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CODEX SKILL THAT FINDS COMPLEXITY HOTSPOTS IN YOUR CODEBASE! I made a Codex skill that finds performance hotspots, proposes one safe optimization, and proves whether it actually worked. Scan your project while Codex creates a repeatable benchmark, asks for approval before changing any files, runs the tests, and compares... show more

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CODEX SKILL THAT FINDS COMPLEXITY HOTSPOTS IN YOUR CODEBASE! I made a Codex skill that analyzes your codebase and reports where performance can be improved safely. Scan your project while Codex checks loops, repeated lookups, render-heavy code, N+1 patterns, and places where complexity can potentially be reduced without breaking behavior. -> codebase complexity analysis -> O(n²), O(n*m), repeated scan detection -> before/after complexity estimates -> safe optimization suggestions -> risk level + tests needed -> report-only mode by default -> one-command install Install: npx --yes codex-complexity-optimizer 100% open source. Repo in Bio.

CODEX SKILL THAT FINDS COMPLEXITY HOTSPOTS IN YOUR CODEBASE! I made a Codex skill that analyzes your codebase and reports where performance can be improved safely. Scan your project while Codex checks loops, repeated lookups, render-heavy code, N+1 patterns, and places where complexity can potentially be reduced without breaking behavior. -> codebase complexity analysis -> O(n²), O(n*m), repeated scan detection -> before/after complexity estimates -> safe optimization suggestions -> risk level + tests needed -> report-only mode by default -> one-command install Install: npx --yes codex-complexity-optimizer 100% open source. Repo in Bio.

Kappaemme

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CODEX SKILL THAT FINDS YOUR STARTUP’S FIRST CUSTOMERS! I made a Codex skill that analyzes your startup and finds potential customers from real public signals. Paste your startup URL while Codex defines your ideal customer, searches public discussions, qualifies each prospect, and generates a polished report with personalized outreach openers. -> ideal customer profile analysis -> public pain + buying signal research -> evidence-backed prospect shortlist -> fit, timing + reachability scores -> original source links for every prospect -> personalized outreach openers -> polished HTML report -> one-command install Install: npx --yes codex-first-customer-finder-skill 100% open source. Repo in Bio.

Kappaemme

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CODEX SKILL THAT FINDS THE BEST PRICING FOR YOUR STARTUP! I made a Codex skill that analyzes your startup and recommends how you should monetize it. Send it your startup URL while Codex researches competitors, compares pricing models, and recommends concrete plans and price hypotheses. -> monthly vs annual pricing -> subscription, one-time, or lifetime decision -> competitor pricing research -> recommended plans and tiers -> concrete price hypotheses -> value metric + upgrade triggers -> free trial and freemium decision -> lifetime deal risk analysis -> complete Pricing Report (.md) -> actionable 14-Day Pricing Roadmap (.csv) -> no automatic billing changes -> one-command install Install: npx --yes codex-startup-pricing-lab 100% open source. Repo in Bio.

CODEX SKILL THAT FINDS THE BEST PRICING FOR YOUR STARTUP! I made a Codex skill that analyzes your startup and recommends how you should monetize it. Send it your startup URL while Codex researches competitors, compares pricing models, and recommends concrete plans and price hypotheses. -> monthly vs annual pricing -> subscription, one-time, or lifetime decision -> competitor pricing research -> recommended plans and tiers -> concrete price hypotheses -> value metric + upgrade triggers -> free trial and freemium decision -> lifetime deal risk analysis -> complete Pricing Report (.md) -> actionable 14-Day Pricing Roadmap (.csv) -> no automatic billing changes -> one-command install Install: npx --yes codex-startup-pricing-lab 100% open source. Repo in Bio.

Kappaemme

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CODEX SKILL TO BRUTALLY TEST ANY STARTUP IDEA! Most startup ideas sound good. This Codex skill tells you why they probably won’t work. Just give Codex your idea and it pressure-tests it for you -> finds the core assumption -> exposes fatal flaws -> checks if the problem is real -> maps real competitors -> plans your first 10 customers -> defines a 2 week MVP Install: npx --yes codex-startup-pressure-test-skill@latest 100% open source. Repo in bio

CODEX SKILL TO BRUTALLY TEST ANY STARTUP IDEA! Most startup ideas sound good. This Codex skill tells you why they probably won’t work. Just give Codex your idea and it pressure-tests it for you -> finds the core assumption -> exposes fatal flaws -> checks if the problem is real -> maps real competitors -> plans your first 10 customers -> defines a 2 week MVP Install: npx --yes codex-startup-pressure-test-skill@latest 100% open source. Repo in bio

Kappaemme

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CODEX SKILL THAT TURNS LOCAL SEARCH INTO CLIENT LEADS! I made a Codex skill that helps find local businesses that may need a website. Search nearby shops, gyms, restaurants, salons, and local activities while Codex checks who has a real website and who only has socials. -> local business prospecting -> website vs social-only checks -> lead scoring -> phone/contact fields -> chat or CSV-style output -> one-command install Install: npx --yes local-client-prospector-skill 100% open source. Repo in Bio.

CODEX SKILL THAT TURNS LOCAL SEARCH INTO CLIENT LEADS! I made a Codex skill that helps find local businesses that may need a website. Search nearby shops, gyms, restaurants, salons, and local activities while Codex checks who has a real website and who only has socials. -> local business prospecting -> website vs social-only checks -> lead scoring -> phone/contact fields -> chat or CSV-style output -> one-command install Install: npx --yes local-client-prospector-skill 100% open source. Repo in Bio.

Kappaemme

323,504 Aufrufe • vor 2 Monaten

WHAT IF CODEX HAD A /GOAL COMMAND FOR BUILDING MVPS? I built a Codex skill that turns “/goal MVP ...” into a full MVP workflow. Write your idea after /goal MVP, and Codex helps scope, build, verify, and prepare a testable MVP. -> trigger it with /goal MVP -> clarify the idea before building -> scope the smallest useful MVP -> create a practical build plan -> build inside your current workspace -> verify with tests/build checks -> prepare demo path + launch notes Install: npm i -g goal-mvp 100% open source. Link in bio.

WHAT IF CODEX HAD A /GOAL COMMAND FOR BUILDING MVPS? I built a Codex skill that turns “/goal MVP ...” into a full MVP workflow. Write your idea after /goal MVP, and Codex helps scope, build, verify, and prepare a testable MVP. -> trigger it with /goal MVP -> clarify the idea before building -> scope the smallest useful MVP -> create a practical build plan -> build inside your current workspace -> verify with tests/build checks -> prepare demo path + launch notes Install: npm i -g goal-mvp 100% open source. Link in bio.

Kappaemme

13,453 Aufrufe • vor 2 Monaten

Introducing 🌸 Kanna – a web ui for Claude Code + Codex running right in your browser (and my first open source project!) It's sort of like if Claude Code & Codex had a baby with a much better ui/ux 💅 Here's what makes Kanna special: 🔀 One-click switch between Claude Code / Codex 🌐 Runs in your browser on localhost - no app switching 🧩 Embedded split terminals, persisted between chats with a beautiful horizontal scrolling UX 📁 Full project file browser built in, embedded editor coming soon too. Coming soon: 🔒 End-to-end encryption + remote access via reverse proxy 🌿 Git integration, diffs, PRs 🧱 Plugins I wanted to replace switching between cursor, the github app, browser & (most recently) the codex app. The browser is hardest to recreate yet also the most flexible so it makes sense to build in the browser itself. Install in 5 seconds: bun install -g kanna-code then just type: kanna Kanna is purely a ui/ux layer. It uses your existing CLIs, is 100% compliant & no data ever leaves your machine. If running claude or codex in your terminal works, kanna works too. Kanna natively supports every tool call, model, reasoning effort, fast mode, plan mode, compaction, user questions, web searching, skills, agents, mcps, everything. There are a few libraries that do this but none that I found as comprehensive. All feedback welcome!

Introducing 🌸 Kanna – a web ui for Claude Code + Codex running right in your browser (and my first open source project!) It's sort of like if Claude Code & Codex had a baby with a much better ui/ux 💅 Here's what makes Kanna special: 🔀 One-click switch between Claude Code / Codex 🌐 Runs in your browser on localhost - no app switching 🧩 Embedded split terminals, persisted between chats with a beautiful horizontal scrolling UX 📁 Full project file browser built in, embedded editor coming soon too. Coming soon: 🔒 End-to-end encryption + remote access via reverse proxy 🌿 Git integration, diffs, PRs 🧱 Plugins I wanted to replace switching between cursor, the github app, browser & (most recently) the codex app. The browser is hardest to recreate yet also the most flexible so it makes sense to build in the browser itself. Install in 5 seconds: bun install -g kanna-code then just type: kanna Kanna is purely a ui/ux layer. It uses your existing CLIs, is 100% compliant & no data ever leaves your machine. If running claude or codex in your terminal works, kanna works too. Kanna natively supports every tool call, model, reasoning effort, fast mode, plan mode, compaction, user questions, web searching, skills, agents, mcps, everything. There are a few libraries that do this but none that I found as comprehensive. All feedback welcome!

Superdaddy

43,285 Aufrufe • vor 4 Monaten

EVERYTHING YOU NEED TO KNOW ABOUT CHATGPT'S "LOVABLE KILLER" CODEX SITES (in 25 mins): TLDR; the coolest part is that apps you build can update themselves autonomously 1. Codex Sites is not Replit or Lovable or Bolt. Those are great for one-prompting a full app. Codex Sites is for building apps that the agent keeps improving without you touching them. 2. Your personal website can update its own stats. Your internal dashboard can refresh its own data. Your product can add features while you sleep. The app is alive. 3. Start by invoking at-sites. Use realistic sample data. Always say "save for review, do not deploy." This unlocks building a real product, not a homepage. 4. Add persistent storage so the app remembers everything between visits. Without this it resets every time. Ask Codex to show you the data model before it builds. 5. Create safe actions. These are the specific things the agent is allowed to do to your app: add data, update cards, move things, score things. You define the boundaries. The agent operates within them. 6. Build skills so any future Codex chat knows how to interact with your app. The skill is basically a manual for the agent. Without it, every new chat starts from zero. 7. Save gate like a video game. Codex doesn't auto-save. Create checkpoints before you deploy so you can roll back if something breaks. 8. Close the autonomous loop. This is the magic. Once memory, safe actions, and skills are set up, the agent can update your app from any chat, any context, without you switching tabs. 9. Use the plugins most people are sleeping on. Figma, Canva, HeyGen for avatar videos, Game Studio for interactive experiences, FAL for image generation, Hugging Face for open source models. Worth adding a few. 10. The big picture: we went from building apps to raising apps. You set up the structure, the guardrails, and the skills. The agent does the rest. That's autonomous product building and it's here right now. Tbh, Codex sites isn't perfect. Still a lot to be desired like domains, db, authentication etc. But it's a glimpse into this idea that apps can be updated/improved upon automonously. And Codex Sites is REALLY good if you live in Codex everyday. Which more and more of are. And that's really cool. Will be interesting to see how Lovable, Bolt, Replit etc react to this. full tutorial on The Startup Ideas Podcast (SIP) 🧃 where you get your pods watch share with a friend i'm rooting for you What do you think of Codex and Codex sites?

GREG ISENBERG

68,813 Aufrufe • vor 2 Monaten

SOMEONE JUST OPEN-SOURCED AN AI THAT SEARCHES EVERY MAJOR PLATFORM AT ONCE Reddit, X, YouTube, Hacker News, TikTok, Polymarket, GitHub not one by one - PARALLEL it ranks results by what real people actually do: - upvotes from Reddit - likes and timing from X - real money predictions from Polymarket - commits and PRs from GitHub then generates ONE brief 28,700+ stars on github 2,400+ forks. MIT license. Google is strong on the web ChatGPT has some access neither of them touches this combination setup guide (2 minutes): Claude Code: > /plugin marketplace add mvanhorn/last30days-skill Hermes: > npx skills add mvanhorn/last30days-skill -g -a hermes Cursor / Codex / Copilot / Gemini CLI: > npx skills add mvanhorn/last30days-skill -g then run it: > /last30days bitcoin > /last30days "OpenAI vs Anthropic" > /last30days [any topic] no API keys needed for Reddit, Hacker News, Polymarket, and GitHub X works if you're already logged into x. com in your browser YouTube: brew install yt-dlp repo in replies

SOMEONE JUST OPEN-SOURCED AN AI THAT SEARCHES EVERY MAJOR PLATFORM AT ONCE Reddit, X, YouTube, Hacker News, TikTok, Polymarket, GitHub not one by one - PARALLEL it ranks results by what real people actually do: - upvotes from Reddit - likes and timing from X - real money predictions from Polymarket - commits and PRs from GitHub then generates ONE brief 28,700+ stars on github 2,400+ forks. MIT license. Google is strong on the web ChatGPT has some access neither of them touches this combination setup guide (2 minutes): Claude Code: > /plugin marketplace add mvanhorn/last30days-skill Hermes: > npx skills add mvanhorn/last30days-skill -g -a hermes Cursor / Codex / Copilot / Gemini CLI: > npx skills add mvanhorn/last30days-skill -g then run it: > /last30days bitcoin > /last30days "OpenAI vs Anthropic" > /last30days [any topic] no API keys needed for Reddit, Hacker News, Polymarket, and GitHub X works if you're already logged into x. com in your browser YouTube: brew install yt-dlp repo in replies

Isra

38,760 Aufrufe • vor 1 Monat

THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this

THIS IS THE MOST COMPLETE CLAUDE CODE SETUP I'VE EVER SEEN someone open-sourced a full agent harness for Claude Code 38 agents. 156 skills. 72 commands. all pre-configured the kind of setup you'd spend weeks building yourself what's inside: → planner - feature implementation planning → architect - system design decisions → tdd-guide - test-driven development workflow → code-reviewer - quality and security review → security-reviewer - vulnerability analysis → build-error-resolver - fixes broken builds → refactor-cleaner - dead code cleanup → doc-updater - keeps docs in sync → chief-of-staff - communication triage → + 29 more agents 156 skills covering token optimization, memory persistence, continuous learning, verification loops, parallelization, subagent orchestration it ships with AgentShield baked in a security auditor that runs vulnerability checks on every agent action before execution works on Claude Code, Cursor, OpenCode, and Codex CLI one repo, one install, configured everywhere one warning: loading 38 agents and 156 skills into your context at once will burn your usage limits in minutes pick the 3-4 you actually need and load only those free. open-source. MIT licensed like + bookmark - you'll need this

self.dll

19,794 Aufrufe • vor 3 Monaten

$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$

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 Aufrufe • vor 1 Monat

If you're hitting Claude Code's context limit, check your terminal output before blaming your project size. Every command Claude runs dumps raw output into your context window. git status : 76% of it is noise. A failed cargo test : 155 lines becomes 3. A grep across your codebase : same story You're not running out of context because your code is too big. You're running out because the commands are bleeding you dry before you get anywhere. RTK intercepts that output, compresses it, and hands Claude a clean version instead. 60–90% fewer tokens. Same information. Runs in the background after a one-command setup. Real numbers from 2,900+ commands: average 89% compression across the board. MIT license. Free. 39k GitHub stars. rtk init -g That's it. Restart Claude Code and every command is compressed automatically.

If you're hitting Claude Code's context limit, check your terminal output before blaming your project size. Every command Claude runs dumps raw output into your context window. git status : 76% of it is noise. A failed cargo test : 155 lines becomes 3. A grep across your codebase : same story You're not running out of context because your code is too big. You're running out because the commands are bleeding you dry before you get anywhere. RTK intercepts that output, compresses it, and hands Claude a clean version instead. 60–90% fewer tokens. Same information. Runs in the background after a one-command setup. Real numbers from 2,900+ commands: average 89% compression across the board. MIT license. Free. 39k GitHub stars. rtk init -g That's it. Restart Claude Code and every command is compressed automatically.

Charlie Hills

16,666 Aufrufe • vor 3 Monaten

Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship. Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor. So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex. Also new: ◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors. ◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays. ◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total. ◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move. Free, open source. Claude Code, Codex, Cursor, and more.

Paul Bakaus

102,226 Aufrufe • vor 2 Monaten

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge

5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge

self.dll

65,079 Aufrufe • vor 1 Monat

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.

Matthew Lam

120,028 Aufrufe • vor 13 Tagen

A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this

A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this

Yarchi

140,729 Aufrufe • vor 1 Monat

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

XBOW raised $117M to build AI hacking agents. Now someone just open-sourced it for FREE. Strix deploys autonomous AI agents that act like real hackers - they run your code dynamically, find vulnerabilities, and validate them through actual proof-of-concepts. Why it matters: The biggest problem with traditional security testing is that it doesn't keep up with development speed. Strix solves this by integrating directly into your workflow: ↳ Run it in CI/CD to catch vulnerabilities before production ↳ Get real proof-of-concepts, not false positives from static analysis ↳ Test everything: injection attacks, access control, business logic flaws The best part? You don't need to be a security expert. Strix includes a complete hacker toolkit - HTTP proxy, browser automation, and Python runtime for exploit development. It's like having a security team that works at the speed of your CI/CD pipeline. The best part is that the tool runs locally in Docker containers, so your code never leaves your environment. Getting started is simple: - pipx install strix-agent - Point it at your codebase (app, repo, or directory) Everything is 100% open-source! I've shared link to the GitHub repo in the replies!

Akshay 🚀

96,762 Aufrufe • vor 9 Monaten

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Dimitris Neocleous

3,003,857 Aufrufe • vor 1 Monat

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

JJ Englert

16,176 Aufrufe • vor 3 Monaten

OpenAI just released Codex Security, an AI agent that scans software projects to fix vulnerabilities while ignoring harmless bugs. Testing on 1.2mn commits found 792 critical flaws and dropped false alarms by 50%. Here is how the Codex Security agent works Initially, it scans your software project to learn how all the different pieces of code connect. It uses that knowledge to build a threat model, which acts as a custom map showing exactly where your software might be exposed to hackers. The agent then hunts for potential security holes based on that specific map. When it spots a possible flaw, it does not just send you a random alert. Instead, it creates a completely safe and isolated copy of your system known as a sandbox. The agent then actively tries to break into that sandboxed copy to prove if the security weakness is a real danger. If the agent successfully exploits its own test environment, it knows the bug is a genuine threat and not a false alarm. After confirming the danger, the agent writes a custom software patch to fix the broken code. It then tests that new patch to guarantee the fix does not accidentally break any other features in your software.

OpenAI just released Codex Security, an AI agent that scans software projects to fix vulnerabilities while ignoring harmless bugs. Testing on 1.2mn commits found 792 critical flaws and dropped false alarms by 50%. Here is how the Codex Security agent works Initially, it scans your software project to learn how all the different pieces of code connect. It uses that knowledge to build a threat model, which acts as a custom map showing exactly where your software might be exposed to hackers. The agent then hunts for potential security holes based on that specific map. When it spots a possible flaw, it does not just send you a random alert. Instead, it creates a completely safe and isolated copy of your system known as a sandbox. The agent then actively tries to break into that sandboxed copy to prove if the security weakness is a real danger. If the agent successfully exploits its own test environment, it knows the bug is a genuine threat and not a false alarm. After confirming the danger, the agent writes a custom software patch to fix the broken code. It then tests that new patch to guarantee the fix does not accidentally break any other features in your software.

Rohan Paul

13,208 Aufrufe • vor 5 Monaten