Made an updated version this weekend Here's how you... do it (raw notes) > Grab Andrej Karpathy's latest gist (in the first comment) > Download Peter Steinberger 🦞 summarize CLI > Download yt-dlp > Download obsidian > Download tobi lutke qmd --> Setup a node or Golang CLI called "brain" --> Have it index all your youtube data, AI agent data (jsonl files) --> Get your X data by requesting an archive in your settings --> Setup vaults for each domain/topic area --> Ask questions with your agent and qmdshow more

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE FABLE 5 +... OBSIDIAN Andrej Karpathy (openai co-founder) shared an architecture that turns Claude into a persistent second brain instead of a basic chat window how it works: > you point Claude Code at an Obsidian vault folder > you drop articles, PDFs, or video transcripts into raw folders > Claude reads the files, updates topic summaries, and cross-references everything > the knowledge base compounds like interest instead of resetting on every new chat the setup is simple: > install and create a local vault directory > open the directory in Claude Code and paste Karpathy's wiki prompt: > > let the agent generate raw, wiki, and CLAUDE.md schema directories > drop any text file into raw and tell the model to ingest it > ask questions across the whole vault and query compiled summaries this eliminates rag database overhead and keeps your local vault organized how do you manage your local knowledge base?show more

The Dawn of a New Era on $SUI (9)... Still in the festive spirit, let’s look at Tusky , Tusky is a storage service that's not controlled by one company. It uses something called WalrusProtocol to keep your data safe. Your data is encrypted from start to finish, so only you can see it. Instead of one place, your data goes to many different spots. This setup makes your data less likely to be lost or stolen. It helps keep your information safe and always available. Tusky gives you control over your files. You can easily manage them with the tools provided. You decide who gets to see your data. This makes it great for personal storage or working with others. It works with SuiNetwork for even more privacy. You can log in without sharing personal info, keeping everything more secure. This means only you can get to your data, with no third party involved. It is growing fast, with 100,000 uploads already. This indicates its increasing acceptance in the tech community focused on data sovereignty. It's good at managing lots of data safely. In tech, where you want to own your data, TuskyTools is popular. It gives users control over their information. This platform helps keep your data secure and gives you freedom.show more

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 +... OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?show more

STOP STORING NOTES. START COMPILING THEM. a pile of... notes isn't memory. it's accumulation wearing memory's clothes. these 7 repos build the other kind, where Claude reads a source once and links it to everything you already have. copy the list: graphify: turns a codebase with its docs, SQL schemas, configs and PDFs into a queryable knowledge graph. 127k stars Understand-Anything: maps a project's code, docs or wiki into a graph you can search and query from your coding assistant. 86k stars claude-obsidian: lets Claude Code build and maintain a Markdown knowledge base in Obsidian, with source-backed notes and grounded answers. 15k stars obsidian-second-brain: gives Claude Code long-term memory as plain markdown notes in an Obsidian vault. 4.8k stars claude-code-memory-setup: an Obsidian vault as persistent memory, graphs for your codebase, and a pipeline that saves insights from your chats. 1k stars second-brain: an Obsidian knowledge base the LLM maintains, built on Karpathy's LLM Wiki pattern. 727 stars llm-wikid: an agent compiles your raw sources into cross-linked wiki pages in Obsidian. 420 stars none of them is your second brain. they're the compiler. the brain is what you feed it pick one and feed it your first 50 sources. before that, a compiled wiki behaves like a plain search engine, so don't quit early. the full setup is in the articleshow more

The Amiko app is live on the Solana dApp... store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →show more

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic... shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.show more

This new skill for Hermes Agent wipes your personal... data off hundreds of broker sites for free, hands-off. It's called Unbroker. I’ve been testing it this week and it's incredible. Here is the full setup: 1. Install it by typing: hermes skills install official/security/unbroker 2. Connect browser automation: Wire up Browserbase so the agent can drive real broker opt-out forms. 3. Do the intake: Give consent, then feed it your info. 4. Fire one prompt: "Use the unbroker skill to remove my data from data brokers. Here is my consent. Run it hands-off." Hundreds of data brokers publish your name, addresses, phone, email, even your relatives. Anyone can find where you live in about ten seconds. The law says brokers have to delete your data if you ask. There's just no easy bulk button for it. Services charge $100’s a year to file the exact requests you could submit yourself for free. Unbroker does the same thing locally, and it's free. It fans out sub-agents, scans every broker site, drafts or sends opt-out emails, and only comes back to you for the ones that need a human. The best part is it loops. Brokers re-list you over time, so it schedules rechecks and re-files on its own. Set it once, it keeps working. One skill, one prompt, dozens of broker sites handled.show more

What if crypto research was as easy as chatting... with ChatGPT, but powered by real market data👑 Introducing CMC AI, a powerful new tool from CoinMarketCap that combines the speed of AI with the depth of live crypto data. It delivers fast, data-backed answers to your questions: ✅ Want to know why Bitcoin's price is rising? ✅ Curious about the latest news on your favorite cryptocurrency? ✅ Need sentiment analysis? It pulls real-time data and explains it in seconds. But it goes far beyond basic Q&A. In the future, you’ll be able to ask anything! For example, you could ask it to: – Discover undervalued tokens based on volume, MC, and sentiment. – Compare Layer 1s or L2s by adoption, speed, and dev activity. – Detect rug-pull risk via wallet distribution and tokenomics red flags. – Break down your portfolio by risk, correlation, and potential return. – Explore new use cases in DeFi, AI, RWA and DePIN And much more! 🔗Try it here: CMC AI changes how you learn, think, and act in Web3🧠show more

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES.... AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇show more

BREAKING: Claude Code + Meta Ads MCP replaced my... $5K/month creative strategist. It connected to my Ad Manager. Looked at my best ads. Then cranked out 12 new ads ready to launch. Took minutes. Here's the full system. No coding required. Most marketers I know still download CSVs from Meta Ads. Paste them into ChatGPT. Ask "what should I change." That worked in 2024. In 2026 you can plug Claude right into your live Meta ad data. Uses something called MCP. Model Context Protocol. Then run it straight into your AI ad making process. Here's what it does. Pulls live campaign data on command. "Show me my top 3 ads by ROAS this week." Answer in seconds. Finds what makes your winning ads work. Looks at the hooks, visuals, and buttons that get clicks from your best stuff. Writes creative briefs automatically. Gives you the angle. The visual style. The pacing. Not spreadsheets. Real creative direction. Makes ready-to-use prompts for HeyOz. Copy and paste into your AI ad tool. Get launch-ready ads from one idea. Spots tired ads before costs spike. Watches click rate, frequency, and spend. Tells you when an ad is dying. Here's the part most people miss. It doesn't just look at your ads. It makes the next batch for you. All in one chat. Setup takes about 15 minutes. No coding required. I don't know why more agencies aren't using this yet. Comment LOOP and I'll send the one-click setup guide.show more

Today we shipped sling, an agent-first CLI for your... GitHub Actions 💫 Right now, if you ask your agent why a CI job failed, it goes and pulls the entire set of GHA logs and eats up your context window. 🪵 > sling why StarSling (YC X25) computes the run's logs and metadata, along with the evidence lines it matched to tell you why a run failed If you want to know what your GHA usage is, you have to go to the GH UI and click around to find it. 💸 > sling bill tells you how many minutes, how much you're spending, and across which runner labels Trying to understand what your slowest job is? You'll need to hunt around the GHA UI and even then you only get an average. 📊 > sling top and sling usage tell you runner minutes and cost across all workflows and repos, with p50, p95, p99, queue wait and a week over week trendshow more

I stack Hermes agents with OpenClaw for financial research,... and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!show more

LangGraph. CrewAI. Agno. Which one to pick? The good... news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.show more

Google's latest spam update wiped sites from search &... AI Overviews the same day What it goes after, and how to fix it 👇 1/ It targets mass-produced pages, hundreds of near-identical ones -> Writing with AI is not the trigger, trying to game Google with mass pages is 2/ It takes out a whole batch of similar pages at once, like all your "best X for Y" pages or all your competitor reviews -> So some part of your website can still survive 3/ Recovery takes months in Google's own wording -> Make the changes once now, then leave the site alone and re-check in November 4/ Fix: ask Claude to check your pages against the spam rules, humanize the mass-produced ones and make them drastically different -> I put an MD file with the guidelines in the first comment, give it to your Claude and ask it to fix your site ⬇ 5/ Small edits will not clear it -> Delete those pages and ask Google to drop them, or merge them into fewer real ones 6/ It does not look at spam backlinks at all -> So no need to worry about your automated backlinks this time At Ryze AI we built an AI agent that runs these checks and fixes what fails Both in the first comment ⬇show more

your AI agent can watch any video now -... paste a URL and it sees every frame, hears every word, all for free 🤯 bradautomates/claude-video gives Claude the ability to watch YouTube, Loom, TikTok, local files - anything yt-dlp supports what people actually use it for: → analyze a competitor launch - what hook, what visuals, what structure → debug from a screen recording - Claude reads the exact frame where it breaks → summarize a 49-min talk in 30 seconds with frame-accurate timestamps → strip the hype from product videos - "what's actually new, skip the pitch" the mechanism: yt-dlp pulls free captions first (zero cost). ffmpeg extracts frames at scene-aware intervals - not uniform sampling, so you don't waste tokens on 12 identical frames of the same slide. Claude reads every frame as an image with timestamp markers. Groq Whisper only kicks in when a video has no caption track how to set up (3 min): > claude code: /plugin marketplace add bradautomates/claude-video then /plugin install watch@claude-video > or npx skills add bradautomates/claude-video -g for codex, cursor, gemini cli > dependencies auto-install on macOS via brew two caveats: free captions cover most but not all videos. past 10 min use --start/--end for focused sections or the token-burner mode for full coverage your buddy still watches every tutorial at 2x speed taking manual notes. you paste a URL and your agent extracts the substance in seconds for $0show more

Your agent asked for a page. Did it get... the one a person sees? Some websites treat datacentre traffic differently from people. A request from a cloud server can come back as a check page, with no price and no stock. Teneo carries the request through an opted-in device in a real household or on a real mobile network, so your product gets the page a person in that market sees. Free 7-day pilot for AI search, RAG and crypto data teams. DM us the markets that give you the most trouble.show more

Made $100K+ by building a simulation that predicts SPX... price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.show more

Claude + Apify MCP is insane 🤯 This setup... allows you to chat with TikTok and Instagram Reels data in natural language. All inside Claude Desktop. Perfect for DTC brands & agencies who need fresh creative angles fast. Here's the problem: You want to know what hooks are working in your niche. But that means hours of scrolling, screenshot folders, and spreadsheets you'll never look at again. This Claude MCP setup solves it: → Ask Claude to scrape 10 viral IG Reels in your niche → It pulls transcripts, engagement, and comments automatically → Then you just... chat with the data → "What pain points keep coming up?" → "Give me the top 5 hooks from these videos" → "What emotional triggers are driving engagement?" No automations. No exports. No learning new tools. What you get: - Instant access to viral content data - Natural language questions, instant answers - Hook formulas backed by millions of views - A full report in under 3 minutes Just ask Claude like you're talking to a research assistant who already watched 100 videos. I recorded a full 11-minute breakdown showing you how to set this up step-by-step. Want access? > Like this post > Comment "CHAT" And I'll sent it right over (must be following so I can DM)show more

motion designers are in serious danger, here is why... an AI agent can now model a 3D scene in Blender, animate a website and render a finished MP4. you describe it, the agent builds it. three repos doing exactly that: -> HyperFrames: write HTML, get a video. made by HeyGen for AI agents. ask for a 10-second product intro with a fade-in title and background music, and the agent plans it, animates it and renders the MP4 -> MCP for Blender: connects Blender to Claude or any other model. the agent creates objects, builds scenes and edits them in real 3D while you watch -> GSAP skills: the official skills that teach coding agents to animate the web properly with GSAP. timelines, scroll effects, the stuff premium landing pages are made of the setup for HyperFrames in Claude Code is two lines: claude plugin marketplace add heygen-com/hyperframes claude plugin install hyperframes@hyperframes then just type /hyperframes:hyperframes and describe your video. the stack in one line: Blender for the 3D, GSAP for the motion, HyperFrames for the final render. bookmark this before your next client asks for a promo video.show more