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@beamnxw • 6,796 subscribers
hyperfixation on AI | always dyor
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this is pure f*cking treasure 15 GitHub projects with 1.21M combined stars that can form a real agent stack specs. memory. web data. documents. context. sandboxes. monitoring. video 01 hermes-agent ▸ 02 OpenSpec ▸ 03 caveman ▸ 04 Scrapling ▸ 05 Docling ▸ 06 PageIndex ▸ 07 mem0 ▸ 08 headroom ▸ 09 Daytona ▸ 10 TrendRadar ▸ 11 Fabric ▸ 12 spec-kit ▸ 13 hyperframes ▸ 14 OpenMontage ▸ 15 AI Engineering Hub ▸ the loop: define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output the entire stack is open source save this to build your own business with the help of an AI employee ⭣
beamnxw ./94,795 次观看 • 1 天前
this is pure f*cking treasure 30 best MCP servers, mapped across 10 categories for building your agent's toolbox search. code. browser automation. files. databases. memory. agents. productivity. research. finance 01 SEARCH & INTERNET Brave Search MCP Server ▸ Google Maps MCP Server ▸ World Monitor MCP ▸ 02 PROGRAMMING Sentry MCP Server ▸ Context7 MCP ▸ GitHub MCP Server ▸ 03 BROWSER & AUTOMATION Fetch MCP Server ▸ Chrome DevTools MCP ▸ Playwright MCP Server ▸ 04 FILES & DOCUMENTS Filesystem MCP Server ▸ Google Drive server ▸ MCP server for Obsidian ▸ 05 DATABASES PostgreSQL ▸ SQLite MCP Server ▸ MCP Toolbox for Databases ▸ 06 MEMORY & RAG Knowledge Graph Memory Server ▸ Graphiti MCP Server ▸ cognee-mcp ▸ 07 AI AGENTS Taskmaster ▸ BlenderMCP ▸ Talk to Figma MCP ▸ 08 WORK & PRODUCTIVITY Google Workspace MCP Server ▸ Todoist AI MCP Server ▸ Thunderbird MCP ▸ 09 RESEARCH & ANALYTICS Phoenix MCP ▸ Zotero MCP ▸ NotebookLM MCP Server ▸ 10 BUSINESS & FINANCE Finance Toolkit ▸ Financial Datasets MCP Server ▸ Stripe MCP Server ▸ the stack covers the full agent loop: find information ⮕ write and inspect code ⮕ operate the browser ⮕ read files ⮕ query data ⮕ remember context ⮕ delegate work ⮕ run operations ⮕ analyze evidence ⮕ handle payments some reference servers above live in archived repositories, so check maintenance and permissions before production use save this, then select your gpt-6 astra use case ⭣
beamnxw ./60,168 次观看 • 3 天前
this work from Google engineers is pure f*cking treasure they built WikiSkill: a system that turns an agent's execution history into persistent knowledge, then compiles it into reusable skills this is basically an experience compiler for agents the loop: run tasks ➜ preserve raw traces ➜ consolidate recurring failures + successful strategies into a wiki ➜ propose one atomic skill update ➜ validate ➜ keep or rollback the clever part: skills can roll back the wiki never does successful strategies, recurring failures, rejected edits and skill impact history survive into the next iteration the result is f*cking amazing my takeaway: the next agent memory layer should distill experience into procedures an agent can execute, validate and transfer raw traces ➜ persistent knowledge ➜ validated skills ➜ a better next run save this, then select your gpt-6 astra use case ⭣
beamnxw ./36,958 次观看 • 3 天前
holy sh*t, this is the whole one-person ai company playbook on one page... this is basically a 7-step blueprint for running a business with ai in 2026 pick one narrow problem talk to 10 potential buyers write the business case publish one clear page win the first customers manually automate what repeats bring in specialists when needed - ai handles research, content, sales prep, support, and operations - you keep control over money, quality, customer relationships, and final decisions save this, then automate everything by building your own AI office ⭣
beamnxw ./54,848 次观看 • 8 天前
this is the most useful gpt-6 astra map on the internet today i turned the entire use cases article into one operational field guide 19 application patterns open it to see where agents fit and where traditional automation starts breaking 11 industry workflows support, sales, finance, commerce, coding, marketing, all mapped one instruction goes in, one completed result comes out 7 questions tell you if the workflow is worth building exceptions go to the agent, final decisions stay with the human start with the workflow, then decide where astra belongs save this, then select your gpt-6 astra use case ⭣
beamnxw ./17,416 次观看 • 5 天前
Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:
beamnxw ./63,432 次观看 • 2 个月前
Kimi K3 is available for FREE on TokenRouter 50,000,000 FREE tokens 1) Sign up at 2) Create an API key in the "API keys" 3) Use these settings in any OpenAI-compatible tool: - Base URL: ' ' - API Key: ' your new key ' - Model: ' moonshotai/kimi-k3-free ' Done. Works in Cursor, Claude Code, Cline, OpenCode and similar tools
beamnxw ./51,489 次观看 • 1 个月前
THIS BUILDER JUST DROPPED 256GB OF RAM INTO A MONSTER THREADRIPPER WORKSTATION TO RUN UNFILTERED LOCAL AI Imagine trying to fit a computer setup into a chassis that is basically the size of a mini fridge. That is the Corsair 1000D tower. The builder crammed an ASUS Pro WS WRX80E-SAGE motherboard inside and slapped a 64-core AMD Threadripper PRO 5995WX right into the socket Why go this heavy? Simple. To make sure local open weights models don't instantly choke standard desktop hardware during heavy reasoning tasks Then things get downright ridiculous with the memory configuration. He unboxes eight separate Kingston DDR4 modules, pinning a massive 256 gigabytes of system RAM directly to the board. Having that kind of local memory headroom is an absolute necessity if a team wants to handle massive datasets or run dense training loops without constantly swapping data to the storage drives Speaking of storage, the system relies on two lightning fast 2TB Samsung 990 PRO NVMe drives For the graphics pipeline, he drops in a top-tier ASUS ROG RTX 4090 boasting 24 gigabytes of VRAM. That is pretty much the gold standard right now if you want to run quick local inference cycles and completely stop paying corporate cloud token fees to OpenAI or Anthropic Powering this whole grid requires a monstrous ASUS ROG 1600W Thor Gen 2 power supply. And to prevent the entire workstation from turning into a space heater under full load, the builder went all out with a 360mm AIO liquid cooling setup and an insane cluster of sixteen Lian Li SL-Infinity RGB fans It looks incredibly flashy, probably sounds like a jet engine when the cores push maximum load What to buy for local AI? => my guide below Bookmark this so you don't lose it
beamnxw ./47,494 次观看 • 2 个月前
i honestly don't f*cking understand why no one has done this yet a second brain inside grok bot 1000+ agents with their own memory one CHIEF that issues the work steal the floor plan: CHIEF → signal deck | inbox, threads, archive → orbit deck | calendar, holds, reminders → vault deck | invoices, receipts, ledger → lens deck | scrape, enrich, dedupe → forge deck | reports, charts, briefs → drift deck | outreach, follow ups, send queue CHIEF assigns. decks execute. i approve 1 login. 1 profile copy this and stop running your company from a chat box save this, then start building ⭣
beamnxw ./14,320 次观看 • 18 天前
GUYS, STOP F*CKING SLEEPING ON GROK BOT I BUILT A FULL ZERO-SALARY AI ENGINEERING TEAM INSIDE A GROK BOT OVERNIGHT Architect ➜ Harness ➜ Memory ➜ Critic ➜ Implementer ➜ Deployer ➜ Chief of Staff Chief owns the final yes Everything irreversible lands in my approval tray first > Architect ➜ Designs the full system architecture and role structure > Harness ➜ Builds evaluation loops, self-tuning and test harnesses > Memory ➜ Owns persistent memory, retrieval and lifelong context > Critic ➜ Finds real failure modes, runs adversarial tests and edge cases > Implementer ➜ Writes the actual prompts, code and routines > Deployer ➜ Ships live, monitors and runs overnight > Chief of Staff ➜ Only point of contact. Routes all work and holds the approval queue I only talk to Chief The rest hand work forward and keep their own memory I wake up to a sorted queue Read the evidence Hit yes or no One human Six specialists Full engineering floor running 24/7 This is GROK FORGE Read the article if you want to do the same ⭣
beamnxw ./16,914 次观看 • 25 天前
this is the most dangerous thing on the internet today.. I built a second brain inside Grok Bot. 1000+ agents. Each one keeps its own permanent memory and works without me in the loop. Chief sits in the center of the graph, issues every task and owns every handoff. I only talk to him. Nothing ships until he routes it and I hit yes Chief ➜ routes the work Signal ➜ reads the live feed Orbit ➜ owns timing Vault ➜ holds receipts and lifelong context Drift ➜ runs outreach Lens ➜ watches numbers and heat Helm ➜ picks the next move Forge ➜ builds Crew ➜ executes I do not manage 1000+ bots. I manage one Chief. The rest keep their state, keep pushing and never drop context 1 human ➜ 1 chief ➜ 1000 specialists the floor never stops don't sleep on this ⭣
beamnxw ./12,397 次观看 • 22 天前
THIS BUILDER JUST LINKED FORTY RTX 3090s IN HIS BEDROOM TO BYPASS THE CLOUD AND RUN 1TB OF LOCAL VRAM. WHY DOES HE HAVE SO MUCH?? Let that number sink in for a minute. Nearly a full terabyte of raw, unadulterated VRAM sitting right next to someone's bed. With forty RTX 3090s linked up in a custom rack, you aren't just running basic local chat models anymore You are essentially operating a localized data center. It completely changes the math on what you can actually execute without a cloud subscription For starters, you can comfortably host giant, fully unquantized open weights models. I'm talking about running massive enterprise reasoning architectures like DeepSeek-R1 671B or dense Llama 405B models at full precision. Most people have to slice these models down, compressing them until they lose their edge. On a rig like this, they run completely uncompressed But it gets weirder. You can launch massive multi-agent autonomous swarms. Imagine spinning up 300 to 400 distinct AI agents simultaneously ➜ each running its own heavy coding or data-scraping loops ➜ and letting them interact in real time + You can build a massive, real-time semantic search engine over your entire digital life. You could feed decades of personal data, code repositories, thousands of books, and full video transcriptions into a localized vector database. The system can keep the entire index permanently hot in the graphics memory. It gives you instant, sub-millisecond semantic search across millions of data points. A true, zero-latency second brain that never phones home and never risks a data leak Bookmark this so you don't lose it
beamnxw ./28,263 次观看 • 2 个月前
HARNESS vs LOOP vs GRAPH ➜ STOP MIXING THEM UP Most people treat these three as the same thing. They’re not 1\ Harness = the machinery around the model (tools, state, permissions, memory, sandboxes, observability) 2\ Loop = the repeated work + evidence + feedback cycle with clear stop rules 3\ Graph = the explicit topology (nodes, branches, joins, controlled cycles) Clean mental model environment → feedback → flow > A raw model can’t maintain state, run tests, or restart failed jobs. Those capabilities come from the harness > Loops turn one-shot calls into managed processes that only stop when evidence proves success > Graphs decide which component is allowed to run next Most production failures that get blamed on the model are actually failures of harness, loop, or graph design Design the three layers together. Diagnose by layer when something breaks Guide in the article below
beamnxw ./19,471 次观看 • 1 个月前
DEEPSEEK JUST DROPPED HARNESS, AND IT ALREADY BECAME ONE OF THE FASTEST-GROWING PROJECTS ON GITHUB: 130,000 STARS IN ONLY 3 DAYS. COMPLETELY FREE Core idea: almost anything can become a plugin ➜ models, sessions, skills, sandboxes, loops, even the interface. Put the pieces in, get a working agent out 1\ Architecture is built on Cordis, so you can swap components at any time 2\ Formula is simple: any model + any component = ready agent 3\ Community already shipped 6,000 ready skills for it You get a flexible environment where you control the building blocks instead of being locked into one rigid setup Installation is simple. Run these commands one by one: ``` git clone cd deepseek-harness pnpm install pnpm run build pnpm dsh web ```
beamnxw ./12,264 次观看 • 1 个月前
GRAPH ENGINEERING: STOP ADDING AGENTS AND START DESIGNING SYSTEMS More agents won’t save you if every step depends on the previous one Graph Engineering forces the better question “Where does the work naturally split? When it does, parallel researchers + independent verifiers + a final merge consistently outperform one overloaded model Verification is more valuable than generation Self-review is usually weaker than dedicated challengers Prompt engineering → Loop engineering → Graph Engineering Are you still designing prompts, or are you designing systems? Bookmark this, then read the article below
beamnxw ./13,723 次观看 • 1 个月前
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