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this GPT-6 Astra setup is getting f...cking dangerous 8 GitHub repos for turning one coding agent into an entire engineering team 01 OpenHands ▸ → autonomous software development 02 SWE-agent ▸ → issue → inspect repo → patch → test BUILD THE WORKERS 03 Aider ▸ → pair programming...

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Lummox

7,834 subscribers

35,870 次观看 • 4 天前 •via X (Twitter)

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17 条评论

Blum 的头像
Blum4 天前

i love Lummox's lists, there's always smth useful in there for me

Lummox 的头像
Lummox4 天前

Fam

Zero 的头像
Zero4 天前

what if openai devs released these repos because they weren’t allowed to ship this officially with gpt?

wast3 的头像
wast34 天前

which operating system does it run on?

Issa 的头像
Issa3 天前

These tools feel powerful but raise reliability concerns, no?

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack4 天前

@Lummox_eth these setups are wild! One thing to watch for: make sure you're setting proper limits on agent execution time.

Ridark 的头像
Ridark4 天前

Your terminal is truly energetic and shows uniqueness in the layout and overall flow for real

Lummox 的头像
Lummox4 天前

With pleasure ridark

Gipp 🦅 的头像
Gipp 🦅4 天前

good repo, bro

Dekos 的头像
Dekos4 天前

It really looks dangerous

Lorden 的头像
Lorden4 天前

you really cooking with these repos

ALEXYZ 的头像
ALEXYZ4 天前

one coding agent becoming a full team

beamnxw ./ 的头像
beamnxw ./4 天前

nice share bro

Karpacho 的头像
Karpacho4 天前

These repos should be banned cause it’s really illegal combination

Perk🥀 的头像
Perk🥀4 天前

this is where coding agents get really interesting, one model becomes an entire team when the right tools are in place

CryptoNinjas 的头像
CryptoNinjas4 天前

OpenHands alone is wild for handling full tasks.

Fares Kaaboubi 的头像
Fares Kaaboubi2 天前

تبارك الله، هادشي واعر بزاف! غدي يسهل الخدمة بزااااف.

相关视频

GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣
0:20

GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣

Lummox

158,670 次观看 • 4 天前

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣
0:24

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 次观看 • 5 天前

ANTHROPIC JUST EXPOSED WHY MOST MULTI AGENT CODING SYSTEMS FAIL BEFORE WRITING A SINGLE LINE OF CODE the model is not the problem the plan is one bad dependency cut gets copied across six agents then three builders produce the wrong result in parallel spec → planner → three builders → critic → scribe → PR → back into the spec the planner runs once every agent downstream is forced to inherit that decision builder A owns the API builder B owns the data layer builder C owns the interface none of them reads another lane that is what makes the system fast and what makes a weak plan catastrophic each builder gets its own read write test loop red stays inside the lane green unlocks the critic the critic does not ask whether the code looks good it asks whether the entire plan should exist then the scribe turns the trace into the PR plan hash three diffs test proof critic notes zero summaries written from memory if the PR fails the reason goes back into the spec as a new constraint the next run starts where the last one failed one human step remains approve or send it back five minutes instead of five hours a good plan scales a bad plan multiplies save this before your next agent build ↓
0:21

ANTHROPIC JUST EXPOSED WHY MOST MULTI AGENT CODING SYSTEMS FAIL BEFORE WRITING A SINGLE LINE OF CODE the model is not the problem the plan is one bad dependency cut gets copied across six agents then three builders produce the wrong result in parallel spec → planner → three builders → critic → scribe → PR → back into the spec the planner runs once every agent downstream is forced to inherit that decision builder A owns the API builder B owns the data layer builder C owns the interface none of them reads another lane that is what makes the system fast and what makes a weak plan catastrophic each builder gets its own read write test loop red stays inside the lane green unlocks the critic the critic does not ask whether the code looks good it asks whether the entire plan should exist then the scribe turns the trace into the PR plan hash three diffs test proof critic notes zero summaries written from memory if the PR fails the reason goes back into the spec as a new constraint the next run starts where the last one failed one human step remains approve or send it back five minutes instead of five hours a good plan scales a bad plan multiplies save this before your next agent build ↓

elune

34,491 次观看 • 1 个月前

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:
0:33

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:

MIKE

130,340 次观看 • 8 天前

this AI agentic stack is f*cking crazy 8 open-source repos that cover everything you need to run AI agents for free. not a random "top tools" dump. each one handles a different part of the job: BUILD > video-use - drop raw footage in a folder, tell Claude Code "make a launch video", get back final.mp4. it cuts filler words, adds subtitles, grades color > > Univer - a full office suite made for agents. they edit your sheets, docs and slides in a draft, you review and merge > ORCHESTRATE > Superpowers - stops your coding agent from writing code blindly. it plans first, splits work into 2–5 min tasks, tests everything > > Treg - OpenRouter, but for agent tools. one key, ~2,600 endpoints from 40+ providers: scraping, SEO, email finding > ACT > Mobile MCP - your agent controls a real iPhone or Android. taps, swipes, installs apps, records the screen > > security-audit-skill - Cloudflare's own skill that turns your coding agent into a security auditor. one agent hunts bugs, a different one double-checks every finding > REMEMBER · TEST · SHIP > Hindsight - memory that actually learns. it doesn't just recall old chats, it builds beliefs and updates them over time > > Graphify - turns your whole codebase into a map your agent can query instead of grepping files blindly. zero LLM cost to build it > the cool part isn't any single repo. it's that your agent can now: edit video -> work in Excel -> plan like a senior dev -> use 2,600 tools -> drive a phone -> audit its own code -> learn -> know your codebase you don't have to build an AI employee from scratch anymore. every part is already on GitHub. you just put it together. bookmark this before you start your next agent.
0:37

this AI agentic stack is f*cking crazy 8 open-source repos that cover everything you need to run AI agents for free. not a random "top tools" dump. each one handles a different part of the job: BUILD > video-use - drop raw footage in a folder, tell Claude Code "make a launch video", get back final.mp4. it cuts filler words, adds subtitles, grades color > > Univer - a full office suite made for agents. they edit your sheets, docs and slides in a draft, you review and merge > ORCHESTRATE > Superpowers - stops your coding agent from writing code blindly. it plans first, splits work into 2–5 min tasks, tests everything > > Treg - OpenRouter, but for agent tools. one key, ~2,600 endpoints from 40+ providers: scraping, SEO, email finding > ACT > Mobile MCP - your agent controls a real iPhone or Android. taps, swipes, installs apps, records the screen > > security-audit-skill - Cloudflare's own skill that turns your coding agent into a security auditor. one agent hunts bugs, a different one double-checks every finding > REMEMBER · TEST · SHIP > Hindsight - memory that actually learns. it doesn't just recall old chats, it builds beliefs and updates them over time > > Graphify - turns your whole codebase into a map your agent can query instead of grepping files blindly. zero LLM cost to build it > the cool part isn't any single repo. it's that your agent can now: edit video -> work in Excel -> plan like a senior dev -> use 2,600 tools -> drive a phone -> audit its own code -> learn -> know your codebase you don't have to build an AI employee from scratch anymore. every part is already on GitHub. you just put it together. bookmark this before you start your next agent.

kiosa

12,887 次观看 • 8 天前

ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.
2:27:56

ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.

Morlex

5,274,321 次观看 • 1 个月前

540,000 lines of Rails is a brutal way to learn the agent era Garry's List shipped with: > 262,000 lines of app code > 276,000 lines of tests > 127 background jobs > 33 cron jobs > a 1,778-line claim verifier Then GStack hit 105k GitHub stars Do not copy the Rails pattern 1. Finish the task with an agent 2. Say `skillify it` 3. Save the markdown skill 4. Keep TypeScript only for deterministic I/O 5. Add a unit test for the code 6. Add an LLM eval for the skill 7. Add an integration test across both 8. Add a resolver so the agent loads the skill next time A Rails feature serves one app A skill pack can run against the next hackathon, repo, customer inbox, research folder, or QA run Audit your repo this week Cut code that only exists to: > re-check model output > retry the same model call > validate what the model already has context for > schedule work because the agent has no reusable procedure > preserve a one-off prompt in chat history Keep the code that touches files, APIs, auth, billing, devices, databases Move the judgment into markdown Test the markdown Reuse the skill
8:28

540,000 lines of Rails is a brutal way to learn the agent era Garry's List shipped with: > 262,000 lines of app code > 276,000 lines of tests > 127 background jobs > 33 cron jobs > a 1,778-line claim verifier Then GStack hit 105k GitHub stars Do not copy the Rails pattern 1. Finish the task with an agent 2. Say `skillify it` 3. Save the markdown skill 4. Keep TypeScript only for deterministic I/O 5. Add a unit test for the code 6. Add an LLM eval for the skill 7. Add an integration test across both 8. Add a resolver so the agent loads the skill next time A Rails feature serves one app A skill pack can run against the next hackathon, repo, customer inbox, research folder, or QA run Audit your repo this week Cut code that only exists to: > re-check model output > retry the same model call > validate what the model already has context for > schedule work because the agent has no reusable procedure > preserve a one-off prompt in chat history Keep the code that touches files, APIs, auth, billing, devices, databases Move the judgment into markdown Test the markdown Reuse the skill

Alpha Batcher

36,878 次观看 • 4 个月前

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇
16:19

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇

MIKE

26,662 次观看 • 3 个月前

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀
0:59

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

André König

36,163 次观看 • 6 个月前

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
0:32

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

kiosa

57,333 次观看 • 2 天前

AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣
0:36

AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ → 66K+ stars 02 Hindsight ▸ → retain → recall → reflect 03 memU ▸ TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ → documents + code + conversations → knowledge graph 05 Graphiti ▸ → remembers how facts change over time 06 OpenViking ▸ MAKE THE AGENT STATEFUL 07 Letta ▸ → memory + identity across sessions 08 Letta Code ▸ REMEMBER ACROSS THE STACK 09 OpenMemory ▸ 10 Agent Memory Benchmark ▸ the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣

Lummox

326,219 次观看 • 8 天前

A 28-year-old in Berlin replaced an entire software agency with 7 AI agents running on a VPS. No office. No desk. No fixed computer. Just a phone and a terminal window connected to agents already working while she sleeps. A client got quoted $74,000 and 6 weeks by a traditional agency. Her agents shipped the validated PR in 19 hours. The agency was still revising the proposal. That’s the scary part about what AI is becoming. Not “ChatGPT writes code.” Entire companies are starting to collapse into one person + automated systems. One agent maps the repo. One writes specs. Two build in parallel. One tests. One audits security. Everything loops automatically until the PR is clean. Copy: She approved the final checkpoint from her phone at midnight. 30 seconds later the system kept running without her.
0:45

A 28-year-old in Berlin replaced an entire software agency with 7 AI agents running on a VPS. No office. No desk. No fixed computer. Just a phone and a terminal window connected to agents already working while she sleeps. A client got quoted $74,000 and 6 weeks by a traditional agency. Her agents shipped the validated PR in 19 hours. The agency was still revising the proposal. That’s the scary part about what AI is becoming. Not “ChatGPT writes code.” Entire companies are starting to collapse into one person + automated systems. One agent maps the repo. One writes specs. Two build in parallel. One tests. One audits security. Everything loops automatically until the PR is clean. Copy: She approved the final checkpoint from her phone at midnight. 30 seconds later the system kept running without her.

Phantom_Defi

17,496 次观看 • 4 个月前

the agent failed while I was asleep and somehow the work still got finished that was the test I actually wanted i gave GPT-6 Astra a workflow that normally needed me between every step inbound came in messy one agent had to sort it, another find the missing context, another build the deliverable, and the last one check everything before it reached me somewhere in the middle, agent 2 got stuck the source it needed wasn’t there normally that kills the entire chain instead, it wrote the missing piece into a blocker file and passed everything else forward the other agents kept working when I came back, most of the pack was finished and the one thing they couldn’t solve was sitting separately waiting for me that felt more important than a perfect demo because an agent completing 10/10 tasks is impressive an agent failing without taking the other 9 down with it is infrastructure
0:30

the agent failed while I was asleep and somehow the work still got finished that was the test I actually wanted i gave GPT-6 Astra a workflow that normally needed me between every step inbound came in messy one agent had to sort it, another find the missing context, another build the deliverable, and the last one check everything before it reached me somewhere in the middle, agent 2 got stuck the source it needed wasn’t there normally that kills the entire chain instead, it wrote the missing piece into a blocker file and passed everything else forward the other agents kept working when I came back, most of the pack was finished and the one thing they couldn’t solve was sitting separately waiting for me that felt more important than a perfect demo because an agent completing 10/10 tasks is impressive an agent failing without taking the other 9 down with it is infrastructure

Lummox

19,046 次观看 • 15 天前

ANTHROPIC LEAKED A 6-AGENT ASSEMBLY LINE WHERE ONE PROMPT GOES IN AND A FINISHED PR COMES OUT your name shows up in this process exactly once - at the very end, for 5 minutes. prompt → planner → coder → tester → critic → scribe → PR Opus 5 plans once - a bad plan executed by five perfect agents is still a bad result. Sonnet 5 runs 3 parallel lanes - and they only work because the planner made the steps independent. tests loop until green and red goes back to the coder - the critic never sees a broken build. Opus 5 as the critic rejects to the plan, not the code - tests ask "does it run", the critic asks "should it exist". the PR description writes itself from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save the setup - 2 models, 6 seats, 2 return paths ↓
0:47

ANTHROPIC LEAKED A 6-AGENT ASSEMBLY LINE WHERE ONE PROMPT GOES IN AND A FINISHED PR COMES OUT your name shows up in this process exactly once - at the very end, for 5 minutes. prompt → planner → coder → tester → critic → scribe → PR Opus 5 plans once - a bad plan executed by five perfect agents is still a bad result. Sonnet 5 runs 3 parallel lanes - and they only work because the planner made the steps independent. tests loop until green and red goes back to the coder - the critic never sees a broken build. Opus 5 as the critic rejects to the plan, not the code - tests ask "does it run", the critic asks "should it exist". the PR description writes itself from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save the setup - 2 models, 6 seats, 2 return paths ↓

Sprytix

39,283 次观看 • 1 个月前

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇
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10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 次观看 • 1 个月前

AI network is getting f…cking illegal 4 GitHub repos building a peer-to-peer market for AI no central inference provider sitting in the middle 01 AntSeed ▸ → P2P discovery + routing + AI inference BUILD THE NETWORK 02 AIPs ▸ → discovery + transport + payments + reputation + verification CONNECT THE AGENTS 03 openclaw-channel-antseed ▸ → turn an OpenClaw agent into a provider on the network ROUTE THE MODELS 04 pi-antseed ▸ → discover AntSeed models directly inside Pi the architecture: AI agent → local router → DHT discovery → provider → model → response providers can compete on: → price → latency → reputation buyers discover them through the network requests move through encrypted P2P connections and the same network can sit underneath existing AI tools the interesting part isn’t another model it’s turning AI inference into an open market models become services → agents find them → the network routes the work ⭣
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AI network is getting f…cking illegal 4 GitHub repos building a peer-to-peer market for AI no central inference provider sitting in the middle 01 AntSeed ▸ → P2P discovery + routing + AI inference BUILD THE NETWORK 02 AIPs ▸ → discovery + transport + payments + reputation + verification CONNECT THE AGENTS 03 openclaw-channel-antseed ▸ → turn an OpenClaw agent into a provider on the network ROUTE THE MODELS 04 pi-antseed ▸ → discover AntSeed models directly inside Pi the architecture: AI agent → local router → DHT discovery → provider → model → response providers can compete on: → price → latency → reputation buyers discover them through the network requests move through encrypted P2P connections and the same network can sit underneath existing AI tools the interesting part isn’t another model it’s turning AI inference into an open market models become services → agents find them → the network routes the work ⭣

Lummox

22,028 次观看 • 4 天前

garry tan just open-sourced a 128k-star repo that basically turns claude code into your entire engineering org it’s called gstack: 23 tools that break a single claude workflow into product, engineering, qa, review, and release lanes > /office-hours turns a rough idea into a real product spec > /plan-ceo-review + /plan-eng-review try to tear the plan apart before any code ships > /review hunts for bugs and patches them > /qa spins up an actual browser and tests the app end to end > /ship runs the final checks and pushes the pr think → plan → build → review → test → ship → reflect garry says this setup let him ship 3 production services and 40+ features in 60 days while still running yc full-time one developer sets the compass. the repo runs the loop.
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garry tan just open-sourced a 128k-star repo that basically turns claude code into your entire engineering org it’s called gstack: 23 tools that break a single claude workflow into product, engineering, qa, review, and release lanes > /office-hours turns a rough idea into a real product spec > /plan-ceo-review + /plan-eng-review try to tear the plan apart before any code ships > /review hunts for bugs and patches them > /qa spins up an actual browser and tests the app end to end > /ship runs the final checks and pushes the pr think → plan → build → review → test → ship → reflect garry says this setup let him ship 3 production services and 40+ features in 60 days while still running yc full-time one developer sets the compass. the repo runs the loop.

CyrilXBT

20,997 次观看 • 1 个月前

launched a swarm of 300 fly-agents and hooked them up to Jev Right before the main execution, the agents took full control of the prep work. In less than two minutes, this digital swarm swept through gigabytes of legacy clutter, aggressively indexing and sorting through forgotten docs, raw notes, and archived codebases across all my directories Then the main dashboard exploded with activity. Three hundred autonomous threads went to work in parallel writing code, hunting bugs, and running edge-case tests. Whenever an agent stumbled on complex logic, Jev instantly stepped in, validated the trace, and injected the exact pattern needed. Zero loops, pure high-speed execution Exactly four minutes in, the terminal dropped to dead silence All 300 tasks completed. Zero errors. 100% test coverage I opened the repo to find a rock-solid architecture, flawless code, and a clean deploy. Closed my laptop with a smile, while the agents, under Jev’s oversight, were already drafting the technical roadmap for the next sprint Bookmark this before you build your next multi-agent workflow
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launched a swarm of 300 fly-agents and hooked them up to Jev Right before the main execution, the agents took full control of the prep work. In less than two minutes, this digital swarm swept through gigabytes of legacy clutter, aggressively indexing and sorting through forgotten docs, raw notes, and archived codebases across all my directories Then the main dashboard exploded with activity. Three hundred autonomous threads went to work in parallel writing code, hunting bugs, and running edge-case tests. Whenever an agent stumbled on complex logic, Jev instantly stepped in, validated the trace, and injected the exact pattern needed. Zero loops, pure high-speed execution Exactly four minutes in, the terminal dropped to dead silence All 300 tasks completed. Zero errors. 100% test coverage I opened the repo to find a rock-solid architecture, flawless code, and a clean deploy. Closed my laptop with a smile, while the agents, under Jev’s oversight, were already drafting the technical roadmap for the next sprint Bookmark this before you build your next multi-agent workflow

Bober_smart

17,409 次观看 • 8 天前

Grok Build for Windows. Your terminal just got a dev teammate. Plans, writes, runs, debugs. Not suggestions. Execution. Multi-agent system. Arena mode ranking outputs. This is real coding inside PowerShell. Set it up on Windows: * Open PowerShell - run as Administrator * Install it - paste the command irm | iex * Go to your project - cd into your repo * Run grok - launch the CLI * Sign in - one-time login with SuperGrok * Start prompting - build, fix, refactor * Let agents run - parallel work, less waiting * Review outputs - diffs, rankings, results Windows dev flow. No excuses. xAI Grok
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Grok Build for Windows. Your terminal just got a dev teammate. Plans, writes, runs, debugs. Not suggestions. Execution. Multi-agent system. Arena mode ranking outputs. This is real coding inside PowerShell. Set it up on Windows: * Open PowerShell - run as Administrator * Install it - paste the command irm | iex * Go to your project - cd into your repo * Run grok - launch the CLI * Sign in - one-time login with SuperGrok * Start prompting - build, fix, refactor * Let agents run - parallel work, less waiting * Review outputs - diffs, rankings, results Windows dev flow. No excuses. xAI Grok

Mario Nawfal

98,671 次观看 • 4 个月前

ANTHROPIC LEAKED A 6-AGENT SETUP THAT TURNS ONE PROMPT INTO A FINISHED PR your name shows up in this process exactly once - at the very end, for 5 minutes. spec → planner → 3 builders → critic → scribe → PR → back into the spec the planner runs once and the whole line inherits its decisions - a bad plan executed by five perfect agents is still a bad result. 3 lanes build in parallel - and they only work because the planner cut the steps so none of them reads another's output. tests loop until green and red goes back to the coder - the critic never sees a broken build. the critic rejects to the plan, not the code - tests ask does it run, the critic asks should it exist. the scribe writes the PR from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. the back edge into the spec is the whole trick - today's failures become tomorrow's constraints without you typing them. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save this and read the full graph engineering course below ↓
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ANTHROPIC LEAKED A 6-AGENT SETUP THAT TURNS ONE PROMPT INTO A FINISHED PR your name shows up in this process exactly once - at the very end, for 5 minutes. spec → planner → 3 builders → critic → scribe → PR → back into the spec the planner runs once and the whole line inherits its decisions - a bad plan executed by five perfect agents is still a bad result. 3 lanes build in parallel - and they only work because the planner cut the steps so none of them reads another's output. tests loop until green and red goes back to the coder - the critic never sees a broken build. the critic rejects to the plan, not the code - tests ask does it run, the critic asks should it exist. the scribe writes the PR from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. the back edge into the spec is the whole trick - today's failures become tomorrow's constraints without you typing them. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save this and read the full graph engineering course below ↓

Hanako

164,222 次观看 • 1 个月前