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@thegreatest_sv • 8,887 subscribers
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Holy sh*t I spent weeks turning Claude Code into a design studio. no Figma license. no agency. no plugins I had to pay for. here's the stack I used and it's embarrassingly simple: -> graphify: teach Claude your brand dump your guidelines, logos, PDFs and old projects into one folder. graphify turns it into a knowledge graph, so every design Claude makes follows your rules instead of guessing. 134k stars, Y Combinator. uv tool install graphifyy -> graphify install -> /graphify . -> Figma-Context-MCP: Figma straight to code it hands your actual Figma file to the agent. the README says it builds designs in one shot, and does it better than pasting screenshots. -> Penpot: the open-source Figma free, open source, and its MCP server lets AI read and edit your designs, then hand them to code and back. -> React Bits: the "expensive" look, for free the biggest open-source library of animated React components. those smooth hover effects and text animations you see on high-end sites? copy, paste, done. -> HyperFrames: designs that move write HTML, get a finished video. made by HeyGen for AI agents. your landing page can now have its own launch video by tonight. claude plugin marketplace add heygen-com/hyperframes -> claude plugin install hyperframes@hyperframes -> MCP for Blender: 3D without learning Blender connects Claude to Blender. describe a scene, the agent builds and edits it in real 3D. the order that works: brand into graphify -> design in Figma or Penpot -> Claude builds it with React Bits -> HyperFrames makes the promo -> Blender adds the 3D hero shot the uncomfortable part: tools used to be the moat. now everyone has them for free. the only thing that still separates good design from AI slop is taste. and taste is the one thing you can't install. bookmark this. your next client will ask why you're suddenly so fast.
kiosa64,534 görüntüleme • 2 gün önce
A GUY MAKES ~$15,000 A MONTH BREAKING INTO SUPERMARKETS - AND THE STORES PAY HIM FOR IT. It starts with a contract: a chain wants to know how a thief beats their system before a real one does it for free. He walks in like a normal customer, basket in hand. In his pocket, a small reader. He holds it near a product and pulls the signal off the tag - the same RFID and anti-theft tech protecting the shelves. Then he probes the weak spots: the gate that should beep and doesn't, the tag readable from too far, the self-checkout that trusts the scan too much, the dead zone by the side exit. He takes nothing. He documents everything, leaves, and comes back at a random hour to see if the gap was real. One report, one invoice. Weeks later the holes are closed - shoppers never know how open it was. Same instinct as a shoplifter, opposite direction. Legal, and it pays. Most of that work now is digital, not a store aisle. Here's how the mindset becomes income - authorized targets only
kiosa1,020,824 görüntüleme • 3 ay önce
THIS GUY MAKES ~$10,000/MONTH FINDING SECURITY HOLES IN A MAJOR GROCERY STORE CHAIN’S SYSTEMS. He walks through a billion-dollar grocery chain he doesn't work for. Not for the groceries. For the cracks in their systems nobody else bothered to look for. He finds them, writes it all down, hands it over Walks away ~$10k richer, having quietly stopped a breach that would've made the news. They never learn his name. They just fix what he found. I built the software version of that instinct with Claude
kiosa1,383,950 görüntüleme • 4 ay önce
I truly don't understand why nobody is using this setup graphify, a tool that turns any folder into a knowledge graph your agent can query, just passed 126k stars on github and got into Y Combinator. people stopped asking ai questions. they're giving it a desk, a memory and a job. you give Claude Code one folder, plug in your email, calendar and a browser, and it turns into an agent. it reads, remembers, acts, and checks itself before it does anything stupid. doesn't matter if you're a founder, a marketer or a student, you need this the full setup: > create a folder called AGENT, open Claude Code inside it and add a CLAUDE.md with one rule: "save every decision, person and project to memory/" > run npm install -g @googleworkspace/cli, then gws auth setup. now it reads your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser to open sites, fill forms and pull data for you > run uv tool install graphifyy, then graphify install, then type /graphify .. everything in the folder becomes a knowledge graph it queries in seconds instead of digging file by file > run npm i -g jev-guard, jev-guard key "...", jev-guard install claude. a tiny model called Jev scores every action before it runs. rm -rf gets blocked, a poisoned email gets flagged, for about 4 cents per 1,000 checks > type /schedule and tell it "every morning at 7:30, send me what came in, what you drafted and what waits for my yes". from now on it works on a routine 15 minutes to set up, and you never start from a blank chat again. every repo is below: -> -> -> -> bookmark this
kiosa109,215 görüntüleme • 12 gün önce
I can't f*cking believe people still use Claude Code like a chat bot. the ones getting real work out of it gave it a folder, tools and a map of everything it knows. that map is graphify. you point it at a folder and it builds a knowledge graph of everything inside. code, docs, PDFs, images, even video. then your agent searches the graph instead of opening files one by one. 134k stars on GitHub. backed by Y Combinator. and most people still haven't tried it. what makes it different: -> the code part runs fully on your machine. nothing gets uploaded -> no vector database, no embeddings. it's a real graph your agent walks through -> every connection is labeled: read straight from the source, or figured out by graphify. you always know which -> you get 3 files: a clickable map you open in your browser, a report with the key ideas and surprising links, and the full graph your agent queries anytime on a public memory benchmark it found the right answer in its top 10 results about half the time. mem0 managed under 5%. and building the graph cost 0 model credits. here's the full setup around it: > create a folder called AGENT, open Claude Code inside it, and add a CLAUDE.md with one line: "save every decision, person and project to memory/" > run uv tool install graphifyy, then graphify install, then type /graphify . and the whole folder becomes a map Claude can search in seconds > run npm install -g @googleworkspace/cli, then gws auth setup and gws auth login. Claude can now read your Gmail, Calendar and Drive > run claude mcp add playwright npx @playwright/mcp@latest. it gets its own browser for sites, forms and data > run npm i -g jev-guard, then jev-guard key "...", then jev-guard install claude. Jev checks every action before it runs. rm -rf gets blocked, emails with hidden instructions get flagged, about 4 cents per 1,000 checks > type /schedule and say "every morning at 7:30, send me what came in, what you drafted and what needs my yes" one folder. 15 minutes. and Claude finally knows where everything is. all the repos: -> -> -> -> bookmark this and try graphify first. it's the piece everyone skips.
kiosa28,321 görüntüleme • 4 gün önce
I don't f*cking understand why motion designers aren't running this setup HyperFrames, HeyGen's tool that turns HTML into finished video, is past 68k stars on GitHub. hand Claude Code one folder, plug in a video engine, an animation library and Blender, and it stops being a chatbot. it storyboards, animates, renders and checks its own frames before you ever see them. motion designer, founder, marketer, doesn't matter. this is for you. here's the whole thing: > make a folder called STUDIO, open Claude Code in it, and add a CLAUDE.md with one rule: "save every brand color, font, logo and approved style to brand/" > run claude plugin marketplace add heygen-com/hyperframes, then claude plugin install hyperframes@hyperframes. type /hyperframes:hyperframes, describe a 10-second product intro, and it plans, animates and renders the MP4 > run npx skills add remotion-dev/skills. now it can also write videos in React with Remotion, the official skills straight from the Remotion team > run npx skills add it learns proper GSAP: timelines, scroll animations, text effects. and GSAP is 100% free now, every plugin included > run npx skills add LottieFiles/motion-design-skill. it picks up real motion principles: timing, easing, choreography, the classic Disney rules adapted for UI. this is where the taste comes from > run curl -LsSf | sh. Claude gets hands inside Blender to build and edit actual 3D scenes > type /schedule and tell it "every Monday at 9, turn last week's launches into 3 short promo clips and wait for my yes". now it runs on a routine 15 minutes of setup, and you never start from a blank timeline again. all the repos: -> -> -> -> -> bookmark this and test asap
kiosa46,359 görüntüleme • 8 gün önce
I couldn't f*cking believe this Stanford AI research team gave Claude Code a human brain setup. JEV is the fast gut brain. It scans everything in milliseconds and makes the easy calls. Claude is the slow thinking brain. It only wakes up for the hard ones. Here are the commands that wire the two together: /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp -> Claude gets Jev as tools: classify, score, check npm install -g @dzhng/jevgrep jg "how are payments retried?" ./my-project -> ask what the code does, get the right files back. the author measured ~30% lower cost on the same tasks npx skills add altryne/jevify --skill jevify -> teaches your agent to hand the bulk yes/no work to Jev on its own > and fast-jev-compaction swaps Claude Code's summary for Jev decisions: it drops stale tool calls and keeps everything else word for word. Faster runs, smaller bills, barely any compute wasted. Save this before your next session.
kiosa86,755 görüntüleme • 17 gün önce
honestly this should be f*cking illegal your LLM takes 30 seconds to think about a trade, but candle closed 29 seconds ago. Jev answers in 70 to 500 milliseconds. it can't write a word. it returns BUY / SELL / HOLD and a probability, and that's all a scalp needs. 3 repos already built on it: -> jev-trader: one AI trade decision every single block, on a live MON-USDC market -> QuantDinger: a full open-source AI trading OS with Jev built in. research, strategy, agent trading -> AIStock: "one person can become their own super-analyst" $0.042 per million input tokens. output is free. that's thousands of decisions for the price of one LLM call. the catch: fast is not the same as right. a wrong call in 200 milliseconds is still a wrong call. paper trade it first. > let the big model do the research. > let Jev pull the trigger. > let code set the stop. bookmark this before your bot is the slow one in the room.
kiosa99,619 görüntüleme • 21 gün önce
Jev dropped barely a week ago and traders are already doing some wild sh*t with it. live order books. real-time signals. trading bots. entire AI hedge funds. here's what happened: > jev-trader · every ~0.3s asks Jev “buy or sell?” on Monad and trades > jev-trade · reads Hyperliquid order book -> Jev picks -> code sends the order > beebots · 3 OKX trading “bees”; every choice is Jev (paper trading by default) > QuantDinger · full AI trading OS; optional Jev gate = “should we trade at all?” > ai-hedge-fund · multi-agent “fund” with a Jev adapter for fast yes/no calls and that’s the funny part: Jev isn’t here to think. it’s here to make the call. the market moves -> Jev picks LONG / SHORT / HOLD -> code executes. no essays. no chain of thought. no expensive model debating every tiny market move. big models figure out the strategy. Jev handles the split-second calls. code makes it real.
kiosa111,388 görüntüleme • 27 gün önce
This is absolutely f*cking insane. A Stanford AI research group joints JEV with Claude Code to sort 100+ billion data points every 15 minutes. the trick is stupidly simple: JEV runs a cheap first pass on everything. Claude only gets the hard cases. so instead of: everything -> Claude -> $$$ it’s: everything -> JEV filters -> hard stuff -> Claude thinks the boring stuff never touches the expensive model. Claude gets a tiny pile that actually deserves deeper analysis. faster. cheaper. way less compute burned. basically, JEV sorts the mail so the genius only opens what matters. LLMs think. JEV decides. code does.
kiosa116,638 görüntüleme • 1 ay önce
this is pure f*cking treasure Anthropic devs just posted 20 GitHub repos that turn your Claude Code into a f*cking engineering team: Exposed for Opus 5.5 1. rtk: squeezes command output before it hits your context. the authors claim 60-90% fewer tokens 2. claude-mem: memory across sessions. records what the agent did and brings back what matters 3. ccusage: shows where your tokens and money went. one command, npx ccusage 4. Serena: search and edit code by meaning, not by text 5. Context7: up-to-date library docs, so Claude stops writing for last year's API 6. claude-context: your whole codebase searchable through MCP 7. Repomix: packs an entire repo into one AI-friendly file skills and method 8. superpowers: a skills framework and a full development methodology. the one almost everyone installs 9. skills: the official Agent Skills repo from Anthropic 10. spec-kit: spec-driven development, from GitHub itself 11. agents: a plugin marketplace for Claude Code and six other harnesses 12. claude-code-templates: ready configs and monitoring from one CLI 13. claude-code-hooks-mastery: hooks explained with working examples 14. awesome-claude-code: the hand-picked map of everything else running more than one agent 15. container-use: a separate container for every agent, so they stop breaking each other 16. claude-squad: several terminal agents in one window 17. Happy: control your agents from your phone, iOS and Android 18. claude-code-router: route requests across different models 19. ccstatusline: a status line that shows what your session is doing 20. tweakcc: edit Claude Code's own system prompts, toolsets and themes the stack in one line: memory keeps the context, skills set the method, containers let you run five agents instead of one. check each repo before you install. save this.
kiosa69,216 görüntüleme • 20 gün önce
whoever built this realized we’ve been using our smartest AI for the dumbest possible jobs your $20-$200/mo frontier model is researching, writing code, planning… and then you’re paying the same brain to decide YES / NO. Jev flips that. it’s a tiny decision model that sits in front of the expensive stuff and handles the boring forks: -> which agent goes next? -> is this worth researching? -> reply or ignore? -> publish, wait or escalate? -> BUY / SELL / HOLD? -> click this or keep looking? then I saw the numbers people are reporting and the whole architecture started making a lot more sense. ~1,000 papers classified for around $0.08 ~500 emails sorted for around $0.035 browser-agent loops demonstrated around 7 seconds for ~$0.0039 and at the quoted pricing, roughly 10,000 ~1K-token decisions comes out around $0.42. that’s not because Jev became smarter than Claude. it’s because nobody asked it to be Claude. the setup is stupidly simple: -> big LLM gets the hard thinking, research and writing -> Jev gets the thousands of tiny decisions between those steps -> code actually clicks, saves, sends and executes and suddenly you start noticing these little decisions everywhere. > your inbox. > X feed. > support queue. > meetings. > leads. > video clips. > browser agents. > even paper-trading loops. that’s the part I think most agent builders are missing. the expensive model doesn’t need to touch every step just because it’s capable of touching every step. > let Claude think. > let Jev decide what deserves Claude. > let code do the actual work. I broke down 12 places you can wire this into an agent today + the exact router setup in the article below
kiosa71,370 görüntüleme • 23 gün önce
Holy sh*t, I wish I’d found this earlier. someone put 300+ AI agent projects into one free GitHub repo because half the time you think you need to build something new… someone already built it. agent memory? there. browser control? there. coding agents, sandboxes, evals, voice, multi-agent teams, local models? all there. so I went through the list and started connecting the pieces: > RUN - Ollama runs the model locally > BUILD - LangChain connects everything > CODE - Aider + Continue work inside your codebase > ORCHESTRATE - CrewAI + AutoGen manage the agent team > ACT - E2B + Composio give agents tools and somewhere safe to run > REMEMBER - Mem0 keeps memory between sessions > WATCH - AgentOps shows what your agents actually did > SHIP - Vercel AI SDK helps turn it into a real product but here’s what clicked for me: the list isn’t really a list. it’s a build-your-own-agent menu. model -> framework -> tools -> sandbox -> memory -> evals -> product missing something? don’t spend the weekend rebuilding it. find the category -> see what already exists -> grab 3 options -> check which one’s still alive -> start there. and if you want to build on top of the list itself, they even give you the ecosystem data in JSON + YAML. > no signup. > no paywall. > MIT licensed. > 300+ projects in one place. basically, before I build anything agent-related now, I’d check this repo first. I pulled out the projects I’d actually start with + the best stuff from each category below
kiosa70,206 görüntüleme • 25 gün önce
Stop building f*cking AI agents. Seriously. (here's why) you spend a weekend wiring up the model, the tools, the prompts and the UI. then you find out someone already built it, open-sourced it and left the code there for free. there's a GitHub repo with 100+ ready-to-run AI agents. not a list of links. actual working code you can clone today. 142k stars. and the best stuff isn't the obvious chatbots. it's the weird, useful ones: got a folder full of side projects you never finished? -> Project Graveyard. it finds every abandoned project, tells you why each one died and helps you pick one to revive writing something and not sure where people stop reading? -> First Reader. it simulates real readers going through your draft and shows you where they lose interest your PR grew way bigger than it was supposed to? -> Scope Creep Detector. it checks if a diff went past its stated goal and tells you what to cut staring at code and wondering why it even exists? -> Commit Archaeologist. it digs through the history and rebuilds the reason that file is there tired of finding out about breaking updates too late? -> Release Radar. an always-on agent that watches your dependencies and briefs you on what's breaking or deprecated want a second opinion that isn't just one model agreeing with itself? -> LLM Panel. three different AI vendors review the same code blind need to dig through public records for red flags? -> Fraud Investigation Agent. it cross-references public records and flags what doesn't add up want an agent that pays for its own data? -> x402 Paying Agent. it has a wallet and pays per call for the data it needs, no API keys the rule I follow now: before I build any agent, I search this repo first. if it's there, I clone it and change what I need. I start on day 2 instead of day 0. free. open source. Apache 2.0. the repo is below
kiosa14,843 görüntüleme • 5 gün önce
f*cking insane. you can now hire an AI chief of staff that keeps working after you close your laptop give it one rule: "find me $50,000 a month in new business". go to sleep. check your phone in the morning. a human chief of staff costs six figures a year. a Grok Bot runs on its own cloud computer with a browser, files and a terminal, and closing your laptop or your phone doesn't stop it you set the goal, it does the steps, you make the calls the rule: "find me $50,000 a month in new business. public data only. contact nobody without my yes" the night you're asking for: -> 9pm split the goal into 4 jobs and give each one a time -> 11pm pull every dental clinic in your city from public listings and cut the chains -> 1am read every site that's left and keep the best 25, each with one specific thing it noticed and where -> 2am write an opener for every one of them, send zero -> 3am build the offer on one page, no made-up results, price left blank for you -> 8am you type the price. 25 clinics × $2,000 = $50,000 a month on the table if they all say yes the blank price is the best part. an AI that guesses your price loses you the deal and it's not one bot. Grok Bots can message each other, share a group chat and hand off tasks, so one can run research while another writes and your chief of staff keeps them in line save the night as a routine and it runs again every Monday on its own it prepares the drafts. you approve them. nothing goes out without your yes someone's chief of staff is already pitching the clinics in your city the whole setup is below and free, steal it
kiosa21,810 görüntüleme • 8 gün önce
It is becoming f*cking dangerous: a model that never writes a single word now judges every move your Claude AI agent makes -> Create a folder named JEV LAB on your PC and launch Claude Code inside it -> Install the Jev CLI: npm install -g jev-axi -> Grab a key at and run: jev-axi config set apiKey YOUR_KEY -> Run jev-axi setup safety so every command Claude runs gets checked first. Dangerous downloads get denied, routine tests pass for free -> Run jev-axi setup git-hooks to stop any commit that sneaks in a password or API key -> Feed it anything your agent reads: jev-axi guard Run jev-axi setup supervise --project to catch the moment your agent quits early or gets stuck in a loop The whole setup takes around 5 minutes Turn Claude into an agent with a second opinion: every command screened, every page checked for prompt injection, every commit scanned for secrets. For a fraction of a cent per check Bonus: pipe a broken build into jev-axi triage and it finds the root cause for you Bookmark this article and check repo below:
kiosa29,602 görüntüleme • 12 gün önce
the NSA put its own hacking tool on GitHub and most people still haven't opened it 5 security repos that show you how exposed you actually are. all free, all open source. want to see every account tied to your username across the internet? -> Sherlock want to take any program apart and read what it really does? -> Ghidra, the reverse engineering framework built by the NSA want to watch everything your phone apps send home? -> mitmproxy want to know if your API keys and passwords are already sitting in your git history? -> TruffleHog want to test your own WiFi and Bluetooth with a $10 chip? -> ESP32 Marauder ~260k stars of tools that professionals get paid to run. none of this is hidden. it's the same kit pentesters use on the job. the catch: it's only legal on things you own or have written permission to test. the best way to learn how you get hacked is to run the attack on yourself first. bookmark this. pick one. point it at your own stuff tonight.
kiosa45,998 görüntüleme • 22 gün önce
this is the list nobody wants you to bookmark. 10 GitHub projects where AI agents actually make you money not another framework zoo. client work, content, leads, voice, trading. -> n8n · AI workflow automation you sell to clients on retainer. the agency backbone -> -> Dify · ship chatbots, RAG apps and agent workflows for businesses without a dev team -> -> Firecrawl · any website -> clean structured data. scraping gigs, lead lists, datasets -> -> browser-use · agents that click through the browser for you. sell the automation, not the hours -> -> TradingAgents · multi-agent trading firm in a repo: analysts, researchers, risk desk (research only - not an atm) -> -> screenshot-to-code · screenshot -> clean HTML / Tailwind / React. landing pages in minutes -> -> Marketing Skills · CRO, copywriting, SEO and growth skills for Claude Code. a marketing team in a folder -> -> Postiz · agentic social media scheduler. run 10 client accounts from one dashboard -> -> VideoLingo · subtitles, translation and dubbing in one click. sell localization to creators -> -> Pipecat · realtime voice agents. AI receptionists and phone bots local businesses pay monthly for -> ~985k stars of "agents that touch revenue" save this. pick one. charge someone for the output.
kiosa42,000 görüntüleme • 23 gün önce
This is completely f*cking wild. A Stanford AI researchers gave Claude Code a second brain with JEV. The result? 80+ billion data points processed every 14 minutes. here's how it works: Jev stands at the door. every row has to get past it first. the easy ones it waves through on the spot. only the ones it can't call get sent upstairs to Claude. the old way: everything -> Claude -> big bill the new way: everything -> Jev -> the tricky few -> Claude Jev also tells you how sure it is on every call. that number decides who goes upstairs. just don't pick it out of thin air. test it first. the 20-minute setup: 1. take 200 real rows from your data 2. run them through Jev AND Claude 3. sort Jev's answers by how sure it was 4. find the point where Jev stops disagreeing with Claude. that's your cutoff 5. above it, Jev's answer is final. below it, the row goes to Claude then do the math. on a public benchmark Jev cost $0.015 per 1,000 rows and Claude cost $0.80. if Jev settles 80% of your rows: $0.015 + (20% × $0.80) = $0.175 per 1,000 rows instead of $0.80 that's ~78% off, with Claude still handling every hard case. 3 rules that keep it working: -> ask all your questions in one Jev call. topic, urgency and sentiment together, not three separate requests -> once a week, send 50 of Jev's "confident" answers to Claude anyway. if they start disagreeing, raise the cutoff -> never let Jev make the final call on anything you can't undo. refunds, bans, deletions go to Claude or a human where this pays off right away: -> support tickets: route by topic and urgency -> leads: hot, warm or skip -> comments: fine, spam or needs a look -> spreadsheets: fix category labels across thousands of rows Claude thinks. Jev picks. code ships.
kiosa13,310 görüntüleme • 6 gün önce
This is absolutely f*cking insane. A Stanford AI research group combines JEV with Claude Code to sort 78+ billion data points every 12 minutes. so the trick is stupidly simple: Jev runs a cheap first pass on everything. Claude only gets the hard cases. instead of: all data -> Claude -> $$$ it's: all data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your filter: -> Jev is 90%+ sure? keep the answer, done -> below that? send it up to Claude you set the cutoff once. the boring stuff never touches the expensive model. where this pays off right away: -> tagging support tickets -> sorting your inbox -> filtering leads -> cleaning a messy spreadsheet -> moderating comments faster. cheaper. way less compute and tokens burned. Jev sorts the mail so the genius only opens what matters. LLMs think. Jev decides. code does the job.
kiosa18,122 görüntüleme • 9 gün önce