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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... 30k tokens), I was seeing sub-150ms TTFT (Time To First Token), that's "instant" for a human. We run this on an office server (one of our Mac Minis) for staff to work with, instant answers on critical, confidential company/client documents on phone, iPads or laptops. Imagine the "What were the 3Q25 profits?", "When did Joe start working for us?", "Please list the project milestones and team count" answered within seconds of hitting the return key. It works with voice too. This example is 17k lines of code from 177 source files plus an index of the entire 1,902 files, the entire Claude Code codebase (oops Anthropic!). Zero loss (unlike Anthropic's security). What this means is the ability to work better on local (private) code bases, off-the-shelf private document analysis with better response than the best public models - all locally.show more

John T Davies 🇪🇺

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You asked for document uploads. We answered with document uploads. This is so much more than a link or a screenshot, Webb actually analyzes the document and adds it to the CONTEXT of the canvas. A serious game changer. Ian Carroll

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This is the future of web design. I generated a website in seconds from a PDF document (with drawings, text, and images). No input from my part. No code. No work whatsoever other than uploading the document. This is incredible.

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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!

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Peter Bakkum

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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.

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

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Jerry Liu

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TBPN

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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 • 1 month ago

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Alex Finn reveals that you need to spend $20,000 to get the equivalent of Opus 4.6 on a local model "The advantages are that you aren’t paying for tokens, all you have to pay for is electricity.” "The fact you're not paying for tokens means you can run your model 24/7 365, you can't do that with Opus" “If you run it locally it’s a completely private system. Nothing you say is private if you're not on a local model”

Mikli

85,005 views • 4 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.

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

39,110 views • 1 month ago

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Shubham Saboo

39,748 views • 8 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 :)

✨ 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 🇺🇸

374,577 views • 3 years 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?

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 • 4 days 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 -

🚨| 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 • 11 months 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

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

544,697 views • 4 months ago