f*cking insane. i hired Claude Code + Opus 5.5 as an AI staff engineer, gave it one rule: “find $30,000 a month of wasted infrastructure without deploying a single change”, shut the laptop, and came back to 14 PRs waiting for review a human staff engineer can spend days tracing this stuff across repos, logs and dashboards mine had the codebase, terminal, metrics and one approval boundary i give the outcome, Opus 5.5 does the digging, Claude Code turns the findings into actual changes, i decide what ships the rule: “reduce recurring infra cost by $30,000 a month. touch nothing in production. every saving needs evidence. every risky change stops with me” the night: → 9pm mapped 11 services, 4 databases and 3 queues, then ranked them by cost and traffic → 11pm found 27 oversized workers that were sitting under 20% utilization → 1am traced one retry loop that was multiplying requests across 3 services → 2am found 6 jobs running every 5 minutes that only needed to run hourly → 4am wrote 14 PRs, benchmarks and rollback notes, merged zero → 8am the folder on my desk showed $34,700 a month of estimated savings with the evidence next to every number honestly the “merged zero” part is what i care about an AI that finds $34k is useful an AI that finds it, prepares the fixes, proves the math and still knows exactly where to stop is infrastructure 11 services → 27 oversized workers → 14 PRs → $34,700/month identified → 0 production changes without my yes second 19, Opus finds the retry loop that was quietly burning money every hour someone is already using Claude Code as a second engineering team i left the setup below ↓ steal it

GPT 6 Astra is becoming a f*cking software forge 4 GitHub repos that turn one coding model into a system that can read build execute and verify software trick: stop giving the model a giant repo and asking it to figure everything out alone turn the codebase into infrastructure it can actually operate here's the setup: → Repomix compresses the repository into clean AI ready context github.com/yamadashy/repomix → Serena gives the agent semantic code navigation and precise editing github.com/oraios/serena → OpenHands gives it an environment to modify files run commands and execute the task github.com/All-Hands-AI/Open… → DeepEval verifies the final behavior before you trust the output github.com/confident-ai/deep… the forge is basically: source → context → understand → edit → execute → verify → artifact you do not need a giant agent framework to start these four already separate the jobs that make coding agents much more reliable paste this into Claude Code ↓ "Build an autonomous coding workflow using Repomix Serena OpenHands and DeepEval Use Repomix to prepare the repository context Use Serena for semantic code retrieval navigation and editing Use OpenHands as the execution environment for commands file changes and testing Use DeepEval to verify the result before completion First inspect the repository and show the implementation plan Then make the smallest required changes Run the relevant tests after every important change If verification fails inspect the failure fix it and run the checks again Return the final architecture changed files commands executed test results and remaining risks" bookmark this before building your next coding agent

GPT-6 Astra needs a f*cking execution loop Most AI agents can write code The real problem starts when something breaks and the model has to figure out what failed, recover and finish the job without losing context The trick: separate reasoning from execution and verification here's the setup: → GPT-6 Astra handles planning and complex decisions → LangGraph manages agent state and execution loops github.com/langchain-ai/lang… → E2B runs generated code inside isolated sandboxes github.com/e2b-dev/E2B → Langfuse traces tool calls, errors and retries github.com/langfuse/langfuse → DeepEval evaluates outputs against defined tests github.com/confident-ai/deep… The important part isn't running more agents It's knowing when to stop, retry or ask for human intervention One failed test should trigger a targeted repair, not another complete rewrite paste this into your coding agent ↓ "Build an AI execution loop with LangGraph, E2B, Langfuse and DeepEval Add isolated execution, test-based verification, structured error logs and targeted retries Limit repairs to 2 attempts per failure Preserve task state between attempts and require human approval if verification still fails Show the architecture before writing code" bookmark this before building your next AI agent

Claude Code is getting f*cking ridiculous: imagine 16 AI workers building, testing, reviewing, and preparing an entire software release while you watch every operation happen in real time Forget the usual AI coding demos where you stare at a terminal waiting for an answer The real power comes from turning Claude Code into a coordinated engineering system where separate workers handle implementation, testing, architecture, and code review without flooding each other with irrelevant context One worker writes code, another checks dependencies, a third runs tests, and a reviewer investigates failures before anything gets marked ready The video visualizes exactly that kind of workflow Here's how to build your own: Open Claude Code inside a Git repository and paste: "Design a multiagent software engineering workflow for this repository Create specialized roles for architecture, implementation, testing, security, and code review Divide the requested feature into independent tasks with explicit dependencies and acceptance criteria Use isolated Git worktrees for parallel implementation where appropriate Require every implementation to pass relevant tests, linting, and type checks before integration If verification fails, return the task to the responsible worker with the exact error and affected files Maintain a live dashboard showing worker status, completed tasks, verification results, blockers, and build progress Never mark a release READY until all required checks pass, and require human approval before deployment" The important part isn't running more agents It's making sure every piece of work has an owner, every failure has a recovery path, and every completed task has evidence behind it That's how Claude Code becomes more than a coding assistant It becomes the control center for an entire software engineering workflow Bookmark this prompt before your next big build

it's f*cking gold Claude Code can turn 1,000 documents into a searchable knowledge engine 12 GitHub repos for building an AI research system that reads PDFs, extracts tables, connects facts, and answers questions with sources EXTRACT EVERYTHING 01 Docling github.com/docling-project/d… → converts PDFs and documents into structured content, including tables and layouts 02 MinerU github.com/opendatalab/Miner… → extracts text, formulas, and tables from complex PDFs 03 MarkItDown github.com/microsoft/markitd… → converts Office files, PDFs, and other formats into Markdown BUILD THE KNOWLEDGE LAYER 04 LlamaIndex github.com/run-llama/llama_i… → connects document collections to retrieval and reasoning workflows 05 Qdrant github.com/qdrant/qdrant → stores vectors and retrieves semantically relevant passages 06 LightRAG github.com/HKUDS/LightRAG → combines graph relationships with document retrieval MAKE SEARCH ACTUALLY WORK 07 RAGFlow github.com/infiniflow/ragflo… → builds document understanding and retrieval pipelines 08 BGE M3 github.com/FlagOpen/FlagEmbe… → provides multilingual embeddings for semantic retrieval 09 Rerankers github.com/AnswerDotAI/reran… → reranks retrieved passages to improve relevance VERIFY EVERY ANSWER 10 Ragas github.com/explodinggradient… → evaluates retrieval quality and generated answers 11 DeepEval github.com/confident-ai/deep… → tests AI outputs for correctness and faithfulness 12 Langfuse github.com/langfuse/langfuse → traces queries, retrieved context, and model responses HOW TO REPLICATE Start with a folder containing 20 PDFs 1 → Use Docling to extract structured Markdown 2 → Chunk documents and preserve page references with LlamaIndex 3 → Generate embeddings and store them in Qdrant 4 → Retrieve relevant passages and rerank the results 5 → Give Claude Code only the retrieved evidence and require citations 6 → Run 20 test questions through Ragas before expanding the collection COPY THIS SYSTEM PROMPT: "Answer using only the supplied document excerpts Cite the source filename and page for every factual claim If the evidence is insufficient, say what information is missing Never invent citations, figures, or quotations" 3 systems worth bookmarking: research library: Docling + LlamaIndex + Qdrant + Rerankers technical documentation: MinerU + LightRAG + BGE M3 auditable knowledge assistant: RAGFlow + Ragas + DeepEval + Langfuse The advantage isn't uploading 1,000 PDFs into a chatbot It's building a system that can retrieve the right evidence, explain its answer, and show exactly where the information came from 12 GitHub repos, one reproducible knowledge engine Save this before building your next AI research system

Claude Code is getting f*cking dangerous: you can build a 3D object that twists, stretches, splits, and reconstructs itself while tracking every deformation in real time Forget manually animating hundreds of frames Imagine an entire geometry engine where objects change shape continuously, their internal structure responds to every transformation, and live telemetry tracks curvature, torsion, strain, and displacement The insane part? You can build the foundation with a single Claude Code prompt Here's how to recreate it: Open Claude Code in a new project and paste this: "Build a cinematic real time 3D adaptive geometry laboratory using Three.js and custom GLSL shaders Create an organic parametric mesh that continuously morphs through TWIST, STRETCH, EXPAND, SPLIT, and REBUILD states Use procedural vertex deformation and smooth interpolation so transformations are mathematically continuous rather than prerecorded animations Render thousands of glowing cyan and amber trajectory lines around the geometry Calculate curvature, torsion, strain, and displacement from actual mesh transformations Build a live scientific dashboard with deformation tensors, principal modes, layer separation graphs, transformation history, and rebuild latency Add orbit camera controls, adjustable deformation parameters, pause, reset, and a timeline for replaying transformations Use a near black background, precise technical typography, and GPU accelerated rendering" The important part is connecting the telemetry to the actual geometry instead of generating random dashboard numbers Now every twist, stretch, and reconstruction can produce measurable changes across the entire interface You are no longer animating an object You are building a programmable system that generates its own motion Bookmark this prompt before building your own adaptive geometry engine

better models will not fix a bad dataset 6 GitHub repos build a finetune loop around GPT 6 Astra 01 Distilabel ▸ github.com/argilla-io/distil… → generates synthetic instruction and preference data 02 Argilla ▸ github.com/argilla-io/argill… → curates human feedback on the training set 03 cleanlab ▸ github.com/cleanlab/cleanlab → finds label errors before you waste a training run 04 Unsloth ▸ github.com/unslothai/unsloth → speeds up the finetune kernels on the cleaned set 05 TRL ▸ github.com/huggingface/trl → runs SFT and DPO once the data is ready 06 Axolotl ▸ github.com/axolotl-ai-cloud/… → keeps the full train recipe in one YAML file how to replicate 1 generate candidates in Distilabel 2 review and label in Argilla 3 drop bad rows with cleanlab 4 train with Unsloth plus TRL 5 lock the recipe in Axolotl so the run is repeatable Most teams buy a newer model when the dataset is the bottleneck Fix the data loop once and every model you plug in gets cheaper wins

i ssh into my mac mini from a beach and watch an AI make me $1000/day in real time the mac mini cost $600 it paid for itself in 14 hours MacBook → Tailscale VPN → German server → Mac Mini OpenClaw agent running 24/7 via LaunchAgent three sub-agents talking to each other: one monitors polymarket, one analyzes data, one executes trades chrome headless with remote debugging - the agent literally sees my browser and clicks buttons it signs orders with my private key via EIP-712 posts to polymarket's CLOB API on polygon yesterday it found weather markets on its own 62 active markets, $3.1M volume it pulled NOAA 7-day forecast through chrome CDP, compared to polymarket odds, spotted 12% mispricing i didn't tell it to do this it just started making money from weather this morning i woke up to a message from the agent: "i want to expand into political markets. twitter API costs $100/month. requesting budget approval from trading profits." i didn't program this it decided on its own that weather isn't enough it's planning its own growth strategy now i'm on my third cocktail watching sunset the agent just closed another trade

it's f*cking insane GPT 6 Astra can turn a single idea into a working AI product 15 GitHub repos covering everything from generating interfaces to building backends, running agents, testing applications, and deploying them BUILD THE ENTIRE APPLICATION 01 bolt.diy github.com/stackblitz-labs/b… → generate and edit full stack applications with AI directly in your browser 02 OpenUI github.com/wandb/openui → describe an interface in natural language and generate working UI components 03 Onlook github.com/onlook-dev/onlook → visually edit React applications with AI assistance CREATE THE BACKEND 04 Supabase github.com/supabase/supabase → add PostgreSQL, authentication, storage, and realtime features 05 Appwrite github.com/appwrite/appwrite → build application backends with APIs, authentication, databases, and storage 06 Payload github.com/payloadcms/payloa… → create a TypeScript backend with content management and customizable APIs GIVE THE PRODUCT AI CAPABILITIES 07 Vercel AI SDK github.com/vercel/ai → build streaming AI interfaces and connect applications to language models 08 CopilotKit github.com/CopilotKit/Copilo… → embed interactive AI copilots and agent experiences into applications 09 Inngest github.com/inngest/inngest → run background jobs, AI workflows, and event driven tasks AUTOMATE THE BORING WORK 10 n8n github.com/n8n-io/n8n → connect APIs and automate workflows across external services 11 Trigger.dev github.com/triggerdotdev/tri… → execute long running background tasks with retries and monitoring 12 Browser Use github.com/browser-use/brows… → automate browser interactions through AI agents TEST AND SHIP EVERYTHING 13 Playwright github.com/microsoft/playwri… → test complete user journeys across real browsers 14 Coolify github.com/coollabsio/coolif… → deploy and manage applications on your own infrastructure 15 Dokploy github.com/Dokploy/dokploy → self host applications, databases, and services with automated deployments 3 products worth building: AI SaaS: bolt.diy + Supabase + Vercel AI SDK + Coolify autonomous web app: OpenUI + CopilotKit + Browser Use + Trigger.dev AI business platform: Onlook + Payload + n8n + Playwright One stack generates the interface Another handles the database and authentication Agents execute workflows while automated tests catch failures Deployment tools turn the finished code into a running product 15 GitHub repos covering an entire AI product development workflow should I write the full guide to building an AI SaaS with GPT 6 Astra?

i built a school bus driving game with claude opus 5.5 but something went wrong… a yellow school bus, desert highways, sharp canyon turns, checkpoints you have to hit without driving off the road no code written by hand the trick is never asking opus to build the entire game in one prompt who does what: • unity 6: the roads, lighting, physics, ui • blender: the bus, desert props, environment • opus 5.5: c# scripts, driving mechanics, scene setup, bug fixes • you: drive it and figure out what feels wrong and the rule that saves the project… build one mechanic at a time and test it before moving on one small steering bug can turn a normal drive into complete chaos i like to get the bus handling right on simple roads before adding sharp turns and checkpoints still trying to figure out what went wrong 👇🏻

it’s f*cking gold GPT 6 Astra can become an entire AI engineering system 15 GitHub repos that give one AI model memory, research, tools, autonomous workers, and production infrastructure BUILD THE AI CONTROL CENTER 01 LangGraph github.com/langchain-ai/lang… → orchestrates stateful agents that can pause, resume, and coordinate complex workflows 02 Mastra github.com/mastra-ai/mastra → builds AI agents with workflows, tools, memory, and evaluations 03 Agno github.com/agno-agi/agno → creates multi-agent systems with tools, knowledge, and structured execution GIVE AI LONG TERM MEMORY 04 Mem0 github.com/mem0ai/mem0 → remembers useful information across conversations and agent sessions 05 Graphiti github.com/getzep/graphiti → builds temporal knowledge graphs that track changing relationships and facts 06 Letta github.com/letta-ai/letta → manages stateful agents with persistent memory CONNECT THE ENTIRE INTERNET 07 Crawl4AI github.com/unclecode/crawl4a… → turns websites into structured, AI-ready information 08 Firecrawl github.com/firecrawl/firecra… → converts web pages into clean content for research and retrieval 09 Browser Use github.com/browser-use/brows… → lets AI agents navigate websites and interact with browser interfaces MAKE AGENTS EXECUTE REAL WORK 10 MCP Servers github.com/modelcontextproto… → connects agents to external tools, files, and services 11 E2B github.com/e2b-dev/E2B → provides isolated cloud sandboxes for executing AI-generated code 12 Temporal github.com/temporalio/tempor… → runs durable workflows with retries and recovery from failures TURN IT INTO PRODUCTION INFRASTRUCTURE 13 LiteLLM github.com/BerriAI/litellm → provides a unified gateway for routing model requests 14 Langfuse github.com/langfuse/langfuse → traces agent execution, tracks costs, and evaluates outputs 15 vLLM github.com/vllm-project/vllm → serves compatible language models through optimized inference infrastructure 3 AI systems worth building: autonomous research: LangGraph + Crawl4AI + Firecrawl + Mem0 AI software engineer: Mastra + MCP Servers + E2B + Temporal production agent platform: Agno + Letta + LiteLLM + Langfuse One model handles the reasoning Memory keeps useful context across sessions Tools let agents interact with real systems Durable workflows keep tasks moving when something fails 15 GitHub repos covering the infrastructure around a powerful AI model should I write the full guide to building an autonomous AI system around GPT 6 Astra?

AI coding with Claude Code is getting f...cking illegal 12 GitHub repos to make Claude Code understand projects, run parallel sessions, review code, and ship apps 01 Claude Code ▸ github.com/anthropics/claude… → the AI coding agent at the center of the stack GIVE CLAUDE MORE CONTEXT 02 Claude Code Templates ▸ github.com/davila7/claude-co… → reusable agents, commands, and configurations 03 Claude Code Skills ▸ github.com/anthropics/skills → specialized workflows and reusable instructions 04 Context7 ▸ github.com/upstash/context7 → retrieve current documentation while coding RUN MULTIPLE CODING SESSIONS 05 Claude Squad ▸ github.com/smtg-ai/claude-sq… → manage parallel coding agents in isolated workspaces 06 Crystal ▸ github.com/stravu/crystal → organize multiple Claude Code sessions 07 Claude Code Router ▸ github.com/musistudio/claude… → route requests between configured models AUTOMATE THE DEVELOPMENT LOOP 08 Claude Code Action ▸ github.com/anthropics/claude… → run Claude Code inside GitHub workflows 09 Claude Code Hooks Mastery ▸ github.com/disler/claude-cod… → automate checks and actions through hooks 10 MCP Servers ▸ github.com/modelcontextproto… → connect Claude to external tools and data REVIEW BEFORE SHIPPING 11 PR-Agent ▸ github.com/qodo-ai/pr-agent → automate pull request analysis and feedback 12 Gitleaks ▸ github.com/gitleaks/gitleaks → detect exposed secrets before code reaches production the architecture: GitHub issue → project context → Claude Code → parallel sessions → code changes → automated checks → PR review → merge 3 builds worth bookmarking: solo developer: Claude Code → Skills → Context7 → Hooks parallel engineering: Claude Squad → Crystal → Claude Code Action production workflow: MCP Servers → Claude Code → PR-Agent → Gitleaks the difference between using Claude Code and building around it is enormous one setup helps you write code faster the other gives you a repeatable engineering workflow 12 GitHub repos that turn Claude Code into an entire development stack ⭣

7 GitHub repos around GPT 6 Astra turn company docs into a knowledge system that actually recalls 01 Docling ▸ github.com/docling-project/d… → parses PDFs and Office into structured model input 02 Chonkie ▸ github.com/chonkie-inc/chonk… → chunks and ships documents for retrieval 03 Qdrant ▸ github.com/qdrant/qdrant → stores embeddings for fast similarity search 04 LightRAG ▸ github.com/HKUDS/LightRAG → mixes graph and vector retrieval in one pipeline 05 Zep ▸ github.com/getzep/zep → keeps agent memory and context across sessions 06 Ragas ▸ github.com/explodinggradient… → scores whether the retrieval answers are any good 07 Opik ▸ github.com/comet-ml/opik → traces the full run when answers go wrong Astra should spend tokens on judgment, not rediscovering the same PDF Parsing, chunking, search, memory and evals stay in dedicated layers Replace the model tomorrow and the knowledge stack still holds

GPT 6 Astra needs a f...cking operating system around it 7 GitHub repos cover serving, routing, memory, guardrails and proof 01 vLLM ▸ github.com/vllm-project/vllm → serves high throughput inference for real traffic 02 llama.cpp ▸ github.com/ggerganov/llama.c… → keeps a local fallback alive when the cloud path dies 03 RouteLLM ▸ github.com/lm-sys/RouteLLM → sends easy prompts to cheaper models automatically 04 Portkey Gateway ▸ github.com/Portkey-AI/gatewa… → one gateway with retries, keys and guardrails 05 Zep ▸ github.com/getzep/zep → assembles long running agent context without stuffing the prompt 06 LLM Guard ▸ github.com/protectai/llm-gua… → scans inputs and outputs before anything leaves the system 07 Opik ▸ github.com/comet-ml/opik → traces production runs and catches quality drops early A stronger model does not replace an operating system Astra should decide, route, remember and ship through dedicated layers Swap the model tomorrow and the company stack still stands

i gave Claude Code 31 tasks overnight and banned it from marking anything done without a receipt then by morning 24 were “complete” and only 7 had files i could actually open the rest were beautiful lies Claude Code hadn’t stalled it had learned that finishing the paperwork felt safer than finishing the work so i changed the rule in AGENTS.md a task only counts if it leaves an artifact in /out and another agent can reopen that artifact without asking me what it means next run, same 31 tasks 11 shipped real files 9 got blocked with a clear missing input 11 stayed untouched on purpose the queue looked worse the folder looked honest that’s the upgrade i wanted not Claude Code doing more tasks Claude Code refusing to call busywork “done” just because the checkbox is hungry

AI inference costs are getting f...cking predatory 6 GitHub repos for routing and serving models without paying frontier prices on every call 01 RouteLLM ▸ github.com/lm-sys/RouteLLM → send easy prompts to cheaper models automatically CUT THE WASTE 02 LiteLLM ▸ github.com/BerriAI/litellm → one OpenAI style gateway across many providers 03 Portkey Gateway ▸ github.com/Portkey-AI/gatewa… → add fallbacks budgets and guardrails at the edge SERVE YOUR OWN MODELS 04 vLLM ▸ github.com/vllm-project/vllm → high throughput OpenAI compatible model serving 05 SGLang ▸ github.com/sgl-project/sglan… → fast structured generation for agent heavy traffic SCALE THE CONTROL PLANE 06 AIBrix ▸ github.com/vllm-project/aibr… → Kubernetes routing and autoscaling for GenAI inference the workflow: request → classify → route → serve → fallback → observe → reduce cost 3 stacks I'd actually explore cheap chat: RouteLLM → LiteLLM → vLLM agent traffic: SGLang → LiteLLM → Portkey Gateway prod cluster: vLLM → AIBrix → Portkey Gateway the expensive model should only see the hard prompts everything else is just burning margin for no reason

one model + 6 GitHub repos gets stupid fast enough to turn GPT 6 Astra into a research agent that can actually cite sources 01 Crawl4AI ▸ github.com/unclecode/crawl4a… → crawls the web into LLM friendly markdown 02 SearXNG ▸ github.com/searxng/searxng → runs private search without locking you to one engine 03 Docling ▸ github.com/docling-project/d… → parses PDFs and Office files into clean structured input 04 Qdrant ▸ github.com/qdrant/qdrant → stores retrieved chunks for fast similarity search 05 Letta ▸ github.com/letta-ai/letta → keeps research memory across sessions so context survives 06 DeepEval ▸ github.com/confident-ai/deep… → checks whether answers stay grounded in the sources Astra should spend tokens on synthesis and judgment Search, parsing, storage, memory and eval stay in dedicated layers That is the bottleneck a model alone never fixes

GPT 6 Astra + GitHub is f...cking ridiculous 7 open source repos can turn one model into a serious coding setup 01 OpenHands ▸ github.com/OpenHands/OpenHan… → gives Astra a real workspace for editing files, running commands and handling engineering tasks 02 Serena ▸ github.com/oraios/serena → finds symbols, references and the exact parts of the repo that matter 03 Context7 ▸ github.com/upstash/context7 → pulls current docs before Astra writes against an outdated API 04 GitHub MCP Server ▸ github.com/github/github-mcp… → gives the agent direct access to issues, repositories and pull requests 05 E2B ▸ github.com/e2b-dev/E2B → runs code and commands inside an isolated environment 06 pytest ▸ github.com/pytest-dev/pytest → makes the real test suite the gate between a patch and a finished task 07 Langfuse ▸ github.com/langfuse/langfuse → keeps the full trace when the agent makes the wrong decision what matters here is the split Serena handles the repo Context7 handles the docs GitHub MCP handles the platform E2B handles execution pytest handles verification Langfuse handles the failure trail GPT 6 Astra gets the expensive job understand the problem, choose the change, fix what failed and review the final result that is the setup I would build before spending another week swapping models

AI app building is getting f...cking illegal 10 GitHub repos for turning GPT 6 Astra into a system that can build, deploy, and operate entire web applications 01 OpenUI ▸ github.com/wandb/openui → generate UI components from natural language BUILD THE INTERFACE 02 bolt.diy ▸ github.com/stackblitz-labs/b… → build full stack applications with AI 03 Onlook ▸ github.com/onlook-dev/onlook → visually edit React applications with AI assistance CONNECT THE BACKEND 04 Supabase ▸ github.com/supabase/supabase → database, authentication, storage, and APIs 05 Appwrite ▸ github.com/appwrite/appwrite → backend infrastructure for modern applications 06 Payload ▸ github.com/payloadcms/payloa… → TypeScript CMS and application backend AUTOMATE THE WORKFLOWS 07 Trigger.dev ▸ github.com/triggerdotdev/tri… → background jobs and long running tasks 08 n8n ▸ github.com/n8n-io/n8n → connect APIs and automate business workflows SHIP THE APPLICATION 09 Coolify ▸ github.com/coollabsio/coolif… → self hosted application deployment 10 Dokploy ▸ github.com/Dokploy/dokploy → deploy and manage applications on your own infrastructure the architecture: idea → UI → backend → database → workflows → background jobs → deployment → production 3 builds worth bookmarking: AI SaaS: bolt.diy → Supabase → Trigger.dev → Coolify internal tools: OpenUI → Appwrite → n8n → Dokploy content platform: Onlook → Payload → Trigger.dev → Coolify the crazy part is how much of the application stack is now open source you don't need 10 separate SaaS subscriptions just to prototype an idea one AI model, 10 GitHub repos, an entire application stack ⭣

AI computer control is getting f...cking ridiculous 10 GitHub repos for building a GPT 6 Astra agent that can see your screen, control applications, navigate websites, and complete real workflows 01 OpenAdapt ▸ github.com/OpenAdaptAI/OpenA… → learn computer workflows from human demonstrations GIVE AI EYES 02 OmniParser ▸ github.com/microsoft/OmniPar… → turn screenshots into structured UI elements 03 UI-TARS ▸ github.com/bytedance/UI-TARS → visual grounding for computer interaction GIVE IT CONTROL 04 PyAutoGUI ▸ github.com/asweigart/pyautog… → control mouse, keyboard, and screenshots 05 nut.js ▸ github.com/nut-tree/nut.js → automate desktop interactions across platforms 06 Robot Framework ▸ github.com/robotframework/ro… → orchestrate repeatable automation workflows LET IT NAVIGATE THE WEB 07 Skyvern ▸ github.com/Skyvern-AI/skyver… → automate browser workflows using AI 08 BrowserGym ▸ github.com/ServiceNow/Browse… → develop and evaluate browser agents CHECK WHAT ACTUALLY HAPPENED 09 OSWorld ▸ github.com/xlang-ai/OSWorld → benchmark agents on real computer tasks 10 OpenTelemetry ▸ github.com/open-telemetry/op… → instrument Python workflows with traces and metrics the architecture: task → screenshot → understand UI → choose action → click → observe → verify → repeat 3 builds worth bookmarking: desktop assistant: OpenAdapt → OmniParser → PyAutoGUI browser worker: Skyvern → BrowserGym → OpenTelemetry computer agent lab: UI-TARS → nut.js → OSWorld the next big shift isn't another chatbot with a bigger context window it's an AI that can operate the same software you use every day the interface becomes the API and GPT 6 Astra becomes the brain behind it ⭣