it to secure assets during legal disputes, ensuring no unauthorized transfers occur. For sanctions, it ensures compliance with global frameworks, preventing tokens from reaching blacklisted entities. These use cases enhance the XRPL’s appeal to institutional players, potentially drawing Circle’s USDC or other major stablecoins to the ledger. The ripple effect—pardon the pun—could be substantial. Increased institutional adoption would boost token issuance, trading volume, and liquidity, reinforcing XRPL’s infrastructure for real-world asset tokenization. This aligns with broader trends in blockchain finance, where compliance-ready platforms are increasingly favored by traditional institutions seeking to integrate digital assets. Conclusion and Implications. In conclusion, DeepFreeze represents a strategic leap forward for the XRP Ledger, harmonizing technological sophistication with regulatory necessity. By equipping issuers with comprehensive control over their tokens, it addresses the compliance and security needs of institutional users, from stablecoin providers to asset tokenizers. As of March 19, 2025, its technical implementation is mature, its community support robust, and its potential to drive XRPL adoption undeniable. Looking ahead, DeepFreeze could position the XRPL as a premier blockchain for regulated financial applications, bridging the gap between decentralized innovation and centralized oversight. Its success will depend on validator adoption of the DeepFreeze amendment and real-world uptake by institutions—a process already underway. For a deeper understanding, refer to the XRPL documentation, the article, and the GitHub discussion linked below. DeepFreeze is more than a feature—it’s a foundation for the XRPL’s future in institutional finance. How do you envision its impact on the blockchain landscape? Your perspectives are welcome. PS: This is by far the most exciting amendment since XLS20, but of course, your average influencer doesn't talk about it in his paid group or while he is siphoning your donations. Unfollow them today. ################## Ressouces: XRPL Docs: XLS-77d: Devto Article: Misunderstandings about Freezes: Amendment voting: If you want to support what I do, follow me and buy me a beer or just use one of the CasinoCoin/LuckyHash 🪝 partners for recreational gaming: Check out my other explainers:
11:51

DeepFreeze on the XRP Ledger – A Comprehensive Examination We need to discuss an amendment that went unnoticed for a long time: DeepFreeze. If you are to lazy to read, just watch the video. Eminence is already voting for its activation, and I urge my fellow node operators and the community to support it. Let’s look at why. Welcome to a detailed examination of DeepFreeze, a transformative feature introduced to the XRP Ledger. This amendment is critical for institutional asset management within the ledger ecosystem. In this analysis, we’ll explore the full scope of DeepFreeze—its definition, technical architecture, institutional significance, community development, and long-term implications for XRPL’s role in financial systems. This is a deep dive into a feature that could redefine blockchain compliance and adoption. What exactly is DeepFreeze? DeepFreeze is an advanced asset-freezing mechanism integrated into the XRPL, tailored explicitly for fungible tokens issued on the ledger, such as stablecoins and tokenised real-world assets. Unlike XRP, which remains unaffected due to its native status, issued tokens fall under the control of their issuers, who can now leverage DeepFreeze for unprecedented oversight. The standard freeze, a pre-existing feature, restricts an account to only receiving tokens, preventing outward transfers. DeepFreeze, however, escalates this control by prohibiting both sending and receiving, effectively isolating the account from all token-related activities except direct transactions with the issuer. According to the XRPL documentation, available at DeepFreeze requires the activation of the DeepFreeze amendment—a network-wide upgrade voted on by XRPL validators. It cannot be applied if the issuer has set the NoFreeze flag on their account, a safeguard that permanently disables freezing capabilities for that issuer’s tokens. This layered design ensures flexibility while prioritising compliance, making DeepFreeze a powerful tool for managing token ecosystems in regulated environments. The significance for Institutions. The significance of DeepFreeze becomes evident when viewed through an institutional lens. For financial entities—such as central banks issuing central bank digital currencies (CBDCs), or stablecoin providers like Ripple’s RLUSD, Societe Generale Group Forge’s EURCV, and Braza Bank’s BBRL—this feature offers a robust mechanism to enforce regulatory compliance. Consider a scenario where an account is identified on an international sanctions list, such as those maintained by the U.S. Office of Foreign Assets Control (OFAC Treasury Department). DeepFreeze allows the issuer to immediately halt all token activity for that account, preventing inflows or outflows that could violate anti-money laundering (AML) or know-your-customer (KYC) regulations. Beyond sanctions, DeepFreeze addresses fraud mitigation. If a stablecoin issuer detects suspicious activity—a hacked account attempting to siphon funds—they can deep-freeze it, stopping the damage while investigations unfold. A article underscores this utility, noting that the standard freeze’s limitation—allowing incoming transfers—falls short for high-stakes compliance needs. DeepFreeze’s total lockdown fills this gap, enhancing security and trust. This capability could attract major regulated entities like Circle, issuer of USDC, to deploy stablecoins on the XRPL, drawn by its compliance-ready infrastructure. Such adoption would increase token volume, liquidity, and the ledger’s utility for real-world asset tokenization—think real estate or commodities—positioning the XRPL as a leader in institutional blockchain applications. The Technical Mechanics. (This is a bit technical) Let’s examine the technical architecture underpinning DeepFreeze, which introduces specific flags to the XRPL’s ledger structure. These flags, detailed in the XRPL documentation, govern trust lines—the bilateral agreements between accounts that enable token holding—and enforce the freeze’s effects. Here’s how they work: The lsfLowDeepFreeze flag is set on the RippleState object to indicate that the low account in a trust line is deep-frozen. This prevents the high account from sending or receiving the token along that trust line, effectively severing its transactional capability. Conversely, the lsfHighDeepFreeze flag marks the high account as deep-frozen, blocking the low account from similar activities. This bidirectional control ensures symmetry in enforcement. In TrustSet transactions, issuers use the tfSetDeepFreeze flag, to apply the DeepFreeze to a specific trust line, activating the lockdown. To reverse this, the tfClearDeepFreeze flag is invoked in a TrustSet transaction, restoring normal functionality to the trust line. These flags have sweeping effects across XRPL operations. Payments to a deep-frozen account fail outright, with the transaction engine returning a tecDSTfrozen error if the destination is locked. Rippling—where tokens pass through intermediary accounts—ceases for deep-frozen trust lines, halting multi-hop transfers. On the decentralized exchange (DEX) and automated market maker (AMM) systems, OfferCreate transactions involving a deep-frozen TakerPays token fail with a tecFROZEN error, and existing offers tied to frozen accounts are implicitly canceled when crossed by new offers, rendering them unfunded. The GitHub discussion at XRPLF/XRPL-Standards #220 adds further nuance, noting impacts on Check transactions—a feature for deferred payments. CheckCash fails if the recipient’s trust line is deep-frozen, protecting against unauthorized redemption, though CheckCreate and CheckCancel remain unaffected, preserving issuer flexibility. This granular control reflects DeepFreeze’s design for precision in compliance-driven scenarios. Community Development. The development of DeepFreeze highlights the XRPL community’s collaborative strength. On August 26, 2024, Shawn Xie of Ripple initiated the XLS-77d proposal in a GitHub discussion, accessible at XRPLF/XRPL-Standards #220. Spanning six comments and seven replies, the thread reveals active engagement. One participant (Wietse Wind - 🪝☝️🛠 Xaman® + XRPL + Xahau) suggested renaming ‘blackholing’—disabling an account permanently—to ‘permafrosting,’ arguing it better conveys the frozen state’s permanence and aligns with DeepFreeze’s theme. This linguistic refinement, while minor, exemplifies community influence on usability. Technical clarifications also emerged. The discussion distinguishes DeepFreeze from GlobalFreeze, which freezes all trust lines for an issuer’s tokens, noting that DeepFreeze targets specific trust lines for finer control. A question arose about rare cases where the standard tfSetFreeze might suffice—such as temporary holds—but the consensus favored DeepFreeze’s comprehensive approach for most compliance needs. The proposal, now in draft status, was merged into the rippled software codebase via pull request XRPLF/rippled #5187, confirming its deployment readiness as of March 19, 2025. This milestone underscores XRPL’s commitment to evolving through community-driven innovation. The Institutional Impact. From an institutional standpoint, DeepFreeze addresses critical gaps in the standard freeze’s functionality. The article explains that the older mechanism, while useful, permitted incoming transfers and balance adjustments, rendering it inadequate for scenarios requiring total isolation—such as sanctions enforcement or fraud containment. DeepFreeze’s ability to block all activity offers a superior solution, tailored to the demands of regulated finance. Consider its applications: a stablecoin issuer like Ripple could deep-freeze an account suspected of laundering funds, halting its operations pending review. A tokenized real estate platform could use it to secure assets during legal disputes, ensuring no unauthorized transfers occur. For sanctions, it ensures compliance with global frameworks, preventing tokens from reaching blacklisted entities. These use cases enhance the XRPL’s appeal to institutional players, potentially drawing Circle’s USDC or other major stablecoins to the ledger. The ripple effect—pardon the pun—could be substantial. Increased institutional adoption would boost token issuance, trading volume, and liquidity, reinforcing XRPL’s infrastructure for real-world asset tokenization. This aligns with broader trends in blockchain finance, where compliance-ready platforms are increasingly favored by traditional institutions seeking to integrate digital assets. Conclusion and Implications. In conclusion, DeepFreeze represents a strategic leap forward for the XRP Ledger, harmonizing technological sophistication with regulatory necessity. By equipping issuers with comprehensive control over their tokens, it addresses the compliance and security needs of institutional users, from stablecoin providers to asset tokenizers. As of March 19, 2025, its technical implementation is mature, its community support robust, and its potential to drive XRPL adoption undeniable. Looking ahead, DeepFreeze could position the XRPL as a premier blockchain for regulated financial applications, bridging the gap between decentralized innovation and centralized oversight. Its success will depend on validator adoption of the DeepFreeze amendment and real-world uptake by institutions—a process already underway. For a deeper understanding, refer to the XRPL documentation, the article, and the GitHub discussion linked below. DeepFreeze is more than a feature—it’s a foundation for the XRPL’s future in institutional finance. How do you envision its impact on the blockchain landscape? Your perspectives are welcome. PS: This is by far the most exciting amendment since XLS20, but of course, your average influencer doesn't talk about it in his paid group or while he is siphoning your donations. Unfollow them today. ################## Ressouces: XRPL Docs: XLS-77d: Devto Article: Misunderstandings about Freezes: Amendment voting: If you want to support what I do, follow me and buy me a beer or just use one of the CasinoCoin/LuckyHash 🪝 partners for recreational gaming: Check out my other explainers:

Daniel "CEO of the XRPL" Keller

163,345 Aufrufe • vor 1 Jahr

TOPIC #106: What Is a “Free Market”? Clarifying the Misconceptions in the Pi Ecosystem I’ve noticed a narrative spreading within parts of the Pi Network community: the idea that Pi’s value in Dapps or ecosystem should fluctuate freely with the exchange market, and that this is what defines a “free market.” They use this "free market" to deny GCV. Let me be clear: this misconception is not only misleading, but it threatens the foundation of the Pi ecosystem we’ve worked so hard to build. It’s time to clarify the truth, not only for our pioneers today but for the economic legacy we’re building for generations to come. What Is a Free Market Really? According to Britannica, a free market is an economic system characterized by minimal government intervention, where prices are determined by the interplay of supply and demand. But even Britannica admits: > “The free market represents a benchmark that does not actually exist… Modern societies only approach this ideal along a spectrum.” — value in relation to In short, a 100% free market is a myth. Every successful economy has rules and frameworks to maintain stability. Without these, markets descend into chaos, not freedom. In Pi Network, “free market” cannot mean price anarchy. And “decentralization” does not mean “do whatever you want.” Let’s break this down: What Pi Network Decentralization Actually Means Pi Network’s decentralization is built on the Stellar Consensus Protocol (SCP) and reflects a healthy distribution of power and particip,ation — not a lack of structure. Key principles of Pi's decentralization: No Single Point of Control No central entity dominates the network. User Participation Pioneers validate transactions and contribute to governance. Resilience The network can survive attacks or failures due to its distributed nature. Censorship Resistance It’s harder for one party to silence or manipulate the system. None of this means that Pi's value can operate in a free market. Any currency must have a fixed value; this is a fundamental concept in economics. Have you ever seen the values of currencies like the USD, CAD, or RMB fluctuate freely based on individual opinions? On the contrary, a fixed value emphasizes the need to protect the economy we are building together. The community-driven GCV illustrates that the value of Pi should derive from its pioneers and merchants, demonstrating the spirit of decentralization. It should not depend on PCT, any government, large corporations, or investors. Furthermore, this structure ensures that no entity can shut down the Pi Network once it becomes fully decentralized, which I believe will occur when it is fully operational and mature. The Danger of Currency Risk: Why Price or Value Chaos Is Destructive In global finance, currency risk refers to the potential loss of value resulting from unstable exchange rates. As the Corporate Finance Institute explains: > “Currency risk refers to the exposure faced by investors or companies operating across different countries due to changes in the value of one currency versus another.” Let’s apply this to Pi. Imagine a Pi Network Dapp marketplace mall merchant collecting a large amount of 10,000 Pi after the Open Mainnet (OM). Customers pay with Pi, but at a value $1. The merchants must know the Pi value because they need to calculate the FIAT cost. Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant total Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant has a total of 10,000 Pi, which is only $1,000, but the cost of investing in products is $9,000 (Sales $10,000 with $1,000 as profit). That’s a massive loss for the merchant $8,000. If you were the merchant, would you feel it was unfair? Will you still support "free market"? Now, imagine the exchange market drops Pi to $0.40. You will lose $5,000. Would you still want to run your business in Pi? Likely not. And neither would other developers or merchants. Unstable value leads to fear. Fear leads to exit. Exit leads to collapse. This is why we must support Global Consensus Value (GCV) — to ensure a unified, trusted economy. Why GCV Exists — and Why $314,159 Matters GCV is not a fantasy. It’s an economic strategy. It functions much like the gold standard once did: England pioneered it. The U.S. adopted it under the Bretton Woods system, fixing the dollar to gold at $35/oz. This standard enabled global trade and trust until 1971. If the free market can work, why did the US adopt the Bretton Woods system at that time to fix the USD's rate with gold? Because if they didn't promise a fixed rate, no country would give its gold to the US. The gold is trust! Here in Pi Network, GCV is a trust! Pi’s GCV of $314,159 per Pi is not random. It’s based on utility, scarcity, and long-term vision. It reflects Pi’s potential as a foundational currency for a real digital economy. Misusing “Free Market” Is Cheating to Ignorant Pioneers Let’s be blunt. Some individuals abuse the term “free market” to justify undervaluing Pi for personal short-term gain, hoarding more Pi, and undermining long-term stability. However, a true economy isn’t built on confusion. Consider the Cayman Islands — a country with no income tax — yet it only accepts USD for settlement. Why? Because multiple currencies lead to confusion, which undermines investor trust. If Pi has no unified value, we will lose merchants, DApps, developers, and the entire vision, except that they just come to hoard Pi, not for the long-term economy, or they really don't understand the economy. The Way Forward: Unity, Strategy, and Patience Here’s how we build the future together for the following strategies before fully OM Strategy #1: Offline Partial GCV Adoption -Fix Pi Value at GCV in Ecosystem for OM GCV Ambassadors around the world are guiding merchants to accept partial GCV, benefiting both sides: Pioneers buy low-cost goods. Merchants enjoy more sales and earn a small profit in FIAT. The ecosystem produces GCV transaction data, creating the real basis for Pi’s future fixed value at OM. Strategy # 2: Online DApps with Utility — at Any Value to Increase Exchange Pi price for OM We support ALL DApps — regardless of the Pi value they use ($1, $100, or floating): As long as the pioneers and merchants are satisfied. As long as real usage is created. As long as the utility grows. As long as more good-quality Dapps are created It will protect and attract more merchants and developers, driving up Pi demand while reducing supply and organically pushing Pi’s market price toward GCV. Strategy #3: Build up GCV Infrastructure The Head of GCV Ambassador builds up your countrywide GCV infrastructure in all provinces, cities, counties, and villages. Strategy #4: Education and Protection of Pi Network Mission and GCV GCV Education Ambassadors: Educate pioneers to HOLD Pi and support GCV usage. GCV Army: Defend GCV and Pi Network on social media, building public trust and global participation. Online Non-GCV pioneers and merchants, or DApp owners, can still enjoy DApps, even if they use low Pi values. They are reducing selling pressure and strengthening the Pi economy. It is said that a person's wealth is closely linked to their knowledge, cognitive abilities, and moral character. We respect and appreciate all DApp owners, merchants, service providers, and pioneers, regardless of whether they share our beliefs in GCV. We are currently in a chaotic period. Before fully transitioning to OM, pioneers, merchants, and DApps will undergo a screening process based on their own judgment and understanding. Those who strongly believe in GCV will become champions and accumulate substantial wealth. Conversely, those who do not believe in GCV may risk losing their wealth by abandoning Pi. This is because if you have a strong belief, you are more likely to hold onto your Pi. If you oppose GCV, it is often due to a lack of long-term confidence in Pi or a current need to accumulate more Pi. It's important to recognize that once you have accumulated enough Pi, you will want to support GCV because no one wishes to hold onto a worthless coin. This approach is fair to everyone. GCV is akin to Noah's Ark, carrying those who have a strong belief in GCV to safety on the mountains of Ararat. A fixed GCV: Attracts real investors Encourages developers and merchants Reduces currency risk Builds global trust and reputation Let’s stop spreading confusion. Let’s stop begging the old system. We are builders. We are visionaries. We are the future. Final Words Together, we build — not beg. Together, we lead, not mislead. Together, we protect Pi for a future that lasts not for years, but centuries. Doris Yin 🪷🪷🪷 July 20th, 2025
1:40

TOPIC #106: What Is a “Free Market”? Clarifying the Misconceptions in the Pi Ecosystem I’ve noticed a narrative spreading within parts of the Pi Network community: the idea that Pi’s value in Dapps or ecosystem should fluctuate freely with the exchange market, and that this is what defines a “free market.” They use this "free market" to deny GCV. Let me be clear: this misconception is not only misleading, but it threatens the foundation of the Pi ecosystem we’ve worked so hard to build. It’s time to clarify the truth, not only for our pioneers today but for the economic legacy we’re building for generations to come. What Is a Free Market Really? According to Britannica, a free market is an economic system characterized by minimal government intervention, where prices are determined by the interplay of supply and demand. But even Britannica admits: > “The free market represents a benchmark that does not actually exist… Modern societies only approach this ideal along a spectrum.” — value in relation to In short, a 100% free market is a myth. Every successful economy has rules and frameworks to maintain stability. Without these, markets descend into chaos, not freedom. In Pi Network, “free market” cannot mean price anarchy. And “decentralization” does not mean “do whatever you want.” Let’s break this down: What Pi Network Decentralization Actually Means Pi Network’s decentralization is built on the Stellar Consensus Protocol (SCP) and reflects a healthy distribution of power and particip,ation — not a lack of structure. Key principles of Pi's decentralization: No Single Point of Control No central entity dominates the network. User Participation Pioneers validate transactions and contribute to governance. Resilience The network can survive attacks or failures due to its distributed nature. Censorship Resistance It’s harder for one party to silence or manipulate the system. None of this means that Pi's value can operate in a free market. Any currency must have a fixed value; this is a fundamental concept in economics. Have you ever seen the values of currencies like the USD, CAD, or RMB fluctuate freely based on individual opinions? On the contrary, a fixed value emphasizes the need to protect the economy we are building together. The community-driven GCV illustrates that the value of Pi should derive from its pioneers and merchants, demonstrating the spirit of decentralization. It should not depend on PCT, any government, large corporations, or investors. Furthermore, this structure ensures that no entity can shut down the Pi Network once it becomes fully decentralized, which I believe will occur when it is fully operational and mature. The Danger of Currency Risk: Why Price or Value Chaos Is Destructive In global finance, currency risk refers to the potential loss of value resulting from unstable exchange rates. As the Corporate Finance Institute explains: > “Currency risk refers to the exposure faced by investors or companies operating across different countries due to changes in the value of one currency versus another.” Let’s apply this to Pi. Imagine a Pi Network Dapp marketplace mall merchant collecting a large amount of 10,000 Pi after the Open Mainnet (OM). Customers pay with Pi, but at a value $1. The merchants must know the Pi value because they need to calculate the FIAT cost. Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant total Then, when the merchant tries to use that Pi to buy a car, only to be told the accepted rate is $0.1 for one Pi, the merchant has a total of 10,000 Pi, which is only $1,000, but the cost of investing in products is $9,000 (Sales $10,000 with $1,000 as profit). That’s a massive loss for the merchant $8,000. If you were the merchant, would you feel it was unfair? Will you still support "free market"? Now, imagine the exchange market drops Pi to $0.40. You will lose $5,000. Would you still want to run your business in Pi? Likely not. And neither would other developers or merchants. Unstable value leads to fear. Fear leads to exit. Exit leads to collapse. This is why we must support Global Consensus Value (GCV) — to ensure a unified, trusted economy. Why GCV Exists — and Why $314,159 Matters GCV is not a fantasy. It’s an economic strategy. It functions much like the gold standard once did: England pioneered it. The U.S. adopted it under the Bretton Woods system, fixing the dollar to gold at $35/oz. This standard enabled global trade and trust until 1971. If the free market can work, why did the US adopt the Bretton Woods system at that time to fix the USD's rate with gold? Because if they didn't promise a fixed rate, no country would give its gold to the US. The gold is trust! Here in Pi Network, GCV is a trust! Pi’s GCV of $314,159 per Pi is not random. It’s based on utility, scarcity, and long-term vision. It reflects Pi’s potential as a foundational currency for a real digital economy. Misusing “Free Market” Is Cheating to Ignorant Pioneers Let’s be blunt. Some individuals abuse the term “free market” to justify undervaluing Pi for personal short-term gain, hoarding more Pi, and undermining long-term stability. However, a true economy isn’t built on confusion. Consider the Cayman Islands — a country with no income tax — yet it only accepts USD for settlement. Why? Because multiple currencies lead to confusion, which undermines investor trust. If Pi has no unified value, we will lose merchants, DApps, developers, and the entire vision, except that they just come to hoard Pi, not for the long-term economy, or they really don't understand the economy. The Way Forward: Unity, Strategy, and Patience Here’s how we build the future together for the following strategies before fully OM Strategy #1: Offline Partial GCV Adoption -Fix Pi Value at GCV in Ecosystem for OM GCV Ambassadors around the world are guiding merchants to accept partial GCV, benefiting both sides: Pioneers buy low-cost goods. Merchants enjoy more sales and earn a small profit in FIAT. The ecosystem produces GCV transaction data, creating the real basis for Pi’s future fixed value at OM. Strategy # 2: Online DApps with Utility — at Any Value to Increase Exchange Pi price for OM We support ALL DApps — regardless of the Pi value they use ($1, $100, or floating): As long as the pioneers and merchants are satisfied. As long as real usage is created. As long as the utility grows. As long as more good-quality Dapps are created It will protect and attract more merchants and developers, driving up Pi demand while reducing supply and organically pushing Pi’s market price toward GCV. Strategy #3: Build up GCV Infrastructure The Head of GCV Ambassador builds up your countrywide GCV infrastructure in all provinces, cities, counties, and villages. Strategy #4: Education and Protection of Pi Network Mission and GCV GCV Education Ambassadors: Educate pioneers to HOLD Pi and support GCV usage. GCV Army: Defend GCV and Pi Network on social media, building public trust and global participation. Online Non-GCV pioneers and merchants, or DApp owners, can still enjoy DApps, even if they use low Pi values. They are reducing selling pressure and strengthening the Pi economy. It is said that a person's wealth is closely linked to their knowledge, cognitive abilities, and moral character. We respect and appreciate all DApp owners, merchants, service providers, and pioneers, regardless of whether they share our beliefs in GCV. We are currently in a chaotic period. Before fully transitioning to OM, pioneers, merchants, and DApps will undergo a screening process based on their own judgment and understanding. Those who strongly believe in GCV will become champions and accumulate substantial wealth. Conversely, those who do not believe in GCV may risk losing their wealth by abandoning Pi. This is because if you have a strong belief, you are more likely to hold onto your Pi. If you oppose GCV, it is often due to a lack of long-term confidence in Pi or a current need to accumulate more Pi. It's important to recognize that once you have accumulated enough Pi, you will want to support GCV because no one wishes to hold onto a worthless coin. This approach is fair to everyone. GCV is akin to Noah's Ark, carrying those who have a strong belief in GCV to safety on the mountains of Ararat. A fixed GCV: Attracts real investors Encourages developers and merchants Reduces currency risk Builds global trust and reputation Let’s stop spreading confusion. Let’s stop begging the old system. We are builders. We are visionaries. We are the future. Final Words Together, we build — not beg. Together, we lead, not mislead. Together, we protect Pi for a future that lasts not for years, but centuries. Doris Yin 🪷🪷🪷 July 20th, 2025

Doris Yin 东方紫莲🪷

30,299 Aufrufe • vor 11 Monaten

let’s start where this actually lives, not where you want it to live. with the math. the last three weeks of dynamite: 👉 765k 👉 730k 👉 654k that is not a ramp. that is not momentum. that is not “heating up” into a conversion window. that is a decline into it. you are looking at a -111k drop in two weeks, roughly 14–15% contraction from the high to the low, at the exact point where a traditional ppv cycle should be stabilizing or expanding. instead, you have compression. average those three weeks and you land at roughly ~716k linear viewers. that is your real, observable, measurable audience. not hypothetical reach not cumulative impressions not social engagement proxies actual people sitting down and watching the show in the time slot that matters for conversion. i’ve already laid out in detail what the max numbers actually represent and how simulcast behavior works, so i’m not going to re-litigate all of that here. but it matters enough to say again clearly: max is not a second audience at scale. it is a distribution extension that splits the same audience. it does not double your reach. it does not materially expand your funnel. it shifts a portion of existing behavior from one pipe to another. the short version is already established: 👉 max is not additive at scale 👉 max is a split of the same audience 👉 max realistically contributes ~50k–150k, ~75k center so your total reachable audience on a good night is: 👉 ~790k–800k all-in that’s the funnel. that’s the ceiling. that’s the number you are working from. that’s the universe that can possibly convert into a paid transaction. not hypothetical not inflated not “what if” that is the real audience number. now take the number dave is floating: 👉 ~143k ppv buys run the only equation that matters: 👉 143k ÷ 800k = ~17.9% conversion stop there for a second Dave Meltzer is unironically asserting that nearly 1 out of every 5 viewers is converting into a $40–$50 transaction in 2026 in a market with: – no centralized ppv infrastructure – subscription-first behavior – widespread piracy – fragmented pricing (domestic, max discount, vpn international) – a declining weekly audience trend that is the claim. not “strong performance.” not “better than expected.” not “up year over year.” the claim is nearly 1 in 5 viewers converting into a $40–$50 transaction in 2026. this is where the conversation should end, because that number does not exist anywhere in modern media behavior. it doesn’t exist in boxing. it doesn’t exist in ufc. it doesn’t exist in any scaled transactional model operating in the current environment. and more importantly, it didn’t exist consistently even when the infrastructure supporting it was intact. again, this is the part people either don’t understand or choose to ignore: ppv is not just a pricing model. it is an infrastructure model. and that infrastructure is gone. for decades, the backbone of ppv was inDemand. that system wasn’t just pushing content out to cable homes. it was aggregating buys, standardizing reporting, and creating a reconciliation layer that allowed promoters, distributors, and networks to operate off the same dataset. if you were serious about this business, you could triangulate numbers. you could get within a narrow band of reality because the pipes were real and the accounting was shared. that system shut down in 2025. 👉 143k ppv buys is not an aggressive take 👉 it is not an optimistic take 👉 it is a structurally impossible take because we know what real conversion looks like. we do not need to guess. it has been modeled across combat sports, boxing, ufc, and multiple transactional platforms. the ranges are stable: 👉 1–3% → normal 👉 3–5% → strong 👉 5%+ → elite and rare, reserved for events with massive cultural heat and crossover appeal those ranges were established when the infrastructure was intact, pricing was more controlled, piracy was less frictionless, and distribution was more centralized. today, every one of those conditions is worse. pricing is fragmented. you have $49.99 standard, $39.99 through max, international pricing accessible through vpn, and a piracy environment where high-quality streams are available instantly. that is a high-friction, high-leakage system. so conversion should compress, not expand. now apply real-world conversion to your real audience: 👉 800k × 2% = 16k buys 👉 800k × 3% = 24k buys 👉 800k × 5% = 40k buys that’s your range. 👉 ~15k–35k realistic 👉 ~20k–30k as the most defensible center 👉 ~40k reasonable given the strength of the headliners now invert the math, because this is where the claim completely collapses. if you want to justify 143k buys at even a healthy 3% conversion rate, you need roughly 4.7 million engaged viewers. so the question becomes extremely simple: where are the other four million people? 👉they are not on linear television. 👉they are not on max. 👉they are not showing up in any digital engagement metrics that correlate with that level of demand. 👉they are not visible anywhere in the ecosystem that would need to exist to support that level of conversion. 👉👉👉👉👉 they do not exist. and this is before you even account for the trend line moving the wrong way. you are not converting off a growing base. you are converting off a shrinking one. you are not building urgency. you are losing reach. that matters, because conversion does not happen in a vacuum. it happens on top of momentum, visibility, and audience expansion. when those inputs are declining, conversion does not spike to historic highs. it compresses further. the biggest fights in the world are no longer relying on ppv as their primary distribution model. they are moving to platforms that guarantee reach and revenue up front. the most recent example should end this conversation for anyone actually paying attention: netflix just locked tyson fury vs anthony joshua for a major global fight this august. that is one of the biggest possible matchups in boxing. in any previous era, that is a premium ppv event with massive buy expectations, heavy marketing, and a full transactional rollout. instead, it is going to a subscription platform. why? because the economics are better. the reach is global. the friction is lower. the platform values engagement at scale over one-off purchases. that is where the industry is. so when you are being asked to believe that a weekly wrestling property with a ~700k linear audience, declining into its ppv window, is somehow generating six-figure transactional buys inside a subscription platform in 2026, you are not just being asked to accept a number. dave is asking you to ignore the direction of the entire market. and that’s before you even bring in platform behavior. max is not a live sports-first platform. it does not behave like one. when max has something it cares about, it promotes it aggressively. homepage rails. push notifications. press releases. talent integration. cross-platform amplification. you do not see that here. and that absence is not accidental. platforms surface what they want you to see. they amplify what drives engagement and revenue. if ppv at scale were happening inside that ecosystem, it would be visible. it would be part of the narrative. it would be monetized loudly. it isn’t. and that’s before you even factor in the other variables i’ve broken down in detail before: 👉 i’ve already debunked why the max numbers people are throwing around don’t make sense 👉 i’ve already explained what the linear number actually represents and what it doesn’t 👉 i’ve already mapped the media rights landscape in 2026, including how these properties are being evaluated ahead of the paramount–wbd merger the wsj reports could close as early as july so when you’re being told by dave that a property with a ~700k weekly audience trending downward into a ppv window is generating six-figure transactional buys at premium pricing inside a subscription platform in 2026, you’re not being given data. you’re being given a number that does not reconcile with anything else in the system. that’s math. stop it - 45
1:00

let’s start where this actually lives, not where you want it to live. with the math. the last three weeks of dynamite: 👉 765k 👉 730k 👉 654k that is not a ramp. that is not momentum. that is not “heating up” into a conversion window. that is a decline into it. you are looking at a -111k drop in two weeks, roughly 14–15% contraction from the high to the low, at the exact point where a traditional ppv cycle should be stabilizing or expanding. instead, you have compression. average those three weeks and you land at roughly ~716k linear viewers. that is your real, observable, measurable audience. not hypothetical reach not cumulative impressions not social engagement proxies actual people sitting down and watching the show in the time slot that matters for conversion. i’ve already laid out in detail what the max numbers actually represent and how simulcast behavior works, so i’m not going to re-litigate all of that here. but it matters enough to say again clearly: max is not a second audience at scale. it is a distribution extension that splits the same audience. it does not double your reach. it does not materially expand your funnel. it shifts a portion of existing behavior from one pipe to another. the short version is already established: 👉 max is not additive at scale 👉 max is a split of the same audience 👉 max realistically contributes ~50k–150k, ~75k center so your total reachable audience on a good night is: 👉 ~790k–800k all-in that’s the funnel. that’s the ceiling. that’s the number you are working from. that’s the universe that can possibly convert into a paid transaction. not hypothetical not inflated not “what if” that is the real audience number. now take the number dave is floating: 👉 ~143k ppv buys run the only equation that matters: 👉 143k ÷ 800k = ~17.9% conversion stop there for a second Dave Meltzer is unironically asserting that nearly 1 out of every 5 viewers is converting into a $40–$50 transaction in 2026 in a market with: – no centralized ppv infrastructure – subscription-first behavior – widespread piracy – fragmented pricing (domestic, max discount, vpn international) – a declining weekly audience trend that is the claim. not “strong performance.” not “better than expected.” not “up year over year.” the claim is nearly 1 in 5 viewers converting into a $40–$50 transaction in 2026. this is where the conversation should end, because that number does not exist anywhere in modern media behavior. it doesn’t exist in boxing. it doesn’t exist in ufc. it doesn’t exist in any scaled transactional model operating in the current environment. and more importantly, it didn’t exist consistently even when the infrastructure supporting it was intact. again, this is the part people either don’t understand or choose to ignore: ppv is not just a pricing model. it is an infrastructure model. and that infrastructure is gone. for decades, the backbone of ppv was inDemand. that system wasn’t just pushing content out to cable homes. it was aggregating buys, standardizing reporting, and creating a reconciliation layer that allowed promoters, distributors, and networks to operate off the same dataset. if you were serious about this business, you could triangulate numbers. you could get within a narrow band of reality because the pipes were real and the accounting was shared. that system shut down in 2025. 👉 143k ppv buys is not an aggressive take 👉 it is not an optimistic take 👉 it is a structurally impossible take because we know what real conversion looks like. we do not need to guess. it has been modeled across combat sports, boxing, ufc, and multiple transactional platforms. the ranges are stable: 👉 1–3% → normal 👉 3–5% → strong 👉 5%+ → elite and rare, reserved for events with massive cultural heat and crossover appeal those ranges were established when the infrastructure was intact, pricing was more controlled, piracy was less frictionless, and distribution was more centralized. today, every one of those conditions is worse. pricing is fragmented. you have $49.99 standard, $39.99 through max, international pricing accessible through vpn, and a piracy environment where high-quality streams are available instantly. that is a high-friction, high-leakage system. so conversion should compress, not expand. now apply real-world conversion to your real audience: 👉 800k × 2% = 16k buys 👉 800k × 3% = 24k buys 👉 800k × 5% = 40k buys that’s your range. 👉 ~15k–35k realistic 👉 ~20k–30k as the most defensible center 👉 ~40k reasonable given the strength of the headliners now invert the math, because this is where the claim completely collapses. if you want to justify 143k buys at even a healthy 3% conversion rate, you need roughly 4.7 million engaged viewers. so the question becomes extremely simple: where are the other four million people? 👉they are not on linear television. 👉they are not on max. 👉they are not showing up in any digital engagement metrics that correlate with that level of demand. 👉they are not visible anywhere in the ecosystem that would need to exist to support that level of conversion. 👉👉👉👉👉 they do not exist. and this is before you even account for the trend line moving the wrong way. you are not converting off a growing base. you are converting off a shrinking one. you are not building urgency. you are losing reach. that matters, because conversion does not happen in a vacuum. it happens on top of momentum, visibility, and audience expansion. when those inputs are declining, conversion does not spike to historic highs. it compresses further. the biggest fights in the world are no longer relying on ppv as their primary distribution model. they are moving to platforms that guarantee reach and revenue up front. the most recent example should end this conversation for anyone actually paying attention: netflix just locked tyson fury vs anthony joshua for a major global fight this august. that is one of the biggest possible matchups in boxing. in any previous era, that is a premium ppv event with massive buy expectations, heavy marketing, and a full transactional rollout. instead, it is going to a subscription platform. why? because the economics are better. the reach is global. the friction is lower. the platform values engagement at scale over one-off purchases. that is where the industry is. so when you are being asked to believe that a weekly wrestling property with a ~700k linear audience, declining into its ppv window, is somehow generating six-figure transactional buys inside a subscription platform in 2026, you are not just being asked to accept a number. dave is asking you to ignore the direction of the entire market. and that’s before you even bring in platform behavior. max is not a live sports-first platform. it does not behave like one. when max has something it cares about, it promotes it aggressively. homepage rails. push notifications. press releases. talent integration. cross-platform amplification. you do not see that here. and that absence is not accidental. platforms surface what they want you to see. they amplify what drives engagement and revenue. if ppv at scale were happening inside that ecosystem, it would be visible. it would be part of the narrative. it would be monetized loudly. it isn’t. and that’s before you even factor in the other variables i’ve broken down in detail before: 👉 i’ve already debunked why the max numbers people are throwing around don’t make sense 👉 i’ve already explained what the linear number actually represents and what it doesn’t 👉 i’ve already mapped the media rights landscape in 2026, including how these properties are being evaluated ahead of the paramount–wbd merger the wsj reports could close as early as july so when you’re being told by dave that a property with a ~700k weekly audience trending downward into a ppv window is generating six-figure transactional buys at premium pricing inside a subscription platform in 2026, you’re not being given data. you’re being given a number that does not reconcile with anything else in the system. that’s math. stop it - 45

Nick LoPiccolo

20,695 Aufrufe • vor 3 Monaten

I understand Tommy Robinson has, once again, shared a documentary in which he *implies* I received money from the filthy, Communist NGO 'Hope Not Hate'. This has been proven in court to be a lie. So, every time Tommy shares the video, I will release evidence proving he is lying to defend a woman-beater. In his film, Tommy never actually says the money I received came from Hope Not Hate - because he knows categorically that it didn't. When I left Britain First in 2019, I went to the Knights Templar Order and asked them to help me get away from the BF leader, Paul Golding. THAT is where the money came from. I had just been released from jail in England for confronting a rape gang, whilst I was inside, the High Court took my house and awarded it to Muslims who had sued Britain First for £500,000. Paul Golding sold my car (and bought himself a Range Rover) and used my accounts / credit cards without my knowledge. When I was released from prison, he attacked me again in Belfast, and I decided that had to be the last time. I knocked on the door of the Templar Priory in Northern Ireland where I was facing jail AGAIN, and I asked them for help. They immediately launched a fundraiser (I still have the emails they sent out and the details of every single person that donated to me) called 'Support Jayda'. They raised $20,000 (approx £17,000) which helped me start my life over, get a solicitor for my Northern Ireland court case and pay off some of the debt that Paul Golding ran up on my cards whilst I was in jail. I had endured over 5 years of violent physical, mental and sexual abuse at the hands of Paul Golding. Some of these details were made public in a Northern Ireland documentary - including Paul Golding admitting to beating two women: myself and the mother of his child. He has also been convicted of attacking his 'security guards' who volunteer to protect him. Thanks to the Knights Templar International, I was able to get away from Paul Golding. He is a deeply disturbed man, and I am forever grateful to them for helping me. I took Paul Golding to court for the years of physical abuse and for the money he stole from me. He was ordered to pay me £75,000 plus damages. Paul Golding, the leader of Britain First, is currently under Police investigation for: Attempted Rape Sexual Assault by touching (3 counts) Controlling and Coercive Behaviour Multiple counts of Assault ABH I am aware of at least one other victim whom Paul Golding has sexually assaulted, that attack is also the subject of police investigation. I am quite sure there are other women he has harmed. Tommy Robinson has offered to give statements to the Police defending Paul Golding - despite the fact that Tommy wasn't there when ANY of the offences took place, he had NEVER been in our home, and had only ever briefly met us 4 or 5 times. In fact, Tommy and Paul hated each other back then. So you might ask yourself, why would Tommy Robinson defend Paul Golding now? The answer is simple: Money. Paul Golding writes Tommy Robinson's fundraiser emails. To be clear: I have NEVER taken a penny from Hope Not Hate and I NEVER would. They are the enemy. The court proved that Tommy's documentary was nothing more than an attempt to smear a woman who had been violently and sexually assaulted for years by his new business associate, Paul Golding. Paul Golding has ADMITTED to beating up women. I took him to court and proved Tommy Robinson was lying to protect him. Of course, I don't have millions of followers on this app, and Tommy Robinson blocked me the MOMENT he was allowed back onto Twitter/ X so that I couldn't respond to his lies about me. So, I know that this is a David and Goliath post, but the truth matters, and patriots deserve to know what kind of men they are supporting. If you made it to the end, thanks for reading! I'm in a much better place than I was back then, I remain a dedicated activist and I'm glad to be connected with so many genuine, honest Nationalists. In the end, we win. Onward Christian Soldiers. x
0:30

I understand Tommy Robinson has, once again, shared a documentary in which he *implies* I received money from the filthy, Communist NGO 'Hope Not Hate'. This has been proven in court to be a lie. So, every time Tommy shares the video, I will release evidence proving he is lying to defend a woman-beater. In his film, Tommy never actually says the money I received came from Hope Not Hate - because he knows categorically that it didn't. When I left Britain First in 2019, I went to the Knights Templar Order and asked them to help me get away from the BF leader, Paul Golding. THAT is where the money came from. I had just been released from jail in England for confronting a rape gang, whilst I was inside, the High Court took my house and awarded it to Muslims who had sued Britain First for £500,000. Paul Golding sold my car (and bought himself a Range Rover) and used my accounts / credit cards without my knowledge. When I was released from prison, he attacked me again in Belfast, and I decided that had to be the last time. I knocked on the door of the Templar Priory in Northern Ireland where I was facing jail AGAIN, and I asked them for help. They immediately launched a fundraiser (I still have the emails they sent out and the details of every single person that donated to me) called 'Support Jayda'. They raised $20,000 (approx £17,000) which helped me start my life over, get a solicitor for my Northern Ireland court case and pay off some of the debt that Paul Golding ran up on my cards whilst I was in jail. I had endured over 5 years of violent physical, mental and sexual abuse at the hands of Paul Golding. Some of these details were made public in a Northern Ireland documentary - including Paul Golding admitting to beating two women: myself and the mother of his child. He has also been convicted of attacking his 'security guards' who volunteer to protect him. Thanks to the Knights Templar International, I was able to get away from Paul Golding. He is a deeply disturbed man, and I am forever grateful to them for helping me. I took Paul Golding to court for the years of physical abuse and for the money he stole from me. He was ordered to pay me £75,000 plus damages. Paul Golding, the leader of Britain First, is currently under Police investigation for: Attempted Rape Sexual Assault by touching (3 counts) Controlling and Coercive Behaviour Multiple counts of Assault ABH I am aware of at least one other victim whom Paul Golding has sexually assaulted, that attack is also the subject of police investigation. I am quite sure there are other women he has harmed. Tommy Robinson has offered to give statements to the Police defending Paul Golding - despite the fact that Tommy wasn't there when ANY of the offences took place, he had NEVER been in our home, and had only ever briefly met us 4 or 5 times. In fact, Tommy and Paul hated each other back then. So you might ask yourself, why would Tommy Robinson defend Paul Golding now? The answer is simple: Money. Paul Golding writes Tommy Robinson's fundraiser emails. To be clear: I have NEVER taken a penny from Hope Not Hate and I NEVER would. They are the enemy. The court proved that Tommy's documentary was nothing more than an attempt to smear a woman who had been violently and sexually assaulted for years by his new business associate, Paul Golding. Paul Golding has ADMITTED to beating up women. I took him to court and proved Tommy Robinson was lying to protect him. Of course, I don't have millions of followers on this app, and Tommy Robinson blocked me the MOMENT he was allowed back onto Twitter/ X so that I couldn't respond to his lies about me. So, I know that this is a David and Goliath post, but the truth matters, and patriots deserve to know what kind of men they are supporting. If you made it to the end, thanks for reading! I'm in a much better place than I was back then, I remain a dedicated activist and I'm glad to be connected with so many genuine, honest Nationalists. In the end, we win. Onward Christian Soldiers. x

Jayda Fransen

106,276 Aufrufe • vor 1 Monat

Here's why $NEAR is a no-brainer in 2025 👇 Everybody loves NEAR Protocol and there is a reason for that (or many). Near is well-positioned to be one of the leading blockchain ecosystems this year. Let’s explore the “whys”. TIMESTAMPS Quick Bio – 00:00:15 Inflation Reduction Proposal – 00:00:43 Technically Speaking – 00:02:40 Near Intents – 00:03:37 Chain Signatures and AI – 00:04:39 Decentralization and DeFi – 00:05:59 I have my Near account since March 2023, but it has been inactive for a while, as I was focused on other stuff. However, the recent inflation halving proposal by HOT DAO (HOT Protocol 🔥) and LiNEAR (LiNEAR Protocol) brought my eyes back to the project and I really like what I’m seeing. So, here’s my first point. If this proposal passes, NEAR could lead the way in what appears to be a market trend of improving the tokenomics, as more and more experts realize holders have been overpaying for these networks' security, with a too high supply inflation. Solana tried something similar, but the proposal was rejected. In my opinion, validators voting favorably to that show a commitment to the chain for the long term. On the other hand, voting against it signals a short-term vision focused on milking the emissions as much as possible, at the ecosystem’s expense. The voting currently goes with 28% “YEA” votes, needing 66.76% to pass. Most of the validators who already cast their votes went with the yes. 2pilot, avb, openshards, qbit, sicmundus, fox, and intear are, so far, the only seven who voted “NAY”. This proposal has the vocal support of most influential figures in the Near ecosystem, including the Near Foundation (NEAR Foundation), led by Illia (root.near) (🇺🇦, ⋈), which makes me believe it will pass and show the power of the halving in getting the market’s attention and presenting a huge investment asymmetry for the native token right now. Is this everything I like about NEAR? Definitely not. This is just what got me looking at it again, just to discover a (very much) thriving ecosystem, full of interesting things happening at the same time. I’ll mention a few, but there is (much) more. Technically speaking, Near is a high-performance blockchain, with really low fees and one of the fastest finalities, with 600ms block time and approximately 1.8s finality. It also has my favorite architecture for internet-scale scalability, using sharding, while keeping a high decentralization standard. As a learning programmer, Near also has one of the best dev experiences (in my limited opinion). The documentation is clear, has a logical journey, presenting from the basic anatomy in details to more complex SDKs and tools. I’m also in love with the near-cli-rs. A command line interface program written in Rust for seamless interaction with the Near blockchain. Allowing wallet creation, chain query, sending transactions, staking, smart contract calls, and more. Near Intents. This was the second thing to get my attention, while studying the project again, and it sets a whole new standard for blockchain interactions, especially cross-chain. Basically, users can declare an intention (for example, swap Ethereum-USDT to Bitcoin) and a network of solvers, running on Near, will find the best path to accomplish this task. We recently saw an impressive 465k-worth swap happening in exactly this example, paying 0.55% of trading fees to thorswap.near and swapkit.near. According to a Dune Dashboard, the protocol accumulates nearly $400 million in volume since its launch not long ago, in November 2024. *obs.: half this volume was achieved in the last month. Massive! Near Intents is possible due to two other very interesting things: (i) Chain abstraction, and (ii) a solid AI infrastructure. Chain abstraction (via Chain Signatures) is a powerful interoperability feature, allowing Near to friendly connect different blockchains as if they were part of a single network. Users and devs benefit from wallet, address, fees, and cross-chain bridges abstractions - not even noticing they are interacting with multiple chains. One wallet that powers everything. Powered by Near. On AI, Near is just built differently. Not for the hype, but for the solution. The team has been looking for AI solutions much before the ChatGPT fever. Actually, they started as an AI company, pivoting to blockchain later. So, being one of the most promising networks for the growing AI economy was just the natural path to follow. There is an extensive and super complete research piece on that topic, recently published by Reflexivity Research (Reflexivity Research) on July 1st. It presents Near as an AI-optimized blockchain, covering AITP, Shade Agents, x402, Near Intents, and more. Definitely worth the reading. Wrapping up this content with one more aspect that really matters to me is how Near remains truthful to decentralization, data ownership, censorship-resistance and open-source primitives that have been increasingly abandoned by other key players. A simple example of that is how the Near Foundation decided to deprecate its public APIs, encouraging the surge of a more decentralized and competitive market of SaaS projects, with a highlight to Lava Network, that recently appeared in my timeline talking about that. DeFi is also huge on Near, leveraging all the previous properties I mentioned, creating a truly decentralized liquidity pool via Rhea Finance, connected with other chains like BTC, Ethereum, ZCash, and more. All that contributes to Near having the second-largest monthly active addresses, with nearly 50 million, only losing to Solana’s nearly 90 million. In the meantime, NEAR, the token, is not even at the 30rd position by market cap. Crazy stuff. To (finally) wrap it up, I also want to mention Near’s consensus decentralization. While having a low node-count, the network has a Nakamoto Coefficient of 11, which is not bad at all. Surely, there is still room for improvement, which is possible as becoming a validator is accessible staking and hardware-wise. If you liked this content, make sure to click the like bottom and share it around. Follow me on X or subscribe to my YouTube channel, both at vinibarbosabr. See ya!
7:52

Here's why $NEAR is a no-brainer in 2025 👇 Everybody loves NEAR Protocol and there is a reason for that (or many). Near is well-positioned to be one of the leading blockchain ecosystems this year. Let’s explore the “whys”. TIMESTAMPS Quick Bio – 00:00:15 Inflation Reduction Proposal – 00:00:43 Technically Speaking – 00:02:40 Near Intents – 00:03:37 Chain Signatures and AI – 00:04:39 Decentralization and DeFi – 00:05:59 I have my Near account since March 2023, but it has been inactive for a while, as I was focused on other stuff. However, the recent inflation halving proposal by HOT DAO (HOT Protocol 🔥) and LiNEAR (LiNEAR Protocol) brought my eyes back to the project and I really like what I’m seeing. So, here’s my first point. If this proposal passes, NEAR could lead the way in what appears to be a market trend of improving the tokenomics, as more and more experts realize holders have been overpaying for these networks' security, with a too high supply inflation. Solana tried something similar, but the proposal was rejected. In my opinion, validators voting favorably to that show a commitment to the chain for the long term. On the other hand, voting against it signals a short-term vision focused on milking the emissions as much as possible, at the ecosystem’s expense. The voting currently goes with 28% “YEA” votes, needing 66.76% to pass. Most of the validators who already cast their votes went with the yes. 2pilot, avb, openshards, qbit, sicmundus, fox, and intear are, so far, the only seven who voted “NAY”. This proposal has the vocal support of most influential figures in the Near ecosystem, including the Near Foundation (NEAR Foundation), led by Illia (root.near) (🇺🇦, ⋈), which makes me believe it will pass and show the power of the halving in getting the market’s attention and presenting a huge investment asymmetry for the native token right now. Is this everything I like about NEAR? Definitely not. This is just what got me looking at it again, just to discover a (very much) thriving ecosystem, full of interesting things happening at the same time. I’ll mention a few, but there is (much) more. Technically speaking, Near is a high-performance blockchain, with really low fees and one of the fastest finalities, with 600ms block time and approximately 1.8s finality. It also has my favorite architecture for internet-scale scalability, using sharding, while keeping a high decentralization standard. As a learning programmer, Near also has one of the best dev experiences (in my limited opinion). The documentation is clear, has a logical journey, presenting from the basic anatomy in details to more complex SDKs and tools. I’m also in love with the near-cli-rs. A command line interface program written in Rust for seamless interaction with the Near blockchain. Allowing wallet creation, chain query, sending transactions, staking, smart contract calls, and more. Near Intents. This was the second thing to get my attention, while studying the project again, and it sets a whole new standard for blockchain interactions, especially cross-chain. Basically, users can declare an intention (for example, swap Ethereum-USDT to Bitcoin) and a network of solvers, running on Near, will find the best path to accomplish this task. We recently saw an impressive 465k-worth swap happening in exactly this example, paying 0.55% of trading fees to thorswap.near and swapkit.near. According to a Dune Dashboard, the protocol accumulates nearly $400 million in volume since its launch not long ago, in November 2024. *obs.: half this volume was achieved in the last month. Massive! Near Intents is possible due to two other very interesting things: (i) Chain abstraction, and (ii) a solid AI infrastructure. Chain abstraction (via Chain Signatures) is a powerful interoperability feature, allowing Near to friendly connect different blockchains as if they were part of a single network. Users and devs benefit from wallet, address, fees, and cross-chain bridges abstractions - not even noticing they are interacting with multiple chains. One wallet that powers everything. Powered by Near. On AI, Near is just built differently. Not for the hype, but for the solution. The team has been looking for AI solutions much before the ChatGPT fever. Actually, they started as an AI company, pivoting to blockchain later. So, being one of the most promising networks for the growing AI economy was just the natural path to follow. There is an extensive and super complete research piece on that topic, recently published by Reflexivity Research (Reflexivity Research) on July 1st. It presents Near as an AI-optimized blockchain, covering AITP, Shade Agents, x402, Near Intents, and more. Definitely worth the reading. Wrapping up this content with one more aspect that really matters to me is how Near remains truthful to decentralization, data ownership, censorship-resistance and open-source primitives that have been increasingly abandoned by other key players. A simple example of that is how the Near Foundation decided to deprecate its public APIs, encouraging the surge of a more decentralized and competitive market of SaaS projects, with a highlight to Lava Network, that recently appeared in my timeline talking about that. DeFi is also huge on Near, leveraging all the previous properties I mentioned, creating a truly decentralized liquidity pool via Rhea Finance, connected with other chains like BTC, Ethereum, ZCash, and more. All that contributes to Near having the second-largest monthly active addresses, with nearly 50 million, only losing to Solana’s nearly 90 million. In the meantime, NEAR, the token, is not even at the 30rd position by market cap. Crazy stuff. To (finally) wrap it up, I also want to mention Near’s consensus decentralization. While having a low node-count, the network has a Nakamoto Coefficient of 11, which is not bad at all. Surely, there is still room for improvement, which is possible as becoming a validator is accessible staking and hardware-wise. If you liked this content, make sure to click the like bottom and share it around. Follow me on X or subscribe to my YouTube channel, both at vinibarbosabr. See ya!

Vini B |「 thecoding 」

40,183 Aufrufe • vor 1 Jahr

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
35:48

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,105 Aufrufe • vor 3 Monaten

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.
35:48

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 Aufrufe • vor 2 Monaten

🚨 China's unemployment crisis in 2026 — by the numbers, and by the faces behind them. The CCP calls it "seasonal fluctuation." The people living it call it despair. 📊 THE OFFICIAL NUMBERS — AND WHY THEY'RE WORSE THAN THEY LOOK In March 2026, China's official youth unemployment rate (ages 16–24, excluding students) rose to 16.9% — a 4-month high, reversing six consecutive months of decline. That's nearly 1 in 6 young people with no job. But here's the catch: China counts anyone who works even ONE hour per week as "employed." The US threshold is 15 hours. France is 20 hours. By any international standard, the real number is far higher. In fact, Peking University economist Zhang Dandan calculated that China's true youth unemployment rate in early 2023 was up to 46.5% — more than double the official figure. There is no reason to believe conditions have improved since. And it's not just the young: — Ages 25–29: 7.7% unemployed (highest since March 2025) — Ages 30–59: 4.3% unemployed (rising) — Overall urban unemployment: 5.4% — a 13-month high as of March 2026 This year, 12.7 million university graduates will enter the job market — 480,000 more than last year. A record high. Into an economy that is shrinking, not growing. 👤 THE FACES BEHIND THE NUMBERS These are not statistics. These are people. A young man sits on a curb in Sichuan, crying. "I genuinely have no money left. Not a single yuan." He has been looking for work for months. He is not in any government database as "unemployed" — because he gave up registering. A migrant worker in his 40s, who spent 20 years building China's cities, returns to his village. The construction site closed. The factory moved. The restaurant shut down. He has no pension. No safety net. Nothing. A fresh graduate, armed with a degree that cost her family years of savings, applies to hundreds of positions. She receives form rejections — or silence. She moves back home. Her parents tell her to "keep trying." She stops telling them how many rejections she has collected. A street vendor in Guangzhou sets up his stall at 6am. By noon, he has sold almost nothing. Foot traffic has collapsed. Everyone around him is cutting spending. He is, technically, "self-employed" — and therefore invisible in the unemployment statistics. 🏭 WHY IS THIS HAPPENING? The CCP's answer: seasonal factors. Global headwinds. Trade friction. The reality: 1. Fixed asset investment — the engine of China's growth for decades — grew only 1.7% in Q1 2026, down from 4.2% in Q1 2025. Investment is collapsing. 2. The property sector, which once drove nearly 30% of economic activity, remains in freefall. Construction has stopped on millions of homes. The workers who built them have nowhere to go. 3. China's major tech companies — Alibaba, Tencent, ByteDance, JD·com — have been cutting headcount for years, under government pressure that made private enterprise feel like a liability, not an asset. 4. Foreign companies are leaving or reducing exposure. The market that once promised unlimited growth now promises unpredictability. 5. Deflation has taken hold. When prices fall, businesses earn less. When businesses earn less, they hire less — or fire more. When people fear job loss, they spend less. The cycle feeds itself. The result: a generation of educated, capable, ambitious young Chinese people — doing nothing. Not because they won't work. Because there is no work. 🔇 WHAT THE CCP DOES INSTEAD OF SOLVING IT When youth unemployment hit a record 21.3% in June 2023, the government didn't fix it. They stopped publishing the data. For months, the numbers disappeared from official releases entirely. When they returned, the methodology had been changed — students were excluded, age brackets were redefined — making direct comparisons harder and the figures look cleaner. In May 2026, authorities began officially renaming homeless people "dispersed persons" (流散人员). Not to help them. To make them statistically disappear. This is the CCP's answer to suffering: rename it. Redefine it. Delete it from the dataset. 📉 THE COST OF "LYING FLAT" A generation of Chinese youth have embraced 躺平 (tǎng píng) — "lying flat." Not as laziness. As rational surrender. Why work 996 hours (9am to 9pm, 6 days a week) for a company that will downsize you anyway? Why compete for jobs that don't exist? Why take on a mortgage for an apartment in a building that may never be completed? The state tells them to be patriotic, to sacrifice, to trust the Party's vision. They've watched that vision fail them. So they lie flat. And the CCP — which created the conditions for this — blames them for lacking ambition. ——— The people in these videos are not failures. They are not lazy. They are not "seasonal fluctuations." They are the cost of a political system that prioritizes control over people, data management over truth, and the Party's image over the lives of 1.4 billion human beings. Share this. The numbers will be deleted again. The faces should not be forgotten. Sources: China National Bureau of Statistics (April 2026) · CNA (April 21, 2026) · World Journal (April 2026) · Peking University / Zhang Dandan (2023 analysis) · Epoch Times (April 21, 2026)· Original post by Aric Chen, views are my own. #ChinaUnemployment #YouthUnemployment #ChinaEconomy #躺平 #LyingFlat #CCP #HumanRightsChina #China2026 #China #RealChina
8:47

🚨 China's unemployment crisis in 2026 — by the numbers, and by the faces behind them. The CCP calls it "seasonal fluctuation." The people living it call it despair. 📊 THE OFFICIAL NUMBERS — AND WHY THEY'RE WORSE THAN THEY LOOK In March 2026, China's official youth unemployment rate (ages 16–24, excluding students) rose to 16.9% — a 4-month high, reversing six consecutive months of decline. That's nearly 1 in 6 young people with no job. But here's the catch: China counts anyone who works even ONE hour per week as "employed." The US threshold is 15 hours. France is 20 hours. By any international standard, the real number is far higher. In fact, Peking University economist Zhang Dandan calculated that China's true youth unemployment rate in early 2023 was up to 46.5% — more than double the official figure. There is no reason to believe conditions have improved since. And it's not just the young: — Ages 25–29: 7.7% unemployed (highest since March 2025) — Ages 30–59: 4.3% unemployed (rising) — Overall urban unemployment: 5.4% — a 13-month high as of March 2026 This year, 12.7 million university graduates will enter the job market — 480,000 more than last year. A record high. Into an economy that is shrinking, not growing. 👤 THE FACES BEHIND THE NUMBERS These are not statistics. These are people. A young man sits on a curb in Sichuan, crying. "I genuinely have no money left. Not a single yuan." He has been looking for work for months. He is not in any government database as "unemployed" — because he gave up registering. A migrant worker in his 40s, who spent 20 years building China's cities, returns to his village. The construction site closed. The factory moved. The restaurant shut down. He has no pension. No safety net. Nothing. A fresh graduate, armed with a degree that cost her family years of savings, applies to hundreds of positions. She receives form rejections — or silence. She moves back home. Her parents tell her to "keep trying." She stops telling them how many rejections she has collected. A street vendor in Guangzhou sets up his stall at 6am. By noon, he has sold almost nothing. Foot traffic has collapsed. Everyone around him is cutting spending. He is, technically, "self-employed" — and therefore invisible in the unemployment statistics. 🏭 WHY IS THIS HAPPENING? The CCP's answer: seasonal factors. Global headwinds. Trade friction. The reality: 1. Fixed asset investment — the engine of China's growth for decades — grew only 1.7% in Q1 2026, down from 4.2% in Q1 2025. Investment is collapsing. 2. The property sector, which once drove nearly 30% of economic activity, remains in freefall. Construction has stopped on millions of homes. The workers who built them have nowhere to go. 3. China's major tech companies — Alibaba, Tencent, ByteDance, JD·com — have been cutting headcount for years, under government pressure that made private enterprise feel like a liability, not an asset. 4. Foreign companies are leaving or reducing exposure. The market that once promised unlimited growth now promises unpredictability. 5. Deflation has taken hold. When prices fall, businesses earn less. When businesses earn less, they hire less — or fire more. When people fear job loss, they spend less. The cycle feeds itself. The result: a generation of educated, capable, ambitious young Chinese people — doing nothing. Not because they won't work. Because there is no work. 🔇 WHAT THE CCP DOES INSTEAD OF SOLVING IT When youth unemployment hit a record 21.3% in June 2023, the government didn't fix it. They stopped publishing the data. For months, the numbers disappeared from official releases entirely. When they returned, the methodology had been changed — students were excluded, age brackets were redefined — making direct comparisons harder and the figures look cleaner. In May 2026, authorities began officially renaming homeless people "dispersed persons" (流散人员). Not to help them. To make them statistically disappear. This is the CCP's answer to suffering: rename it. Redefine it. Delete it from the dataset. 📉 THE COST OF "LYING FLAT" A generation of Chinese youth have embraced 躺平 (tǎng píng) — "lying flat." Not as laziness. As rational surrender. Why work 996 hours (9am to 9pm, 6 days a week) for a company that will downsize you anyway? Why compete for jobs that don't exist? Why take on a mortgage for an apartment in a building that may never be completed? The state tells them to be patriotic, to sacrifice, to trust the Party's vision. They've watched that vision fail them. So they lie flat. And the CCP — which created the conditions for this — blames them for lacking ambition. ——— The people in these videos are not failures. They are not lazy. They are not "seasonal fluctuations." They are the cost of a political system that prioritizes control over people, data management over truth, and the Party's image over the lives of 1.4 billion human beings. Share this. The numbers will be deleted again. The faces should not be forgotten. Sources: China National Bureau of Statistics (April 2026) · CNA (April 21, 2026) · World Journal (April 2026) · Peking University / Zhang Dandan (2023 analysis) · Epoch Times (April 21, 2026)· Original post by Aric Chen, views are my own. #ChinaUnemployment #YouthUnemployment #ChinaEconomy #躺平 #LyingFlat #CCP #HumanRightsChina #China2026 #China #RealChina

Aric Chen

53,709 Aufrufe • vor 2 Monaten

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.
2:41

The multi-leader blockchain endgame: competitive information inclusion as a self-reinforcing mechanism for global price discovery - how we got here, and why Aptos is leading the charge Onchain trading is the killer app In the nine years since the launch of programmable transactions on the Ethereum blockchain, onchain trading has revealed itself as the killer use case for blockchains: onchain listings, volume, and total value locked are all growing with no signs of slowing down, due to the censorship-resistant, permissionless, 24/7/365 qualities afforded by decentralized (DeFi) systems. Monolithic parallelism is key In 2020 Solana was first to market with monolithic, parallel execution (as opposed sharded execution which offers parallelism by partitioning global state into separate information silos), establishing a new design paradigm that raised the bar for throughput and latency: put all of the information in one replicated state machine and make it run as fast as possible. This design produces a single, global hub for activity, liquidity, and token launches, a kind of financial data whiteboard in the sky, where anyone can come and trade at any time with everybody else who has plugged into the system. DEXes are becoming more competitive Historically decentralized systems have been juxtaposed with centralized ones since the latter eliminates the overhead associated with distributed systems coordination. And yet despite this overhead, Solana as a decentralized exchange (DEX) is still pulling in billions of trading volume per day, exceeding that of all but the largest centralized crypto exchanges (CEXs), that simply can't compete with the giant DEX in the sky on token listings or fees. After all, CEXs have to pay for server space, salaries, and lawyers, while a DEX outsources everything. The colocation arms race The one place where CEXs have an advantage over DEXs is on end-to-end latency for colocation applications, or in other words: someone sets up a trading bot in the same data center as the exchange, and their trades get to the exchange faster than everyone else's. When there is only one data ingestion point the fastest trader wins, and after the arms race has played out everyone ends up huddling around the trading hub, effectively cutting off the rest of the world from playing the latency trading game. This is the model that traditional securities exchanges like the Nasdaq or the NYSE 🏛 employ, and because they own the server they can effectively charge whatever they want for access to it. The colocation arms race is also why L2s will probably never decentralize: running the sequencer is practically the same as running the NASDAQ, with the same monopoly on transaction fees collected from a nearby cluster of trading bots (I understand from conversations with Logan Jastremski that the Arbitrum arms race has already hit a Nash Equilibrium in Portland, Oregon). Colocation is a trap But once the colocation arms race has played out, trades become less about incorporating new information in the market and more about skimming off the top by spoofing all of the trades coming in from the other bots. High-frequency trading (HFT) bots located in the NYSE New Jersey data center, for example, are constantly placing buys and sell orders that they have no intention of executing, just to spoof the other colocated bots who are playing the same adversarial game. Information inclusion, on the other hand, the synthesis of real-time world events into prices, takes a back seat because anyone who tries to include new information first needs to batch up their order and send it through a series of middlemen before it ultimately ends up on the exchange: you, I, or practically any other individual can not actually "trade on the NASDAQ", no, we have to express our intent to someone like Robinhood, who then sells our order flow to @CitadelSecurities, who then sends it to the exchange, oh and by the way it doesn't actually even "clear" or "settle" once it "executes" because for whatever reason the whole systems splits these things up and prevents them from happening instantaneously even though it's 2024 and we have computers. Onchain trading cuts out middlemen This whole mess is why we have onchain trading, and why it's starting to win: if you want a mainline to the exchange, without setting up a server, and you want to trade on a news event without getting immediately frontrun by an HFT bot that is sniffing out the trades of every other HFT bot who is easing in batched up order flow on their own terms, then you submit your order to a node in the blockchain and the information gets included in the price upon ingestion. Oh, and by the way the trade is actually fully complete: settled, cleared, reconciled, done, whatever you want to call it, because the people who build decentralized finance (DeFi) build it how it should actually work, not in a way that creates a million incumbents and charges exorbitant rents for access to the system. Onchain trading better for price discovery And the beautiful part about this is that even if a distributed system has more latency than a centralized system, DeFi still ends up incorporating more information into the price faster than centralized finance, because with DeFi the information gets included in the system as soon as it is submitted, not after it has been batched up and sent through a series of middlemen. The consensus mechanism of the blockchain disseminates the information around the world in the form of a price update, while the centralized exchange model requires information about the event to first get propagate to the region of the trading hub, then to get submitted to the colocation server. This means that in terms of global price discovery, onchain trading is strictly a better system because the entire consensus model is based around accelerated information propagation. Because price discovery is a global phenomenon, blockchains, which are global, are actually better than the centralized status quo, on a performance basis, not just from an ideological or convenience-based view. And it has to be multi-leader In practice, effective global information synthesis of information has an additional key requirement: multi-leader architecture. That is, in a single-leader blockchain like Solana, where one validator at a time has a monopoly on ordering transactions into blocks, for their duration as a leader they effectively function as a colocation server. This means that if the current leader is in New York, someone in Singapore who wants to trade on local news as soon as it breaks will still need to get their order all the way around the world to the leader, who is effectively serving as the chain's data ingestion point, before the order can start propagating through the network. But this is issue solved by the introduction of multiple distributed leaders, because then anyone with access to new information can submit their order to the leader closest to them, yielding faster information inclusion in the form of price updates. Multi-leader is also required for fair markets A multi-leader architecture is also required for fair markets, because in a single-leader system the leader has the power to censor transactions, reorder them to their advantage, or even replace transactions with copycats that extract maximum value by replacing the sender's address with their own. For example if someone wants to capture an arbitrage opportunity between two onchain DEXes, they'll need to submit a transaction to the leader and trust that the leader won't simply copy the transaction and submit it themselves. But when there are two or more leaders, users whose transactions are censored by one leader will simply work with a different leader the next time around, eventually cutting off transaction fee flow to the extractive leader. Beyond just strict inclusion, in a multi-leader architecture validators are also forced to compete with each other on latency, because the leader who is fastest at disseminating users' transactions across the network will over time gobble up the largest share of the order flow. Transparent priority fees are a must, or a private mempool will emerge But in order to make this work, a multi-leader architecture must also offer users the ability to pay priority fees AKA "tips" or "bribes" to move their transaction to the front of the line: if there is a $5 arbitrage opportunity onchain, users need to have assurance that they if they pay a 4.99 priority fee to take that arb, they will get priority over a different user who is only willing to tip 4.98. If the native blockchain system does not offer this fair market priority fee mechanism, then it is only a matter of time before one spontaneously emerges in the form of a private mempool like Jito, which can create centralization pressures and undermine the integrity of the system as a whole. Competitive payment for order flow is the stable solution With the right architecture in place, the end result is a competitive environment where endpoints running maximum extractable value (MEV) bots compete with one to offer users the best price for their order flow. In other words, if a user wants to submit an order that can get sandwich attacked for as much as $2 of MEV, then the order should ultimately go to the endpoint bot that is willing to pay the user as much as $1.99 for the right to process their transaction. The price that the provider is willing to pay is ultimately a function of how much in priority fees they might need to pay to the current leader (0 they are the current one), but notably at each stage there is a competitive market for order flow, whether in the form of retail trader's orders, or priority fees among bots that might be forwarding orders to one of the leaders. AptosLabs is already building all this With a public mempool and transaction priority fees, Aptos additionally includes a pipelined architecture that already includes concurrent batching of transactions into blocks, with a single consensus leader who propagates the batched blocks out to the network. And the team is already researching running multiple instances of the consensus algorithm in parallel, yielding multiple consensus leaders who can compete with each other on latency and inclusion - just ask pranav | Shelby, Alexander Spiegelman, and Zekun Li. This means that block times can shrink as the number of consensus leaders grows, with each leader having its own geographical radius of inclusion beyond which it makes more sense to submit to a different leader. The starting point? Something like 60 ms blocks and 3 consensus leaders, partitioning the global information space into competitive and constantly-rotating regions of information inclusion. Messaging is important With concurrent pipelined transaction batching, a public mempool, priority fees, and a clear path to a multi-leader architecture, Aptos leads the industry in onchain trading infrastructure that can truly supplant the centralized colocation paradigm that has heretofore dominated global finance - by offering a truly superior product. And I am hopeful that this deep dive is the first step in communicating not how or that superior product is getting built, but what it means from a bigger picture perspective. If blockchains have found product market fit in anything, it is in trading, and the trading game can only be won by building the biggest, baddest, most high performance system that has as its north star a single, concrete goal: constantly reducing, ever lower toward zero, time time it takes to incorporate information from anywhere in the world into the global price discovery computer. Whoever does this, even 1 ms faster than the competitor, wins the price discovery game, as other blockchains are left in the dust, their DEXes arbed away to zero against the fastest chain on the block. And sure, the blockchain that can rise to this challenge can also handle useful things like payments, NFTs, or other solutions that benefit from permissionlessness and low gas costs, but I want to impress that at the core of this pursuit must be the urge to drive down information inclusion latency to the absolute minimum afforded by the laws of physics through a competitive, market-driven environment. I call on avery.apt 🇺🇸 , CTO of Aptos Labs, to lean in on this messaging, to make it clear that Aptos is here for this singular mission, to build the most performant price discovery engine in history, as a rallying call for alignment in development efforts across the ecosystem and broader industry. Where does this go? As the latencies drop, the spreads tighten, and the information inclusion increases with every incremental increase in network bandwidth, we can expect a new class of competing techno-financial hubs that aggregate around the world's largest information sources: New York, Washington DC, London, Tokyo, etc., commanding stake distribution commensurate with the density of information flow in these respective locales. With the right incentives in place, competing concurrent leaders will invest ever more in infrastructure to get their packets out to the network faster than the rest, yielding clusters of fiber optic cable around the world's financial hubs, neurons in the global financial brain connecting not just HFT firms to servers in their city, but connecting every city with every other city, to move pricing information across oceans and continents. And retail traders, who have been left out of the colocation game, will only benefit: this entire system gets faster, more inclusive, with tighter spreads and lower fees, and it is such an amazing opportunity to watch all of this unfold in real time. The future of blockchains is the future of trading, is the future of competitive information inclusion in real-time, is the future of truly unified global markets, because at the the core of this industry is a simple idea: connect the computers, and see where the incentives lead. They lead to this, and Aptos is leading the charge, because its tech is purpose-built for this exact purpose. So tell the world about it.

Alex Kahn

24,432 Aufrufe • vor 1 Jahr

This is absolutely fascinating: Jason Furman, one of the foremost economists in the U.S. and former chair of the Council of Economic Advisers, explains why the so-called "China shock" is a myth. According to him, "85 to 95% of Americans benefited" from trade with China, and "China has been part of helping [the US economy] work, not hurting it work." In other words, the narrative that China "stole" American jobs and wages is the exact opposite of reality. Furman's logic is pretty ironclad: 1) He points out, which is factual, that "the slowdown of wage growth and the rise of inequality began in the 1970s, when there basically was no trade with China." It then accelerated in the 1980s-90s when China trade was small, and **slowed down** after 2000. And "since about 2013," when trade with China was at its highest, "we've had pretty fast real wage growth," with "the fastest real wage growth for moderate income households." In other words, the timing doesn't fit: if China was the cause, the problem should have gotten worse as trade with China increased. Instead, it got better. 2) A common narrative one hears about China is "who cares about affordable goods, we need well-paying jobs." But Furman points out it's actually one and the same thing: "the way we measure jobs is how much your wages can buy. If you improve purchasing power, you are making every single job in the economy better." In very concrete terms, if salaries stay flat but Chinese imports make goods 10% cheaper, your purchasing power just went up 10%, as if you got a 10% wage hike. This makes every single job in the economy better. In effect "jobs vs. cheap goods" is a false dichotomy: cheap goods ARE better jobs. 3) Furman also points out, rightly, that the majority of what U.S. imports from China isn't consumer goods: "more than half of what we import is actually inputs into the manufacturing process itself." In other words, Chinese imports make U.S. manufacturing MORE competitive as it decreases their input costs. If you were to cut all Chinese imports, you'd cripple U.S. manufacturing as it would no longer be able to compete on price with anyone. And, as per point 2 above, you'd also destroy Americans' purchasing power, making every single U.S. worker worse off. 4) Last but not least, Furman says that the "China shock" literature is fundamentally flawed, as it "doesn't answer the most important question, which is what the net effect was." It "doesn't consider other causes for the job losses, doesn't look at all the places that gained jobs and wages, and doesn't integrate the consumer side." All in all, he believes that if one were to actually calculate the net effect of trade with China on the U.S. economy, it'd show that "85 to 95% of Americans benefited." And even for the 5-15% who lost out, Furman says these people were failed by "our labor policies, our social safety net" - not by China. What Furman is saying is more relevant than ever because, both in the U.S. and in Europe, this notion that China is somehow "stealing" Western jobs and prosperity has become the unquestioned premise of so many of today's policies. Nobody even debates it anymore, it's almost universally assumed correct. In my own country France, Macron keeps repeating it all the time, leading the charge in Europe to slap tariffs on Chinese imports, warning that China is "killing its own customers" and that it's a question of life or death for European industry ( He literally called last week for the EU to build its own version of America's Section 301 - the same protectionist tool Trump uses ( BUT, if Furman is right, and the data strongly suggests he is, France and Europe are about to inflict economic self-harm in the name of a problem that doesn't exist. Much more affordable cars, for instance, would literally give every single European a big wage hike. It's Furman's argument on "85 to 95% benefiting" vs 5% to 15% losing out: the vast majority of Europeans would see their money go further, while a small number of jobs in legacy automakers would be disrupted. Instead of helping those workers transition, Europe wants to prevent making everyone better off. Anyhow, please do watch the whole podcast, which has many other fascinating insights because Furman also debates with Justin Yifu Lin, the former Chief Economist of the World Bank and State Council Counsellor of China. They're both interviewed by my friend Hansong Li - also a professor and an immensely smart man - in his excellent new podcast "worldviews" (imho one of the best new podcasts our there). The video is here:
3:29

This is absolutely fascinating: Jason Furman, one of the foremost economists in the U.S. and former chair of the Council of Economic Advisers, explains why the so-called "China shock" is a myth. According to him, "85 to 95% of Americans benefited" from trade with China, and "China has been part of helping [the US economy] work, not hurting it work." In other words, the narrative that China "stole" American jobs and wages is the exact opposite of reality. Furman's logic is pretty ironclad: 1) He points out, which is factual, that "the slowdown of wage growth and the rise of inequality began in the 1970s, when there basically was no trade with China." It then accelerated in the 1980s-90s when China trade was small, and **slowed down** after 2000. And "since about 2013," when trade with China was at its highest, "we've had pretty fast real wage growth," with "the fastest real wage growth for moderate income households." In other words, the timing doesn't fit: if China was the cause, the problem should have gotten worse as trade with China increased. Instead, it got better. 2) A common narrative one hears about China is "who cares about affordable goods, we need well-paying jobs." But Furman points out it's actually one and the same thing: "the way we measure jobs is how much your wages can buy. If you improve purchasing power, you are making every single job in the economy better." In very concrete terms, if salaries stay flat but Chinese imports make goods 10% cheaper, your purchasing power just went up 10%, as if you got a 10% wage hike. This makes every single job in the economy better. In effect "jobs vs. cheap goods" is a false dichotomy: cheap goods ARE better jobs. 3) Furman also points out, rightly, that the majority of what U.S. imports from China isn't consumer goods: "more than half of what we import is actually inputs into the manufacturing process itself." In other words, Chinese imports make U.S. manufacturing MORE competitive as it decreases their input costs. If you were to cut all Chinese imports, you'd cripple U.S. manufacturing as it would no longer be able to compete on price with anyone. And, as per point 2 above, you'd also destroy Americans' purchasing power, making every single U.S. worker worse off. 4) Last but not least, Furman says that the "China shock" literature is fundamentally flawed, as it "doesn't answer the most important question, which is what the net effect was." It "doesn't consider other causes for the job losses, doesn't look at all the places that gained jobs and wages, and doesn't integrate the consumer side." All in all, he believes that if one were to actually calculate the net effect of trade with China on the U.S. economy, it'd show that "85 to 95% of Americans benefited." And even for the 5-15% who lost out, Furman says these people were failed by "our labor policies, our social safety net" - not by China. What Furman is saying is more relevant than ever because, both in the U.S. and in Europe, this notion that China is somehow "stealing" Western jobs and prosperity has become the unquestioned premise of so many of today's policies. Nobody even debates it anymore, it's almost universally assumed correct. In my own country France, Macron keeps repeating it all the time, leading the charge in Europe to slap tariffs on Chinese imports, warning that China is "killing its own customers" and that it's a question of life or death for European industry ( He literally called last week for the EU to build its own version of America's Section 301 - the same protectionist tool Trump uses ( BUT, if Furman is right, and the data strongly suggests he is, France and Europe are about to inflict economic self-harm in the name of a problem that doesn't exist. Much more affordable cars, for instance, would literally give every single European a big wage hike. It's Furman's argument on "85 to 95% benefiting" vs 5% to 15% losing out: the vast majority of Europeans would see their money go further, while a small number of jobs in legacy automakers would be disrupted. Instead of helping those workers transition, Europe wants to prevent making everyone better off. Anyhow, please do watch the whole podcast, which has many other fascinating insights because Furman also debates with Justin Yifu Lin, the former Chief Economist of the World Bank and State Council Counsellor of China. They're both interviewed by my friend Hansong Li - also a professor and an immensely smart man - in his excellent new podcast "worldviews" (imho one of the best new podcasts our there). The video is here:

Arnaud Bertrand

160,393 Aufrufe • vor 1 Monat

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything Most smart people don’t fail because they’re dumb. They fail because they apply clean logic to a messy world — and the world punishes that mistake with a smile. The messy truth is that modern life is shaped less by individual intent and more by systems: incentives, competition, scaling effects, path dependence, and statistical weirdness. These systems produce outcomes that feel unfair or mysterious until you learn the underlying “laws” — a set of lenses that let you predict how things actually behave. This is not about becoming cynical. It’s about becoming accurate. Once you internalize these lenses, you start noticing that most disagreements aren’t about values. They’re about which hidden force you think dominates: Do incentives matter more than morals? Do networks scale value more than craftsmanship? Do rare events matter more than averages? Do systems evolve, or can they be designed? This article is a guided map through those forces — told as one story. 1) The seduction of “doing the obvious thing” Imagine you’re in charge of improving something important: a company, a city, a hospital, a school, a product, maybe even your own life. You do what responsible people do: you define a goal. You pick a metric. And you tell everyone: we’re going to win on this number. This is where the first trap snaps shut. Goodhart’s Law: the metric stops being real When a measure becomes a target, it stops being a good measure. Before it became a target, the metric was an instrument: a thermometer. After it becomes a target, it becomes a game. Hospitals improve “wait times” by changing intake rules. Companies improve “engagement” by nudging addiction. Schools improve test scores by teaching to the test. Police departments improve crime stats by changing what counts as a crime. Not because anyone is evil. Because the system rewards it. The principal–agent problem: the doers don’t pay This is the deeper engine under Goodhart. The person deciding is not the person suffering the consequences. Executives chase quarterly optics; employees deal with the chaos. Politicians chase election cycles; citizens live with the long-term effects. Managers chase easy metrics; customers absorb the frustration. Once you see principal–agent problems, you start seeing why seemingly intelligent organizations keep doing self-destructive things: the incentives are miswired. The Cobra Effect: perverse incentives grow cobras Sometimes this miswiring gets darkly funny. Reward outcomes and people will manufacture the appearance of outcomes. In the original parable, a colonial government offered a bounty for dead cobras — and people began breeding cobras. This isn’t historical trivia; it’s a universal pattern: Reward bug counts → people file junk bugs. Reward convictions → plea bargains + overcharging. Reward content volume → SEO sludge. Reward “delivery” → rushed work + tech debt. The world is full of cobra farms. 2) Why fixing things often makes them worse Okay, so: choose better metrics, align incentives, done. Not quite. Because even well-intentioned fixes trigger the next law: second-order effects. Chesterton’s Fence: don’t remove constraints you don’t understand You walk into an old system and see “stupid rules.” You want to clean house. You want to simplify. But: why is that rule there? Don’t remove a fence until you know why it was built. A lot of institutional weirdness is scar tissue from past disasters. The rule might be dumb — but if you don’t understand it, you don’t know what disaster you’re re-inviting. This is why naive reformers are dangerous: they confuse “not understanding a thing” with “the thing being pointless.” Gall’s Law: complex systems must grow from simple working ones Even if the fence is removable, you still hit the next problem: A complex system that works is always evolved from a simple system that worked. This demolishes a common fantasy: that you can design complexity from scratch. Most large redesigns fail for one reason: They try to create a finished organism instead of growing a living embryo. If the system matters, you don’t “implement” the final form. You build something simpler that works. Then you iterate. Gall’s law is harsh, but kind: it explains why so many ambitious “transformations” flame out. 3) Efficiency doesn’t save you (and sometimes consumes you) Now suppose you do manage to improve a system. You make it cheaper, faster, more efficient. Surely this reduces resource usage? Often, no. Jevons Paradox: efficiency increases total consumption When you make something more efficient, you often make people use more of it. Make lighting cheaper → people illuminate more spaces. Make driving more fuel-efficient → people drive farther. Make computing cheaper → people compute vastly more. Efficiency doesn’t always shrink the pie. It can expand it. This is one of the most important and least emotionally intuitive truths about progress: efficiency changes behavior. 4) Some things don’t get more efficient — and get expensive forever Now meet the mirror image of Jevons: not everything can get dramatically more productive. Some work is bottlenecked by time, humans, and attention. Baumol’s Cost Disease: sectors that don’t scale inflate A string quartet takes as long to play Beethoven as it did 200 years ago. A therapist can’t 10× their clients without breaking the thing. A teacher can’t “scale” classroom attention the way software scales distribution. Meanwhile, other industries do scale — manufacturing, computing, logistics. So as society grows richer, productivity sectors get cheaper and cheaper… and human-time sectors get relatively more expensive. That’s why: healthcare education legal services childcare eldercare …feel like they eat the world. Baumol isn’t “a problem to solve” so much as a physics constraint: certain value comes from human presence. And presence doesn’t compress easily. 5) The invisible accelerant: networks At this point you might feel like everything is doom and friction. It’s not. Some forces make systems wildly better as they grow. The biggest one is networks. Metcalfe’s Law: value scales with connections A phone is useless alone. A fax machine is useless alone. A social app is useless without other humans. As users increase, connections increase faster than users do. That creates accelerating value. Reed’s Law: groups scale even faster than connections But it’s not just one-to-one links. Once people can form groups — communities, coalitions, companies, subcultures — the number of potential groupings explodes. That’s Reed’s law: group-forming networks can scale with frightening speed. This is why networked platforms can go from “niche” to “dominant” almost overnight: the product isn’t just features — it’s the social graph. 6) Progress has a heartbeat: learning curves Not all progress comes from networks. Some comes from repetition. Wright’s Law: cost falls with cumulative production This is the law behind why solar, batteries, and manufacturing tech get cheaper and cheaper: Every doubling of cumulative production yields a predictable cost reduction. The implications are enormous: the future is shaped by what we manufacture at scale volume is not just output; it’s learning building the thing teaches you to build the thing better Strategy through Wright’s law becomes: maximize learning rate. Not “be brilliant,” but “iterate relentlessly.” 7) Cooperation is rare — and competition forces ugliness Now we move from economics into game theory and moral physics. Even with good metrics, good redesign, good scaling… Sometimes the system makes people do bad things. Prisoner’s Dilemma: defecting is rational If you and I cooperate, we both win. But if I suspect you might defect, I should defect first. So we both defect. We both lose. This structure appears everywhere: labor vs management nations vs nations companies vs companies roommates siblings Twitter discourse It’s tragedy-by-incentives. Moloch: the god of coordination failure “Moloch” is the poetic version of the same idea: systems where competition forces everyone into worse behavior, even if nobody wants it. No one wants the attention economy. But creators compete for attention. Platforms compete for engagement. So everyone converges on outrage and addiction. Moloch doesn’t need villains. It only needs incentives. 8) The biggest mistake smart people make: believing in averages Now we arrive at the statistical heart of why forecasts fail. Most planning assumes the world behaves like a bell curve: most outcomes are near the average, extremes are rare. In many domains, that’s false. Fat tails: extremes happen way more than you think In fat-tailed worlds, the “average” is a comforting lie. Outliers dominate: venture returns blockbuster movies bestselling authors company outcomes war and peace pandemics market crashes In a fat-tailed world: one event can erase ten years of progress or create it overnight Black swans: surprise + impact + fake hindsight A black swan isn’t just an outlier. It’s an outlier we didn’t know how to model. The signature of black swans is: huge impact surprise beforehand “it was obvious” afterward We are story machines. We can rationalize anything after it happens. Survivorship bias: you’re studying the winners This is why business advice is mostly nonsense. We read biographies of billionaires and imitate their habits — forgetting the cemetery of equally hardworking, equally smart people who lost. Survivorship bias turns randomness into “wisdom.” A good thinker always asks: what am I not seeing because it died? 9) The final set of tools: tradeoffs, simplicity, and time After you’ve internalized incentives, scaling, networks, and tail risk, you earn the right to something important: Less ideology. More judgment. That’s what these last lenses provide. Pareto efficiency: every improvement has a cost At some point, you stop making “free” gains and enter a world of tradeoffs. If you want more of A, you give up B. This is what breaks utopian thinking: more safety can mean less liberty more speed can mean less quality more fairness can mean less efficiency more growth can mean more inequality Smart people aren’t the ones who avoid tradeoffs. They’re the ones who name the tradeoff out loud. Occam’s Razor: don’t add gears without proof Now that you’re thinking in systems, you could easily overcomplicate. Occam is your brake pedal: prefer the simplest explanation that predicts. It’s not “simplicity is truth.” It’s: don’t hallucinate complexity. Lindy: time is the best filter we have In fragile worlds, “new” is often a synonym for “untested.” The Lindy effect says: the longer something has survived, the longer it’s likely to survive. Ideas, books, institutions, even practices: time is a stress test. Lindy isn’t anti-innovation. It’s pro-robustness. Comparative advantage: specialization beats self-reliance Finally, comparative advantage gives you the social version of Occam. Even if you’re worse at everything than someone else… trade can still make both better off, because efficiency comes from relative differences. That lens dissolves a lot of macho self-sufficiency myths. So what does this worldview do? It does three things. First: it replaces naive optimism with durable optimism Not “everything will work out.” But: we can build systems that don’t collapse under their own incentives. Second: it changes what you fear Not competitors. Not critics. Not even failure. You start fearing: bad metrics misaligned incentives brittle complexity tail risks coordination failure Which are the real predators. Third: it gives you a usable strategy A decision-making style that looks like this: Start simple (Gall) Measure carefully (Goodhart) Align incentives (principal–agent) Expect adaptation (cobra effect) Respect old constraints (Chesterton) Model scaling honestly (Metcalfe/Reed/Wright) Don’t assume efficiency saves you (Jevons/Baumol) Prepare for tails (fat tails / black swans) Don’t trust winner stories (survivorship bias) Name tradeoffs and keep models simple (Pareto + Occam + Lindy) That list is more than theory. It’s a survival kit for reality. Closing: the meta-law If I had to compress this entire worldview into one sentence, it would be: Outcomes come from incentives and scaling under uncertainty—not from intentions and plans. Most people live inside stories. This toolkit makes you live inside systems. And once you do, you become harder to fool — including by yourself.
1:38

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything Most smart people don’t fail because they’re dumb. They fail because they apply clean logic to a messy world — and the world punishes that mistake with a smile. The messy truth is that modern life is shaped less by individual intent and more by systems: incentives, competition, scaling effects, path dependence, and statistical weirdness. These systems produce outcomes that feel unfair or mysterious until you learn the underlying “laws” — a set of lenses that let you predict how things actually behave. This is not about becoming cynical. It’s about becoming accurate. Once you internalize these lenses, you start noticing that most disagreements aren’t about values. They’re about which hidden force you think dominates: Do incentives matter more than morals? Do networks scale value more than craftsmanship? Do rare events matter more than averages? Do systems evolve, or can they be designed? This article is a guided map through those forces — told as one story. 1) The seduction of “doing the obvious thing” Imagine you’re in charge of improving something important: a company, a city, a hospital, a school, a product, maybe even your own life. You do what responsible people do: you define a goal. You pick a metric. And you tell everyone: we’re going to win on this number. This is where the first trap snaps shut. Goodhart’s Law: the metric stops being real When a measure becomes a target, it stops being a good measure. Before it became a target, the metric was an instrument: a thermometer. After it becomes a target, it becomes a game. Hospitals improve “wait times” by changing intake rules. Companies improve “engagement” by nudging addiction. Schools improve test scores by teaching to the test. Police departments improve crime stats by changing what counts as a crime. Not because anyone is evil. Because the system rewards it. The principal–agent problem: the doers don’t pay This is the deeper engine under Goodhart. The person deciding is not the person suffering the consequences. Executives chase quarterly optics; employees deal with the chaos. Politicians chase election cycles; citizens live with the long-term effects. Managers chase easy metrics; customers absorb the frustration. Once you see principal–agent problems, you start seeing why seemingly intelligent organizations keep doing self-destructive things: the incentives are miswired. The Cobra Effect: perverse incentives grow cobras Sometimes this miswiring gets darkly funny. Reward outcomes and people will manufacture the appearance of outcomes. In the original parable, a colonial government offered a bounty for dead cobras — and people began breeding cobras. This isn’t historical trivia; it’s a universal pattern: Reward bug counts → people file junk bugs. Reward convictions → plea bargains + overcharging. Reward content volume → SEO sludge. Reward “delivery” → rushed work + tech debt. The world is full of cobra farms. 2) Why fixing things often makes them worse Okay, so: choose better metrics, align incentives, done. Not quite. Because even well-intentioned fixes trigger the next law: second-order effects. Chesterton’s Fence: don’t remove constraints you don’t understand You walk into an old system and see “stupid rules.” You want to clean house. You want to simplify. But: why is that rule there? Don’t remove a fence until you know why it was built. A lot of institutional weirdness is scar tissue from past disasters. The rule might be dumb — but if you don’t understand it, you don’t know what disaster you’re re-inviting. This is why naive reformers are dangerous: they confuse “not understanding a thing” with “the thing being pointless.” Gall’s Law: complex systems must grow from simple working ones Even if the fence is removable, you still hit the next problem: A complex system that works is always evolved from a simple system that worked. This demolishes a common fantasy: that you can design complexity from scratch. Most large redesigns fail for one reason: They try to create a finished organism instead of growing a living embryo. If the system matters, you don’t “implement” the final form. You build something simpler that works. Then you iterate. Gall’s law is harsh, but kind: it explains why so many ambitious “transformations” flame out. 3) Efficiency doesn’t save you (and sometimes consumes you) Now suppose you do manage to improve a system. You make it cheaper, faster, more efficient. Surely this reduces resource usage? Often, no. Jevons Paradox: efficiency increases total consumption When you make something more efficient, you often make people use more of it. Make lighting cheaper → people illuminate more spaces. Make driving more fuel-efficient → people drive farther. Make computing cheaper → people compute vastly more. Efficiency doesn’t always shrink the pie. It can expand it. This is one of the most important and least emotionally intuitive truths about progress: efficiency changes behavior. 4) Some things don’t get more efficient — and get expensive forever Now meet the mirror image of Jevons: not everything can get dramatically more productive. Some work is bottlenecked by time, humans, and attention. Baumol’s Cost Disease: sectors that don’t scale inflate A string quartet takes as long to play Beethoven as it did 200 years ago. A therapist can’t 10× their clients without breaking the thing. A teacher can’t “scale” classroom attention the way software scales distribution. Meanwhile, other industries do scale — manufacturing, computing, logistics. So as society grows richer, productivity sectors get cheaper and cheaper… and human-time sectors get relatively more expensive. That’s why: healthcare education legal services childcare eldercare …feel like they eat the world. Baumol isn’t “a problem to solve” so much as a physics constraint: certain value comes from human presence. And presence doesn’t compress easily. 5) The invisible accelerant: networks At this point you might feel like everything is doom and friction. It’s not. Some forces make systems wildly better as they grow. The biggest one is networks. Metcalfe’s Law: value scales with connections A phone is useless alone. A fax machine is useless alone. A social app is useless without other humans. As users increase, connections increase faster than users do. That creates accelerating value. Reed’s Law: groups scale even faster than connections But it’s not just one-to-one links. Once people can form groups — communities, coalitions, companies, subcultures — the number of potential groupings explodes. That’s Reed’s law: group-forming networks can scale with frightening speed. This is why networked platforms can go from “niche” to “dominant” almost overnight: the product isn’t just features — it’s the social graph. 6) Progress has a heartbeat: learning curves Not all progress comes from networks. Some comes from repetition. Wright’s Law: cost falls with cumulative production This is the law behind why solar, batteries, and manufacturing tech get cheaper and cheaper: Every doubling of cumulative production yields a predictable cost reduction. The implications are enormous: the future is shaped by what we manufacture at scale volume is not just output; it’s learning building the thing teaches you to build the thing better Strategy through Wright’s law becomes: maximize learning rate. Not “be brilliant,” but “iterate relentlessly.” 7) Cooperation is rare — and competition forces ugliness Now we move from economics into game theory and moral physics. Even with good metrics, good redesign, good scaling… Sometimes the system makes people do bad things. Prisoner’s Dilemma: defecting is rational If you and I cooperate, we both win. But if I suspect you might defect, I should defect first. So we both defect. We both lose. This structure appears everywhere: labor vs management nations vs nations companies vs companies roommates siblings Twitter discourse It’s tragedy-by-incentives. Moloch: the god of coordination failure “Moloch” is the poetic version of the same idea: systems where competition forces everyone into worse behavior, even if nobody wants it. No one wants the attention economy. But creators compete for attention. Platforms compete for engagement. So everyone converges on outrage and addiction. Moloch doesn’t need villains. It only needs incentives. 8) The biggest mistake smart people make: believing in averages Now we arrive at the statistical heart of why forecasts fail. Most planning assumes the world behaves like a bell curve: most outcomes are near the average, extremes are rare. In many domains, that’s false. Fat tails: extremes happen way more than you think In fat-tailed worlds, the “average” is a comforting lie. Outliers dominate: venture returns blockbuster movies bestselling authors company outcomes war and peace pandemics market crashes In a fat-tailed world: one event can erase ten years of progress or create it overnight Black swans: surprise + impact + fake hindsight A black swan isn’t just an outlier. It’s an outlier we didn’t know how to model. The signature of black swans is: huge impact surprise beforehand “it was obvious” afterward We are story machines. We can rationalize anything after it happens. Survivorship bias: you’re studying the winners This is why business advice is mostly nonsense. We read biographies of billionaires and imitate their habits — forgetting the cemetery of equally hardworking, equally smart people who lost. Survivorship bias turns randomness into “wisdom.” A good thinker always asks: what am I not seeing because it died? 9) The final set of tools: tradeoffs, simplicity, and time After you’ve internalized incentives, scaling, networks, and tail risk, you earn the right to something important: Less ideology. More judgment. That’s what these last lenses provide. Pareto efficiency: every improvement has a cost At some point, you stop making “free” gains and enter a world of tradeoffs. If you want more of A, you give up B. This is what breaks utopian thinking: more safety can mean less liberty more speed can mean less quality more fairness can mean less efficiency more growth can mean more inequality Smart people aren’t the ones who avoid tradeoffs. They’re the ones who name the tradeoff out loud. Occam’s Razor: don’t add gears without proof Now that you’re thinking in systems, you could easily overcomplicate. Occam is your brake pedal: prefer the simplest explanation that predicts. It’s not “simplicity is truth.” It’s: don’t hallucinate complexity. Lindy: time is the best filter we have In fragile worlds, “new” is often a synonym for “untested.” The Lindy effect says: the longer something has survived, the longer it’s likely to survive. Ideas, books, institutions, even practices: time is a stress test. Lindy isn’t anti-innovation. It’s pro-robustness. Comparative advantage: specialization beats self-reliance Finally, comparative advantage gives you the social version of Occam. Even if you’re worse at everything than someone else… trade can still make both better off, because efficiency comes from relative differences. That lens dissolves a lot of macho self-sufficiency myths. So what does this worldview do? It does three things. First: it replaces naive optimism with durable optimism Not “everything will work out.” But: we can build systems that don’t collapse under their own incentives. Second: it changes what you fear Not competitors. Not critics. Not even failure. You start fearing: bad metrics misaligned incentives brittle complexity tail risks coordination failure Which are the real predators. Third: it gives you a usable strategy A decision-making style that looks like this: Start simple (Gall) Measure carefully (Goodhart) Align incentives (principal–agent) Expect adaptation (cobra effect) Respect old constraints (Chesterton) Model scaling honestly (Metcalfe/Reed/Wright) Don’t assume efficiency saves you (Jevons/Baumol) Prepare for tails (fat tails / black swans) Don’t trust winner stories (survivorship bias) Name tradeoffs and keep models simple (Pareto + Occam + Lindy) That list is more than theory. It’s a survival kit for reality. Closing: the meta-law If I had to compress this entire worldview into one sentence, it would be: Outcomes come from incentives and scaling under uncertainty—not from intentions and plans. Most people live inside stories. This toolkit makes you live inside systems. And once you do, you become harder to fool — including by yourself.

Carlos E. Perez

102,951 Aufrufe • vor 6 Monaten