#AgentAI

YOU WON'T HAVE A JOB IN 2027 IF YOU DON'T LEARN THIS 7 GitHub repos that will save you from being replaced by AI: 1. career-ops (80k stars) an agent that scans job boards, scores every role against your CV and tailors your resume for each one 2. Agent Reach (107k stars) gives your agent eyes on YouTube, Reddit, GitHub, LinkedIn and the open web with one command 3. OfficeCLI (32k stars) lets agents create and edit Word, Excel and PowerPoint files without Office installed 4. Hypit (26k stars) clones any viral video into an editable workflow - footage, captions, b-roll, effects 5. AI Website Cloner (37k stars) turns any website URL into a clean Next.js app you can edit 6. Strix (72k stars) autonomous AI hackers that find vulnerabilities in your code and prove them with working exploits 7. Paperclip (110k stars) run a whole company of AI agents - org chart, budgets, approvals, one dashboard want more videos like this? like it, share it with a friend and tell me in the comments which agents you want me to visualize next

You have no f*cking idea how easy it is to set up a live dashboard for your Grok Bot agent teams I did it with a single prompt and it took only 2% of my Grok Bot usage, so make sure you save this now I see the second a bot gets stuck or needs me, instead of opening 15 chats, here's all you need to do: - paste it into your Grok Bot, it becomes the manager of your office - it creates your own private channel, nobody else can read it - it adds one status rule to every bot, removable with one message - it sends your roster, teams and looks, each team moves into its floor - open your link, done every new bot you create shows up in the office automatically, you can see its status, which team it's in and how much of your usage it eats you can find the prompt below, then go further with my Grok Bot article

We built Pando, a new Git backend for our monorepo, to handle more pushes and fetches without changing how our engineers and agents use Git. Pando cut push latency by ~80% and now serves nearly a petabyte of Git data a day. We can grow Git capacity alongside agent usage.

HOLY F*CK. you're still building AI agents from scratch while an entire army of ready-made bots is sitting on GitHub for free. I found 20 GitHub repos that can save you weeks of building, configuring, and connecting your own Grok Bot team. We're talking about ready-made agents for coding, research, sales, trading, marketing, operations, and even entire multi-agent companies. And most people are completely sleeping on this. Here are 20 projects worth bookmarking: 01. grok-skills 195 reusable playbooks for coding, debugging, research, writing, planning, and shipping. 02. botteams Create named bots, group chats, and team routines using a simple installer prompt. 03. grok-bot-templates 49 templates, 10 teams, 47 skills, and 8 routines. Ready-made roles with clear deliverables and boundaries. 04. hypergrok-trading-desk 7 specialist bots and 17 skills for market research, strategy testing, risk, execution, and review. 05. awesome-grokbot A searchable collection of public Grok Bot configurations organized by job. 06. grok-bot-profiles Includes Spec Writer, Bug Reproducer, PR Producer, and a separate PR Verifier. 07. root-agent-skill-framework Builds specialist agents around specific tasks and delegates daily management to team leads. 08. grok-research Persistent equity researchers, scout reports, reviewed views, and a shared research database. 09. rosterroom 82 ready-to-use team setups for founders, operations, security, finance, and research. 10. grokbot-for-gtm Connects prospect research, email, LinkedIn outreach, replies, and meeting tracking. 11. grok-bot-token-saver Replaces huge conversation histories with compact role cards and handoffs between sessions. 12. gojiberryai-sales-os 13 sales roles covering prospect research, buying signals, follow-ups, qualification, and pipeline analysis. 13. crew-contract Gives every agent a mission, owner, evidence-based handoff, completion check, and stopping rule. 14. awesome-grokbot by mergisi Ready-made engineering, sales, marketing, customer success, operations, and personal teams. 15. botskills Ready-made BOT.md configurations with integrations and clear approval boundaries. 16. grok-bot-cli Create bots and groups, send messages, and inspect threads through scripts. 17. grok-bot-skill Lets a coding agent create, message, and manage Grok Bot teammates. 18. awesome-grok-bot by majiayu000 A searchable collection of public bot configurations, team recipes, and setup guides. 19. akeru-bot A self-hosted alternative with named teammates, individual tools, instructions, and persistent memory. 20. guaca A desktop workspace where agents share conversations, delegate tasks, and work with MCP tools. But here's what most people get wrong: Having 20 agents doesn't mean you have a working AI company. You need a system: → 1 coordinator to break down objectives → Specialist agents to execute tasks in parallel → Shared handoffs so work doesn't restart from zero → Independent verification before results are accepted → Clear stopping rules to prevent endless retry loops That's how you turn a folder full of bots into an actual AI workforce. The repositories are free. Running them may still require an AI subscription, API usage, integrations, or hosting. Start with one workflow. Make it work. Then scale. Bookmark this before you waste another week building something that already exists.

THIS IS F*CKING INSANE... MOST PEOPLE ARE STILL TRADING CRYPTO MANUALLY WHILE YOU CAN BUILD A GROK BOT THAT RUNS AN ENTIRE TRADING DESK 24/7. And you don't need to build every component from scratch. Here are 10 GitHub repos to build your own automated crypto trading system: 01 Cryptofeed → Stream live trades, prices and order book updates from crypto exchanges so your system can react to market activity. 02 CCXT → Connect your trading system to multiple exchanges through one unified API. Fetch market data, check balances and manage orders. 03 Freqtrade → Build, backtest and run crypto trading strategies with position tracking, dry-run mode and a built-in dashboard. 04 VectorBT → Test thousands of entry and exit combinations against historical data to compare returns, trades and drawdowns. 05 TA-Lib → Calculate technical indicators like RSI, MACD and moving averages to turn raw price data into usable signals. 06 skfolio → Optimize portfolio allocations while accounting for constraints like transaction costs, turnover and position weights. 07 QuantStats → Generate performance reports showing drawdowns, win rate, volatility and how your strategy compares with a benchmark. 08 DuckDB → Store market snapshots, signals and trade records so you can investigate exactly what happened during every trading session. 09 APScheduler → Schedule market-data updates, account checks and automated reports without constantly asking an AI agent to wake up. 10 TradingView Lightweight Charts → Build a lightweight trading dashboard with live price updates, indicators and trade markers. Connect the tools through Python, APIs or MCP. Let Grok Bot orchestrate the workflow while your code handles execution and risk limits. Backtest first. Dry-run before going live. Never give an AI unrestricted access to your funds. Market data → signals → execution → automated reports. Build your own AI trading desk ↓

Deepseek just revealed every AI agent secret Incredibly important article to read if you're building anything AI agent related Here's basically everything you need to know about building a Grok Bot quality AI agent: 1. Agents are split between routers and workers. The user talks to the router, then gives the work to the workers. The router never does work. This is why the chief of staff model in Grok Bot works SO well 2. Proactive means acting on events, not chats. The agent should always be watching for events and react based on every event that comes through it. Routing to the right worker/action 3. Messages should be in a queue. Users should be able to send messages even while the agent is working, and each message queues/steers the agent 4. Besides the router and workers, there should be a watchdog layer. The watchdog makes sure the agent is always up and fixes it when there are issues. This is why Hermes succeeded and OpenClaw struggled. Constantly broke and couldn't fix itself 5. Protect human attention. Only message the user when it needs to act or the user can use the result. Lighten their cognitive load. Another thing Grok Bot is excellent at. When you message it you can be confident the next message you get will be it saying the task is complete. Carve off an hour and read this incredible article

Anthropic engineers just released the best 90-minute course on building AI agents from scratch I've ever seen: • 00:00 - why the brain and the hands should be separate • 07:45 - the architecture behind Claude Managed Agents • 23:40 - building a research agent step by step • 54:00 - agents that run on a schedule and "dream" between tasks • 1:10:23 - evals, CI/CD and running agents for many customers at once This alone will replace every $500 agent tutorial you can find on the internet. Watch it, then go further and build a system that improves itself with the article below.

Harness Engineering 2.0 is here! After protocols for tools, agent communication, and agent interfaces, harnesses now have one too. Developers just built UHP (Unified Harness Protocol), a standardized interface for running agent tasks across any agent harness. HarnessRouter is its open-source server implementation. Here’s how it works: - The product backend sends a task with a `harness_id`. - HarnessRouter starts the selected harness in its own session. - The harness runs its native agent loop and streams back progress. - Files, artifacts, failures, and the final output return through the same interface. Without UHP, integrating every harness needs separate handling for task execution, sessions, streaming, files, recovery, and output formats. With UHP, Codex, Claude Code, Gemini CLI, Hermes, Pi, and other harnesses can plug into the same product backend while keeping their native tools, skills, models, and agent loops. This means developers can: - Switch harnesses without rebuilding the product integration. - Run the same task across different harnesses. - Add new harnesses without maintaining another execution stack. HarnessRouter currently supports 30 harness bases. Here’s the repo: (don’t forget to star it ⭐) As per official benchmarks, changing only the harness while keeping the model fixed reduced costs by 25% to 64% across several healthcare compliance tasks. And changing both the harness and model widened the difference to 99.8% between the highest and lowest-cost configurations. Developers can test their own workloads across different configurations using Harness Arena. Alibaba Cloud's Qoder has also adopted UHP, making it available as an agent backend through the HarnessRouter API. If you want to dive deeper, I already wrote an article that explains why model routing is different from harness routing and what supporting multiple agent runtimes actually involves. It also covers UHP, the local setup, a working API call, and how sessions and files work. Read it below.

Holy f*ck. SpaceXAI senior engineer Lauren Tan: "Grok Bot is the most powerful agent tool we've ever released, and only 1% of users know how to use it." In this 40-minute session, she shows how she set up her team before she scaled it Job → Chief of Staff → 3 Managers → 11 Workers → 200+ Agents* Most people add another bot when the first one fails. That just multiplies the mistakes Give every bot one job and a manager that checks it, instead of paying for the same failure every run Worth more than most $1,000 agent engineering courses Bookmark and watch it today Then read the full setup: "Grok Bot: 6 Bots, 200+ Agents, a Smaller Bill" ↓

Your Grok Bot is not expensive Your setup is 75% of my Grok Bot tokens went to re-reading old chat. About 1% went to actual work I broke down the 6-bot setup the Grok Bot team runs, and the 9 leaks eating your bill ↓

holy f*ck, I just sold this single AI-generated 3D neuron for $2,000 to a private biotech lab. a private lab saw my visuals on X and reached out. they just ordered a prototype for their modeling work. designers who can't do this will be looking for a job in 2027. save this before its too late: 7 design repos that make Claude Code feel like a whole design team: 1. Awesome DESIGN.md 84 design systems pulled from real sites, each one a single markdown file. drop it in your project, say "build me a page that looks like this" and it actually does 2. Hallmark a design skill from Together AI that refuses to look AI-generated. 50 themes, builds the page around what the visitor came to do, then checks it in a real browser. /hallmark redesign rebuilds any old page 3. Huashu Design type one sentence, get a launch animation, a clickable app prototype, an editable PPT or an infographic. it shows you 6 directions first, you pick 3, it builds them for real 4. video-shotcraft tell Claude "make a promo for my product". it screenshots your real pages, storyboards, animates, syncs to the beat, adds sound and checks every frame. you get an MP4 you can keep editing 5. OpenPencil an open-source Figma alternative that opens real .fig files. describe what you want in chat and the AI builds the design, and Claude Code can drive it directly 6. Motion2Code show it a motion design video and it rebuilds it as clean SVG + GSAP code, checked frame by frame against the original. a 10-second animation ends up around 24 KB 7. Blender Agent Skills 22 pro Blender workflows for Claude Code: modeling, materials, rigging, animation, lighting, rendering. the full 3D pipeline, not just "make a cube" the neuron in this video? I built it myself with Claude Code and a few 3D and motion tools and sold it for 2K. no studio, no team. want to make something like this? like + save this post, comment "NEURON" or DM me and I'll send you the exact prompt.

watch a coding agent with zero intrusions turn 1 picture into this: 9 beats, 6 tentacles, an arc torch at 3200 K, 20 seconds. I tried the method from this article on an octopus lander. it's in my setup now, and I'd recommend it to anyone building 3D in Claude Code. stop letting one agent grade its own build from memory. what I ran: → ChatGPT gives one reference picture. it sets the look and nothing else → Agent reads a job description file before every build → Agent writes a 10 to 12 line checklist for this one picture → Agent renders a still, puts it next to the reference, grades every line yes, partial or no → Agent counts dark pixels. under 0.8 of the reference is a no, however good it "looks" → the film is one script file, every beat a line Tethys TX-8 came out of that last step: land, limbs, grab, weld, wings, optics, teardown, assembly, done. You only look at three moments: → after round 2. the stills and the grades. in the article the hydra sat at 0.57 of its reference, one round aimed at the number took it to 0.80 → before the film. no yes, no render. a still costs about 400,000 tokens, a film another 150,000 to 400,000 → when you correct it. the fix goes into the file as a number. his piranha came back as a crocodile with jaws at 0.52 of the body, 4 measured numbers brought it to 0.35 his own count: 9 builds started, 5 shipped, one moth burned about 430,000 tokens before he cut it. that's why I run 2 at a time, not 4. full prompt in the reply. the part worth copying is one line: the film waits for my yes. on your last build, which one did you skip: the round 2 review, the yes before the film, or writing the fix into the file?

holy f*ck, web designers have NO IDEA what’s coming after them in 2027 the ones learning AI tools right now are getting ahead. the rest might be fighting for jobs they used to take for granted. 7 GitHub repos every designer should know before it’s too late: 1. Graphify 137k stars and in Y Combinator. point it at your old projects, brand PDFs and screenshots, and Claude gets a memory of everything you've ever designed. "what did this client hate last time?" answered in seconds 2. Awesome DESIGN.md 84 design systems pulled from real sites, each one a single markdown file. drop it in your project, say "build me a page that looks like this", done 3. Hallmark a design skill from Together AI that refuses to look AI-generated. 50 themes, checks every page in a real browser, and /hallmark redesign rebuilds any old page 4. Huashu Design one sentence in, a launch animation, clickable app prototype, editable PPT or infographic out. it shows you 6 directions first, you pick 3 5. Designer Skills Pack 273 skills and 76 commands for Claude Code: research, design systems, UI, interaction. a whole design department in one install 6. Motion Design Skill LottieFiles taught AI to think like a motion director: timing, easing, choreography, Disney's 12 principles adapted for UI. this is where the "feel" comes from 7. Blender Agent Skills 22 pro Blender workflows for Claude Code: modeling, materials, rigging, animation, rendering. the full 3D pipeline, not just "make a cube" the cell you're watching? built solo with Claude Code plus a few of these tools. no agency, no team, no After Effects. want to make something like this? like + save this post, comment "CELL" or DM me and I'll send you the exact prompt.

f*cking insane. I hired an AI chief of staff for $0, gave it one rule: "find me $50,000 a month or you're fired", went to sleep, and Opus 5.5 had it on my desk by morning a SpaceXAI engineer said one line that hit 5 million views: "i have a Chief of Staff agent. he knows about all my other bots and manages everything" a human one costs $200,000 a year. mine runs on Opus 5.5 inside Viktor, on his own computer in the cloud, with my laptop shut i give the goal, he does the steps, i only make the calls the rule: "find me $50,000 a month of new business. public data only. contact nobody without my yes" the night: → 9pm broke the goal into 4 jobs and gave each a day and a time, i wrote none of them → 11pm pulled 240 dental clinics in Austin from Google Maps and cut every chain → 1am read every site that was left and kept 25, each with one thing he noticed and where he saw it → 2am wrote an opener for every one of them, sent zero → 3am built the offer on one page, no invented results, price left blank for me instead of guessing → 8am i typed $2,000 a month. 25 clinics × $2,000 = $50,000 a month on the table honestly the blank price is what i'd pay for, an AI that guesses your price loses you the deal and he does it again every Monday: 100 new clinics a month, and he never forgets to start 240 clinics → 25 worth it → 1 offer → $50,000 a month on the table → 0 sent without my yes second 12, he leaves the price blank and hands it to me someone's chief of staff is already pitching the clinics in your city i left the whole setup below ↓ free, steal it

here's a visual explainer of DeepSeek's article about Super-Agent:

Andrej Karpathy: “A 1B model can be much more effective than most current LLMs. 90% of current models waste a ton of capacity and money on remembering stuff that doesn’t matter.” here is his 5 core thesis about Small LLM’s: 1. the intelligence was never the knowledge. Strip the memorized internet, keep the reasoning. What’s left is the cognitive core, and it’s small. 2. the core might fit in ~1B params. Maybe less. “Plenty of room at the bottom.” We’ve been paying for a library when we needed a brain. 3. size stopped being the bottleneck. What makes an agent work is the tools it can call and how tight the feedback loop is, not parameter count. 4. intelligence lives in the system, not the weights. A raw LLM is a CPU with no OS. Small core + good harness > giant model alone. 5. you can own it. A model on your hardware is one no one can rate-limit, price-hike, or deprecate on you. in 12 months the gap between teams shipping on-device and teams paying per-token frontier bills won’t be capability. TwIL an 8.8B model at 90%+ of frontier isn’t a benchmark flex. It’s the thesis Karpathy’s been repeating all year, made real. study more on this below ↓

Modest intelligence is arriving faster than most people realize. Today, we're open-sourcing TwIL-8B. Just 8.8 billion parameters, yet approaching frontier-model scores on several difficult benchmarks: • 93% of frontier performance on coding • 92% on instruction following • 90% on contest mathematics All from a model small enough to run on your own hardware. But the benchmarks aren't the most important part of this announcement. For years, the AI industry has focused on building the most capable model, regardless of the cost required to run it. I believe that equation is changing. The next era of AI will be defined by the cost of completing work. Not simply the intelligence of the model, but how efficiently that intelligence can accomplish the task. If a model with 8 billion parameters can reliably complete the same work as one with hundreds of billions, why pay for the larger model? As smaller models become more capable, intelligence becomes something we can distribute, specialize, and run across the hardware we already own. That's the future we're building at @thewebAI TwIL-8B is available today. Open weights. And this is just the first of several announcements we're making today. HF Link:

An agent on every desktop

Introducing agent-desktop agent-browser, for your whole computer → Computer Use for any coding agent → macOS, Windows and Linux → Runs in bg while you keep working → Agents use your computer in parallel, each with its own cursor

Hermes Agent v0.23 was just released! The biggest headline new feature is that you can now host the desktop app as a webapp! Access your agent over your web browser from anywhere. See the full release notes below!

Hermes Agent v0.23.0: The Hearth Release Highlights: - Desktop in a browser tab - One gateway for every conversation - /Rewind - Group Chats with automatic routing - Spend limits for paid tools - Lighter and faster and more Changelog below

This is f*cking insane. Anthropic senior engineer: "Writing prompts isn't the job anymore. Most people open a single chat and call that an agent. My agents live inside /loops + /graphs + /harness. Same model, same tokens, 10x the finished work. It plans, checks itself, and ships while I'm offline." Prompts → Agents → Loops → Graphs → Harness In a 1-hour course, she builds a team of agents that fix their own failures and improve without you touching them. Better than any $1,500 agent course out there. Watch it before your feed buries it. Full Opus 5.5 harness engineering guide below ↓