Yarchi
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Anthropic will pay you $85,000 to learn AI, and this is the kind of opportunity you don't let pass It's called Claude Corps. Anthropic just launched it, and it's a 12-month paid fellowship for people at the very start of their careers. They train you to use Claude from scratch, then place you inside a nonprofit to do real work with it for a year. You get paid $85,000 plus benefits the whole time. They're basically paying you to master the most in-demand skill on the planet right now, then handing you real-world experience using it. The barrier to entry is almost nothing. Over 18, less than two years of full-time work experience. No degree, no AI background needed. If that's you, don't sit on this one. Apply here: Deadline: July 17 Bookmark this
Yarchi2,443,988 Aufrufe • vor 1 Monat
THE GUY WHO WON ANTHROPIC'S HACKATHON JUST GAVE AWAY HIS ENTIRE CLAUDE CODE PLAYBOOK FOR FREE. 10 MONTHS OF WORK, ALL PUBLIC Affaan Mustafa won the Anthropic x Forum Ventures hackathon by building a full startup in 8 hours with Claude Code. Then he open-sourced the exact setup that did it. It's called Everything Claude Code, and it turns Claude from one assistant into an entire engineering team Repo: affaan-m/ecc This isn't a prompt pack. It's a system he refined over 10+ months of daily use shipping real products What's inside: A huge library of skills, dozens of specialized subagents, and ready-made commands, all working together. Each piece does one job. One subagent reviews security against OWASP standards. One optimizes memory so Claude stops forgetting earlier decisions around hour three. One learns from your past sessions and projects so the setup gets smarter the more you use it. Others handle planning, test-driven development, and language-specific code review Instead of one assistant writing code, you get an orchestrated team. A main session delegates to the right specialist when the task calls for it, the way a real dev team splits work The best part: it's not locked to one tool. It runs in Claude Code, Cursor, Codex and OpenCode, across Windows, Mac and Linux. Free, MIT licensed This is the difference between using Claude like a search box and running it like a team that ships. The guy spent 10 months figuring out what actually works so you don't have to Bookmark this
Yarchi813,194 Aufrufe • vor 2 Monaten
ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.
Yarchi460,553 Aufrufe • vor 1 Monat
ANTHROPIC JUST OPEN-SOURCED 11 PLUGINS THAT TURN CLAUDE INTO YOUR WHOLE BACK OFFICE These plugins turn Claude into an actual coworker for your job, whatever your job is. Repo: /anthropics/knowledge-work-plugins Anthropic built and shipped 11 plugins, the same ones inspired by how their own teams work. One for each role: Sales, Marketing, Engineering, Data, Design, Product, HR, Support, Productivity, and more. Each one comes loaded with the real workflows that role needs. The sales one researches prospects, preps your calls, reviews your pipeline, drafts outreach, builds competitive battlecards. The data one writes your SQL, explores datasets, builds dashboards. The productivity one learns your people, your projects, your shorthand, so you say "ask Todd to do the PSR for Oracle" and Claude knows exactly who and what you mean. They work standalone right away, and get supercharged when you connect your tools (Slack, Notion, HubSpot, Linear, Figma, Snowflake, whatever you use). This is straight from the people who build Claude. Not a third-party copy. Bookmark this
Yarchi120,923 Aufrufe • vor 1 Monat
A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this
Yarchi140,729 Aufrufe • vor 1 Monat
Claude Fable 5 just hacked its way past a security system it wasn't supposed to cross, by accident A man set up an isolated network for AI experiments and gave Fable 5 SSH access to a test router. Basic config changes worked fine. Then he asked for settings SSH didn't expose. He'd basically forgotten to give Claude access to them. A normal model would just say "I can't reach that." Fable didn't. Here's what makes it wild. Routers lock most of their real settings behind the manufacturer's firmware, not SSH. To change them, you normally need the web interface or you have to poke at the firmware directly. Fable took the hard path: it reverse-engineered the router's own system, found how the restricted settings were stored, and rewrote them from the inside. It gave itself the access it was never handed. When he asked why it took so long, it said it had fully unlocked the router and could now change anything, even settings that were off-limits before. Then it tripped a safety warning and dropped to Opus 4.6. But the job was already done. To be clear: his own router, his own network. But think about what that shows. Give this model a wall and no key, and it doesn't stop. It figures out how to build its own key.
Yarchi98,531 Aufrufe • vor 1 Monat
NVIDIA just dropped free API keys for every top AI model You don't need your own GPU and you don't pay per token. GLM-5.2, MiniMax, Kimi, DeepSeek, OpenAI, all running on NVIDIA's servers, called through a normal API. Link: How to use one: 1. Create a free NVIDIA account. 2. Pick a Free Endpoint model and open its Build tab. You'll see ready-to-copy code with the base URL 3. Hit Generate API Key, copy it and paste that base URL and key into Claude Code, Cursor, or Cline. Bonus: NVIDIA also dropped 237 official skills that install into Claude Code and Codex in one command. Bookmark this.
Yarchi62,979 Aufrufe • vor 1 Monat
THIS GUY BUILT A REAL JARVIS THAT TURNS HIS IDEAS INTO FINISHED PROJECTS ON ITS OWN, AND HE JUST SHOWED HOW THE WHOLE THING WORKS The whole system runs on one clean split he calls a blood-brain barrier. The second brain, his Obsidian vault, is only for things that aren't real yet. Ideas. Each one sits in a project-shaped folder, waiting to be processed and turned into something real. Nothing executes there. The vault is the place where ideas get shaped, not built. Once an idea is signed off, it crosses over to the execution pipeline. That lives on his actual machine, or an always-on box, or a Raspberry Pi, depending on what the project needs. Inside that machine is a fully Claude-native file system. The key piece is one CLAUDE.md file sitting in the root of his documents folder, acting as the map to everything in there. So he just opens Claude and says "go to my projects folder," and it already knows the whole structure, because that root file explains it. Two zones, one rule: the brain holds what isn't real, the machine runs what is. That separation is what keeps the ideas from tangling with the work. Bookmark this
Yarchi87,075 Aufrufe • vor 1 Monat
HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this
Yarchi106,174 Aufrufe • vor 2 Monaten
THIS GUY HAS CLAUDE DOING $500-700 A DAY SELLING ONE DIGITAL PRODUCT, WITH ZERO SPENT ON ADS He sells one digital product and lets Claude run the content that drives the sales. No ad spend, sales starting within a day or two. The product is the easy part. The content engine is what actually sells it, and that's the whole method The exact steps: 1. Find the niche. Ask Claude for specific problems people already pay to solve in a space you know, then check Gumroad and Etsy search to confirm people are buying. Specific beats generic every time 2. Build the product with Claude. Have it design a Notion template for one exact buyer, sections and dashboard included. Recreate it in Notion, set it to "anyone can duplicate." 3. List it on Gumroad. Free account, New Product, paste the Notion link, price it, grab your checkout link 4. Run the 3-prompt content engine. This is the part that sells. First prompt: competition mining, have Claude find the top-selling competitor products and pull the most viral content mentioning them. Second: have it extract the structures and frameworks from that content into reusable templates. Third: feed it your product and those templates and have it write fresh viral content for you 5. Rerun it weekly. The niche shifts, so refresh the templates every week. The winning ones you reuse again and again Build once, post daily, let the content do the selling Bookmark this
Yarchi100,572 Aufrufe • vor 2 Monaten
You can build a person who doesn't exist and run their entire social media as them AI influencers are real now, fully invented people with the same face across every post. Lil Miquela, a virtual character, has 2.6M followers and real brand deals. The hard part was consistency, keeping one identical face. That's solved. Define the character once, lock the face, generate any pose or outfit. Tools: Higgsfield, The Influencer AI, or ComfyUI for local control. If it's 100% AI, you own it Bookmark this
Yarchi71,761 Aufrufe • vor 1 Monat
Turns out Fable 5 isn't expensive, you're just using it wrong. The Claude team just showed how they run it Use Fable 5 as the brain, Sonnet or Opus as the hands. Fable makes one or two calls to set direction, other model does the rest of the work cheap. Near-Fable results at half the cost, from their own benchmarks below Same logic as a good manager: your best person steers the decisions, not every task
Yarchi46,359 Aufrufe • vor 1 Monat
THE GUY WHO RUNS THE ACCELERATOR BEHIND AIRBNB, COINBASE, AND STRIPE OPEN-SOURCED THE EXACT AI SECOND BRAIN HE RUNS HIS BUSINESS ON Garry Tan runs Y Combinator, the startup factory behind Airbnb, Coinbase, Stripe, and 5000 another companies He also runs an AI second brain with 146,000 pages in it, ingesting his meetings, emails, and notes while he sleeps, and he open-sourced it. Repo: /garrytan/gbrain But you don't start there. Here are the five levels, simplest to most complex: Level 1: routing. A CLAUDE.md that acts as a map telling the AI where things live, plus a few markdown folders. You find things by exact name. Most people never actually need more than this. Level 2: the wiki. 30+ notes you keep forgetting, ingested into a linked wiki with an index, the Karpathy pattern. The AI follows a trail instead of guessing. Level 3: semantic search. When you search different words than you wrote and it whiffs, vector search matches by meaning, not exact words. Level 4: the knowledge graph. When you need to trace relationships, this person works at that company that competes with this one, you add typed connections, not just backlinks. Level 5: autonomous. An always-on brain that refreshes itself while you sleep, like Garry's. The top, but not automatically best. More context can do more harm than good. The real lesson: higher isn't better. Find the lowest level that fixes a pain you actually have. And your vault doesn't have to be one level, one folder can be level 2 while another is level 4. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this
Yarchi53,539 Aufrufe • vor 1 Monat
If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.
Yarchi32,840 Aufrufe • vor 27 Tagen
THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
Yarchi58,186 Aufrufe • vor 2 Monaten
THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
Yarchi55,345 Aufrufe • vor 1 Monat
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
Yarchi54,840 Aufrufe • vor 2 Monaten
DRONE VIDEOGRAPHERS CHARGE $10K FOR THIS SHOT. HE PULLS IT FROM GOOGLE EARTH AND A PROMPT You never buy a drone, book a pilot, or leave the house. You pick any city on Earth, trace the flight path you want, and let Gemini render it as real-looking FPV footage. Clients pay thousands for this shot. You make it from a screenshot Here is the exact process: 1. Open Google Earth. Find the city or building you want. Frame the angle you'd want a drone to start from and take a screenshot 2. Draw the path. On that screenshot, draw a red line showing exactly where the drone should fly through the scene. This line is what the AI follows 3. Open Gemini and drop in the screenshot. Use the video generation in the Gemini app, the part that animates a still image into motion. Nano Banana handles images, the video engine is what turns your shot into footage 4. Paste the prompt. Tell it to follow the red flight path through the city, fast smooth motion, banking around buildings, golden-hour light, motion blur, 9:16 vertical, real FPV drone look. Full prompt is in the comments 5. Generate and clean it up. One clip is a few seconds. Stitch a couple together for a full flythrough and you have a reel Set the prompt once and you can re-run it for any location on the planet Who pays for this: Real estate agents, hotels, restaurants and event venues all need aerial b-roll and almost none can afford a real drone shoot Pull listings or venues with flat, ground-level photos and zero aerial footage. Send a free sample flythrough of their own location, then charge per clip or a monthly rate for ongoing reels One agent with ten listings is a recurring client, fully online Full prompt in the comments Bookmark this
Yarchi53,000 Aufrufe • vor 2 Monaten
A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this
Yarchi32,476 Aufrufe • vor 1 Monat
ANTHROPIC'S PRODUCT CHIEF HAS USED CLAUDE FABLE 5 FOR MONTHS BEFORE ANYONE ELSE. HERE'S WHAT HE LEARNED ABOUT THE MOST POWERFUL MODEL YET Mike Krieger co-founded Instagram and now runs product at Anthropic. He's had Claude Fable 5 for two months before the public, and his takeaway is that it changes how you have to work, not just how much you get done. Here's what stood out, and what to actually do with it 1. It holds the whole project, so stop chopping tasks small. The old habit was breaking work into model-sized pieces and stitching them. Fable keeps the whole thing in context. What to do: stop pre-slicing your prompts into tiny steps. Hand it the full goal and the intent behind it, the way you'd brief a senior engineer, and let it sequence the work itself 2. Delegate big, async, and overnight. He sets it on a hard task at night and wakes to it finished, including the model getting itself unstuck when a service died, scaffolding a workaround, and documenting it. What to do: stop babysitting one prompt at a time. Kick off long jobs and walk away. Run several sessions at once instead of one you watch 3. The skill is planning now, not typing. His day moved to long architecture conversations up front, then execution in chunks. What to do: spend your first prompts planning, not building. Then ask it to output an HTML page or markdown doc of the plan so your team aligns before any code is written. That early alignment is the new leverage 4. Match the effort level to the task. Fable's range is wide, so a heavy reasoning pass on a tiny UI tweak is overkill (and pricey). What to do: dial effort down for small jobs, save the deep thinking for hard ones. And don't use your most expensive model for quick questions, keep a fast model for those 5. Verification is the real bottleneck now. The hard part isn't getting output, it's trusting it. What to do: make every change ship with proof. Have Claude attach a screenshot or video of what it built, so you can see the result instead of reading the diff. Then stand behind the decisions yourself before you merge 6. Cost is per-result, not per-turn. Fable is expensive per call but often one-shots what other models need ten turns to get right. What to do: judge cost by what it takes to finish the task to your satisfaction, not the price of a single message. Give it a real task and see how far it gets before you jump in His bigger point: software engineering isn't over, it's different. The craft moved from writing code to owning intent, taste, and what actually ships. The floor rose so anyone can build, and the ceiling rose so experts go further than before Bookmark this
Yarchi30,958 Aufrufe • vor 1 Monat