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The Fogo Thesis -> Zero Compromise Latency became accepted Fairness became negotiable wtf happened? Crypto is supposed to fix finance. If you’re a trader, 400ms isn't fast enough. 100ms isn't even fast enough. Once you pull back a couple layers it's wild what's going on🤯 You’re fighting for PnL...

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Jared

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73,534 views • 8 months ago •via X (Twitter)

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The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.
0:54

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

Muriel Medard

44,395 views • 1 month ago

My conversation with Max Resnick As a researcher pushing the frontier of Solana's market structure, Max is relentlessly focused on the engineering required to turn a general-purpose L1 into credibly neutral, high-performance financial infrastructure. He and Anza are doing the hard protocol level work to mitigate bad MEV, eliminate colocation advantages, and bring all of finance onchain We spend a lot of time unpacking the shift from single-leader to Multi-Concurrent Leader (MCL) designs and why this change could fundamentally redefine global market structure At the center of this conversation is the Constellation Proposal, 50ms protocol-enforced economic ticks and T+0 settlement, and the belief that true DeFi requires moving beyond the constraints of traditional data center colocation We discuss: - Why multi-proposer designs are necessary to solve the single-leader bottleneck - The end of colocation and what it means for truly decentralized trading - Multiple Concurrent Leaders as a solution to censorship and MEV - The Constellation Proposal, 50ms economic ticks, and latency reduction - Why Solana is the preferred home for high-throughput trading and T+0 settlement - Market making dynamics and dual-flow batch auctions - FCFS vs. priority ordering and the future of on-chain finance Enjoy! Timestamps: 0:00 - Introduction: The Vision for a Decentralized NASDAQ 3:40 - Multi-Proposer Designs: Solving the Single-Leader Bottleneck 11:07 - Constrained Optimization: Balancing Decentralization with Reality 23:50 - Why Solana? High-Performance Trading & T+0 Settlement 32:47 - Deep Dive: The Constellation Proposal & 50ms Economic Ticks 46:00 - Market Making & Dual-Flow Batch Auctions 59:13 - FCFS vs. Priority Ordering: The Future of High-Throughput Finance
1:04:37

My conversation with Max Resnick As a researcher pushing the frontier of Solana's market structure, Max is relentlessly focused on the engineering required to turn a general-purpose L1 into credibly neutral, high-performance financial infrastructure. He and Anza are doing the hard protocol level work to mitigate bad MEV, eliminate colocation advantages, and bring all of finance onchain We spend a lot of time unpacking the shift from single-leader to Multi-Concurrent Leader (MCL) designs and why this change could fundamentally redefine global market structure At the center of this conversation is the Constellation Proposal, 50ms protocol-enforced economic ticks and T+0 settlement, and the belief that true DeFi requires moving beyond the constraints of traditional data center colocation We discuss: - Why multi-proposer designs are necessary to solve the single-leader bottleneck - The end of colocation and what it means for truly decentralized trading - Multiple Concurrent Leaders as a solution to censorship and MEV - The Constellation Proposal, 50ms economic ticks, and latency reduction - Why Solana is the preferred home for high-throughput trading and T+0 settlement - Market making dynamics and dual-flow batch auctions - FCFS vs. priority ordering and the future of on-chain finance Enjoy! Timestamps: 0:00 - Introduction: The Vision for a Decentralized NASDAQ 3:40 - Multi-Proposer Designs: Solving the Single-Leader Bottleneck 11:07 - Constrained Optimization: Balancing Decentralization with Reality 23:50 - Why Solana? High-Performance Trading & T+0 Settlement 32:47 - Deep Dive: The Constellation Proposal & 50ms Economic Ticks 46:00 - Market Making & Dual-Flow Batch Auctions 59:13 - FCFS vs. Priority Ordering: The Future of High-Throughput Finance

Logan Jastremski

19,780 views • 4 months ago

I love the Arcade1Up Official cabinets that Trista has given me the last couple years. Yes, you can run free emulators on almost any device, but having the games running in a cabinet with arcade controls is a much better experience, even though it is just a packaged emulator. I was pretty decent back in the day, but after playing these for a while, I got farther than young-John ever did. Recently I played an original Joust machine at Cidercade and on my second game, I blew away my previous best score at home — 158k! The subtle control latency of the emulated experience versus the real thing matters! I measured the press-to-flap latency at home, and it looks like about 80ms. It isn’t blatantly obvious, but it shows up in the game feel and control error rate. I know there is a hard core community around emulator optimization, and with high refresh rate monitors it is possible to get objectively lower latency than the original CRT based hardware, but there is no reason the popular consumer versions can’t get most of the way there. This is probably just a matter of backing up a triple buffered swap chain or extra layers of image scaling / UI compositor getting in the way. Phase sync to the last quarter or so of the video interval and swap to the actual display should cut that latency in half. Doing the bit plane graphics and scaling directly to /dev/fb0 with software would be a guaranteed low latency path if you can get vsync timing. Trivia: The real Joust, and all the classic Williams games, didn’t even page flip, they just drew straight to the frame buffer, paying attention to the scan time.
0:23

I love the Arcade1Up Official cabinets that Trista has given me the last couple years. Yes, you can run free emulators on almost any device, but having the games running in a cabinet with arcade controls is a much better experience, even though it is just a packaged emulator. I was pretty decent back in the day, but after playing these for a while, I got farther than young-John ever did. Recently I played an original Joust machine at Cidercade and on my second game, I blew away my previous best score at home — 158k! The subtle control latency of the emulated experience versus the real thing matters! I measured the press-to-flap latency at home, and it looks like about 80ms. It isn’t blatantly obvious, but it shows up in the game feel and control error rate. I know there is a hard core community around emulator optimization, and with high refresh rate monitors it is possible to get objectively lower latency than the original CRT based hardware, but there is no reason the popular consumer versions can’t get most of the way there. This is probably just a matter of backing up a triple buffered swap chain or extra layers of image scaling / UI compositor getting in the way. Phase sync to the last quarter or so of the video interval and swap to the actual display should cut that latency in half. Doing the bit plane graphics and scaling directly to /dev/fb0 with software would be a guaranteed low latency path if you can get vsync timing. Trivia: The real Joust, and all the classic Williams games, didn’t even page flip, they just drew straight to the frame buffer, paying attention to the scan time.

John Carmack

61,855 views • 1 year ago

New interview: Reiner Pope, co-founder/CEO of MatX A counterintuitive throughput insight: “Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.” We get into: • The hybrid SRAM + HBM bet, and why pipeline parallelism finally works • Why sparse MoE drives MatX to “the most interconnect of any announced product” • Why frontier labs are willing to bet on an AI ASIC startup • Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not) • Why 95% of model-side news is noise for chip design • The biggest challenges ahead 00:00 “We left Google one week before ChatGPT” 00:24 Intro: who is MatX 01:17 Origin story: leaving Google for LLM chips 02:21 GPT-3 and the “too expensive” problem 04:25 Why buy hardware that is not a GPU 05:52 Overcoming the CUDA moat 08:46 Early investors 09:35 The name MatX 09:59 The chip: matrix multiply + hybrid SRAM/HBM 12:11 Why pipeline parallelism finally works 14:22 Reading papers and Google going dark 15:20 Research agenda: attention and numerics 17:06 Five specs and meeting customers where they are 19:24 Why frontier labs are the natural first customer 20:32 Workloads: training, prefill, decode 22:18 Little’s law and the throughput case for low latency 24:29 Interconnect and MoE topology 26:35 Inside the team: 100 people, full stack 28:32 Agentic AI: 95% noise for hardware 30:35 KV cache sizing in an agentic world 32:11 How MatX uses AI for chip design (Verilog + BlueSpec) 34:23 Go to market: proving credibility under NDA 35:12 Porting effort for frontier labs 36:34 Biggest skepticism: manufacturing at gigawatt scale 37:32 Hiring plug Vikram Sekar
38:57

New interview: Reiner Pope, co-founder/CEO of MatX A counterintuitive throughput insight: “Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.” We get into: • The hybrid SRAM + HBM bet, and why pipeline parallelism finally works • Why sparse MoE drives MatX to “the most interconnect of any announced product” • Why frontier labs are willing to bet on an AI ASIC startup • Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not) • Why 95% of model-side news is noise for chip design • The biggest challenges ahead 00:00 “We left Google one week before ChatGPT” 00:24 Intro: who is MatX 01:17 Origin story: leaving Google for LLM chips 02:21 GPT-3 and the “too expensive” problem 04:25 Why buy hardware that is not a GPU 05:52 Overcoming the CUDA moat 08:46 Early investors 09:35 The name MatX 09:59 The chip: matrix multiply + hybrid SRAM/HBM 12:11 Why pipeline parallelism finally works 14:22 Reading papers and Google going dark 15:20 Research agenda: attention and numerics 17:06 Five specs and meeting customers where they are 19:24 Why frontier labs are the natural first customer 20:32 Workloads: training, prefill, decode 22:18 Little’s law and the throughput case for low latency 24:29 Interconnect and MoE topology 26:35 Inside the team: 100 people, full stack 28:32 Agentic AI: 95% noise for hardware 30:35 KV cache sizing in an agentic world 32:11 How MatX uses AI for chip design (Verilog + BlueSpec) 34:23 Go to market: proving credibility under NDA 35:12 Porting effort for frontier labs 36:34 Biggest skepticism: manufacturing at gigawatt scale 37:32 Hiring plug Vikram Sekar

Semi Doped

19,439 views • 4 months ago

The Head of Innovation at Janus Henderson thinks blockchain is the most disruptive tech in asset management We sat down with Nick Cherney to discuss why financial services are moving onchain, what Janus Henderson is already building, and what's still in the way Timestamps 👇 0:00 Intro 0:38 Meet Nick Cherney, Head of Innovation at Janus Henderson 1:43 From ETFs to tokenization 3:05 Why blockchain is more disruptive than AI 4:05 Tokenization explained simply 4:51 What Janus Henderson does onchain 7:03 Why the choice of blockchain matters 9:05 The real friction points for institutions 10:05 Why he says no blockchain has ever been hacked 11:20 Blockchain is not crypto 13:08 The settlement gap holding tokenization back 15:20 What institutions need from a high-performance chain 16:54 The next inflection point for RWAs 18:19 Closing thoughts
19:37

The Head of Innovation at Janus Henderson thinks blockchain is the most disruptive tech in asset management We sat down with Nick Cherney to discuss why financial services are moving onchain, what Janus Henderson is already building, and what's still in the way Timestamps 👇 0:00 Intro 0:38 Meet Nick Cherney, Head of Innovation at Janus Henderson 1:43 From ETFs to tokenization 3:05 Why blockchain is more disruptive than AI 4:05 Tokenization explained simply 4:51 What Janus Henderson does onchain 7:03 Why the choice of blockchain matters 9:05 The real friction points for institutions 10:05 Why he says no blockchain has ever been hacked 11:20 Blockchain is not crypto 13:08 The settlement gap holding tokenization back 15:20 What institutions need from a high-performance chain 16:54 The next inflection point for RWAs 18:19 Closing thoughts

Monad

81,741 views • 1 month ago

I gave Claude Fable 5 just $100 to test the simplest arbitrage. Left my laptop opened for 24 hours. Session PnL: clean $1,571.66 On Polymarket, YES + NO Aren't there thousands of arbitrage bots already? Absolutely. You're not competing against people. You're competing against infrastructure. The biggest improvement wasn't a new strategy. It was rebuilding the entire execution pipeline. The bot runs on a VPS close to the APIs it talks to instead of my local machine. It keeps persistent connections open instead of reconnecting every request. Market metadata is cached in memory so it only refreshes live prices. Orders are prepared before the opportunity exists. When YES + NO finally drops below $1, there's almost nothing left to calculate. It checks liquidity, order book and submits both legs immediately. Will that beat firms spending millions on low-latency infrastructure? Probably not. It doesn't have to. There are hundreds of active prediction markets, constant volatility and plenty of opportunities that last long enough for a well-engineered bot to compete. That's probably the biggest thing Claude Fable 5 changed for me. I stopped asking it to build me a trading bot and started asking where am I wasting milliseconds. That single shift made the bot noticeably more competitive than any new trading strategy I could have invented. Arbitrage is the king. Irt lives everywhere. The only question is are you ready to study it. Leaving a full build + latency tips guide below this post.
0:46

I gave Claude Fable 5 just $100 to test the simplest arbitrage. Left my laptop opened for 24 hours. Session PnL: clean $1,571.66 On Polymarket, YES + NO Aren't there thousands of arbitrage bots already? Absolutely. You're not competing against people. You're competing against infrastructure. The biggest improvement wasn't a new strategy. It was rebuilding the entire execution pipeline. The bot runs on a VPS close to the APIs it talks to instead of my local machine. It keeps persistent connections open instead of reconnecting every request. Market metadata is cached in memory so it only refreshes live prices. Orders are prepared before the opportunity exists. When YES + NO finally drops below $1, there's almost nothing left to calculate. It checks liquidity, order book and submits both legs immediately. Will that beat firms spending millions on low-latency infrastructure? Probably not. It doesn't have to. There are hundreds of active prediction markets, constant volatility and plenty of opportunities that last long enough for a well-engineered bot to compete. That's probably the biggest thing Claude Fable 5 changed for me. I stopped asking it to build me a trading bot and started asking where am I wasting milliseconds. That single shift made the bot noticeably more competitive than any new trading strategy I could have invented. Arbitrage is the king. Irt lives everywhere. The only question is are you ready to study it. Leaving a full build + latency tips guide below this post.

Oracle Boar

18,091 views • 26 days ago

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.
2:15

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

Ivan Burazin

22,655 views • 4 months ago

Yesterday i publicly deposited $100 to a new wallet and asked Claude Fable 5 to double it on risk free arbitrage. Today it's sitting at $1,787.21 made overnight. I went through every code line and was shocked. It used just one formula to make 17x in one day: YES + NO Run the bot on a VPS close to the exchange instead of your laptop > Keep WebSocket connections alive instead of reconnecting every loop > Cache market metadata in memory and only update prices > Prepare both orders before the opportunity exists That last one changed everything. Most bots see YES + NO < $1, then build the trade. Mine already has the orders ready. When the spread appears, it only checks liquidity and submits both legs. No rebuilding. No unnecessary calculations. Just execution. Will you beat firms with million-dollar low-latency infrastructure? Probably not. But you don't need to. Most developers lose opportunities long before network latency becomes the bottleneck. They lose them inside their own code. Claude Fable 5 didn't give me a smarter strategy. It taught me how to stop wasting milliseconds. And in arbitrage, that's often the only edge that matters. Make sure to explain it to Claude if you wanna rebuild the same one. Leaving a full guide with every aspect covered below.
0:43

Yesterday i publicly deposited $100 to a new wallet and asked Claude Fable 5 to double it on risk free arbitrage. Today it's sitting at $1,787.21 made overnight. I went through every code line and was shocked. It used just one formula to make 17x in one day: YES + NO Run the bot on a VPS close to the exchange instead of your laptop > Keep WebSocket connections alive instead of reconnecting every loop > Cache market metadata in memory and only update prices > Prepare both orders before the opportunity exists That last one changed everything. Most bots see YES + NO < $1, then build the trade. Mine already has the orders ready. When the spread appears, it only checks liquidity and submits both legs. No rebuilding. No unnecessary calculations. Just execution. Will you beat firms with million-dollar low-latency infrastructure? Probably not. But you don't need to. Most developers lose opportunities long before network latency becomes the bottleneck. They lose them inside their own code. Claude Fable 5 didn't give me a smarter strategy. It taught me how to stop wasting milliseconds. And in arbitrage, that's often the only edge that matters. Make sure to explain it to Claude if you wanna rebuild the same one. Leaving a full guide with every aspect covered below.

Oracle Boar

101,882 views • 24 days ago

INTERVIEW: Is $LIT Cheap? | Will Price & Flip David Hoffman sits down with flip and Will Price to unpack why Lighter may be one of the most underpriced bets in the perps market. They talk about: - Whether $LIT is just a beta trade to $HYPE - Why zero-fee trading changes Lighter’s distribution strategy - The U.S. perps market that still hasn’t opened - Lighter buybacks, revenue, and why the valuation may not reflect the opportunity [TIMESTAMPS] 0:00 Intro 3:05 Why Lighter Is Different 5:32 ZK Rollup Advantages 6:46 Charging Traders, Not Retail 12:07 Fairness Without MEV 14:50 Why Hyperliquid Can’t Copy 18:03 Distribution Beyond Crypto 22:08 Latency & Flow Quality 26:50 Technical Edge Across the Stack 30:31 RWAs & Pre-IPO Markets 34:07 U.S. Perps Market Play 36:05 Back-End For Big Brokers 38:28 Bootstrapping Liquidity 41:54 Lighter As A Platform 46:34 Token Value & Buybacks 50:11 Market Cap Versus FDV 53:02 Valuing LIT Properly 57:51 Cracked Team & Better Comms 58:13 Joining The Lighter Community 58:51 What Comes Next
1:00:47

INTERVIEW: Is $LIT Cheap? | Will Price & Flip David Hoffman sits down with flip and Will Price to unpack why Lighter may be one of the most underpriced bets in the perps market. They talk about: - Whether $LIT is just a beta trade to $HYPE - Why zero-fee trading changes Lighter’s distribution strategy - The U.S. perps market that still hasn’t opened - Lighter buybacks, revenue, and why the valuation may not reflect the opportunity [TIMESTAMPS] 0:00 Intro 3:05 Why Lighter Is Different 5:32 ZK Rollup Advantages 6:46 Charging Traders, Not Retail 12:07 Fairness Without MEV 14:50 Why Hyperliquid Can’t Copy 18:03 Distribution Beyond Crypto 22:08 Latency & Flow Quality 26:50 Technical Edge Across the Stack 30:31 RWAs & Pre-IPO Markets 34:07 U.S. Perps Market Play 36:05 Back-End For Big Brokers 38:28 Bootstrapping Liquidity 41:54 Lighter As A Platform 46:34 Token Value & Buybacks 50:11 Market Cap Versus FDV 53:02 Valuing LIT Properly 57:51 Cracked Team & Better Comms 58:13 Joining The Lighter Community 58:51 What Comes Next

Bankless

15,304 views • 2 months ago

In 2026, 90% of all Polymarket profits will be taken by Python scripts.. And this is not a prediction. It’s already happening. So if you think political pundits and sports gurus are making profits in these areas, let me be the bearer of bad news. Studies have revealed that merely “16% of users are profitable.” More importantly, “most of these users are not human.” How bots are exactly taking your money: Speed. One bot made $313 into $438K in a month. It’s a simple trick: the bot would look at the btc price a few seconds before the price update on Polymarket by checking the price on Binance. There’s no strategy or intelligence involved: simply beating the latency of the system. Risk-Free Arbitrage It looks for markets where "YES + NO" equals less than $1. "94 cents," for example. The bot buys both sides of the market and makes off with 6 cents guaranteed. This occurs thousands of times daily. Not gambling but math. Stream parsing. In esports, the script is faster than the blink of an eye when parsing the stream for games like Dota 2 and League of Legends. A team fight appears on the screen. The bot has already placed its bets on the winner using the old odds. What is meant by the turning point of 2026? Dynamic fees were introduced on Polymarket to get rid of simple bots. But what happened? The difficulty level on this marketplace simply increased. Today, it is not only fast scripts that win. Full-on AI robots have joined this game. They read news and respond to certain events within a millisecond. But here comes the painful part: barrier to entry is dirt cheap. Virtual private servers for $60 per month. Libraries written in Python waiting on GitHub. But here’s the thing: You don’t have to create a bot of your own. All you have to do is copy those which are already winning. PolyCop helps you to track the most profitable wallets and replicate their trades automatically. No code. No infrastructure. Just tap into the wallets that are already dominating. → Copy the winners: Humans deal on intuition and vibes. Bots play on numbers and network latency. In this game, "intuition" always loses against "code." You have two choices here. You could learn how to code or you could “copy” people who have done it before you
0:29

In 2026, 90% of all Polymarket profits will be taken by Python scripts.. And this is not a prediction. It’s already happening. So if you think political pundits and sports gurus are making profits in these areas, let me be the bearer of bad news. Studies have revealed that merely “16% of users are profitable.” More importantly, “most of these users are not human.” How bots are exactly taking your money: Speed. One bot made $313 into $438K in a month. It’s a simple trick: the bot would look at the btc price a few seconds before the price update on Polymarket by checking the price on Binance. There’s no strategy or intelligence involved: simply beating the latency of the system. Risk-Free Arbitrage It looks for markets where "YES + NO" equals less than $1. "94 cents," for example. The bot buys both sides of the market and makes off with 6 cents guaranteed. This occurs thousands of times daily. Not gambling but math. Stream parsing. In esports, the script is faster than the blink of an eye when parsing the stream for games like Dota 2 and League of Legends. A team fight appears on the screen. The bot has already placed its bets on the winner using the old odds. What is meant by the turning point of 2026? Dynamic fees were introduced on Polymarket to get rid of simple bots. But what happened? The difficulty level on this marketplace simply increased. Today, it is not only fast scripts that win. Full-on AI robots have joined this game. They read news and respond to certain events within a millisecond. But here comes the painful part: barrier to entry is dirt cheap. Virtual private servers for $60 per month. Libraries written in Python waiting on GitHub. But here’s the thing: You don’t have to create a bot of your own. All you have to do is copy those which are already winning. PolyCop helps you to track the most profitable wallets and replicate their trades automatically. No code. No infrastructure. Just tap into the wallets that are already dominating. → Copy the winners: Humans deal on intuition and vibes. Bots play on numbers and network latency. In this game, "intuition" always loses against "code." You have two choices here. You could learn how to code or you could “copy” people who have done it before you

Blaze

66,385 views • 7 months ago

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust.​ where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows.​ if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic.​ first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge.​ what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots.​ - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow).​ Dedicated Polygon RPC node so your orders don't die in public rate limits.​ - VPS physically close to Polymarket's infra (ping is literally part of your edge).​ where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine.​ the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds.​ for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed.​ if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure.​ log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you.​ where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds.​ BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic.​ two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions.​ self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market.​ how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market.​ start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade.​ use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys
0:21

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust.​ where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows.​ if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic.​ first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge.​ what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots.​ - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow).​ Dedicated Polygon RPC node so your orders don't die in public rate limits.​ - VPS physically close to Polymarket's infra (ping is literally part of your edge).​ where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine.​ the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds.​ for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed.​ if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure.​ log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you.​ where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds.​ BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic.​ two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions.​ self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market.​ how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market.​ start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade.​ use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys

0xCryptoGirl

25,454 views • 7 months ago

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 views • 1 year ago

this is a lecture on how to hunt for investors, cofounders & new hires complete breakdown: 00:00 8 billion people don't know you exist 00:24 No one knows you. No one cares. 01:09 Stop being a farmer 01:51 The one thing all great hunters share 02:10 How I got my co-founder in 30 seconds 03:30 You + them = 3 people 05:36 Missionaries vs mercenaries 10:31 Why you have to be generous with equity 12:35 The right vesting schedule 13:17 Always trial before you commit 14:19 The 9-to-5 specialist red flag 15:05 Why logos mean nothing 17:46 Hunt them on GitHub 19:37 Niche Discords and subreddits 20:58 Why paid referrals work 22:10 Hiring friends: the trap 24:39 Why advisors are bullshit 31:29 Should you raise at all? 35:06 Investors only buy growth stories 35:58 Fundraising is running an auction 37:36 The luck factor (and Twitter lies) 39:33 Angels vs VCs 47:21 The hierarchy: inbound > warm > cold 48:00 Never ask a VC for intros if they pass 48:59 The double opt-in rule 52:32 Fundraising is a full-time job 53:33 Why a 4-week VC chat is a "no" 56:22 The leverage trick 01:00:24 Fundraising advisors = scam 01:02:21 The one trait of every great founder 01:03:18 Your homework: a list of 100
1:04:08

this is a lecture on how to hunt for investors, cofounders & new hires complete breakdown: 00:00 8 billion people don't know you exist 00:24 No one knows you. No one cares. 01:09 Stop being a farmer 01:51 The one thing all great hunters share 02:10 How I got my co-founder in 30 seconds 03:30 You + them = 3 people 05:36 Missionaries vs mercenaries 10:31 Why you have to be generous with equity 12:35 The right vesting schedule 13:17 Always trial before you commit 14:19 The 9-to-5 specialist red flag 15:05 Why logos mean nothing 17:46 Hunt them on GitHub 19:37 Niche Discords and subreddits 20:58 Why paid referrals work 22:10 Hiring friends: the trap 24:39 Why advisors are bullshit 31:29 Should you raise at all? 35:06 Investors only buy growth stories 35:58 Fundraising is running an auction 37:36 The luck factor (and Twitter lies) 39:33 Angels vs VCs 47:21 The hierarchy: inbound > warm > cold 48:00 Never ask a VC for intros if they pass 48:59 The double opt-in rule 52:32 Fundraising is a full-time job 53:33 Why a 4-week VC chat is a "no" 56:22 The leverage trick 01:00:24 Fundraising advisors = scam 01:02:21 The one trait of every great founder 01:03:18 Your homework: a list of 100

Founders Inc

19,492 views • 3 months ago

Ludvig Åberg breaks down his strategy for every hole at Augusta National in TGJ Podcast #207: 00:01:11 But first, some LEGO talk. 00:10:52 No. 1: The hardest pin on the course. 00:14:42 No. 2: Left is dead, so you have to love the bunker. 00:18:36 No. 3: "I've got trust issues with banking it into the hill." 00:22:49 No. 4: The most visually deceptive hole at Augusta. 00:25:02 No. 5: "It's long and has one of the hardest greens." 00:27:07 No. 6: Hit it high to fit the swirling wind 00:29:46 No. 7: The simplest hole, but only if you hit the fairway. 00:31:22 No. 8: More uphill than you think. 00:33:31 No. 9: "It sets up for a low cut or low skank." 00:39:04 No. 10: The left side of the fairway is the place to be. 00:41:56 No. 11: "If I could ever take back one shot, it'd be here." 00:43:50 No. 12: Limit the external factors as much as you can. 00:48:23 No. 13: Ludvig is a reformed doubter of the pull of Rae's Creek. 00:52:15 No. 14: It's birdie or bust. 00:54:20 No. 15: The layup debate in the caddie room is real. 00:57:56 No. 16: "The highlights make the left pin look automatic. It’s not." 00:59:26 No. 17: Rae's Creek has some mythical pull up here too. 01:01:15 No. 18: Rory's winning putt was harder than you think. 01:07:57 The last book Ludvig read is exactly what you'd expect.
1:10:36

Ludvig Åberg breaks down his strategy for every hole at Augusta National in TGJ Podcast #207: 00:01:11 But first, some LEGO talk. 00:10:52 No. 1: The hardest pin on the course. 00:14:42 No. 2: Left is dead, so you have to love the bunker. 00:18:36 No. 3: "I've got trust issues with banking it into the hill." 00:22:49 No. 4: The most visually deceptive hole at Augusta. 00:25:02 No. 5: "It's long and has one of the hardest greens." 00:27:07 No. 6: Hit it high to fit the swirling wind 00:29:46 No. 7: The simplest hole, but only if you hit the fairway. 00:31:22 No. 8: More uphill than you think. 00:33:31 No. 9: "It sets up for a low cut or low skank." 00:39:04 No. 10: The left side of the fairway is the place to be. 00:41:56 No. 11: "If I could ever take back one shot, it'd be here." 00:43:50 No. 12: Limit the external factors as much as you can. 00:48:23 No. 13: Ludvig is a reformed doubter of the pull of Rae's Creek. 00:52:15 No. 14: It's birdie or bust. 00:54:20 No. 15: The layup debate in the caddie room is real. 00:57:56 No. 16: "The highlights make the left pin look automatic. It’s not." 00:59:26 No. 17: Rae's Creek has some mythical pull up here too. 01:01:15 No. 18: Rory's winning putt was harder than you think. 01:07:57 The last book Ludvig read is exactly what you'd expect.

The Golfer's Journal

157,909 views • 4 months ago

NEW Solana Sessions dives into Harmonic’s plan to bring Nasdaq onchain Its cofounder Ben ⌛ explains how it’s creating an open, competitive marketplace on Solana with plans to scale to 1M TPS Timestamps 00:00 – Intro: Solana’s progress & how Temporal fits in 01:03 – Creating an “onchain Nasdaq” for Solana 02:15 – Ben’s background: from math & CS student to interning at Citadel 03:19 – What drew him from traditional finance to crypto 04:11 – First impressions of Solana’s speed and performance 05:25 – Why Ben left HFT firms to build in decentralized finance 06:20 – Problems with centralized markets and the appeal of DeFi transparency 07:04 – How Solana achieved real efficiency and low fees 08:50 – Understanding more about Harmonic 10:15 – Why sequencing and inclusion latency matter for DeFi apps 11:35 – Standardizing Solana’s transaction rules with ACE (Application Controlled Execution) 13:13 – Why open sequencing helps apps like DEXs and liquidators 14:04 – Bringing predictability and competition to Solana’s block production 15:32 – How multiple builders competing improves network throughput 16:51 – The goal: Solana reaching 10 to 100x throughput with stability 18:06 – Long-term potential can be 1 million TPS and sustainable decentralization 19:22 – How open sequencing improves inclusion, latency and efficiency 20:57 – Building trust between validators, builders, and applications 22:13 – Preventing toxic block-building practices 23:46 – Why transparency and fairness increase validator revenue 25:04 – What “success” looks like for Solana developers and apps 26:33 – Why Solana’s user and institutional adoption is accelerating 28:10 – The role of liquidity providers and market competition 29:12 – Institutional capital, DeFi adoption & network maturity 31:04 – Advice from Ben: ignore short-term noise, keep building 32:33 – Closing thoughts & future of open blockchain design Watch on X:
32:44

NEW Solana Sessions dives into Harmonic’s plan to bring Nasdaq onchain Its cofounder Ben ⌛ explains how it’s creating an open, competitive marketplace on Solana with plans to scale to 1M TPS Timestamps 00:00 – Intro: Solana’s progress & how Temporal fits in 01:03 – Creating an “onchain Nasdaq” for Solana 02:15 – Ben’s background: from math & CS student to interning at Citadel 03:19 – What drew him from traditional finance to crypto 04:11 – First impressions of Solana’s speed and performance 05:25 – Why Ben left HFT firms to build in decentralized finance 06:20 – Problems with centralized markets and the appeal of DeFi transparency 07:04 – How Solana achieved real efficiency and low fees 08:50 – Understanding more about Harmonic 10:15 – Why sequencing and inclusion latency matter for DeFi apps 11:35 – Standardizing Solana’s transaction rules with ACE (Application Controlled Execution) 13:13 – Why open sequencing helps apps like DEXs and liquidators 14:04 – Bringing predictability and competition to Solana’s block production 15:32 – How multiple builders competing improves network throughput 16:51 – The goal: Solana reaching 10 to 100x throughput with stability 18:06 – Long-term potential can be 1 million TPS and sustainable decentralization 19:22 – How open sequencing improves inclusion, latency and efficiency 20:57 – Building trust between validators, builders, and applications 22:13 – Preventing toxic block-building practices 23:46 – Why transparency and fairness increase validator revenue 25:04 – What “success” looks like for Solana developers and apps 26:33 – Why Solana’s user and institutional adoption is accelerating 28:10 – The role of liquidity providers and market competition 29:12 – Institutional capital, DeFi adoption & network maturity 31:04 – Advice from Ben: ignore short-term noise, keep building 32:33 – Closing thoughts & future of open blockchain design Watch on X:

Talking Tokens Podcast

23,219 views • 9 months ago

"I haven't seen a real new idea in trading in at least 15 years." Tom Costello (Tom Coste) ran money at Tudor, Moore Capital, and Caxton. Built one of the first NLP-driven equity systems in 2003. 20 years managing capital, never had a down year. "Comparing what a retail trader does to what a quantitative hedge fund does is like comparing driving a bus on the New Jersey Turnpike to winning a Formula One race." We cover: - His hot take: no genuinely new trading idea in 15 years — only better people doing the same things faster - Why everyone in quant finance is a genius — and why that makes you ordinary, not special - Crypto is "super smart guys cosplaying at finance" — built for retail, which is exactly why it's the easiest money in finance right now - Why AGI won't beat the hedge fund industry — all the readily-capturable alpha is already captured - The status trap: why the path that made Paul Tudor Jones a billionaire won't work for the kid trying to copy it in 2026 - His friend the investment banker who'd quit it all to run a 10-employee ambulance supply company worth $150M - Why excitement is "wildly overbid" in finance — and why wanting an exciting trading job is itself a disqualifier - The most honest end of the financial industry — and why the media has it exactly backwards Thanks so much to Tom for coming on Odds on Open! Highlights: 00:00 Intro 01:18 Building institutional credibility for early-stage managers 03:01 The Pareto distribution of hedge fund returns 04:25 Applying the Unified Field Theory of Finance to fair value 08:14 Trading against human incentives in a deterministic market 13:54 Why allocators don’t steal alpha from prospective PMs 25:16 Evaluating career edge in quantitative finance for 2026 30:48 Paul Tudor Jones and the art of game selection 33:42 Analyzing the economic viability of starting a new fund 35:16 Identifying common retail pitfalls: Mean reversion and arbitrage 38:55 Why there hasn't been a new trading idea in 15 years 50:33 Managing tail risk: Physics vs. deterministic financial distributions 59:10 Career pathing for PMs after a fund blow-up 1:07:53 SBF and FTX: Credibility vs. the "Founder-Genius" archetype 1:13:44 Establishing proof-of-concept through audited multi-year returns
1:15:58

"I haven't seen a real new idea in trading in at least 15 years." Tom Costello (Tom Coste) ran money at Tudor, Moore Capital, and Caxton. Built one of the first NLP-driven equity systems in 2003. 20 years managing capital, never had a down year. "Comparing what a retail trader does to what a quantitative hedge fund does is like comparing driving a bus on the New Jersey Turnpike to winning a Formula One race." We cover: - His hot take: no genuinely new trading idea in 15 years — only better people doing the same things faster - Why everyone in quant finance is a genius — and why that makes you ordinary, not special - Crypto is "super smart guys cosplaying at finance" — built for retail, which is exactly why it's the easiest money in finance right now - Why AGI won't beat the hedge fund industry — all the readily-capturable alpha is already captured - The status trap: why the path that made Paul Tudor Jones a billionaire won't work for the kid trying to copy it in 2026 - His friend the investment banker who'd quit it all to run a 10-employee ambulance supply company worth $150M - Why excitement is "wildly overbid" in finance — and why wanting an exciting trading job is itself a disqualifier - The most honest end of the financial industry — and why the media has it exactly backwards Thanks so much to Tom for coming on Odds on Open! Highlights: 00:00 Intro 01:18 Building institutional credibility for early-stage managers 03:01 The Pareto distribution of hedge fund returns 04:25 Applying the Unified Field Theory of Finance to fair value 08:14 Trading against human incentives in a deterministic market 13:54 Why allocators don’t steal alpha from prospective PMs 25:16 Evaluating career edge in quantitative finance for 2026 30:48 Paul Tudor Jones and the art of game selection 33:42 Analyzing the economic viability of starting a new fund 35:16 Identifying common retail pitfalls: Mean reversion and arbitrage 38:55 Why there hasn't been a new trading idea in 15 years 50:33 Managing tail risk: Physics vs. deterministic financial distributions 59:10 Career pathing for PMs after a fund blow-up 1:07:53 SBF and FTX: Credibility vs. the "Founder-Genius" archetype 1:13:44 Establishing proof-of-concept through audited multi-year returns

Ethan Kho

1,187,584 views • 3 months ago

hockeystick moments are the biggest opportunity of your career. It's exciting and kinda scary. Everything changes! I wrote Scaling Fast: Software Engineering Through the Hockeystick as your guide. 16 years of startup experience condensed into 240 pages Scaling Fast is based on startup war stories, academic research papers, talking to my mentors, and reading industry insights. It shows you what it takes to survive and thrive through the hockeystick. You'll learn about scaling teams and scaling code, how they influence each other, and why none of it matters if the business is bad. Scaling Fast: Software Engineering Through the Hockeystick is organized in 3 sections: - Scaling the Business - Scaling the Team - Scaling the Tech The business part, that's to help you evaluate the companies you join, this isn't a business book. The team part, that's key to shaping your everyday. We talk about delegating decisions, empowering engineers, working smooth instead of fast, finishing things all the way to done, good code review culture, and shipping incrementally without risky big bang releases. The tech part, that's my favorite. We talk about good abstractions, architectural complexity, observing your systems break, making steady improvements to your code without huge refactoring sprints, why tests don't solve everything, what's even worth testing, how your team structure impacts what you can do with the code, and why solving today's problem is more important than building for an imagined future. None of it is about numerical scaling [name|]. That's easy in 2025. Computers are fast. The challenge is building complex systems that don't fit in any one person's brain without breaking the business. oftware engineering when your whole company changes every 6 months is the fun part of this gig. And that's what Scaling Fast is about. 👉
3:35

hockeystick moments are the biggest opportunity of your career. It's exciting and kinda scary. Everything changes! I wrote Scaling Fast: Software Engineering Through the Hockeystick as your guide. 16 years of startup experience condensed into 240 pages Scaling Fast is based on startup war stories, academic research papers, talking to my mentors, and reading industry insights. It shows you what it takes to survive and thrive through the hockeystick. You'll learn about scaling teams and scaling code, how they influence each other, and why none of it matters if the business is bad. Scaling Fast: Software Engineering Through the Hockeystick is organized in 3 sections: - Scaling the Business - Scaling the Team - Scaling the Tech The business part, that's to help you evaluate the companies you join, this isn't a business book. The team part, that's key to shaping your everyday. We talk about delegating decisions, empowering engineers, working smooth instead of fast, finishing things all the way to done, good code review culture, and shipping incrementally without risky big bang releases. The tech part, that's my favorite. We talk about good abstractions, architectural complexity, observing your systems break, making steady improvements to your code without huge refactoring sprints, why tests don't solve everything, what's even worth testing, how your team structure impacts what you can do with the code, and why solving today's problem is more important than building for an imagined future. None of it is about numerical scaling [name|]. That's easy in 2025. Computers are fast. The challenge is building complex systems that don't fit in any one person's brain without breaking the business. oftware engineering when your whole company changes every 6 months is the fun part of this gig. And that's what Scaling Fast is about. 👉

Swizec Teller

57,865 views • 8 months ago

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)
0:40

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,593 views • 2 months ago

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24
46:40

OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24

Dan Shipper 📧

15,588 views • 5 months ago