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MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some...

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John T Davies 🇪🇺

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85,589 views • 4 months ago •via X (Twitter)

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Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:
0:35

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

385,706 views • 5 days ago

A small project I'm publishing for Neovim people: 🐠 butterfish.nvim This is a Neovim plugin to use language models for very fluent code editing. It veers away from the chatbox or codebase Q/A approaches and instead focuses on specific local edits, for example, select and rewrite a block of code. Do powerful things with few keypresses, without leaving the Neovim buffer!
4:56

A small project I'm publishing for Neovim people: 🐠 butterfish.nvim This is a Neovim plugin to use language models for very fluent code editing. It veers away from the chatbox or codebase Q/A approaches and instead focuses on specific local edits, for example, select and rewrite a block of code. Do powerful things with few keypresses, without leaving the Neovim buffer!

Peter Bakkum

29,069 views • 2 years ago

$200,000 a year on a dev team. Fable 5 does the same thing in one night for $0. My friend who works as a Claude Code engineer made $700,000 without a single developer on his team. 10:27 One person wrote one PRD document. Fable 5 did the rest - 21,000 lines of TypeScript, 90+ commits, a full 3D game in the browser with zero downloads. Anthropic gave free access to this for 7 days and nobody is talking about it. I watched the demo and couldn't sleep. For two years I was hiring people for what one document and one night can do better. Watch the video below then read the article.
11:58

$200,000 a year on a dev team. Fable 5 does the same thing in one night for $0. My friend who works as a Claude Code engineer made $700,000 without a single developer on his team. 10:27 One person wrote one PRD document. Fable 5 did the rest - 21,000 lines of TypeScript, 90+ commits, a full 3D game in the browser with zero downloads. Anthropic gave free access to this for 7 days and nobody is talking about it. I watched the demo and couldn't sleep. For two years I was hiring people for what one document and one night can do better. Watch the video below then read the article.

Noisy

11,153 views • 1 month ago

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
13:23

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

55,345 views • 2 months ago

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.
1:09

President of Ukraine: We did indeed have a positive meeting with President Trump and his team. First of all, to be honest, I raised the issue of air defense. And I think that the US will return with a positive result. I will definitely talk about it when the result is in Ukraine. Second, our security guarantees. They are indeed ready, and the agreement is ready for signing. Of course, there will be many different technical details. And based on this document, additional documents will appear, but the main, core agreement on security guarantees is in place. Now I am waiting for President Trump to set the date and place. It's up to him. We are ready to sign such an important [document] for us. I emphasize once again that I am confident this is a historic document. Why did I mention the many details? You know that such a document awaits ratification by the US Congress and the Ukrainian Parliament. Therefore, there are still many steps ahead on this path.

Anton Gerashchenko

27,842 views • 6 months ago

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,618 views • 4 months ago

The 20 points for ending the war form a fundamental document. We are actively working on the key steps – they must be doable. From this fundamental document, we are developing at least two additional ones. The first is on security – regarding security guarantees with the United States. The second is on the economy, covering reconstruction and joint investment. Today, we focused specifically on the economic document. The principles of the economic document are absolutely clear, and we are fully aligned with the American side on this. Thank you. An economic action plan will follow. Europe will also be involved in reconstruction. In the near future, we plan to move forward on the other two documents as well. Rustem Umerov will continue the discussions.
4:34

The 20 points for ending the war form a fundamental document. We are actively working on the key steps – they must be doable. From this fundamental document, we are developing at least two additional ones. The first is on security – regarding security guarantees with the United States. The second is on the economy, covering reconstruction and joint investment. Today, we focused specifically on the economic document. The principles of the economic document are absolutely clear, and we are fully aligned with the American side on this. Thank you. An economic action plan will follow. Europe will also be involved in reconstruction. In the near future, we plan to move forward on the other two documents as well. Rustem Umerov will continue the discussions.

Volodymyr Zelenskyy / Володимир Зеленський

470,337 views • 8 months ago

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.
1:32

This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4 with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.

Blaze

29,917 views • 3 months ago

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.
1:55

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.

divyansh tiwari

39,303 views • 2 months ago

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)
0:51

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

Sam Hogan 🇺🇸

374,653 views • 3 years ago

🚨| Sir Lewis Hamilton on these past weeks between races and what he has been working on with the team in the background, including development for 2026. "I've sent documents, so I've done through the year. After the first few races, I did a full document for the team. Then during this break, I had another two documents that I sent in, and so then I come in and want to address those. Some of its structural adjustments that we need to make as a team in order to get better and all the areas that we want to improve. Then the other one was really about the car, the current issues that I have with this car, some things that you do want to take on to the next year's car and some that you need to work on changing for next year." "We did development for, tried the 2026 car for the first time and started work on that... so big, big push, and otherwise just training." - F1 2025 Spa -
1:56

🚨| Sir Lewis Hamilton on these past weeks between races and what he has been working on with the team in the background, including development for 2026. "I've sent documents, so I've done through the year. After the first few races, I did a full document for the team. Then during this break, I had another two documents that I sent in, and so then I come in and want to address those. Some of its structural adjustments that we need to make as a team in order to get better and all the areas that we want to improve. Then the other one was really about the car, the current issues that I have with this car, some things that you do want to take on to the next year's car and some that you need to work on changing for next year." "We did development for, tried the 2026 car for the first time and started work on that... so big, big push, and otherwise just training." - F1 2025 Spa -

sim

74,210 views • 1 year ago

Head of Engineering for Claude Code at Anthropic. Fiona Fung: "After a while, it could start being a lonely experience - we were all just working with our agents so much." Claude now writes 80% of Anthropic's code. The team ships 8x more. An engineer, in Anthropic's own report: "On the days everything works, I can't help but think nothing I do matters." She names the fix in the episode. The dream was less typing - is this what you wanted?
28:47

Head of Engineering for Claude Code at Anthropic. Fiona Fung: "After a while, it could start being a lonely experience - we were all just working with our agents so much." Claude now writes 80% of Anthropic's code. The team ships 8x more. An engineer, in Anthropic's own report: "On the days everything works, I can't help but think nothing I do matters." She names the fix in the episode. The dream was less typing - is this what you wanted?

shmidt

33,041 views • 1 month ago

Good morning from Washington. Time for a quick tour. You know what you won’t find in my office? A desk. Or a chief of staff. We have an open office with all day interaction and collaboration between my employees. As for the chief of staff? I tried having a single point of failure before. Didn’t love it. Now our four directors report directly to me. Our office works faster, more efficient and better than it ever has under this set up. Everyone in the office has a voice and they are encouraged to use it. Our entire office is on the same page with the way we serve our constituents. And we love it. Now
2:27

Good morning from Washington. Time for a quick tour. You know what you won’t find in my office? A desk. Or a chief of staff. We have an open office with all day interaction and collaboration between my employees. As for the chief of staff? I tried having a single point of failure before. Didn’t love it. Now our four directors report directly to me. Our office works faster, more efficient and better than it ever has under this set up. Everyone in the office has a voice and they are encouraged to use it. Our entire office is on the same page with the way we serve our constituents. And we love it. Now

Rep. Nancy Mace

545,601 views • 6 months ago

Collison Brothers Explain Why Stripe is Better Off Staying Private John's (John Collison) take: " People generally make the argument that public companies run in a more disciplined fashion. I think that's hogwash." "If you need a 25-year old Fidelity analyst asking you to double click on your CapEx, blah, blah, blah, to run the company with discipline, something is horribly wrong at the company and you need new management." "It used to be the case that to do any return of capital to shareholders, or if you needed any kind of large sums of money, you needed the public markets." "That's obviously not true today, where stable private markets exist." " This is our life's work. We're not going anywhere. We'll be very happily running Stripe in 10 years time, in 20 years time." Patrick's (Patrick Collison) take: "We're a company at the intersection of financial services and technology. Being private for a long time is the norm." "What matters is less kind of the returns in a given year, and more duration. And so the question is, what enables the best compounding on a 10 year time horizon?" "And what's best for shareholders as you really take the longer-term perspective? And then what's best for customers?" " At this juncture, with the business growing at this rate, we want to spend the marginal hour with some customer."
1:02

Collison Brothers Explain Why Stripe is Better Off Staying Private John's (John Collison) take: " People generally make the argument that public companies run in a more disciplined fashion. I think that's hogwash." "If you need a 25-year old Fidelity analyst asking you to double click on your CapEx, blah, blah, blah, to run the company with discipline, something is horribly wrong at the company and you need new management." "It used to be the case that to do any return of capital to shareholders, or if you needed any kind of large sums of money, you needed the public markets." "That's obviously not true today, where stable private markets exist." " This is our life's work. We're not going anywhere. We'll be very happily running Stripe in 10 years time, in 20 years time." Patrick's (Patrick Collison) take: "We're a company at the intersection of financial services and technology. Being private for a long time is the norm." "What matters is less kind of the returns in a given year, and more duration. And so the question is, what enables the best compounding on a 10 year time horizon?" "And what's best for shareholders as you really take the longer-term perspective? And then what's best for customers?" " At this juncture, with the business growing at this rate, we want to spend the marginal hour with some customer."

The All-In Podcast

407,004 views • 1 year ago

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.
0:45

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.

Santiago

155,179 views • 2 years ago

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
0:31

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,856 views • 1 year ago

We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!
0:58

We built an AI agent that lets you vibe-code document extraction - high accuracy and citations over the most complex documents. Our latest release lets you upload documents as context. All you then have to do is describe what you want extracted in natural language. 💡 Our agent will then read the document with file tools to infer the right schema, validation rules, and other pre/postprocessing logic. ✅ It will give you back a workflow that can extract over thousands/millions of documents at scale. You can still of course review and edit every output before approving. Stop handling paperwork manually; just upload files, describe your task, and let our agent handle the rest. Our vision for LlamaAgents is to provide the most advanced and easy-to-use way for you to orchestrate document work. Walkthrough: Check it out: If you’re interested in reducing the operational burden of document extraction (invoices, claims, onboarding forms), come talk to us!

Jerry Liu

20,857 views • 5 months ago

Full 10 Min Report, Experts CONFIRM Barack Obama Birth Certificate is FAKE Obama’s Presidency Was A Fraud & Has “9 Points Of Forgery” Everything Obama Did Should Be Undone “Today you're going to hear lots of information that some of you are going to understand and going to be able to tell the true story. In fact, please know that this is a very technical, but the evidence is clear if you'll pay attention. Please note you're going to hear about two separate experts. These experts are two separate continents with no knowledge of each other and they draw similar conclusions. Again, that said, I know some of you are going to get this story and are going to tell the story the way it was.” —- “We and anyone else who dared to question the document have been the line falsely labeled grossly criticized in the bulk of the media on certain internet sources for years. Today we're going to set the record straight. I believe you will be shocked by what you hear and see today.” —- “Like the sheriff just told you, when you conduct criminal investigations, you have to let the evidence lead you. You never lead the evidence. And in doing this, my motive was to clear the document. Because to be quite honest with you, I didn't believe it. I didn't believe this was possible. I didn't think this would ever happen in this nation. I didn't believe it.” — “Back in 2012, I told you about Reed Hayes, a document examiner. Let me tell you about Reed Hayes, a man with 40 years, since 1974, 40 plus years of experience in examining forensic document, handwriting, a man who's well respected in his expertise, a court recognized expert, a document examiner. He is the man you go to when somebody gives you a bad check with a bad signature. This is the guy you run to. — Mr. Hayes told me right off the bat, I'm an Obama supporter. I voted for him twice. He goes, and I will never do anything to hurt the President of the United States. What I had said to him was, Reed, I am not asking you to hurt the President of the United States. I'm asking you to take a look at this document and clear it and tell me there's nothing wrong with it. Would you at least do that? And he took a look at it. And when he called me back, he told me, Mike.” “I can't clear this, there's something wrong with it." And I asked him, I said, Reed, would you continue? I said, I know your position, but would you continue? And his answer to me was, this is what I do. I'll look at it, I'll do it. That's a man of integrity, respecting what his ability is to get to the truth. Because for Sheriff Apoyo and myself, this was never about Barack Obama. This is about a document. You take that document and you remove the name, Barack Hussein Obama, and put your name on there. If it was your document and it was brought to us, we would do the same thing with this document.” — You are looking at two long-form birth certificates from the state of Hawaii. According to the dates on the certificates, these births took place in Honolulu during the month of August of 1961, just 16 days apart. The birth certificate on the left belongs to Barack Obama. The birth certificate on the right belongs to Johanna Ane. After five years of intense investigation, which included consultation with one of America's most respected forensic document examiners, and a team of European media forensic experts, the Maricopa County Sheriff's Office has reached a conclusion utilizing forensic techniques both old and new. It is the opinion of the Maricopa County Sheriff's Office that the birth certificate on your right, belonging to Johanna Ani, was in fact used as a source document in the digital creation of Barack Obama's long-form birth certificate. Nine points of forgery in which words, letters, and hand-placed date stamps have been digitally copied from the Adneid long-form birth certificate and pasted onto Obama's long-form birth certificate” They COULD NOT clear the document. Much more info in this video if you watch it all
10:00

Full 10 Min Report, Experts CONFIRM Barack Obama Birth Certificate is FAKE Obama’s Presidency Was A Fraud & Has “9 Points Of Forgery” Everything Obama Did Should Be Undone “Today you're going to hear lots of information that some of you are going to understand and going to be able to tell the true story. In fact, please know that this is a very technical, but the evidence is clear if you'll pay attention. Please note you're going to hear about two separate experts. These experts are two separate continents with no knowledge of each other and they draw similar conclusions. Again, that said, I know some of you are going to get this story and are going to tell the story the way it was.” —- “We and anyone else who dared to question the document have been the line falsely labeled grossly criticized in the bulk of the media on certain internet sources for years. Today we're going to set the record straight. I believe you will be shocked by what you hear and see today.” —- “Like the sheriff just told you, when you conduct criminal investigations, you have to let the evidence lead you. You never lead the evidence. And in doing this, my motive was to clear the document. Because to be quite honest with you, I didn't believe it. I didn't believe this was possible. I didn't think this would ever happen in this nation. I didn't believe it.” — “Back in 2012, I told you about Reed Hayes, a document examiner. Let me tell you about Reed Hayes, a man with 40 years, since 1974, 40 plus years of experience in examining forensic document, handwriting, a man who's well respected in his expertise, a court recognized expert, a document examiner. He is the man you go to when somebody gives you a bad check with a bad signature. This is the guy you run to. — Mr. Hayes told me right off the bat, I'm an Obama supporter. I voted for him twice. He goes, and I will never do anything to hurt the President of the United States. What I had said to him was, Reed, I am not asking you to hurt the President of the United States. I'm asking you to take a look at this document and clear it and tell me there's nothing wrong with it. Would you at least do that? And he took a look at it. And when he called me back, he told me, Mike.” “I can't clear this, there's something wrong with it." And I asked him, I said, Reed, would you continue? I said, I know your position, but would you continue? And his answer to me was, this is what I do. I'll look at it, I'll do it. That's a man of integrity, respecting what his ability is to get to the truth. Because for Sheriff Apoyo and myself, this was never about Barack Obama. This is about a document. You take that document and you remove the name, Barack Hussein Obama, and put your name on there. If it was your document and it was brought to us, we would do the same thing with this document.” — You are looking at two long-form birth certificates from the state of Hawaii. According to the dates on the certificates, these births took place in Honolulu during the month of August of 1961, just 16 days apart. The birth certificate on the left belongs to Barack Obama. The birth certificate on the right belongs to Johanna Ane. After five years of intense investigation, which included consultation with one of America's most respected forensic document examiners, and a team of European media forensic experts, the Maricopa County Sheriff's Office has reached a conclusion utilizing forensic techniques both old and new. It is the opinion of the Maricopa County Sheriff's Office that the birth certificate on your right, belonging to Johanna Ani, was in fact used as a source document in the digital creation of Barack Obama's long-form birth certificate. Nine points of forgery in which words, letters, and hand-placed date stamps have been digitally copied from the Adneid long-form birth certificate and pasted onto Obama's long-form birth certificate” They COULD NOT clear the document. Much more info in this video if you watch it all

Wall Street Apes

2,526,348 views • 2 years ago