Promix @Promixyz
I notice everything when others are still asleeping Washington, USA Joined September 2026-
Tweets66
-
Followers770
-
Following33
-
Likes644
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-lab… → 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/onl… → visually edit React applications with AI assistance CREATE THE BACKEND 04 Supabase github.com/supabase/supab… → add PostgreSQL, authentication, storage, and realtime features 05 Appwrite github.com/appwrite/appwr… → build application backends with APIs, authentication, databases, and storage 06 Payload github.com/payloadcms/pay… → 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/Cop… → 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/… → execute long running background tasks with retries and monitoring 12 Browser Use github.com/browser-use/br… → automate browser interactions through AI agents TEST AND SHIP EVERYTHING 13 Playwright github.com/microsoft/play… → test complete user journeys across real browsers 14 Coolify github.com/coollabsio/coo… → 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/l… → orchestrates stateful agents that can pause, resume, and coordinate complex workflows 02 Mastra github.com/mastra-ai/mast… → 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/craw… → turns websites into structured, AI-ready information 08 Firecrawl github.com/firecrawl/fire… → converts web pages into clean content for research and retrieval 09 Browser Use github.com/browser-use/br… → lets AI agents navigate websites and interact with browser interfaces MAKE AGENTS EXECUTE REAL WORK 10 MCP Servers github.com/modelcontextpr… → 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/tem… → 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/langf… → traces agent execution, tracks costs, and evaluates outputs 15 vLLM github.com/vllm-project/v… → 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/cla… → the AI coding agent at the center of the stack GIVE CLAUDE MORE CONTEXT 02 Claude Code Templates ▸ github.com/davila7/claude… → reusable agents, commands, and configurations 03 Claude Code Skills ▸ github.com/anthropics/ski… → specialized workflows and reusable instructions 04 Context7 ▸ github.com/upstash/contex… → retrieve current documentation while coding RUN MULTIPLE CODING SESSIONS 05 Claude Squad ▸ github.com/smtg-ai/claude… → 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/cla… → route requests between configured models AUTOMATE THE DEVELOPMENT LOOP 08 Claude Code Action ▸ github.com/anthropics/cla… → run Claude Code inside GitHub workflows 09 Claude Code Hooks Mastery ▸ github.com/disler/claude-… → automate checks and actions through hooks 10 MCP Servers ▸ github.com/modelcontextpr… → connect Claude to external tools and data REVIEW BEFORE SHIPPING 11 PR-Agent ▸ github.com/qodo-ai/pr-age… → automate pull request analysis and feedback 12 Gitleaks ▸ github.com/gitleaks/gitle… → 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-projec… → parses PDFs and Office into structured model input 02 Chonkie ▸ github.com/chonkie-inc/ch… → 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/explodinggradi… → 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/v… → serves high throughput inference for real traffic 02 llama.cpp ▸ github.com/ggerganov/llam… → 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/gat… → 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-… → 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/gat… → add fallbacks budgets and guardrails at the edge SERVE YOUR OWN MODELS 04 vLLM ▸ github.com/vllm-project/v… → high throughput OpenAI compatible model serving 05 SGLang ▸ github.com/sgl-project/sg… → fast structured generation for agent heavy traffic SCALE THE CONTROL PLANE 06 AIBrix ▸ github.com/vllm-project/a… → 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/craw… → 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-projec… → 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/d… → 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/Open… → 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/contex… → pulls current docs before Astra writes against an outdated API 04 GitHub MCP Server ▸ github.com/github/github-… → 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/pyt… → makes the real test suite the gate between a patch and a finished task 07 Langfuse ▸ github.com/langfuse/langf… → 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-lab… → build full stack applications with AI 03 Onlook ▸ github.com/onlook-dev/onl… → visually edit React applications with AI assistance CONNECT THE BACKEND 04 Supabase ▸ github.com/supabase/supab… → database, authentication, storage, and APIs 05 Appwrite ▸ github.com/appwrite/appwr… → backend infrastructure for modern applications 06 Payload ▸ github.com/payloadcms/pay… → TypeScript CMS and application backend AUTOMATE THE WORKFLOWS 07 Trigger.dev ▸ github.com/triggerdotdev/… → 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/coo… → 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/Op… → learn computer workflows from human demonstrations GIVE AI EYES 02 OmniParser ▸ github.com/microsoft/Omni… → turn screenshots into structured UI elements 03 UI-TARS ▸ github.com/bytedance/UI-T… → visual grounding for computer interaction GIVE IT CONTROL 04 PyAutoGUI ▸ github.com/asweigart/pyau… → 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… → orchestrate repeatable automation workflows LET IT NAVIGATE THE WEB 07 Skyvern ▸ github.com/Skyvern-AI/sky… → automate browser workflows using AI 08 BrowserGym ▸ github.com/ServiceNow/Bro… → develop and evaluate browser agents CHECK WHAT ACTUALLY HAPPENED 09 OSWorld ▸ github.com/xlang-ai/OSWor… → benchmark agents on real computer tasks 10 OpenTelemetry ▸ github.com/open-telemetry… → 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 ⭣
GPT 6 Astra + GitHub is getting ridiculous 8 open source repos turn it into a real coding system 01 LangGraph ▸ github.com/langchain-ai/l… → stateful loops for long coding jobs 02 Docling ▸ github.com/docling-projec… → turns PDFs and office docs into text 03 Crawl4AI ▸ github.com/unclecode/craw… → scrapes live docs into markdown 04 Qdrant ▸ github.com/qdrant/qdrant → retrieves the right repo chunks 05 Graphiti ▸ github.com/getzep/graphiti → keeps project knowledge over time 06 FastMCP ▸ github.com/PrefectHQ/fast… → turns local tools into MCP servers 07 smolagents ▸ github.com/huggingface/sm… → agents that write actions as code 08 Phoenix ▸ github.com/Arize-ai/phoen… → shows what broke in a failed run Astra should spend tokens on the hard judgment calls Docs, retrieval, memory, tools and traces stay in dedicated layers bookmart the list of paper right now
i gave GPT-6 Astra 32 agents one task and banned them from agreeing too early then in 11 minutes they produced 68 competing decisions, killed 51 of them, and still couldn’t agree on the final answer that was exactly what i wanted the strongest agents weren’t the ones producing more ideas they were the ones finding reasons other agents were wrong bad sources got killed weak assumptions got killed confident answers with no evidence got killed by the final round, only 3 paths were left then i forced those 3 to attack each other again one survived i used to think better AI meant getting the right answer faster now i’d rather build a system where the wrong answer has to survive 67 chances to die
AI agents are getting f...cking dangerous with real data 9 GitHub repos for building a GPT 6 Astra system that can explore databases, write SQL, run analysis, and turn raw numbers into decisions 01 Vanna ▸ github.com/vanna-ai/vanna → natural language questions into SQL queries GIVE AI ACCESS TO DATA 02 DB-GPT ▸ github.com/eosphoros-ai/D… → build AI applications around private databases 03 MindsDB ▸ github.com/mindsdb/mindsdb → connect AI workflows to enterprise data MAKE THE DATA USABLE 04 DuckDB ▸ github.com/duckdb/duckdb → run analytical SQL directly on local files 05 Polars ▸ github.com/pola-rs/polars → process large datasets with fast DataFrame operations 06 DataFusion ▸ github.com/apache/datafus… → build custom SQL query engines TURN ANALYSIS INTO ACTION 07 PandasAI ▸ github.com/sinaptik-ai/pa… → ask questions about datasets in natural language 08 Evidence ▸ github.com/evidence-dev/e… → turn SQL queries into interactive reports 09 Metabase ▸ github.com/metabase/metab… → explore metrics and build dashboards the architecture: question → schema → SQL → query → validate → analyze → visualize → decision 3 builds worth stealing: finance: Vanna → DuckDB → Evidence business: MindsDB → Polars → Metabase research: DB-GPT → DataFusion → PandasAI here's what makes this interesting most AI agents can explain your business very few can actually inspect the numbers behind it GPT 6 Astra + the right GitHub stack changes what you can build the next useful AI assistant won't just answer questions it'll know where to look for the answer ⭣
AI agents are getting f...cking illegal 10 open-source GitHub repos for making GPT 6 Astra spend less compute without making the system dumber 01 LiteLLM ▸ github.com/BerriAI/litellm → route requests across models through one gateway ROUTE THE EASY WORK 02 RouteLLM ▸ github.com/lm-sys/RouteLLM → send simple prompts to cheaper models 03 Martian ▸ github.com/withmartian/ro… → benchmark which routing strategies actually work CACHE WHAT YOU ALREADY PAID FOR 04 GPTCache ▸ github.com/zilliztech/GPT… → reuse previous LLM responses 05 LMCache ▸ github.com/LMCache/LMCache → reuse KV cache across inference requests MAKE INFERENCE FASTER 06 SGLang ▸ github.com/sgl-project/sg… → high performance serving for LLM workloads 07 vLLM ▸ github.com/vllm-project/v… → high throughput model serving RUN SMALLER WHEN YOU CAN 08 llama.cpp ▸ github.com/ggml-org/llama… → run quantized models locally 09 bitsandbytes ▸ github.com/bitsandbytes-f… → lower precision inference and training WATCH THE BILL 10 OpenLLMetry ▸ github.com/traceloop/open… → trace LLM calls, latency, and usage the efficiency loop: request → classify → route → cache → infer → measure → optimize the expensive architecture sends every problem to the smartest model the better one knows when GPT 6 Astra is actually needed more intelligence ⭣
GPT 6 Astra gets f...cking treasure when one agent stops doing everything 7 GitHub repos for building the switchyard around it 01 langchain ai/langgraph ▸ github.com/langchain-ai/l… → route tasks through stateful workflows where different nodes can parse, plan, execute, inspect, and continue GitHub ONE AGENT IS DOING 4 JOBS 02 microsoft/agent framework ▸ github.com/microsoft/agen… → build specialized agents and orchestrate them as workflows instead of forcing one model through every stage GitHub OUTPUT HAS NO SHAPE 03 pydantic/pydantic ai ▸ github.com/pydantic/pydan… → constrain tools, dependencies, instructions, and structured outputs so each worker has a defined contract GitHub THE AGENT CANNOT TOUCH REAL SYSTEMS 04 ComposioHQ/composio ▸ github.com/ComposioHQ/com… → gives agents 1000+ toolkits with auth, sessions, triggers, search, and execution GitHub BUILD NEEDS A REAL WORKSPACE 05 OpenHands/OpenHands ▸ github.com/OpenHands/Open… → turns coding agents into workers that can operate across repositories, commands, tasks, and development environments GitHub THE NEXT WORKER FORGOT THE LAST ONE 06 mem0ai/mem0 ▸ github.com/mem0ai/mem0 → adds persistent memory so useful state can survive beyond a single model call or session GitHub 10 TASKS FINISHED BUT YOU HAVE NO IDEA WHY 07 langfuse/langfuse ▸ github.com/langfuse/langf… → traces prompts, model calls, tools, retrieval, latency, and evaluations so the full execution rail stays inspectable GitHub the switchyard queue → parse → route → plan → build → verify → inspect → continue the useful part is not adding more agents it is giving every task a station, every station a contract, and every failure somewhere to go next GPT 6 Astra becomes much more useful when intelligence is shared across a system instead of trapped inside one giant loop
OPENAI JUST LAUNCHED GPT-6 ASTRA AND THE AGENT ARCHITECTURE IS F...CKING DIFFERENT one GitHub task now gets split across planning, research, execution, and verification while Astra keeps every return inside the same running state step 1 → the request enters the coordinator first, so Astra decides how the work should be divided before any agent starts touching the repo step 2 → the task splits into dedicated lanes for planning, research, build, and verification instead of forcing one agent to carry the entire workflow step 3 → research inspects the repo and returns useful context, so later actions start from actual project state instead of guessing what is inside the codebase step 4 → the build lane executes changes through tools and runners while the other lanes can keep working around the same task step 5 → every return feeds back into shared state, so discoveries, actions, and corrections become available to the rest of the swarm without restarting the job step 6 → routing, permissions, and budgets control what each lane can do, keeping specialized agents bounded instead of giving every agent unrestricted access step 7 → verification checks unit, integration, safety, and quality paths before work is allowed to move toward the final result step 8 → Astra merges the returns back into one execution path, so parallel work becomes a coordinated result instead of four isolated agent outputs i think the shared return path is the part people will underestimate most parallel agents matter much less if the system cannot keep their work synchronized
GPT 6 Astra + GitHub is getting f...cking ridiculous I’d build the entire coding loop around 8 open source repos 01 OpenHands ▸ github.com/All-Hands-AI/O… → takes the issue and runs the engineering task 02 Serena ▸ github.com/oraios/serena → finds symbols, references and the exact code Astra needs to touch 03 Context7 ▸ github.com/upstash/contex… → pulls current docs and examples before Astra writes against an outdated API 04 Browser Use ▸ github.com/browser-use/br… → researches anything missing from the repo or docs 05 E2B ▸ github.com/e2b-dev/E2B → runs the generated code inside an isolated sandbox 06 Ruff ▸ github.com/astral-sh/ruff → catches formatting, lint and common Python problems before testing 07 pytest ▸ github.com/pytest-dev/pyt… → checks whether the patch actually works 08 Langfuse ▸ github.com/langfuse/langf… → keeps the trace when the agent screws something up Give each of those jobs to a dedicated layer and the model gets one job Understand the problem and write the right change That is the stack I’d clone before trying another model
GPT-6 Astra + GitHub can replace a ridiculous amount of coding work 8 open-source repos for building an AI engineer that can enter a codebase and actually finish the job 01 Serena ▸ github.com/oraios/serena → map and navigate the codebase 02 Context7 ▸ github.com/upstash/contex… → pull current documentation 03 Repomix ▸ github.com/yamadashy/repo… → package the repo into useful context 04 LangGraph ▸ github.com/langchain-ai/l… → control the execution loop 05 OpenHands ▸ github.com/All-Hands-AI/O… → turn tasks into actual code changes 06 E2B ▸ github.com/e2b-dev/E2B → run everything inside isolated sandboxes 07 SWE-bench ▸ github.com/SWE-bench/SWE-… → test against real software issues 08 Langfuse ▸ github.com/langfuse/langf… → trace what the agent did and why the build: issue → understand → retrieve → plan → code → execute → test → inspect → retry → PR the valuable part isn't having 8 AI tools open it's connecting them so one GitHub issue can travel through the entire stack without a human rebuilding the context at every step ⭣
Thế Anh Bùi @ThAnhBi97382844
704 Followers 2K Following
Tirae @TnL2008
2 Followers 16 Following
Stephanie Goldstein @Stephan87413759
180 Followers 886 Following
Leon Le @leon_le49692
0 Followers 11 Following
coldfloat @coldfloatrp
10 Followers 50 Following ai systems in the real world. money when the numbers are real. the rest is noise.
El Viejo Nerd | IA si... @ElViejoNerd
29 Followers 199 Following IA, tecnología y cultura nerd sin humo. Vengo de cuando internet hacía ruido para conectarse. Te cuento lo nuevo con memoria de lo viejo.
Alter Ego @AlterEgo_eth
3K Followers 109 Following AI & ML Engineering From architectures to production systems Sharing what actually works Dm always are open
Volkan @VolkanTekicf3u
525 Followers 614 Following
Huy Vũ @PhanVanHuyVu
23 Followers 2K Following
hiro.kawa @hirkawas
1 Followers 5 Following
Sebastian Hardy | AI ... @sebastianhardy_
236 Followers 449 Following Scale your influence without being there. Helping you actually use AI. Then become a Limitless Human 👇
jamon pointer @jordenthepimp
1 Followers 19 Following
Hugo @Hugoxckochev
20 Followers 442 Following
chapman @chapman98181233
4 Followers 159 Following
Sink Positive @brebster1
108 Followers 712 Following BⒶT DⒶDDY BOOM - gegen Dosenfutter. DDD-Die Dicken Deutschen, haben kein Übergewicht, sondern jeder hat sein individuelles Neutralgewicht.
Genesis @frogbetgenesis
151 Followers 83 Following 150% Welcome Bonus | Quick Payouts | No KYC | 100 Free Spins @frogbetcom
duRArulz @duRArulz
0 Followers 6 Following
Rose @frogbetrose
143 Followers 69 Following No KYC & Instant Payouts • 150% Welcome Bonus + 100 Free Spins @frogbetcom
Gaara Sunagakure @kazekage91
0 Followers 7 Following
Kris @Kris_hyd
6 Followers 80 Following
Santiago Sánchez @visanto1
10 Followers 557 Following 💡 Exploring the world of Artificial Intelligence. AI ethics, and tech innovations. 📈 Sharing insights, news, and advancements 🚀 #AI #TechEnthusiast
Kudi @hayrat_jawhax
112 Followers 1K Following Every warrior has his battle fields where the legend of bravery comes from. This is what Kazakhstan means to me. I belong to here, it is my destiny.
Srini @SriniBlitz
0 Followers 19 Following
Luis Evangelista @luisfelv
85 Followers 243 Following 🇵🇪 #Peruvian dude 🖥 #Fullstack #developer 🐕 #Doglover 🍕 #Pizza monster ❄ #Winter man 🎸 #Guitar player
xavicrip @xavicrip
165 Followers 304 Following
Đức Lê @cL0861778913327
0 Followers 11 Following
AgentsAtWork @AgentsAtWorkHQ
4 Followers 21 Following AI agents doing real tasks. What works, what breaks, and how to actually use them. No hype, just field notes.
박호성 @Kresnik_00
2K Followers 3K Following 🎓Ph.D in Computer Science, 💻AI Engineer,In-house faculty @Samsung Fire&Marine Insurance 🏎️🚴♂️🤿 ✈️🇺🇸🇯🇵🇨🇳🇹🇷🇭🇷🇸🇪🇰🇭🇬🇧🇸🇬🇮🇩🇻🇳
. @mh_md67
8 Followers 342 Following
Cüneyt İnan @ArcCuneytinan
29 Followers 256 Following
Ajinkya Bhintade @ajibhin
30 Followers 165 Following
Mar @TchMarcin
12 Followers 327 Following
Open Source Daily @OpenS_Daily
0 Followers 32 Following aily open-source GitHub repos worth starring
nopanic @0518MOkCJZ1e44R
53 Followers 5K Following
Bartłomiej Pichalski @Namedix
156 Followers 460 Following Solo iOS dev building 0xCal, an AI calorie tracker. 180k+ meals logged. I test every new AI model on a real app and post the numbers.
Alter Ego @AlterEgo_eth
3K Followers 109 Following AI & ML Engineering From architectures to production systems Sharing what actually works Dm always are open
rompasso @0xrompasso
81 Followers 46 Following
Leonard Rodman @RodmanAi
35K Followers 864 Following Agentic AI builder. Loud opinions. Useful tools. 61k on LinkedIn • 41k on YouTube • Collabs DM or [email protected]
whaley @whaleyxbt
2K Followers 406 Following 18 // software engineer // ai workflows content creator // dms open
shmidt @shmidtqq
12K Followers 635 Following predicting the future with code / ai × finance / ex xAI / tg: shmidtqq
Mura @0x_mura
8K Followers 310 Following AI & Automation Creator | Vibe Coding | Making powerful tools feel effortless
marfin @marfinxx
5K Followers 294 Following ai enthusiast || full-time in tech || researcher || content creator
𝐌𝐈𝐌 ♡ @0xmim9
28K Followers 16K Following Web3 Learner & Builder || Content Creator || Ambassador @ActionModelAI @NewSightHQ || DM for Collabs
RR @RRweb3
33K Followers 17K Following AI • DeFi • RWA • NFT • Creator AMB @ZARGATES DM for collabs 📩
Master @Coinmaster100x
65K Followers 25K Following Kol // PR // BD || OTC || VC || Listing Manager || @OKX Creator || @EVEDEX // @Rivr_DEX Ambassador || CEO @lunalogisticllc || $SC & $retire whale.
Mikadzyki🌙 @Mikadzyki_NFT
7K Followers 3K Following Laugh,and the world laughs with you. Weep, and you weep alone.
Fokki @0x_fokki
15K Followers 1K Following AI & Tech Creator | Vibe Coding | Making AI simple, useful & exciting for everyone | DM for Collab
Fluixo @fluixoo
5K Followers 3K Following Marketing & AI | Founder @firsayai & @0penoffers | BD @frameonx | Building @PlayTerra_ | Amb @1inch
Roan @RohOnChain
80K Followers 423 Following building my life around AI agents, LLMs & quant systems for prediction markets + crypto
Grootex @Grootexbt
107 Followers 49 Following
Lummox @Lummox_eth
8K Followers 398 Following developing internet money / building interesting things / dm open
You might like
