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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...

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Isra

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38,760 просмотров • 3 месяцев назад •via X (Twitter)

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Introducing - /last30days v3.11 - the biggest release yet. Last30Days - the agentic search engine with ⭐51K stars. v3.11: your agent now reads X, Reddit, YouTube, TikTok, Instagram, arXiv, Techmeme, Hacker News, Digg, and Polymarket in ONE command. NEW: - Codex: now a first class native plugin (thank you Trevin Chow) - arXiv.org + Techmeme + Digg: free, no keys - free Reddit is back with real upvotes and top comments - best comments from TikTok, Instagram and YouTube when you add your Scrape Creators key, also LinkedIn articles
1:13

Introducing - /last30days v3.11 - the biggest release yet. Last30Days - the agentic search engine with ⭐51K stars. v3.11: your agent now reads X, Reddit, YouTube, TikTok, Instagram, arXiv, Techmeme, Hacker News, Digg, and Polymarket in ONE command. NEW: - Codex: now a first class native plugin (thank you Trevin Chow) - arXiv.org + Techmeme + Digg: free, no keys - free Reddit is back with real upvotes and top comments - best comments from TikTok, Instagram and YouTube when you add your Scrape Creators key, also LinkedIn articles

Matt Van Horn

76,498 просмотров • 2 месяцев назад

v3 of /last30days is here. 20,000+⭐ on GitHub. The biggest upgrade yet. An AI agent-led search engine scored by upvotes, likes, and real money - not editors. Reddit comments, X posts, and YouTube transcripts are now FREE. No API keys needed for the core sources. v3 killer feature: intelligent search. Before it searches, a Python pre-research brain resolves X handles, subreddits, TikTok hashtags, and YouTube channels for your topic. It finds the RIGHT places to search before the LLM judge assembles the report. Shout out to Jeffrey Sperling for building this engine New in v3: - Free Reddit, X, and YouTube (no API keys) - Intelligent pre-research engine - Best Takes (the funniest Reddit comments are first-class) - Cross-source cluster merging - Single-pass comparisons (X vs Y in 5 min, not 12) - GitHub person-mode - ELI5 mode
5:26

v3 of /last30days is here. 20,000+⭐ on GitHub. The biggest upgrade yet. An AI agent-led search engine scored by upvotes, likes, and real money - not editors. Reddit comments, X posts, and YouTube transcripts are now FREE. No API keys needed for the core sources. v3 killer feature: intelligent search. Before it searches, a Python pre-research brain resolves X handles, subreddits, TikTok hashtags, and YouTube channels for your topic. It finds the RIGHT places to search before the LLM judge assembles the report. Shout out to Jeffrey Sperling for building this engine New in v3: - Free Reddit, X, and YouTube (no API keys) - Intelligent pre-research engine - Best Takes (the funniest Reddit comments are first-class) - Cross-source cluster merging - Single-pass comparisons (X vs Y in 5 min, not 12) - GitHub person-mode - ELI5 mode

Matt Van Horn

334,449 просмотров • 5 месяцев назад

This guy literally automated his entire social media with one free AI agent. No team. No scheduling tools. No VA. Here's exactly how he did it 👇 The agent is called Hermes. It's free and open source. Here's what it's running on autopilot right now: → Scans Reddit every 24 hours for trending topics → Writes posts based on breaking news → Generates custom images with Nano Banana 2 → Posts directly to X, TikTok, and Instagram with captions and hashtags → Pulls daily analytics and flags what's working → Researches competitors and shows what's getting engagement One post already hit 1,000 views. He didn't write it. He didn't post it. He didn't even check it. Here's the part most people skip: Day one it won't be good. You give it feedback. It writes notes on what went wrong. It improves the skill file. By day 7 it's better. By day 30 it knows your voice. It's called a closed learning loop. Every task makes the next one sharper. The setup takes an afternoon: Install Hermes from GitHub. Connect a free Qwen 3.6 API key from OpenRouter. Add Firecrawl for web research. Create your first skill. Give it feedback. Let it run. That's the whole system. Free. Open source. Gets better every single day. Save this. Then go install it.
39:35

This guy literally automated his entire social media with one free AI agent. No team. No scheduling tools. No VA. Here's exactly how he did it 👇 The agent is called Hermes. It's free and open source. Here's what it's running on autopilot right now: → Scans Reddit every 24 hours for trending topics → Writes posts based on breaking news → Generates custom images with Nano Banana 2 → Posts directly to X, TikTok, and Instagram with captions and hashtags → Pulls daily analytics and flags what's working → Researches competitors and shows what's getting engagement One post already hit 1,000 views. He didn't write it. He didn't post it. He didn't even check it. Here's the part most people skip: Day one it won't be good. You give it feedback. It writes notes on what went wrong. It improves the skill file. By day 7 it's better. By day 30 it knows your voice. It's called a closed learning loop. Every task makes the next one sharper. The setup takes an afternoon: Install Hermes from GitHub. Connect a free Qwen 3.6 API key from OpenRouter. Add Firecrawl for web research. Create your first skill. Give it feedback. Let it run. That's the whole system. Free. Open source. Gets better every single day. Save this. Then go install it.

Julian Goldie SEO

27,164 просмотров • 5 месяцев назад

Every morning I open my laptop and i get served with Alpha in Crypto and Ai.. basically, i got tired of checking a dozen tabs every morning just to find what's actually moving in AI and crypto. so i built my own radar using GPT 5.6 sol ( cc: Sam Altman ) And i have named it Signal Radar.. it sweeps 4 platforms at once.. will add more.. - X - hacker news - github - and reddit. throws out the noise, keeps only AI and crypto, and ranks what's left by what's ACTUALLY pulling.. loudest posts by likes, trending repos by the stars they got today, the reddit threads bubbling in the subs my people actually sit in.. and it doesn't just link them.. it reads them. click any card and the full thing's already pulled in, the whole thread, the whole post. so i'm not opening multiple tabs or social media platforms to catch up.. it did that overnight. this sweep alone: 77 signals in, 49 flagged as actually worth the click.. runs on my own machine. free. no api keys, no subscription, nothing. running on my own setup for now. want it? say so below and i'll make it open source
0:35

Every morning I open my laptop and i get served with Alpha in Crypto and Ai.. basically, i got tired of checking a dozen tabs every morning just to find what's actually moving in AI and crypto. so i built my own radar using GPT 5.6 sol ( cc: Sam Altman ) And i have named it Signal Radar.. it sweeps 4 platforms at once.. will add more.. - X - hacker news - github - and reddit. throws out the noise, keeps only AI and crypto, and ranks what's left by what's ACTUALLY pulling.. loudest posts by likes, trending repos by the stars they got today, the reddit threads bubbling in the subs my people actually sit in.. and it doesn't just link them.. it reads them. click any card and the full thing's already pulled in, the whole thread, the whole post. so i'm not opening multiple tabs or social media platforms to catch up.. it did that overnight. this sweep alone: 77 signals in, 49 flagged as actually worth the click.. runs on my own machine. free. no api keys, no subscription, nothing. running on my own setup for now. want it? say so below and i'll make it open source

Axel Bitblaze 🪓

17,126 просмотров • 1 месяц назад

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.
1:49

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

394,457 просмотров • 5 месяцев назад

One of the best skills to install right now is my friend Peter Yang's no AI slop skill. It's an editor. It hunts for the patterns that make writing feel AI generated and strips them out, while trying to preserve your actual voice. The second part is the hard one. Most writing tools make you cleaner and sand off the interesting parts, so everyone ends up sounding the same. You already know the smell. The grammar is fine, the syntax is fine, and it still reads like a keynote from a fake SaaS conference. It writes "it's not x but it's y." It uses "quietly" a lot. Here's how I run it: 1) Install it: npx skills add, then the GitHub link. 2) Write a rough draft yourself. An outline is fine, messy is fine. 3) Get your real points down, the ones only you would make 4) Ask the skill to remove the AI patterns and keep your voice. Step 4 only works if step 2 is real. If you ask AI to write the whole thing, there's no voice left to preserve. If you're building products, you're writing constantly. Tweets, landing pages, cold emails, launch posts, product updates, onboarding copy, investor updates. Nobody replies to say "this was written by AI." They just trust you less and keep scrolling. Write the messy draft, run the skill, then post it.
3:35

One of the best skills to install right now is my friend Peter Yang's no AI slop skill. It's an editor. It hunts for the patterns that make writing feel AI generated and strips them out, while trying to preserve your actual voice. The second part is the hard one. Most writing tools make you cleaner and sand off the interesting parts, so everyone ends up sounding the same. You already know the smell. The grammar is fine, the syntax is fine, and it still reads like a keynote from a fake SaaS conference. It writes "it's not x but it's y." It uses "quietly" a lot. Here's how I run it: 1) Install it: npx skills add, then the GitHub link. 2) Write a rough draft yourself. An outline is fine, messy is fine. 3) Get your real points down, the ones only you would make 4) Ask the skill to remove the AI patterns and keep your voice. Step 4 only works if step 2 is real. If you ask AI to write the whole thing, there's no voice left to preserve. If you're building products, you're writing constantly. Tweets, landing pages, cold emails, launch posts, product updates, onboarding copy, investor updates. Nobody replies to say "this was written by AI." They just trust you less and keep scrolling. Write the messy draft, run the skill, then post it.

The Startup Ideas Podcast (SIP) 🧃

40,041 просмотров • 5 дней назад

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.
25:52

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.

JJ Englert

29,322 просмотров • 5 месяцев назад

Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.
4:44

Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.

Santiago

37,546 просмотров • 6 месяцев назад

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.
0:47

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,330 просмотров • 1 месяц назад

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!
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Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

87,048 просмотров • 4 месяцев назад

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.
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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 просмотров • 5 месяцев назад

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:
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Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:

AlphaCartel

30,801 просмотров • 4 месяцев назад

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this
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REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,840 просмотров • 3 месяцев назад

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.
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HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,200 просмотров • 20 дней назад

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼
51:23

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

JJ Englert

572,894 просмотров • 5 месяцев назад

Announcing Meta Ads skills in Claude 🦢 This is a suite of skills that turns Claude into a Meta Ads expert. You invoke it with a single command /gooseworks and Claude can now access skills and APIs like an expert ad team. Here's what it can do 👇 --- To use it, run npx gooseworks install --claude RESEARCH & INTEL 1. /gooseworks Find trending topics in my niche to run ads on right now Skill used: trending-ad-hook-spotter 2. /gooseworks Scrape my competitors' ads and reverse-engineer their funnel Skill used: competitor-ad-intelligence 3. /gooseworks Mine my reviews and Reddit for the angles that actually convert Skill used: ad-angle-miner 4. /gooseworks Research my brand and build a reusable context pack for ads Skill used: brand-research --- DIAGNOSE PERFORMANCE 5. /gooseworks Diagnose my Meta campaigns — what to cut, keep, or scale Skill used: meta-ads-analyzer 6. /gooseworks Find what's wasting budget across Google, Meta, and LinkedIn Skill used: ad-campaign-analyzer 7. /gooseworks Show true CAC per qualified lead, not vanity CPA Skill used: ad-lead-quality-analyzer 8. /gooseworks Check if my ad's promise carries through to the landing page Skill used: ad-to-landing-page-auditor --- PLAN AND REVIEW 9. /gooseworks Tell me which 1–2 paid channels to start with and a 90-day plan Skill used: paid-channel-prioritizer 11. /gooseworks Build Meta audiences, ad sets and copy from my ICP Skill used: meta-ads-campaign-builder 12. /gooseworks Pre-flight my ad copy against Meta policy before I spend Skill used: meta-ad-policy-checker --- MAKE CREATIVES 13. /goose-ads Turn that research into branded static ad creatives for my product Skill used: goose-ads All directly in Claude Code or Cowork. Start by installing npx gooseworks install --claude Comment Goose below and follow, and I'll DM you the full open-source library with 100+ skills to turn Claude into your growth team.
0:32

Announcing Meta Ads skills in Claude 🦢 This is a suite of skills that turns Claude into a Meta Ads expert. You invoke it with a single command /gooseworks and Claude can now access skills and APIs like an expert ad team. Here's what it can do 👇 --- To use it, run npx gooseworks install --claude RESEARCH & INTEL 1. /gooseworks Find trending topics in my niche to run ads on right now Skill used: trending-ad-hook-spotter 2. /gooseworks Scrape my competitors' ads and reverse-engineer their funnel Skill used: competitor-ad-intelligence 3. /gooseworks Mine my reviews and Reddit for the angles that actually convert Skill used: ad-angle-miner 4. /gooseworks Research my brand and build a reusable context pack for ads Skill used: brand-research --- DIAGNOSE PERFORMANCE 5. /gooseworks Diagnose my Meta campaigns — what to cut, keep, or scale Skill used: meta-ads-analyzer 6. /gooseworks Find what's wasting budget across Google, Meta, and LinkedIn Skill used: ad-campaign-analyzer 7. /gooseworks Show true CAC per qualified lead, not vanity CPA Skill used: ad-lead-quality-analyzer 8. /gooseworks Check if my ad's promise carries through to the landing page Skill used: ad-to-landing-page-auditor --- PLAN AND REVIEW 9. /gooseworks Tell me which 1–2 paid channels to start with and a 90-day plan Skill used: paid-channel-prioritizer 11. /gooseworks Build Meta audiences, ad sets and copy from my ICP Skill used: meta-ads-campaign-builder 12. /gooseworks Pre-flight my ad copy against Meta policy before I spend Skill used: meta-ad-policy-checker --- MAKE CREATIVES 13. /goose-ads Turn that research into branded static ad creatives for my product Skill used: goose-ads All directly in Claude Code or Cowork. Start by installing npx gooseworks install --claude Comment Goose below and follow, and I'll DM you the full open-source library with 100+ skills to turn Claude into your growth team.

Shiv

106,577 просмотров • 2 месяцев назад

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

56,177 просмотров • 2 месяцев назад

I solved building decks with AI agents — by giving them a CLI tool like Powerpoint or Google Slides. AI could already make a beautiful deck if you asked it to using Ant's pptx skill. The problem was working with it. If it made one alignment mistake, fixing it on one slide would break something on another, and it became a game of whack-a-mole. One time I spent two days playing AI roulette, hoping the next prompt would finally fix the thing, and ended up building the whole deck by hand because I was on a deadline. So I built Hands-on Deck. And the reason it works is that this isn't just a skill — this is PowerPoint. The actual application: PowerPoint, Google Slides, Keynote, whatever you use. This is that, but for an agent, presented as a CLI. Every gesture you make in a deck app maps to a command. Click a box and type, drag a shape from here to there, look at a slide – agent can do it all in a command. And that changes how the agent behaves. With this CLI it works and thinks like a designer — it looks, makes an edit, looks again, makes another surgical edit. Compare that to Anthropic's pptx skill, built on the idea that Claude is a great programmer: it literally writes code to manipulate the deck, hand-editing XML and hoping it doesn't break anything else in the middle. The real test isn't creating something once — it's whether it can make surgical edits like you want. That's what I did in this video walkthrough and my claude crushed it! Check it out for yourself. So decks can be built like a designer now — with real flavor and taste. If you spend hours every week on decks, this gives those hours back. You can install it as a skill in Claude Code, Codex, whatever you use. Works every harness that supports skills. Let me know if you make something cool with it.
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I solved building decks with AI agents — by giving them a CLI tool like Powerpoint or Google Slides. AI could already make a beautiful deck if you asked it to using Ant's pptx skill. The problem was working with it. If it made one alignment mistake, fixing it on one slide would break something on another, and it became a game of whack-a-mole. One time I spent two days playing AI roulette, hoping the next prompt would finally fix the thing, and ended up building the whole deck by hand because I was on a deadline. So I built Hands-on Deck. And the reason it works is that this isn't just a skill — this is PowerPoint. The actual application: PowerPoint, Google Slides, Keynote, whatever you use. This is that, but for an agent, presented as a CLI. Every gesture you make in a deck app maps to a command. Click a box and type, drag a shape from here to there, look at a slide – agent can do it all in a command. And that changes how the agent behaves. With this CLI it works and thinks like a designer — it looks, makes an edit, looks again, makes another surgical edit. Compare that to Anthropic's pptx skill, built on the idea that Claude is a great programmer: it literally writes code to manipulate the deck, hand-editing XML and hoping it doesn't break anything else in the middle. The real test isn't creating something once — it's whether it can make surgical edits like you want. That's what I did in this video walkthrough and my claude crushed it! Check it out for yourself. So decks can be built like a designer now — with real flavor and taste. If you spend hours every week on decks, this gives those hours back. You can install it as a skill in Claude Code, Codex, whatever you use. Works every harness that supports skills. Let me know if you make something cool with it.

Nityesh

70,086 просмотров • 2 месяцев назад

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

JJ Englert

53,969 просмотров • 2 месяцев назад

Send this to ANYONE on your team using AI agents with Claude/Codex skills: If I were you, I'd put your BEST AI skills in a GitHub repo, turn that repo into a PLUGIN, and have your team INSTALL it in Claude/Codex with auto update on. Why? 1. Everyone gets the same AI SOPs instead of 10 versions floating around Slack 2. When one person improves a skill, the whole team gets the better version. 3. New hires can start with your best workflows instead of a blank AI setup (this is a BIG deal). 4. If someone breaks a skill, you can roll it back with version control. 5. Personal skills can stay personal, while team skills become shared company infrastructure (and an asset!). 6. Your best AI processes stay with the company when someone leaves. 7. You can chain skills together for bigger workflows, like titles -> thumbnails -> descriptions -> YouTube publish. 8. You can track which skills are actually being used and delete the ones collecting dust. 9. You get less slop because the agent has real instructions, examples, taste, and process. 10. Your team moves from single-player AI to multiplayer AI. (thanks to AI with Remy | Learn AI for coming onto The Startup Ideas Podcast (SIP) 🧃) Watch full breakdown here (clearly explained): I don't know why I didn't do this before. The more I think about it, the more obvious it feels: your AI workflows should be version controlled company assets. It kinda feels like the difference between “we use AI” and “we actually operate with AI". Enjoy.
32:26

Send this to ANYONE on your team using AI agents with Claude/Codex skills: If I were you, I'd put your BEST AI skills in a GitHub repo, turn that repo into a PLUGIN, and have your team INSTALL it in Claude/Codex with auto update on. Why? 1. Everyone gets the same AI SOPs instead of 10 versions floating around Slack 2. When one person improves a skill, the whole team gets the better version. 3. New hires can start with your best workflows instead of a blank AI setup (this is a BIG deal). 4. If someone breaks a skill, you can roll it back with version control. 5. Personal skills can stay personal, while team skills become shared company infrastructure (and an asset!). 6. Your best AI processes stay with the company when someone leaves. 7. You can chain skills together for bigger workflows, like titles -> thumbnails -> descriptions -> YouTube publish. 8. You can track which skills are actually being used and delete the ones collecting dust. 9. You get less slop because the agent has real instructions, examples, taste, and process. 10. Your team moves from single-player AI to multiplayer AI. (thanks to AI with Remy | Learn AI for coming onto The Startup Ideas Podcast (SIP) 🧃) Watch full breakdown here (clearly explained): I don't know why I didn't do this before. The more I think about it, the more obvious it feels: your AI workflows should be version controlled company assets. It kinda feels like the difference between “we use AI” and “we actually operate with AI". Enjoy.

GREG ISENBERG

79,539 просмотров • 19 дней назад