HTTP 429 isn’t an error. It’s a decision. And I built the system that makes that decision. Most developers have hit rate limits. Very few understand how they actually work under the hood. So I built a production-grade 𝐀𝐏𝐈 𝐑𝐚𝐭𝐞 𝐋𝐢𝐦𝐢𝐭𝐞𝐫 from scratch — not a clone, not a tutorial. ➣ What it does Controls how many requests a client can make within a time window: • Within limits → HTTP 200 ✅ • Cross the limit → HTTP 429 🚫 This is what protects real APIs from: ✓ bot traffic ✓ abuse ✓ infrastructure overload ➣ 3 algorithms. 3 different trade-offs. • 𝐓𝐨𝐤𝐞𝐧 𝐁𝐮𝐜𝐤𝐞𝐭 → absorbs burst traffic (user-facing APIs) • 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰→ fair distribution, no boundary exploits • 𝐋𝐞𝐚𝐤𝐲 𝐁𝐮𝐜𝐤𝐞𝐭 → strict constant rate (payments, critical systems) 👉 Switch between them LIVE — no restart, no downtime. ➣ Where theory meets reality 1. 𝐑𝐚𝐜𝐞 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧: Two requests see “1 token left” → both pass → Fixed using serialized writes + DB transactions 2. 𝐖𝐫𝐢𝐭𝐞 𝐥𝐨𝐜𝐤 𝐜𝐨𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧 : High traffic = silent failures → Fixed with retry logic + scoped transactions 3. 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐬𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠: Changing logic without breaking user state or API keys → Required careful state isolation 🛠️ 𝐒𝐭𝐚𝐜𝐤 : Python · FastAPI · SQLite · Vanilla JS · Chart.js 🔗 Live Demo: https://lnkd.in/dmK_WF6V 💻 GitHub: https://lnkd.in/dnK5AAPZ Built this from scratch to understand how production systems think about traffic control. 🚀 Would really appreciate feedback — especially from engineers who've worked on distributed systems or high-traffic APIs. #BackendEngineering #Python #FastAPI #SystemDesign #SoftwareEngineering #Backend

I built a RAG layer for Claude Code that cuts token usage by 80–90%                            Most devs using Claude Code don't realize they're burning tokens on files Claude doesn't need to read.                                                Ask Claude "how does auth work?" and it reads 3 full files — 1,500+ tokens just to answer with 40 relevant lines. I fixed that. What I built: A local hybrid RAG system that sits between Claude and your codebase:  → Late chunking — splits every file into overlapping 40-line windows  → Dense retrieval — semantic search with all-MiniLM-L6-v2 (runs fully local, no API key)  → BM25 sparse retrieval — keyword matching for exact symbol names  → Cross-encoder reranking — picks the 3 best chunks from 20 candidates  → File watcher — auto-rebuilds the index within 2 seconds of any file save Claude Code reads the CLAUDE.md and knows: run pip package before opening any file. It gets back 3 precise snippets with file path + line range. It reads only those lines. Nothing else. Real numbers on my Volta Engine project (76 files):  - Without RAG: 17,235 chars across 3 files for one question  - With RAG: 3,073 chars the exact 3 chunks that matter  - 82% fewer tokens. Same answer. The whole thing runs offline. No cloud embeddings. No API calls. Just a one time pip install and run it. Stack: sentence-transformers · rank-bm25 · watchdog · Python If you use Claude Code daily on a real codebase, this pays for itself in the first session. DM me if you want the scripts. 🧠  #AI #ClaudeCode #RAG #DeveloperTools #Python #LLM #Productivity

Most of us have requests baked into our muscle memory, but as web standards move toward HTTP/3 and high-concurrency, the "old reliable" is starting to show its age. I’ve been diving into Niquests, and it’s a serious contender for the new standard. It’s designed as a drop-in replacement, meaning you get a massive performance boost without the headache of a refactor. What makes it a "pro" choice: - Protocol Support: It handles HTTP/2 and HTTP/3 natively. If you're hitting modern APIs, this isn't just a "nice to have"—it’s a massive efficiency gain. - Multiplexing: You can send multiple requests over a single connection. This eliminates the handshake overhead that usually slows down bulk data fetching. - True Async Compatibility: Unlike the original requests library, this is built to play nice with asyncio, making it ideal for high-traffic backend services. - Performance: In standard benchmarks, it significantly outperforms HTTPX and AIOHTTP in request-heavy loops. If you’re building production-grade scrapers, microservices, or data pipelines, the switch is almost a no-brainer. It’s the same API we love, just supercharged for 2026. Check out the project on GitHub: https://lnkd.in/d98Zy_cc #Python #SoftwareEngineering #Backend #Performance #DataEngineering #OpenSource

Database errors will humble you. No matter how confident you are. Today was one of those days. Everything looked correct: logic made sense API routes were fine schema was clean But nothing worked the way it should. Hours went into: Prisma validation errors weird TypeScript issues (never type…) data not updating even though queries looked right And the funniest part? The actual issue is always something small. One missing field. One wrong assumption. One mismatch between frontend and backend. That’s it. But it’ll cost you hours. What I’ve learned (again): Debugging databases is not just coding. It’s patience + clarity + brutal honesty with your own logic. You have to slow down and ask: “What is actually happening?” “What am I assuming?” “Where is the data breaking?” Finally fixed it. And yeah, that moment when it works? Worth it. But still… database errors are a pain in the ass. #webdev #programming #debugging #buildinpublic

🚀 rst-queue v0.1.6: Scaling Terabytes with Megabytes In a world of bloated data systems, we often find ourselves throwing more hardware at software problems. But what if our tools were engineered to be small, grounded, and incredibly powerful? Introducing rst-queue v0.1.6, a high-performance async queue system built for the modern developer who values efficiency above all else. Inspired by the psychology of the Leafcutter Ant, this project is the first major release from the Datarn initiative. Why rst-queue? Most Python-based queues are limited by the Global Interpreter Lock (GIL) and high memory overhead. rst-queue is different. By using Rust and the Crossbeam framework, we’ve built a system that: ⚡ Bypasses the GIL: Achieve true parallelism with native Rust worker pools. 🐜 Microscopic Footprint: 30-50x less memory usage than traditional message brokers. 🛡️ Dual Modes: Choose between AsyncQueue (In-memory for 1M+ items/sec) or the new AsyncPersistenceQueue (Durable storage with Sled KV). Grounded in the Kernel The secret to our speed is "Simple OS Layering." We’ve designed rst-queue to sit as close to the OS kernel as possible, utilizing direct system calls and memory-mapped I/O. This isn't just a library; it's a high-velocity data crossing (Taran) for your most critical applications. Get Started in Seconds We believe in zero-setup excellence. You can add high-performance queuing to your Python project with a single command: Bash pip install rst-queue==0.1.6 Join the Datarn Movement At Datarn, we are building a suite of "Small but Mighty" tools for data-intensive domains like B2B e-commerce and real-time analytics. rst-queue is just the beginning. Explore the project on PyPI: https://lnkd.in/d54yqdea Contribute on GitHub: https://lnkd.in/d_x3E-zj #Python #RustLang #DataEngineering #OpenSource #Efficiency #Datarn #PerformanceOptimization #SoftwareArchitecture

I just published a very detailed and comprehensive analysis of Claude Code's source (leaked a few days ago) that I found on the internet. When Anthropic's Claude Code source leaked via npm source maps on March 31, people started hyping "secret features" they found — an AI Tamagotchi pet, a dreaming assistant, and multi-session communication over Unix sockets. I decided to actually check. I read 1,902 TypeScript files, 20,000 lines of Rust rewrite, and 29 subsystem metadata archives. I traced every claim to actual source lines. Here's what I found: -> BUDDYBUDDY (the AI Tamagotchi) What people said: 18 species, rarity tiers, CHAOS and SNARK stats What's actually there: 6 React components with no backend The "teaser drop date": April 1, 2026. Make of that what you will. -> KAIROS (the dreaming assistant) What people said: Claude remembers across sessions and consolidates memories while you sleep What's actually there: Two boolean config flags — autoMemoryEnabled and autoDreamEnabled. That's it. Zero execution logic. I described it as "two light switches installed in a house with no wiring." -> UDS Inbox (inter-session communication) What people said: Multiple Claude sessions talk to each other over Unix domain sockets What I found: Zero references. Anywhere. Not in TypeScript, not in Python, not in Rust, not even in comments. This feature was never built, never started, never even planned in code. But here's what IS real (and nobody's talking about): - ULTRAPLAN — a planning command that gives Claude full tool access to read your entire codebase before making a plan. Actually works. - Sub-agent spawning — real thread-based agent spawning with JSON manifest tracking - The Hook Pipeline — fully implemented PreToolUse/PostToolUse system that... nobody connected to the main conversation loop. A fire alarm where the sensors and sprinklers work but aren't connected. I published the full analysis with architecture diagrams, code walkthroughs, and honest verdicts on every feature. The lesson: in the AI hype cycle, always check the source. Most of the time, the hype outpaces the code by miles. Comment with "Claude" and I will send it to you in DM straight away

I turned 512,000 lines of Claude Code's reverse-engineered source code into a 26-chapter book. When Anthropic accidentally shipped a debug file in their npm package on March 31, the full source of Claude Code became public. 1,900 files. Half a million lines. Wide open. I wrote a 26-chapter tutorial to make sense of it all (for myself). Here are the parts that genuinely surprised me: How do you add new capabilities to Claude Code? Write a markdown file. Drop it in a folder. Add some YAML at the top. That's it — it becomes a slash command. No build step, no framework. Four directories are checked in priority order: enterprise, user, project, and built-in. How does it survive 8-hour sessions without blowing the context window? A three-tier compression system: Tier 1 — After every turn, it trims bloated tool outputs. That 800-line file listing? Trimmed to 50 lines. Saves 10–30% of tokens every turn. Tier 2 — At ~80% capacity, it generates a structured summary, inserts a boundary marker, and frees everything above it. 50–80% reduction in one move. Tier 3 — At ~95%, it saves the session to disk and starts fresh. 90%+ reduction. Detail is gone but recoverable later. How does it keep token costs low? Every tool output has a budget — massive results get truncated before the API sees them. Unused tools stay deferred and don't load their schemas. The system prompt is split into a static zone and a dynamic zone. After the first turn, ~70% is a cache hit. Does the public version include everything? Not even close. Compile-time feature flags physically remove code from the build. Behind them: autonomous background agents (codenamed KAIROS), voice input, multi-agent swarms, and an ML classifier that auto-approves safe tool calls. What else stood out? — Terminal UI built with React — actual components rendered as terminal characters — 45 tools share the same interface. Adding one doesn't touch the engine — Read-only ops run in parallel (up to 10). Writes run one at a time — Permissions layered eight levels deep — Tools start running while the API response is still streaming But here's what impressed me most. I went through Anthropic's official docs at code.claude.com. The skill system, memory layers, permission modes, compaction behavior — every pattern matched the public docs down to the field names. They're not just telling developers "here's how to build with our tool." They actually built it that way. It's rare to see a company follow its own best practices this closely. The biggest takeaway? None of this is new computer science. Caching strategies, plugin architectures, streaming pipelines, defensive defaults — patterns decades old. The innovation is in how carefully they're combined. Good AI tooling turns out to be really good software engineering.

Your API is slow because it's doing too much before it responds. A user places an order. Your endpoint saves it, charges payment, sends an email, generates an invoice, updates inventory. Then it responds. That payment call? 5 to 25 seconds. Thousands of requests during a flash sale? Thousands of blocked threads. Provider goes down? Your entire API goes down. But the user only needs one answer: "Did you get my order?" That's it. Everything else can happen after. The fix is one architectural shift: → API saves the order to the database → Queues the heavy work for a background worker → Returns "received" in ~50ms The worker picks it up and handles the rest:   Charge payment   Send email   Generate invoice   Update inventory If something fails, it retries with exponential backoff. If all retries fail, the user gets notified AND the engineering team gets an alert with the full traceback. Nobody is left in the dark. Three things I learned building this in production: 1. Save to the database before queuing. If the worker crashes, the order still exists. The DB is your safety net. 2. Use Celery's on_failure() hook. Define it once in a custom base class. When retries run out, it automatically notifies users and alerts your team. No scattered try/except blocks. 3. Your API is a receptionist, not a worker. It takes the request, confirms receipt, and hands it off. The real work happens in the background. What's the slowest thing your API does before responding? ↓ Full blog post with architecture diagram and code in the comments #Python #SoftwareEngineering #SystemDesign #BackendDevelopment #Celery

A bit more tweaking, and I added a brain. The rules engine now supports logic operators and I added rule examples to the README. Here it is in action. I added a progress bar because otherwise it looks like it hung in the terminal. This is also CI ready with defined exit codes, and emits JSON if you ask it to. You probably already knew about the .pth payload, that's the one that made the news. What's less discussed is what else was in there. proxy/proxy_server[.]py, line 130: a 34,460-character base64-alphabet string with Shannon entropy of 5.61 bits/character. That's CRITICAL by the rules engine because that entropy profile is consistent with compressed, encrypted, or encoded content. I am still working on tuning the rules, so if this interests you and you want to tell me how it worked for you, I would love to get your feedback. The current version on PyPI is 0.1.7 Zero dependencies, Python 3.11+. If you're running supply-chain tooling in CI and want to close this gap, I'd genuinely like to hear how it works for you. On GitHub: nuclear-treestump/pydep-vector-runner ( https://lnkd.in/gvPkUsMx ) I build this kind of tooling and I'm looking for my next role in platform security or developer tooling. If that's relevant to you, my inbox is open.

Your FastAPI backend is fast to build. But is it fast to run? Most developers find out the answer at the worst possible moment when real users hit it at the same time. Endpoints slow down. Requests pile up. Users drop off. Not because the code is wrong. Because it is blocking. Here is what blocking actually looks like in production: Your user hits an endpoint. FastAPI calls the database. That query takes 200ms. During those 200ms your server is frozen. Not slow. Frozen. Every other request sits in a queue waiting for that one query to finish. 100 users hit your API at the same time. User 1 gets served. Users 2 to 100 wait in line. That is sync. That is blocking I/O. FastAPI was built to never work that way. With async/await while your database query runs in the background, your server is already picking up the next request. And the next. And the next. 200ms of database wait becomes invisible to every other user. In real backend terms. SYNC — blocks: def get_orders(user_id: int): return db.query(user_id) ASYNC — non blocking: async def get_orders(user_id: int): return await db.query(user_id) Same logic. Same database. Same server. But now 100 users get served in the time it used to take to serve 1. This matters even more when your endpoints call external services. 1. Payment gateway 300ms wait. 2. AI model response 2 to 3 seconds wait. 3. Email service 500ms wait. Sync every user feels every millisecond of every one of those waits. with Async none of them do. FastAPI gives you non-blocking I/O natively. No extra setup. No plugins. No workarounds. Just write async. Add await. Let FastAPI handle the rest. Your backend was already fast to build. Now make it fast to run. Are you using async endpoints in your FastAPI projects? 👇 #FastAPI #Python #BackendDevelopment #AsyncProgramming #SoftwareEngineering #APIDesign #PythonDeveloper #WebDevelopment #TechIn2026 #BuildInPublic