Promix

It's becoming f*cking insane : Claude Code turns a boring nutrition textbook into an interactive 3D world where food comes alive, ingredients react to your clicks, energy levels change in real time, and every lesson becomes something you can actually play with Forget reading 40 pages just to understand how your body uses energy Imagine opening a digital book and watching an entire miniature kitchen emerge from its pages Food appears in 3D, shelves unfold, ingredients become interactive, and a live dashboard tracks different energy stores as you explore This Every Bite concept shows what happens when educational content stops behaving like a PDF and starts behaving like a game And the interesting part? You can build something similar with Claude Code Instead of hardcoding dozens of animations, create a reusable system where each chapter controls its own objects, interactions, and learning objectives Here's the prompt worth saving: "Build an interactive 3D educational pop up book called Every Bite using React, Three.js, React Three Fiber, and TypeScript Create a beautifully illustrated book that opens into a miniature 3D nutrition laboratory Include animated food models, colorful ingredient shelves, interactive meals, and a dashboard showing carbohydrate, fat, and protein energy stores Let users click ingredients, assemble meals, and explore how different foods contribute to energy intake Create chapters covering food energy, energy storage, and basic nutrition concepts Add smooth page transitions, camera zooms, object animations, tooltips, and interactive quizzes Make the experience responsive, use a warm editorial design, and keep the nutrition calculations grounded in clearly stated educational assumptions Separate the chapter content from the 3D engine so new lessons can be generated without rebuilding the application" The important part isn't making a prettier textbook It's creating a reusable engine that can turn almost any subject into an interactive learning experience Biology, physics, chemistry, engineering, all built around the same system Save this prompt before building your next educational app

here’s how to build and animate a medieval knight in blender with claude opus 5.5... a royal knight with polished silver armor, blue and gold heraldry, a feathered helmet, layered metal plates, and a complete walking animation tested inside one review scene the tricky part isn't modeling the armor, it's making every rigid plate follow the body without clipping into the neighboring pieces here’s the workflow: step 1: connect opus 5.5 to blender through blender mcp and provide the knight's concept art step 2: build the character in layers: boots > greaves > chestplate > shoulder armor > helmet > blue tabard step 3: create the humanoid skeleton, then attach rigid armor pieces to the appropriate bones step 4: animate the walking cycle, adjusting hip rotation, foot placement and upper body movement step 5: add secondary motion to the blue fabric, waist panels and helmet feathers step 6: create a review scene with a tiled floor, fixed lighting and a camera that shows the full character step 7: have opus inspect the animation for foot sliding, armor intersections and unnatural joint movement step 8: export the rigged character and walking animation as FBX who does what: concept art: the visual reference opus 5.5: modeling, rigging, animation, inspection blender: geometry, materials, skeleton, timeline test armored characters in motion before polishing their materials, a knight can look perfect standing still and completely fall apart the moment the hips and shoulders start moving

here’s how to build and animate a jet engine in blender with claude opus 5.5… a fully exposed turbine with rotating compressor blades, a glowing combustion chamber, an animated exhaust plume, and every stage displayed inside one engineering dashboard the trick is building the engine as separate systems before animating the airflow here’s the workflow: • step 1: connect opus 5.5 to blender through blender mcp and provide a jet engine cross section as reference • step 2: build the engine part by part: intake > fan > compressor > combustion chamber > turbine > nozzle • step 3: separate the rotating shafts and blade assemblies so each section can move independently • step 4: animate the compressor and turbine rotation, then add airflow moving through the engine • step 5: add the combustion effect with orange emission, heat distortion and exhaust particles • step 6: create a technical dashboard with a fixed camera, component labels, pressure graphs and engine status • step 7: have opus inspect blade clearance, rotation direction and airflow continuity • step 8: render the complete engine sequence with synchronized telemetry who does what: reference diagram: the engineering target opus 5.5: modeling, animation, scene inspection blender: geometry, materials, simulation, rendering animate the engine as connected systems, not one spinning object, the compressor, combustion chamber and turbine should each explain a different part of how thrust is generated

It's becoming f*cking insane how Claude Code can turn a boring nutrition textbook into an interactive 3D world where food comes alive, ingredients react to your clicks, energy levels change in real time, and every lesson becomes something you can actually play with Forget reading 40 pages just to understand how your body uses energy Imagine opening a digital book and watching an entire miniature kitchen emerge from its pages Food appears in 3D, shelves unfold, ingredients become interactive, and a live dashboard tracks different energy stores as you explore This Every Bite concept shows what happens when educational content stops behaving like a PDF and starts behaving like a game And the interesting part? You can build something similar with Claude Code Instead of hardcoding dozens of animations, create a reusable system where each chapter controls its own objects, interactions, and learning objectives Here's the prompt worth saving: "Build an interactive 3D educational pop up book called Every Bite using React, Three.js, React Three Fiber, and TypeScript Create a beautifully illustrated book that opens into a miniature 3D nutrition laboratory Include animated food models, colorful ingredient shelves, interactive meals, and a dashboard showing carbohydrate, fat, and protein energy stores Let users click ingredients, assemble meals, and explore how different foods contribute to energy intake Create chapters covering food energy, energy storage, and basic nutrition concepts Add smooth page transitions, camera zooms, object animations, tooltips, and interactive quizzes Make the experience responsive, use a warm editorial design, and keep the nutrition calculations grounded in clearly stated educational assumptions Separate the chapter content from the 3D engine so new lessons can be generated without rebuilding the application" The important part isn't making a prettier textbook It's creating a reusable engine that can turn almost any subject into an interactive learning experience Biology, physics, chemistry, engineering, all built around the same system Save this prompt before building your next educational app

It's becoming f*cking dangerous how Claude Code can turn a single prompt into an entire robotic factory, where a 3D mechanical arm picks up objects, moves them between stations, stacks components, and tracks every operation in real time No robotics degree, no expensive industrial software, no manually animating hundreds of frames Just a programmable factory running directly in your browser The demo shows a robotic arm completing a 9 step production cycle, from grabbing individual blocks to positioning, lowering, and stacking them while the dashboard tracks each operation The crazy part? You can recreate this entire concept with Claude Code and Three.js Instead of manually animating the robotic arm, give it a sequence of commands and let the simulation calculate the movement between each station Every object has a position, every operation has a state, and every completed action triggers the next step Here's the prompt worth saving: "Build an interactive 3D robotic factory using React, Three.js, and TypeScript Create a 4 axis industrial robotic arm with articulated joints, a functional gripper, and smooth inverse kinematics Design a production platform with colored blocks, pickup stations, and a stacking zone Implement a 9 step autonomous sequence covering initialization, pickup, lifting, transportation, placement, stacking, and cycle completion Use deterministic state machines to control every movement, with collision checks and precise object positioning Build a premium live dashboard showing the current operation, execution logs, completed steps, cycle progress, and production telemetry Add pause, resume, reset, adjustable speed, manual controls, and emergency stop Keep the robot controller separate from the 3D renderer and make the entire simulation reproducible" The most important detail is that the robotic arm shouldn't just play a prerecorded animation Its movements should respond to actual commands, object positions, and execution states Now you have a reusable foundation for simulating assembly lines, warehouse automation, and industrial robotics One prompt, one robotic arm, an entire production workflow you can modify Bookmark this before building your own AI factory

NVIDIA'S GB200 IS A $30,000 MONSTER AND I JUST TURNED IT INTO A 20 SECOND AI FACTORY SIMULATION 11 components, live assembly, simulated hardware failures, real time diagnostics and a fully interactive 3D GPU rack The craziest part is watching the system build itself

stop spending 20 hours rebuilding interfaces by hand when Claude Code can turn a screenshot into a working React app 5 GitHub repos can automate most of the process, from extracting the visual structure to testing the final interface (1/5) React ▸ github.com/facebook/react build reusable components instead of manually recreating every element (2/5) Tailwind CSS ▸ github.com/tailwindlabs/tail… reconstruct layouts, spacing, typography, colors, and responsive behavior (3/5) shadcn/ui ▸ github.com/shadcn-ui/ui use customizable components for buttons, dialogs, forms, and navigation (4/5) Lucide ▸ github.com/lucide-icons/luci… recreate consistent icons without drawing everything from scratch (5/5) Playwright ▸ github.com/microsoft/playwri… let Claude Code open the app, capture screenshots, test interactions, and identify visual differences you don't need to manually inspect every pixel or keep explaining what looks wrong Claude Code can render the interface, compare it with your reference, fix mismatched components, and repeat the process here's the exact prompt I'd use : "Rebuild the attached screenshot as a functional React application using Tailwind CSS and shadcn/ui, extract the design tokens, create reusable components, implement responsive layouts and interactions, then use Playwright to capture screenshots and iteratively correct visual differences against the reference" the screenshot becomes the specification react builds the structure playwright provides the feedback Claude Code connects the entire workflow save these repos before your next UI project

It's becoming f*cking terrifying how Claude Code can turn a swarm of independent AI workers into one coordinated system that routes tasks, catches failures, tracks dependencies, and visualizes the whole network as a living 3D brain Forget juggling 10 terminals just to figure out which agent is doing what Imagine an entire AI workforce operating inside one shared system, where specialized workers communicate, execute tasks, and report progress while a central control layer monitors everything The crazy part? You can build a working simulation of this architecture with Claude Code This Swarm Cortex concept turns multiagent orchestration into a live 3D network, where information moves between thousands of visual connections and every worker has its own execution state Here's how the system works : DISPATCH tasks to specialized agents based on their capabilities ROUTE messages without flooding every worker's context TRACK dependencies, execution progress, and completed jobs RECOVER from failures through retries and task reassignment VISUALIZE agent activity through a living 3D neural network MONITOR throughput, queue depth, and system health in real time The important part is that no individual worker needs to understand the entire project Each agent receives only the context required to complete its assigned task Want to recreate this? Open Claude Code in a new project and paste: "Build an interactive AI swarm orchestration simulator using React, Three.js, and TypeScript Create 6 specialized agent roles with independent task queues, execution states, and message histories Implement a central dispatcher with dependency tracking, asynchronous messaging, retry logic, and failure recovery Visualize the network as a rotating 3D brain made of thousands of interconnected particles Animate message propagation based on actual simulation events rather than random visual effects Build a live dashboard showing agent activity, completed tasks, throughput, queue depth, and failed operations Add controls for worker failures, task injection, pause, replay, and simulation speed Separate orchestration logic from rendering and include automated tests for scheduling and recovery" Start with simulated workers, verify the coordination logic, then connect real Claude Code sessions through an orchestration layer The real breakthrough isn't making individual agents smarter It's building an architecture where dozens of workers can coordinate without drowning each other in context Bookmark this prompt before building your own AI swarm Make the hook more concrete

TODAY I LET AI RUN MY ENTIRE PRODUCT LAUNCH, AND I CAN’T F*CKING BELIEVE HOW MUCH WORK IT TOOK OFF MY PLATE I decided to run an experiment. Instead of jumping between five different chats, chasing approvals, answering questions and trying to keep everyone moving, I wanted to see what would happen if AI handled the coordination while I made the decisions. So I gave it one task: get my Friday launch ready. Here’s how it went: → CONTENT PARTNER: the first thing I needed was a clear creative direction. My AI picked up the conversation, collected the available options and brought me one simple decision. Friday launch. One teaser. One caption. Clean type or bold type. I chose the direction, and the conversation moved forward without me having to manage every message. → FREELANCER: then came the part that usually wastes my afternoon. The package was $600 with one revision included. Normally, I'd be jumping between messages, checking the scope, confirming delivery dates and making sure nothing got misunderstood. This time, AI organized the terms and asked for my approval. I checked the budget, approved it, and the terms were locked. → DESIGN STUDIO: this is where things got interesting. Two visual directions came back. A was minimal, with the message doing most of the work. B was louder, with bigger typography and a much stronger visual impact. My AI didn't bury me in a 30-message discussion about fonts and colors. It showed me both options and asked which one felt right. I picked B. The design direction was approved, and the project kept moving. → LAUNCH CREW: now everything had to come together. The team needed confirmation that the visuals and copy were ready. AI checked the handoff, coordinated the final pieces and brought the Friday publish decision back to me. No endless back-and-forth. No searching through conversations to figure out who was waiting on whom. → AND THEN CAME MY FAVORITE PART: my personal life. Right in the middle of everything, a message came in asking if we were still having dinner that night. My AI didn't blindly make a decision for me. It surfaced the conversation, let me respond, and kept the work moving separately. That's when the whole concept clicked. Five conversations. A $600 budget. Multiple creative decisions. One Friday launch. And instead of manually coordinating every single person, I was just making the decisions that actually mattered. The final screen said it perfectly: "The work is ready. The next move is mine." That's the difference between AI that just answers questions and AI that actually helps run your workflow. I don't want another chatbot that makes me copy messages between five windows. I want one that keeps everything organized, handles the coordination and knows when to ask for my approval. Bookmark this if you're tired of managing conversations instead of getting actual work done.

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