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Search across a half million git repos at Algolia-level speeds ⚡ I'm never using GitHub code search ever again.

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Steven Tey

58,273 subscribers

156,650 Aufrufe • vor 3 Jahren •via X (Twitter)

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Remember the guy who lost 7,500 BTC because he threw away his old hard drive? That’s James Howells, and today those coins are worth over $500 million forever locked behind a seed phrase he can’t recover. We have heard stories like that. panic, loss, phone wipe One mistake, and it’s game over. Now imagine never having to write down a seed phrase again. That’s what Vultisig is building. A seedless, multi-chain vault where your keys are split across trusted devices using MPC/TSS tech meaning no single point of failure, ever. It’s crypto security built for the AI Agent era: → self-custodial automation → multi-chain access → multi-factor protection → zero seed phrases. ever. The next evolution of self-custody is here. And it’s powered by Vultisig.

OHJAY ⭕️ || 🇬🇧

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Every serious Claude Code user is using this repo. if you're not, you're leaving 90% of Claude Code's power on the table. It's called claude-code-best-practice - 84 sourced tips, implementation examples for every major feature, workflow comparisons across 8 major repos, and the actual tips from Boris Cherny (creator of Claude Code) compiled in one place. Here's what's actually in it: → 84 tips organized by category -- prompting, planning, CLAUDE.md, agents, commands, skills, hooks, workflows, debugging, utilities, daily habits → best practice + implemented examples for every core concept: subagents, commands, skills, hooks, MCP servers, plugins, settings, memory, checkpointing, CLI flags → workflow comparison table -- Superpowers, BMAD-METHOD, Get Shit Done, OpenSpec, gstack, HumanLayer -- what makes each unique, how many agents/commands/skills each has → orchestration workflow -- Command → Agent → Skill pattern with a live demo → Boris Cherny tips compiled across 3 tweet threads (13 + 10 + 12 tips) and 5 podcast/video appearances → "billion dollar questions" section -- open questions about CLAUDE.md, agents vs commands vs skills, specs -- that nobody has definitively answered yet here's a few of the tips that actually change how you use it: → use subagents with "say use subagents" to throw more compute at a problem -- offload tasks to keep your main context clean → spin up a second Claude to review your plan as a staff engineer before executing → CLAUDE.md should target under 200 lines -- wrap domain-specific rules in ` ` tags so Claude doesn't ignore them as files grow → compress KV context at max 50%, not at the end -- avoid the "agent dumb zone" by doing manual /compact proactively → after a mediocre fix: "knowing everything you know now, scrap this and implement the elegant solution" was #1 trending on GitHub in March 2026. 19.7K GitHub stars. 1.7K forks. MIT license. 100% open source. (link in the comments)
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Every serious Claude Code user is using this repo. if you're not, you're leaving 90% of Claude Code's power on the table. It's called claude-code-best-practice - 84 sourced tips, implementation examples for every major feature, workflow comparisons across 8 major repos, and the actual tips from Boris Cherny (creator of Claude Code) compiled in one place. Here's what's actually in it: → 84 tips organized by category -- prompting, planning, CLAUDE.md, agents, commands, skills, hooks, workflows, debugging, utilities, daily habits → best practice + implemented examples for every core concept: subagents, commands, skills, hooks, MCP servers, plugins, settings, memory, checkpointing, CLI flags → workflow comparison table -- Superpowers, BMAD-METHOD, Get Shit Done, OpenSpec, gstack, HumanLayer -- what makes each unique, how many agents/commands/skills each has → orchestration workflow -- Command → Agent → Skill pattern with a live demo → Boris Cherny tips compiled across 3 tweet threads (13 + 10 + 12 tips) and 5 podcast/video appearances → "billion dollar questions" section -- open questions about CLAUDE.md, agents vs commands vs skills, specs -- that nobody has definitively answered yet here's a few of the tips that actually change how you use it: → use subagents with "say use subagents" to throw more compute at a problem -- offload tasks to keep your main context clean → spin up a second Claude to review your plan as a staff engineer before executing → CLAUDE.md should target under 200 lines -- wrap domain-specific rules in ` ` tags so Claude doesn't ignore them as files grow → compress KV context at max 50%, not at the end -- avoid the "agent dumb zone" by doing manual /compact proactively → after a mediocre fix: "knowing everything you know now, scrap this and implement the elegant solution" was #1 trending on GitHub in March 2026. 19.7K GitHub stars. 1.7K forks. MIT license. 100% open source. (link in the comments)

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113,759 Aufrufe • vor 4 Monaten

PLUGGED CLAUDE CODE INTO OBSIDIAN AND MY DEAD VAULT STARTED FIRING LIKE A NEURAL NETWORK 13,000 notes written by hand over 4 years, and search worked fine that was the problem, because search only gives back what you already remember well enough to ask for The graph view never helped either, just a pretty picture nobody actually reads Here is what almost nobody uses: Obsidian keeps your entire vault as plain markdown in a folder on disk, with no database, no export, no API to beg for access So an agent doesn't need permission, it needs a path I gave Claude Code the vault directory and one rule don't answer me, read the links It stopped treating notes as files and started treating them as a net, where backlinks became edges and tags became weights and every note it opened dragged 20 more into context Then it wrote a file I never asked for One note welding two clusters 4 years apart an old client teardown and a half-dead idea buried in a daily note from last winter, a connection I had circled for months and never closed The agent closed it on the first pass, not because it was smarter, but because it had no memory of writing either one That's the part nobody says out loud: a second brain is not a memory problem, it's a distance problem, and you are standing too close to your own notes Everyone builds a second brain, nobody gives it a nervous system
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PLUGGED CLAUDE CODE INTO OBSIDIAN AND MY DEAD VAULT STARTED FIRING LIKE A NEURAL NETWORK 13,000 notes written by hand over 4 years, and search worked fine that was the problem, because search only gives back what you already remember well enough to ask for The graph view never helped either, just a pretty picture nobody actually reads Here is what almost nobody uses: Obsidian keeps your entire vault as plain markdown in a folder on disk, with no database, no export, no API to beg for access So an agent doesn't need permission, it needs a path I gave Claude Code the vault directory and one rule don't answer me, read the links It stopped treating notes as files and started treating them as a net, where backlinks became edges and tags became weights and every note it opened dragged 20 more into context Then it wrote a file I never asked for One note welding two clusters 4 years apart an old client teardown and a half-dead idea buried in a daily note from last winter, a connection I had circled for months and never closed The agent closed it on the first pass, not because it was smarter, but because it had no memory of writing either one That's the part nobody says out loud: a second brain is not a memory problem, it's a distance problem, and you are standing too close to your own notes Everyone builds a second brain, nobody gives it a nervous system

Spike 1%

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My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!
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My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!

Santiago

247,833 Aufrufe • vor 1 Jahr

Obsidian became a neural network with 184,320 links and now runs as the most powerful second brain ever built for $0. 12,480 notes in 1 vault, 0 folders, 0 tabs, and he hasn't opened Notion in 7 months. Every night at 2 AM Claude Code walks the vault, reads all 12,480 notes, embeds them, and hunts for the pairs that should be linked but never were. Last night it found 61 and he accepted all 61. That's the move nobody makes by hand, because 12,480 notes means 77 million possible pairs, and a human links maybe 4 a day while the agent links 61 while he sleeps. Capture takes 2 seconds, a voice memo lands as a note in 1.4, a clip becomes a linked node on its own friction is what kills a second brain so he deleted the friction. Recall latency sits at 38 milliseconds, which means the graph answers before he finishes doubting himself. 36 clusters formed without him, decay score holds at 0.03, and 97% of everything he ever wrote is still reachable, while most people's notes rot past 90% in a year and turn into a graveyard with good search. Yesterday it surfaced 9 forgotten notes, one of them 412 days old, and that one became a product he shipped 48 hours later. Plain .md files on a laptop, no cloud, no subscription, no company that can delete his brain on a Tuesday. The graph doesn't store what he knows. It stores what he'd never have connected alone.
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Obsidian became a neural network with 184,320 links and now runs as the most powerful second brain ever built for $0. 12,480 notes in 1 vault, 0 folders, 0 tabs, and he hasn't opened Notion in 7 months. Every night at 2 AM Claude Code walks the vault, reads all 12,480 notes, embeds them, and hunts for the pairs that should be linked but never were. Last night it found 61 and he accepted all 61. That's the move nobody makes by hand, because 12,480 notes means 77 million possible pairs, and a human links maybe 4 a day while the agent links 61 while he sleeps. Capture takes 2 seconds, a voice memo lands as a note in 1.4, a clip becomes a linked node on its own friction is what kills a second brain so he deleted the friction. Recall latency sits at 38 milliseconds, which means the graph answers before he finishes doubting himself. 36 clusters formed without him, decay score holds at 0.03, and 97% of everything he ever wrote is still reachable, while most people's notes rot past 90% in a year and turn into a graveyard with good search. Yesterday it surfaced 9 forgotten notes, one of them 412 days old, and that one became a product he shipped 48 hours later. Plain .md files on a laptop, no cloud, no subscription, no company that can delete his brain on a Tuesday. The graph doesn't store what he knows. It stores what he'd never have connected alone.

Spike 1%

11,546 Aufrufe • vor 8 Tagen

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!
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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

Daily Dose of Data Science

23,748 Aufrufe • vor 4 Monaten

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!
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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

Akshay 🚀

36,317 Aufrufe • vor 5 Monaten

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)
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most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,994 Aufrufe • vor 1 Jahr

We release Diamond Maps💎 unlocking accurate and efficient guidance for diffusion models. Our experiments show that our methods scale incredibly well. Excited to see what people will build with this! Accurate guidance has been a notoriously hard problem, but in this work, we’re bringing TWO (!) solutions to the table. The recipe for success: 1️⃣ Speed: Use distilled models (flow maps, mean flows, consistency models). 2️⃣ Exploration: Inject stochasticity to properly explore your search space. Because this fundamentally improves anything using flow matching and diffusion, we see a lot of potential for applications across audio, robotics, molecules, and beyond. Paper: Code: Huge thanks to an amazing team: Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong (Kelly) He, Zeynep Akata, Tommi Jaakkola, Nicholas Boffi, and Max Simchowitz. It was awesome bringing this to life together!
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We release Diamond Maps💎 unlocking accurate and efficient guidance for diffusion models. Our experiments show that our methods scale incredibly well. Excited to see what people will build with this! Accurate guidance has been a notoriously hard problem, but in this work, we’re bringing TWO (!) solutions to the table. The recipe for success: 1️⃣ Speed: Use distilled models (flow maps, mean flows, consistency models). 2️⃣ Exploration: Inject stochasticity to properly explore your search space. Because this fundamentally improves anything using flow matching and diffusion, we see a lot of potential for applications across audio, robotics, molecules, and beyond. Paper: Code: Huge thanks to an amazing team: Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong (Kelly) He, Zeynep Akata, Tommi Jaakkola, Nicholas Boffi, and Max Simchowitz. It was awesome bringing this to life together!

Peter Holderrieth

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