Claude Code + GPT-6 fixed an async race condition with just 3 lines of code Two overlapping API requests were causing stale responses to overwrite newer results 3 failing tests → 6 passing tests → 0 failures Here's how to reproduce it 👇 step 1 → Inspect the request flow Give Claude Code your repo and let it trace API calls, async handlers, and state updates step 2 → Reproduce the race Trigger two overlapping requests Request 01 starts first but finishes last Request 02 starts later and finishes first The older response arrives and overwrites the newer result step 3 → Add a latest-request guard Assign each request an ID and track the most recent one Before updating the UI, check: if (id !== latest) return; Now outdated responses can't overwrite the latest request's result step 4 → Verify the fix Use GitHub Actions to run regression tests covering: → Single requests → Overlapping requests → Latest response wins → Stale response ignored → Backtracking → State isolation The demo ends with: 3 lines added 6 tests passing 0 failures The interesting part? The API wasn't broken The model wasn't hallucinating The application was trusting whichever response arrived last, even when it belonged to an older request Copy this prompt into Claude Code: "Inspect my async request flow for race conditions where older responses can overwrite newer results. Reproduce the failure with deterministic tests, implement the smallest safe latest-request guard, and verify that stale responses cannot update current UI state. Run the full test suite and show the diff." 3 lines can prevent an entire class of stale-response bugs Save this before your next AI-powered UI starts displaying outdated answers

Claude Code just got a 10x cheaper rival for 3D building Same rocket design prompt Two AI models Two very different price tags Claude Code vs Kimi 3.1 «Claude Code reaches $83 Kimi 3.1 reaches $8.30 Both generate a detailed metallic rocket scene Geometry, materials and assembly are visible side by side» The interesting part isn't just the price It's how much of the 3D production workflow can be handled by a cheaper AI model before you actually need to pay for a more expensive one If the output is good enough for your next iteration, the economics of AI assisted 3D creation start looking very different

Claude Code + Blender explained every part of a mechanical keyboard keycaps, switches, plate, pcb, foam, case. explode. fly through. knoll. (sound on)

Claude Code can turn a blank project into a 6v6 volleyball simulation, and the hardest part has nothing to do with drawing the players The real challenge is making 12 independent players react to the same ball, obey the rules and coordinate without scripting every rally BUILD → generate the court, net, player positions and live scoreboard PHYSICS → simulate ball trajectories, gravity, collisions and net interactions DECIDE → handle receiving, setting, attacking and defensive positioning SYNC → keep all 12 players reacting to the same simulation state VERIFY → detect illegal touches, out of bounds shots and scoring conditions A volleyball rally looks simple until you try to program one The receiver has to predict where the ball will land before it gets there The setter needs to choose a target while the opposing blockers reposition And the attacker has to time the jump against a moving ball, not a fixed animation Traditional game development often means adjusting movement, animation and physics separately until everything looks believable Claude Code can help generate the logic that connects those systems The breakthrough is not making 12 characters move It is making 12 independent decisions produce one believable rally Try building the foundation with Claude Code "Create a 6v6 volleyball simulation with autonomous players, realistic ball physics, three touch rules, receiving, setting, spiking, blocking and a live scoreboard Separate physics, player decisions, match rules and rendering into independent modules, with automated tests for scoring and ball contacts" Save this if you want to experiment with AI assisted game development

Claude Code is turning game development into a programming problem instead of a manual production pipeline The interesting part is not generating a game It is building the simulation, movement logic, camera system and interface as one connected piece of software BUILD → generate the pitch, player objects and game environment MOVE → define acceleration, collisions and ball physics through code THINK → give players positioning, passing and defensive behaviors RENDER → connect the simulation to a live camera and match interface The demo uses Liverpool and Real Madrid, but the teams are almost irrelevant What matters is the system underneath Traditional game development involves constant switching between scene editors, animation tools, physics settings and gameplay scripts Claude Code can help write and modify the code connecting those systems, so changing the movement rules does not mean manually rebuilding every interaction The hardest part is not drawing 22 players It is making the ball, players, decisions and game clock behave consistently in the same simulation The real shift is that you are no longer just asking AI to write game code You are using code to define an entire playable world Try building the foundation with Claude Code using this prompt "Create a top down football simulation with 22 players, two teams, ball physics, autonomous movement, passing logic, a live scoreboard and a camera that follows the action Separate the simulation state, decision logic and rendering into independent modules so each system can be modified without rewriting the others" Save this if you want to experiment with AI assisted game development

it's f*cking gold Claude Code can turn 20 motion design skills into an AI video studio 15 GitHub repos for building animations, rendering scenes, generating visuals, and exporting finished MP4s BUILD THE MOTION DESIGN BRAIN 01 Motion Design Agent Skills github.com/lottiefiles/motio… → reusable motion design skills for AI coding agents 02 Remotion github.com/remotion-dev/remo… → generate videos programmatically with React 03 Motion Canvas github.com/motion-canvas/mot… → create animated scenes with TypeScript ANIMATE THE INTERFACE 04 Motion github.com/motiondivision/mo… → animate UI elements, layouts, and transitions 05 GSAP github.com/greensock/GSAP → build precise timelines and complex motion sequences 06 Lottie Web github.com/airbnb/lottie-web → render vector animations in the browser BUILD CINEMATIC SCENES 07 Three.js github.com/mrdoob/three.js → create 3D environments, lighting, and camera movements 08 React Three Fiber github.com/pmndrs/react-thre… → build reusable 3D scenes with React 09 Theatre.js github.com/theatre-js/theatr… → animate cameras, objects, and scene properties with visual timelines GENERATE VISUAL ASSETS 10 SVG.js github.com/svgdotjs/svg.js → generate and manipulate vector graphics 11 Fabric.js github.com/fabricjs/fabric.j… → compose editable graphics and canvas elements 12 Rough.js github.com/rough-stuff/rough → create hand drawn visual effects and illustrations RENDER AND EXPORT 13 Puppeteer github.com/puppeteer/puppete… → automate browser based scene rendering 14 FFmpeg github.com/FFmpeg/FFmpeg → encode frames, synchronize audio, and export video 15 Sharp github.com/lovell/sharp → process images and prepare assets for rendering HOW TO REPLICATE Create a Remotion project npx create-video@latest Give Claude Code this prompt: Build a 20 second motion design video using Remotion Use a 1600×2000 canvas at 60 FPS Create five connected scenes with animated typography, moving data panels, and smooth camera transitions Keep all text readable and prevent overlapping elements Render a final MP4 and inspect the output for visual errors Start with Motion Design Agent Skills for animation guidance Use GSAP or Motion Canvas for precise timing, Three.js for dimensional scenes, and FFmpeg for final processing the part worth saving You don't need 15 separate AI agents generating disconnected clips You need one coherent scene system with reusable animation components, consistent timing, and a reliable rendering pipeline 15 GitHub repos covering the infrastructure of a programmable motion design studio should I write the full Claude Code video production guide?

i gave Grok Bot 128 documents and told it one thing don’t summarize them then 2,774 nodes became 10,199 links, the raw files stayed untouched, and the bot started building a second brain around me while i was doing other work that’s what this video is showing sources stay locked new facts get pulled into separate pages projects connect to research decisions connect to context people connect to conversations and every useful relationship gets written back into the graph instead of dying inside one chat the part i care about most is that nothing has to “remember everything” the system only needs to know where the truth already lives by the end, 128 documents weren’t 128 documents anymore they were a knowledge engine i could keep adding to without rebuilding the context from zero i used to think AI memory meant giving a model a bigger context window now i think the better version is making sure useful knowledge has somewhere permanent to go

Tesla + GPT 6 Astra could turn car design into a f*cking engineering pipeline Imagine describing a futuristic electric vehicle and watching AI assemble the chassis, install the wheels, construct the body, and run a cinematic flight test No manually animating every assembly stage The video explores a wild concept where an electric vehicle is built layer by layer, from an exposed battery platform to a complete aerodynamic body, before lifting off with four glowing thrusters But the interesting part isn't the flying Tesla It's how a single programmable 3D system could handle the entire design sequence Here's how the concept breaks down: ENGINEER the battery platform and structural chassis ASSEMBLE suspension, wheels, and mechanical components INTEGRATE the aerodynamic body and glass surfaces VALIDATE geometry, clearances, and assembly states SIMULATE a fictional vertical flight sequence with animated thrusters Instead of modeling every frame independently, you can generate the components once and control their assembly through code Want to recreate something similar? Open Claude Code with Blender installed and paste: "Create a cinematic Tesla inspired electric vehicle concept using Blender Python Begin with an exposed battery skateboard chassis, then procedurally assemble the suspension, wheels, body panels, windows, and lighting Build six distinct animation stages covering engineering, assembly, integration, visual inspection, thruster activation, and vertical flight Use reusable geometry, realistic metallic materials, controlled camera movement, and cinematic studio lighting Make each stage parameterized so I can change vehicle proportions, assembly timing, and camera angles without rebuilding the scene Render a premium vertical 60 FPS animation" The real opportunity isn't asking AI to invent a flying car It's turning complex product visualization into a repeatable production workflow One design system, endless variations Bookmark this prompt before your next AI engineering experiment

it's f*cking gold Claude Code can turn reverse engineering into an AI powered investigation lab 18 GitHub tools to inspect Android apps, analyze binaries, trace network traffic, and rebuild software from verified evidence GIVE AI THE FULL TOOLBOX 01 REA ▸ github.com/ReverseEngineerin… → connect an AI assistant to reverse engineering tools through MCP DECOMPILE THE CODE 02 Ghidra ▸ github.com/NationalSecurityA… → decompile binaries and inspect program structure 03 GhidraMCP ▸ github.com/LaurieWired/Ghidr… → expose Ghidra analysis tools to AI agents 04 IDA Pro MCP ▸ github.com/mrexodia/ida-pro-… → let agents inspect functions and cross references in IDA REVERSE ENGINEER MOBILE APPS 05 JADX ▸ github.com/skylot/jadx → recover Java like source from Android APKs 06 Apktool ▸ github.com/iBotPeaches/Apkto… → decode and rebuild Android application resources 07 Frida ▸ github.com/frida/frida → instrument running applications for dynamic analysis 08 ILSpy ▸ github.com/icsharpcode/ILSpy → decompile and inspect .NET assemblies ANALYZE BINARIES 09 radare2 ▸ github.com/radareorg/radare2 → inspect machine code, functions, and binary structures 10 x64dbg ▸ github.com/x64dbg/x64dbg → debug Windows applications at runtime 11 ImHex ▸ github.com/WerWolv/ImHex → inspect binary formats with patterns and hex analysis 12 angr ▸ github.com/angr/angr → explore program behavior using symbolic execution TRACE NETWORK BEHAVIOR 13 mitmproxy ▸ github.com/mitmproxy/mitmpro… → inspect HTTP traffic in authorized test environments 14 dnSpyEx ▸ github.com/dnSpyEx/dnSpy → debug and inspect managed .NET applications 15 rizin ▸ github.com/rizinorg/rizin → analyze executable files and disassembly 16 raddebugger ▸ github.com/EpicGamesExt/radd… → debug native programs with a visual interface REBUILD FROM EVIDENCE 17 Awesome AI Reverse Engineering ▸ github.com/ReverseEngineerin… → discover AI assisted reverse engineering tools 18 Awesome Reverse Engineering ▸ github.com/tylerha97/awesome… → find curated resources for binary analysis Start with software you own or have permission to analyze 1 → Decompile the target with Ghidra or JADX 2 → Collect function names, cross references, and observed behavior 3 → Feed the evidence into Claude Code with a strict analysis prompt 4 → Write SPEC.md describing inputs, outputs, and edge cases 5 → Implement an independent version from the specification 6 → Run comparison tests and repeat until the documented behavior matches COPY THIS PROMPT: " Analyze the supplied reverse engineering evidence Separate verified behavior from assumptions Produce SPEC.md with inputs, outputs, state transitions, and edge cases Propose an independent implementation based only on authorized evidence Generate tests for every documented behavior Flag anything that cannot be verified " The expensive part of reverse engineering isn't opening a decompiler It's turning thousands of disconnected observations into a specification you can actually implement and test 18 tools, one investigation workflow, a reproducible path from evidence to working code should I write the full guide to building this with Claude Code?

10 GitHub repos can turn Claude Code into an entire AI development team Give it better code navigation, live documentation, reusable skills, browser debugging, and automated PR workflows (1/10) Claude Code the foundation for an AI coding agent that works directly inside your codebase ▸ github.com/anthropics/claude… (2/10) Superpowers add structured planning, test driven development, and systematic debugging ▸ github.com/obra/superpowers (3/10) Serena navigate large codebases using symbols and semantic code intelligence ▸ github.com/oraios/serena (4/10) Context7 pull current library documentation instead of guessing outdated APIs ▸ github.com/upstash/context7 (5/10) Anthropic Skills equip Claude with reusable instructions, scripts, and specialized workflows ▸ github.com/anthropics/skills (6/10) Playwright automate browser tests and catch UI regressions ▸ github.com/microsoft/playwri… (7/10) Chrome DevTools MCP inspect browser errors, network requests, and performance traces ▸ github.com/ChromeDevTools/ch… (8/10) Claude Code Action bring Claude into GitHub issues and pull requests ▸ github.com/anthropics/claude… (9/10) Semgrep scan code for security issues and risky patterns ▸ github.com/semgrep/semgrep (10/10) Langfuse trace and evaluate LLM workflows when your project uses model APIs ▸ github.com/langfuse/langfuse The interesting part? The biggest problem with AI coding isn't generating code It's knowing whether the code actually works Give Claude Code a repeatable process: 1 → Read the codebase before making changes 2 → Retrieve current documentation 3 → Write a failing test before fixing the bug 4 → Implement the smallest working change 5 → Run tests and inspect the result before opening a PR Start with Claude Code + Serena + Context7, then add Superpowers for structured development You don't need all 10 repos on day one Save these 10 repos before your next Claude Code project

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 👇🏻