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Sibyl Memory Plugin. closed beta is opening. one command, any harness. -95.1% on longmemeval. -file-based. -zero vectors. -hierarchical schema. early data is showing a 52% reduction in token usage. bounty board goes live tomorrow. come learn, earn & build.

SIBYL

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31,059 görüntüleme • 3 ay önce •via X (Twitter)

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MICROSOFT QUIETLY SHIPPED THE RAG PIPELINE EVERYONE KEEPS TRYING TO BUILD FROM SCRATCH it is called Kernel Memory, and it turns any pile of files into an AI memory that answers with citations. drop your data in and Microsoft's pipeline does the rest: > ingest PDFs, Word, PPT, Excel, web pages, even images > extract the text, chunk it, embed it into vectors > store the vectors with metadata and keep the originals in blob storage > ask a question and it pulls the right chunks with semantic plus hybrid search then it hands the answer back grounded, with links to the exact source and full token usage. and it plugs into whatever you already run: > Azure OpenAI, OpenAI, Ollama, or Anthropic for the models > Qdrant, pgvector, Redis, or Azure AI Search for the vectors > every step is a swappable handler, so you can replace any part with your own one heads up: Microsoft archived the repo in June 2026 and ships it as a reference blueprint, not production. but as a map for how memory and RAG are actually built, nothing is cleaner. run it in one command: docker run kernelmemory/service bookmark this. Microsoft already drew the blueprint, you just build on it.

MICROSOFT QUIETLY SHIPPED THE RAG PIPELINE EVERYONE KEEPS TRYING TO BUILD FROM SCRATCH it is called Kernel Memory, and it turns any pile of files into an AI memory that answers with citations. drop your data in and Microsoft's pipeline does the rest: > ingest PDFs, Word, PPT, Excel, web pages, even images > extract the text, chunk it, embed it into vectors > store the vectors with metadata and keep the originals in blob storage > ask a question and it pulls the right chunks with semantic plus hybrid search then it hands the answer back grounded, with links to the exact source and full token usage. and it plugs into whatever you already run: > Azure OpenAI, OpenAI, Ollama, or Anthropic for the models > Qdrant, pgvector, Redis, or Azure AI Search for the vectors > every step is a swappable handler, so you can replace any part with your own one heads up: Microsoft archived the repo in June 2026 and ships it as a reference blueprint, not production. but as a map for how memory and RAG are actually built, nothing is cleaner. run it in one command: docker run kernelmemory/service bookmark this. Microsoft already drew the blueprint, you just build on it.

NO1ennn

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Today on MCG: tekkaadan | Founder of Litcoin Tekkaadan is a solo founder building a data marketplace where AI agents mine verified data across 22 verticals and get paid in Bitcoin The token has zero supply increase baked in from day one 👀 00:06 - Founder introduction 01:14 - Saw Botcoin and got inspired 02:10 - Ten years in crypto 02:50 - What is Litcoin? 03:44 - AI agents mine "blocks" 04:03 - The 22 verticals explained 04:54 - How do you verify the data is accurate? 05:19 - The sandbox 05:48 - 10+ miners live and submitting 07:20 - What contaminated data is 08:49 - Miners get paid in Bitcoin 10:19 - Token design 10:50 - Zero supply increase 11:01 - 5.5 billion tokens 12:26 - Mining token prices connect to protocol growth 15:08 - Explorer page 16:31 - "We have very unique data" 23:09 - OpenAI just said they're going to need more data 23:31 - Can a company come and request specific data? 23:42 - The bounty system 24:52 - 10% of bounties flow back into the ecosystem 32:31 - The data marketplace is just one part of the bigger picture 33:20 - Valuation breakdown 35:56 - North Korea and bad actors 41:58 - GPT-5 agents producing better data 42:50 - Tekkaadan's port 44:02 - Takeaways

Today on MCG: tekkaadan | Founder of Litcoin Tekkaadan is a solo founder building a data marketplace where AI agents mine verified data across 22 verticals and get paid in Bitcoin The token has zero supply increase baked in from day one 👀 00:06 - Founder introduction 01:14 - Saw Botcoin and got inspired 02:10 - Ten years in crypto 02:50 - What is Litcoin? 03:44 - AI agents mine "blocks" 04:03 - The 22 verticals explained 04:54 - How do you verify the data is accurate? 05:19 - The sandbox 05:48 - 10+ miners live and submitting 07:20 - What contaminated data is 08:49 - Miners get paid in Bitcoin 10:19 - Token design 10:50 - Zero supply increase 11:01 - 5.5 billion tokens 12:26 - Mining token prices connect to protocol growth 15:08 - Explorer page 16:31 - "We have very unique data" 23:09 - OpenAI just said they're going to need more data 23:31 - Can a company come and request specific data? 23:42 - The bounty system 24:52 - 10% of bounties flow back into the ecosystem 32:31 - The data marketplace is just one part of the bigger picture 33:20 - Valuation breakdown 35:56 - North Korea and bad actors 41:58 - GPT-5 agents producing better data 42:50 - Tekkaadan's port 44:02 - Takeaways

MCG

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Memory vs. Graphs, clearly explained! memory is great, and the ceiling arrives quietly: it stores what happened. it does not store what to do about it. six runs later your file has fifty lines, and the model reloads all of them before it does anything. Graph engineering fixes this by changing what memory is: not a place things are kept, but an edge that runs backwards. you need both, and here is the sentence that resolves the whole confusion: a store keeps what happened. an edge keeps what to do about it. ↳ a store grows with every run, and every line is reloaded before the next one ↳ an edge carries one derived rule, and the rule replaces the run that produced it Prompts → Context → Harness → Loops → Graphs the transcript goes away, the constraint stays. and the constraint is smaller, because "adapters preserve keyword args exactly" is four hundred tokens shorter than the run that proved it. the same four blocks work on anything you can cut into lanes. i pointed them at token launches on Robinhood Chain, open source, nothing leaves your terminal the trick is knowing what deserves to survive. an output is not memory. "ported the utils slice, green on first pass" tells the next run nothing it can act on. the rule you derived from it does. one thing to know before you scale it. what you write down is not what comes back. ↳ the root rules file and auto memory are re-injected from disk. they come back intact, every time ↳ path-scoped rules live in message history. they get summarized away and do not return until a matching file is read again so a rule that must persist cannot be path-scoped. move it to the root and pay the always-loaded cost, or accept that it is advisory in any long session. and the one that eats whole nights: a memory file that has never had a line deleted is not memory. it is a tax on every run you will ever make, and nobody reads it back. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this, and the repo that runs it is below ↓

Memory vs. Graphs, clearly explained! memory is great, and the ceiling arrives quietly: it stores what happened. it does not store what to do about it. six runs later your file has fifty lines, and the model reloads all of them before it does anything. Graph engineering fixes this by changing what memory is: not a place things are kept, but an edge that runs backwards. you need both, and here is the sentence that resolves the whole confusion: a store keeps what happened. an edge keeps what to do about it. ↳ a store grows with every run, and every line is reloaded before the next one ↳ an edge carries one derived rule, and the rule replaces the run that produced it Prompts → Context → Harness → Loops → Graphs the transcript goes away, the constraint stays. and the constraint is smaller, because "adapters preserve keyword args exactly" is four hundred tokens shorter than the run that proved it. the same four blocks work on anything you can cut into lanes. i pointed them at token launches on Robinhood Chain, open source, nothing leaves your terminal the trick is knowing what deserves to survive. an output is not memory. "ported the utils slice, green on first pass" tells the next run nothing it can act on. the rule you derived from it does. one thing to know before you scale it. what you write down is not what comes back. ↳ the root rules file and auto memory are re-injected from disk. they come back intact, every time ↳ path-scoped rules live in message history. they get summarized away and do not return until a matching file is read again so a rule that must persist cannot be path-scoped. move it to the root and pay the always-loaded cost, or accept that it is advisory in any long session. and the one that eats whole nights: a memory file that has never had a line deleted is not memory. it is a tax on every run you will ever make, and nobody reads it back. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this, and the repo that runs it is below ↓

Hanako

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ANNOUNCING: The Floki Trading Bot Closed Beta Mainnet Launch We’re pleased to announce the launch of the Closed Beta of the Floki Trading Bot on the mainnets of the Ethereum, BNB, and Base blockchains! The beta is currently open to an initial 150 users on a first come first served basis. The Floki Trading Bot is an innovative multi-chain Telegram on-chain trading bot designed to provide a seamless trading experience. With just a few taps, you can buy and sell cryptocurrencies in seconds across different chains. Our aim is to advance the way you trade, making it faster, easier, and more efficient. The Floki Trading Bot charges a 1% fee on every trade and uses FLOKI as its main utility token: 50% of this fee buys and burns $FLOKI, while the rest goes to the Floki Treasury. This will enhance the utility of the FLOKI token while accelerating its deflation. The closed beta will run for a period of two weeks, after which we intend to release a public version of the bot to everyone. Why a Closed Beta? The Closed Beta allows us to create a controlled environment where we can: - Identify and fix early bugs (should there be any!) - Collect invaluable user feedback to improve user experience - Validate our market assumptions Closed Beta User Assignments: - Submit at least 2 feedback/bug reports every week - Trade at least 4 times every week - Submit the End of Beta Survey (we will share it near the end of Closed Beta) Reward: Participants who complete the assignments will receive a reward in their Floki Trading Bot primary wallet at the end of the Closed Beta period. Details about the reward will be communicated later. 🚨 Safety Notice: Be vigilant against scam links/pages below that may appear similar to ours. Do not trust any messages regarding airdrops or asking you to connect your wallet to any site. Stay safe and beware of fraudulent activities. Join the Closed Beta Waitlist To join the Closed Beta, please use the link below: You can read the Floki Trading Bot documentation here: We look forward to having you onboard and hearing your feedback as we continue to aggressively expand the Floki ecosystem and work toward becoming the world's most known and used cryptocurrency.

FLOKI

327,039 görüntüleme • 2 yıl önce

INTRODUCING PONZIMON: So this is a new twist on miner-ponzi-games with battle tested contracts. So new and improved gameplay, I have wanted to test a few new core mechanics and fix some of the issues I saw with Ponzi games. So issue one with any Ponzi game is the game theory correct thing to do is rush in, buy a lot of the tokens early, and get the best miners so you always have a big % of the emissions and then sell these as the token price goes up. But this makes the game less fun because 1. It’s a downward drag on token price early 2. It’s harder for newer players with smaller bank rolls to catch up. So I added a RNG element. In PonziMon, you pay the native token to open packs—->the tokens you pay are burned—-> you get a miner (PonziMon)—-> you place him and earn a % of the tokens emissions so you “mine” the token just like in BigCoin. There is also a leader board, that incentivizes people with further rewards so people. Also beefed up the ref-codes. So if you refer, you get 25% of what your buddy spends on opening packs. I think this should increase the sharring and viral nature of the game. Also the creatures are cute. 😊 Let’s run it back turbo.

INTRODUCING PONZIMON: So this is a new twist on miner-ponzi-games with battle tested contracts. So new and improved gameplay, I have wanted to test a few new core mechanics and fix some of the issues I saw with Ponzi games. So issue one with any Ponzi game is the game theory correct thing to do is rush in, buy a lot of the tokens early, and get the best miners so you always have a big % of the emissions and then sell these as the token price goes up. But this makes the game less fun because 1. It’s a downward drag on token price early 2. It’s harder for newer players with smaller bank rolls to catch up. So I added a RNG element. In PonziMon, you pay the native token to open packs—->the tokens you pay are burned—-> you get a miner (PonziMon)—-> you place him and earn a % of the tokens emissions so you “mine” the token just like in BigCoin. There is also a leader board, that incentivizes people with further rewards so people. Also beefed up the ref-codes. So if you refer, you get 25% of what your buddy spends on opening packs. I think this should increase the sharring and viral nature of the game. Also the creatures are cute. 😊 Let’s run it back turbo.

Skely

23,446 görüntüleme • 1 yıl önce

🚨 memU bot is live. A better alternative to OpenClaw🦞 (formerly Moltbot / Clawdbot) 👉Get instant access to the memU bot: 🕒 A 24/7 proactive assistant memU bot runs continuously on your machine and works as a proactive assistant. It takes action based on your behavior and context — instead of waiting for explicit commands. 🧠 Highly personal, built for you memU bot learns from your long-term usage and memory, and gradually adapts to your work style and preferences. It becomes your assistant — not a generic AI. ⚡ Very easy to use — download and run No complex setup. No configuration. Even non-technical users can simply download and run memU bot. 🔒 Local-first and secure, with no server dependency memU bot runs locally on your device. Your data never needs to be uploaded to public networks or third-party servers. 💸 Lower LLM token cost (more efficient than OpenClaw🦞) While supporting always-on and proactive behavior, memU bot is designed to reduce LLM calls and token usage — so it runs cheaper than OpenClaw, without sacrificing performance. 🧠 "Always-on" is the real key to a proactive agent. And memory is what gives it true proactivity. With memory, an agent is no longer generic. It becomes personal — shaped by who you are. This is how a user-intention-driven proactive agent is born: before you even issue a command, it can already anticipate what kind of help you’ll need, based on your past, your habits, your context. 🔮 A 24/7 process that can observe 👀, remember 📝, and act ⚡ — not just wait for prompts. 🤖 memU bot is our attempt at a user-intention-driven proactive agent — one that lives beyond the chat box.

🚨 memU bot is live. A better alternative to OpenClaw🦞 (formerly Moltbot / Clawdbot) 👉Get instant access to the memU bot: 🕒 A 24/7 proactive assistant memU bot runs continuously on your machine and works as a proactive assistant. It takes action based on your behavior and context — instead of waiting for explicit commands. 🧠 Highly personal, built for you memU bot learns from your long-term usage and memory, and gradually adapts to your work style and preferences. It becomes your assistant — not a generic AI. ⚡ Very easy to use — download and run No complex setup. No configuration. Even non-technical users can simply download and run memU bot. 🔒 Local-first and secure, with no server dependency memU bot runs locally on your device. Your data never needs to be uploaded to public networks or third-party servers. 💸 Lower LLM token cost (more efficient than OpenClaw🦞) While supporting always-on and proactive behavior, memU bot is designed to reduce LLM calls and token usage — so it runs cheaper than OpenClaw, without sacrificing performance. 🧠 "Always-on" is the real key to a proactive agent. And memory is what gives it true proactivity. With memory, an agent is no longer generic. It becomes personal — shaped by who you are. This is how a user-intention-driven proactive agent is born: before you even issue a command, it can already anticipate what kind of help you’ll need, based on your past, your habits, your context. 🔮 A 24/7 process that can observe 👀, remember 📝, and act ⚡ — not just wait for prompts. 🤖 memU bot is our attempt at a user-intention-driven proactive agent — one that lives beyond the chat box.

memU

818,472 görüntüleme • 7 ay önce

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

Matt Dancho (Business Science)

12,736 görüntüleme • 1 yıl önce

run agent harnesses 100% private & offline. (no token costs, no API keys, 100% open-source) your agent runs locally. the model doesn't. every prompt, every file, and every secret still leaves your machine before the agent does anything with it. Magnitude fixes that. it's an open source inference server that runs models on your own hardware and plugs into the coding agent you already use. setup is one command. it profiles your machine, measures the memory bandwidth that sets your token rate, and hands back complete configurations instead of a list of models. each one names a model, a compression level, a context size, and a speed range you can expect. pick one and start working. it doesn't replace your harness. setup asks which one you want and writes that config for you. Pi, OpenCode, Claude Code, Codex, and Cline all work, and there's a built-in one tuned for local models if you don't have a harness yet. that one uses your shell, edits files, and runs scripts out of the box. add skills and it handles Excel, PowerPoint, PDFs, or Chrome. everyday work it covers: → analyze sensitive data → manage private notes → review code and logs → search and organize files → build docs or slides Apache 2.0. no rate limits, and nothing leaves the machine. 𝗻𝗽𝗺 𝗶 -𝗴 @𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲𝗱𝗲𝘃/𝗰𝗹𝗶 the repo is here: (don't forget to star 🌟) i wrote the full breakdown of why picking the configuration is the hard part. the article is quoted below.

run agent harnesses 100% private & offline. (no token costs, no API keys, 100% open-source) your agent runs locally. the model doesn't. every prompt, every file, and every secret still leaves your machine before the agent does anything with it. Magnitude fixes that. it's an open source inference server that runs models on your own hardware and plugs into the coding agent you already use. setup is one command. it profiles your machine, measures the memory bandwidth that sets your token rate, and hands back complete configurations instead of a list of models. each one names a model, a compression level, a context size, and a speed range you can expect. pick one and start working. it doesn't replace your harness. setup asks which one you want and writes that config for you. Pi, OpenCode, Claude Code, Codex, and Cline all work, and there's a built-in one tuned for local models if you don't have a harness yet. that one uses your shell, edits files, and runs scripts out of the box. add skills and it handles Excel, PowerPoint, PDFs, or Chrome. everyday work it covers: → analyze sensitive data → manage private notes → review code and logs → search and organize files → build docs or slides Apache 2.0. no rate limits, and nothing leaves the machine. 𝗻𝗽𝗺 𝗶 -𝗴 @𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲𝗱𝗲𝘃/𝗰𝗹𝗶 the repo is here: (don't forget to star 🌟) i wrote the full breakdown of why picking the configuration is the hard part. the article is quoted below.

Akshay 🚀

55,693 görüntüleme • 14 gün önce

Happy weekend, everyone. Long Ge’s out in the countryside chilling today. It’s freezing cold today—probably the last cold snap before the Year of the Snake wraps up. Tomorrow will be another chilly day, and then it’ll be clear and sunny. I was casually scrolling through my feed and saw the latest update from idOS , and I gotta say, I’m pretty hyped. TGE goes live directly on Arbitrum on Mar 5. This isn’t just another random token launch. The team is building real identity infrastructure for the stablecoin economy. One identity verification, seamless usage across every ecosystem, self-custodial privacy, and users truly own their own data. They also dropped a detailed token audit report a few days ago: clear positioning, plenty of room for future iterations. The market’s sluggish right now, but projects that build steadily and solve real industry pain points are the ones that stand the test of time. Guys, go check it out early. Don’t wait till the TGE pumps and end up regretting it! #idos idOS

Happy weekend, everyone. Long Ge’s out in the countryside chilling today. It’s freezing cold today—probably the last cold snap before the Year of the Snake wraps up. Tomorrow will be another chilly day, and then it’ll be clear and sunny. I was casually scrolling through my feed and saw the latest update from idOS , and I gotta say, I’m pretty hyped. TGE goes live directly on Arbitrum on Mar 5. This isn’t just another random token launch. The team is building real identity infrastructure for the stablecoin economy. One identity verification, seamless usage across every ecosystem, self-custodial privacy, and users truly own their own data. They also dropped a detailed token audit report a few days ago: clear positioning, plenty of room for future iterations. The market’s sluggish right now, but projects that build steadily and solve real industry pain points are the ones that stand the test of time. Guys, go check it out early. Don’t wait till the TGE pumps and end up regretting it! #idos idOS

空投龙 | GoldenHoarder

21,883 görüntüleme • 7 ay önce

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓

Charly Wargnier

203,456 görüntüleme • 1 ay önce

Get PAID to use NECT Nectar (NECT), the native Berachain CDP stablecoin powered by PoL is seeing huge growth through integrations, LP’s being set up and explosive usage! Here is a secret most don’t know. You can earn POLLEN by using NECT. Here are a few ways to start earning today. 🟠Set up ANY token LP with NECT, start earning POLLEN. 🟠Integrate NECT into your dApp, start earning POLLEN with usage. 🟠Not sure what stablecoin to hold? Choose the one that pays you. NECT. No need to ask for permission. Just use NECT. How does this work? Every NECT in circulation is earning interest rates inside the Beraborrow Protocol, POLLEN is then distributed to users of NECT. Interest rates are then passed through the POLLEN flywheel that uses PoL to maximise the value distributed back to POLLEN. Allowing for the continued sustainable explosive growth to occur! Resulting in more NECT usage -> more interest rates -> more POLLEN to NECT users. NECT usage is picked up automatically with incentives claimable once POLLEN is live! Post TGE, we have something cooking to make it even easier to get your POLLEN! What truly sets NECT apart is its zero-friction approach: you can start using it today with no governance hoops to jump through or prior approvals required. Incentives are emitted based on TVL, ensuring a seamless user experience for both new and established projects. Get NECT or get REKT. Ooga Matata.

Get PAID to use NECT Nectar (NECT), the native Berachain CDP stablecoin powered by PoL is seeing huge growth through integrations, LP’s being set up and explosive usage! Here is a secret most don’t know. You can earn POLLEN by using NECT. Here are a few ways to start earning today. 🟠Set up ANY token LP with NECT, start earning POLLEN. 🟠Integrate NECT into your dApp, start earning POLLEN with usage. 🟠Not sure what stablecoin to hold? Choose the one that pays you. NECT. No need to ask for permission. Just use NECT. How does this work? Every NECT in circulation is earning interest rates inside the Beraborrow Protocol, POLLEN is then distributed to users of NECT. Interest rates are then passed through the POLLEN flywheel that uses PoL to maximise the value distributed back to POLLEN. Allowing for the continued sustainable explosive growth to occur! Resulting in more NECT usage -> more interest rates -> more POLLEN to NECT users. NECT usage is picked up automatically with incentives claimable once POLLEN is live! Post TGE, we have something cooking to make it even easier to get your POLLEN! What truly sets NECT apart is its zero-friction approach: you can start using it today with no governance hoops to jump through or prior approvals required. Incentives are emitted based on TVL, ensuring a seamless user experience for both new and established projects. Get NECT or get REKT. Ooga Matata.

Beraborrow (🐻⛓️🍯)

21,805 görüntüleme • 1 yıl önce

Introducing my newest app, TethrX, made with Grok 4.5 to access Grok Build on your phone. TethrX connects to Grok Build running on your own computer, so you can start a task from anywhere, follow Grok's reasoning as it works, approve every command before it runs, and review the code it writes. Your code never leaves your machine. The public TestFlight is now open, and a demonstration is below. You pair your phone by scanning a QR code, either on your local network or from anywhere through Tailscale. From there TethrX streams Grok's reasoning, tool calls, command output and file changes as they happen, and asks your approval before anything runs. Plan mode lets you read the plan before the work begins. When a task finishes you can review exactly what changed. TethrX lists the modified files in your project, shows a diff for each one, and lets you commit or discard the work without leaving your phone. The app supports slash commands, including /compact and any skills you have installed, along with voice dictation, queued messages and reusable prompts. Sessions can be searched and organised into folders, and you can pair several computers and switch between them. Siri can start a task or tell you what Grok is doing without opening the app, a home screen widget shows whether Grok is working, and a Live Activity tracks progress on your lock screen and Dynamic Island. Every session reports its context window, token usage and cost, the app can be locked behind Face ID, and your computer is kept awake for as long as a task is running. TethrX requires Grok Build installed and signed in on your computer, together with Node.js 20 or newer. A single command starts the bridge: npx tethrx-bridge TethrX is open source under the Apache License 2.0. Both the iOS client and the local bridge are available here: Grok 4.5 helped a lot, thanks to SpaceXAI for making Grok 4.5 exceptional.

Introducing my newest app, TethrX, made with Grok 4.5 to access Grok Build on your phone. TethrX connects to Grok Build running on your own computer, so you can start a task from anywhere, follow Grok's reasoning as it works, approve every command before it runs, and review the code it writes. Your code never leaves your machine. The public TestFlight is now open, and a demonstration is below. You pair your phone by scanning a QR code, either on your local network or from anywhere through Tailscale. From there TethrX streams Grok's reasoning, tool calls, command output and file changes as they happen, and asks your approval before anything runs. Plan mode lets you read the plan before the work begins. When a task finishes you can review exactly what changed. TethrX lists the modified files in your project, shows a diff for each one, and lets you commit or discard the work without leaving your phone. The app supports slash commands, including /compact and any skills you have installed, along with voice dictation, queued messages and reusable prompts. Sessions can be searched and organised into folders, and you can pair several computers and switch between them. Siri can start a task or tell you what Grok is doing without opening the app, a home screen widget shows whether Grok is working, and a Live Activity tracks progress on your lock screen and Dynamic Island. Every session reports its context window, token usage and cost, the app can be locked behind Face ID, and your computer is kept awake for as long as a task is running. TethrX requires Grok Build installed and signed in on your computer, together with Node.js 20 or newer. A single command starts the bridge: npx tethrx-bridge TethrX is open source under the Apache License 2.0. Both the iOS client and the local bridge are available here: Grok 4.5 helped a lot, thanks to SpaceXAI for making Grok 4.5 exceptional.

Myrhe𝕩

13,099 görüntüleme • 1 ay önce

ANTHROPIC LEAKED A TRADING DESK FILE THAT TAKES YOU FROM $10 TO $10,000 AND COSTS $1.50 A DAY Anthropic spent 5 years building this file on closed tech, it costs $1.50 a day and it can return $1.2M a year rules → reading → code → critic → journal → back to rules $10 to $10,000 is 1000×, which is ten doublings in a row at a coin flip that is 1 chance in 1,024, and a single loss anywhere sends you to zero instead of back one step by week four the balance reads $5,400 and only 0.8% are still alive on that rung the 1,024 who tried are not visible in the feed, only the one who arrived is you write the rules yourself: size, max loss, and what you never touch Haiku 4.5 reads the market and summarises it, and never once passes judgement Opus 5 works as the critic and attacks your own backtest instead of praising it the journal logs every trade and why, and it is what rewrites the rules for tomorrow the desk does not raise your odds on any single flip, it lowers how many flips you have to take save this and paste it into Claude Code - let it build your DESK.md ↓

ANTHROPIC LEAKED A TRADING DESK FILE THAT TAKES YOU FROM $10 TO $10,000 AND COSTS $1.50 A DAY Anthropic spent 5 years building this file on closed tech, it costs $1.50 a day and it can return $1.2M a year rules → reading → code → critic → journal → back to rules $10 to $10,000 is 1000×, which is ten doublings in a row at a coin flip that is 1 chance in 1,024, and a single loss anywhere sends you to zero instead of back one step by week four the balance reads $5,400 and only 0.8% are still alive on that rung the 1,024 who tried are not visible in the feed, only the one who arrived is you write the rules yourself: size, max loss, and what you never touch Haiku 4.5 reads the market and summarises it, and never once passes judgement Opus 5 works as the critic and attacks your own backtest instead of praising it the journal logs every trade and why, and it is what rewrites the rules for tomorrow the desk does not raise your odds on any single flip, it lowers how many flips you have to take save this and paste it into Claude Code - let it build your DESK.md ↓

Sprytix

22,861 görüntüleme • 13 gün önce

How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]

How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]

Varun

22,963 görüntüleme • 5 ay önce

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.

m0h

70,509 görüntüleme • 3 ay önce

Context vs. Graphs, clearly explained! context engineering is great, but it has a ceiling: you can make one window perfect. there is still only one of it. every technique on that layer is rationing the same scarce thing. compact, retrieve less, delete, defer. all of it is deciding what to drop. Graph engineering fixes this by moving the decision up a layer: not what goes in the window, but how many windows there are and what each one is for. you need both. here's how it works: ↳ inside a window: context engineering. what loads, in what order, what gets compacted ↳ between windows: the graph. how many lanes, what each one is allowed to see, what comes back Prompts → Context → Harness → Loops → Graphs each lane gets a clean window, nothing in one competes with anything in another, and your main thread stops filling up. the trick is being selective about what comes back. a subagent reads six thousand tokens of files and hands you a four hundred token summary. that ratio is the whole point. send back the raw material instead and you have moved the problem, not solved it. one thing to know before you scale it. not everything survives compaction equally, and almost nobody knows the table. ↳ the project-root rules file and auto memory are re-injected from disk. they come back intact ↳ path-scoped rules and nested rules files live in message history. they get summarized away and do not return until a matching file is read again so a rule that genuinely must persist cannot be path-scoped. move it to the root and pay the always-loaded cost, or accept that it is advisory in any long session. and the one that eats whole nights: shared context makes parallel agents converge. four auditors on one window produce one opinion with three echoes. you paid four times for it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Context vs. Graphs, clearly explained! context engineering is great, but it has a ceiling: you can make one window perfect. there is still only one of it. every technique on that layer is rationing the same scarce thing. compact, retrieve less, delete, defer. all of it is deciding what to drop. Graph engineering fixes this by moving the decision up a layer: not what goes in the window, but how many windows there are and what each one is for. you need both. here's how it works: ↳ inside a window: context engineering. what loads, in what order, what gets compacted ↳ between windows: the graph. how many lanes, what each one is allowed to see, what comes back Prompts → Context → Harness → Loops → Graphs each lane gets a clean window, nothing in one competes with anything in another, and your main thread stops filling up. the trick is being selective about what comes back. a subagent reads six thousand tokens of files and hands you a four hundred token summary. that ratio is the whole point. send back the raw material instead and you have moved the problem, not solved it. one thing to know before you scale it. not everything survives compaction equally, and almost nobody knows the table. ↳ the project-root rules file and auto memory are re-injected from disk. they come back intact ↳ path-scoped rules and nested rules files live in message history. they get summarized away and do not return until a matching file is read again so a rule that genuinely must persist cannot be path-scoped. move it to the root and pay the always-loaded cost, or accept that it is advisory in any long session. and the one that eats whole nights: shared context makes parallel agents converge. four auditors on one window produce one opinion with three echoes. you paid four times for it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

36,970 görüntüleme • 25 gün önce

A developer figured out how to give AI permanent memory for $0.40 a year. No complex vector databases. No massive cloud storage. Just a single 4,000-token file that refuses to grow. The secret? True memory isn’t about storing everything. It’s about aggressive deletion. Instead of endlessly appending data, his system rewrites just six core fields on every turn: > IDENTITY: Who you are and what you build (300 tokens) > STATE: Current active task and focus (400 tokens) > DECISIONS: Settled choices—zero re-arguing (800 tokens) > CORRECTIONS: Every "no, do it this way" moment (600 tokens) > PEOPLE: Key names, roles, and open dependencies (500 tokens) > GRAVEYARD: Dead attempts, so bad ideas never return (400 tokens) Total size: 3,000 tokens. Absolute hard ceiling: 4,000 tokens. When a category hits its limit, the model compresses it. Data is never stacked—only pruned and replaced. Using Kimi K2.5 caching rates ($0.10/M tokens), running this budget memory costs $0.0004 per turn. That is 2,500 interactive turns for a single dollar. The real breakthrough lies in the CORRECTIONS field. Standard AI models waste compute making the same mistakes twice. This schema forces the assistant to remember its failures instantly. While the rest of the industry burns millions searching through bloated history logs, he pays pennies to keep his context razor-sharp. Your memory system isn't defined by what you save. It’s defined by what you have the discipline to throw away. Bookmark this breakdown. You will need it for your next build.

A developer figured out how to give AI permanent memory for $0.40 a year. No complex vector databases. No massive cloud storage. Just a single 4,000-token file that refuses to grow. The secret? True memory isn’t about storing everything. It’s about aggressive deletion. Instead of endlessly appending data, his system rewrites just six core fields on every turn: > IDENTITY: Who you are and what you build (300 tokens) > STATE: Current active task and focus (400 tokens) > DECISIONS: Settled choices—zero re-arguing (800 tokens) > CORRECTIONS: Every "no, do it this way" moment (600 tokens) > PEOPLE: Key names, roles, and open dependencies (500 tokens) > GRAVEYARD: Dead attempts, so bad ideas never return (400 tokens) Total size: 3,000 tokens. Absolute hard ceiling: 4,000 tokens. When a category hits its limit, the model compresses it. Data is never stacked—only pruned and replaced. Using Kimi K2.5 caching rates ($0.10/M tokens), running this budget memory costs $0.0004 per turn. That is 2,500 interactive turns for a single dollar. The real breakthrough lies in the CORRECTIONS field. Standard AI models waste compute making the same mistakes twice. This schema forces the assistant to remember its failures instantly. While the rest of the industry burns millions searching through bloated history logs, he pays pennies to keep his context razor-sharp. Your memory system isn't defined by what you save. It’s defined by what you have the discipline to throw away. Bookmark this breakdown. You will need it for your next build.

shmidt

17,304 görüntüleme • 21 gün önce

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 görüntüleme • 1 yıl önce

Once you learn these three things, you can build nearly anything yourself. Skills, chains, and plugins. Learn them once and you can automate a real part of your week yourself. Here's the system: A skill is a standard operating procedure. It's one long, reusable prompt that does one thing well, like writing a newsletter, setting up a PPC campaign, triaging your inbox, or drafting a note to the board. You write it once and reuse it forever. The trick is to feed it your real work. For my writing skill, I gave Claude posts I admire and my own exported analytics with the winners marked, so it could see the patterns. Show it what good looks like and it nails your voice. Skip that step and it guesses. A chain connects skills into an automation. One skill's output feeds the next, in order. A copywriting skill writes the messaging, then hands it to a PPC-setup skill that builds the campaign. That's an automation, and you built it without writing code. A plugin is the harness that holds it all. It bundles your skills and chains into one thing you share with your team. Instead of sending 15 separate prompts around, you send one plugin, and it knows when to use each skill on its own. Skill, chain, plugin. It's one step up the ladder, and once you're up there it's easy. Now the worked example. I call it the Daily Driver. It's five skills chained together: email triage, a writer, Slack triage, a thinking partner, and a setup skill that connects my tools and personalizes everything. I chained them into one morning brief and scheduled it to run at 9am and message me the list. Two things made the biggest difference. Give it memory. I made plain docs for About Me, my Brand Voice, and my working preferences. I had Claude interview me and saved each answer as a file. Now every output sounds like me and knows my projects and my team. Connect your tools. The plugin reads my inbox, Slack, and calendar through MCP connectors for Gmail, Slack, and Google Calendar. That's what turns "summarize my morning" into a real brief instead of an empty wish. This is the highest-ROI thing an operator can set up this week. I run my YouTube channel, podcast, community, and newsletter on plugins I built myself, all on the $100 Max plan with no engineer in sight. I made a full walkthrough that shows the whole build start to finish. Want the Daily Driver plugin to start from? Comment PLUGIN below and I'll send it to you. #AI #automation #ClaudeCode

JJ Englert

53,969 görüntüleme • 3 ay önce