Structured Logging with Timberlogs: Simplify Debugging and Audits

Most developers overcomplicate background tasks. When I first started using Celery, I'd create a separate task for everything — sending an email, updating a record, generating a report. Result? 40+ tasks, half of them doing almost the same thing, impossible to debug. Here's what I learned after 3 years of production Celery: 1. Group similar operations into one task with parameters — not 10 separate ones 2. Always set hard time limits — one stuck task can block your entire queue 3. Use task retries with exponential backoff — networks fail, APIs timeout, it's normal 4. Monitor your queues — if you don't watch them, they'll surprise you at 3 AM Simple beats clever. Every time. What's your biggest Celery lesson? 👇 #Python #Celery #BackendDevelopment #SoftwareEngineering #Django

The useful part of most AI code review tools fits in about 300 lines of Python. The other 100,000 are SaaS overhead. Fetch the diff. Chunk by file. One prompt per chunk with the PR description for context. One LLM call. Structured output posted as comments. Three cents. Three dependencies. Runs as a GitHub Action. A generic tool reviews generic code. Yours should know your conventions, your edge cases, your architecture. I open-sourced a reference implementation. Fork it, strip it, own it. Stop buying LLM wrappers. https://lnkd.in/gqgqY2Sk #AIEngineering #MultiAgentSystems #SoftwareArchitecture #OpenSource

AntroCode Launches Zero-Dependency Single-File DeepSeek UI for Developers 📌 A 12-year-old developer just dropped a revolutionary tool: AntroCode, a zero-dependency, single-file DeepSeek UI that runs in your browser with one command. No servers, no installs - just python AntroCode_1.py and instant access to AI chat, CoT reasoning, and token tracking. Already trending on Hacker News, it’s redefining lightweight AI workflows for devs who hate setup. 🔗 Read more: https://lnkd.in/dNzkDQV8 #Antrocode #Deepseek #Python #Singlefile #Zerodependency

**Feature Spotlight: Simplifying Log Ingestion with Timberlogs** Are you wrestling with multiple data formats and disparate logging sources? Say goodbye to complexity with Timberlogs' seamless log ingestion capability. Whether your data comes in JSON, CSV, plain text, or any other common format, Timberlogs ingests it all effortlessly. Why does this matter? A unified ingestion process means less friction, fewer errors, and faster insights. When logs from Python, Go, or even Rust flow smoothly into a single platform, your focus can shift back to what truly matters—solving problems. Give it a try; it’s not just easy—it’s essential. Start your journey with our free tier: [timberlogs.dev](https://timberlogs.dev) #structuredlogging #devtools #typescript #webdev

This is something that I worked on during my time at the Algorand Foundation. If you've written #Algorand #SmartContracts, you know the Puya #compiler turns your Python/Typescript code into TEAL bytecode. What you don't see is the ~15 intermediate stages it goes through to get there -- SSA transformations, optimizations, register allocation, all of it. I hadn't worked with compilers as complex as Puya's, so I took to visualizing the pipeline to better understand it. Luckily, the good work done by the MakerX folks made debugging and visualization easy. You can try it by pasting your contract, compiling it, and then stepping through the entire pipeline stage by stage. You can compile with different Puya versions to see the differences. Built with SolidJS, CodeMirror 6, and a FastAPI backend. Runs on Cloud Run. Feedback and PRs welcome! https://lnkd.in/eKf-umGt

🚀 I built a Mission Control dashboard for Claude Code #ClaudeCode #AI #DevTools #Automation #FullStack #Python #React #SelfHosted If you've ever worked with Claude Code on multiple projects at the same time, you know the pain: dozens of terminal tabs, losing track of which session needs input, constantly switching context. So I built Vestal — a self-hosted orchestration platform that lets you manage multiple Claude Code sessions from a single browser tab, across local and remote servers. How it works under the hood: Each Claude Code session runs inside a tmux session. Vestal's backend

I’ve developed a Python package called "#Aurora-Vault" (MashAllah). The goal behind it is to simplify the process of building RAG pipelines. When developers build AI agents, they often rely on RAG to reduce hallucination and bias. Since LLMs naturally tend to hallucinate, this becomes a serious challenge, especially when accurate and real-time responses are required. To address this, I built Aurora-Vault as a lightweight building block rather than a full standalone system, so developers can easily integrate it into their own custom RAG workflows. I have used total 6.5 Million data to make this package. This version 1...!! It’s working smoothly alongside LLMs, Alhamdulillah. Visit: https://lnkd.in/gfBcNG85

🚀 Day 38/60 — LeetCode Discipline Problem Solved: Build an Array With Stack Operations Difficulty: Medium Today’s problem revolved around simulating stack operations to construct a target array using a stream of numbers. By carefully deciding when to Push and when to Pop, I built the required sequence efficiently. 💡 Focus Areas: • Stack fundamentals (Push & Pop) • Simulation-based problem solving • Conditional logic building • Understanding sequence construction • Improving step-by-step thinking ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) Every number entered the stack… but not all were meant to stay. Some were pushed forward, others quietly removed— until only the desired sequence remained. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Stack #Simulation #CodingJourney #ProblemSolving #Python #Developers #TechGrowth #Consistency

Today seems like a good day to mention we’ve built our own litellm-esque model library, and that its completely open source. It provides one Python SDK to access any provider. It's simple, fully typed, and modular by design. At a glance: - Simple, modular design (easy to read + contribute to) - Fully typed, LLM-agnostic Pydantic I/O - Built-in model metadata + granular cost tracking (cache read/write, reasoning tokens, per-turn totals) - Agent loop with tool execution + hooks - Image + file support across providers - Retry strategies, including token-aware retries It’s built for simplicity and transparency — with a built-in agent loop + tool hooks, image/file support, and granular cost tracking. It is intentionally small and modular - we wanted it to be easy to understand, verify, and extend. 🔗 Link in the comments!

I put together a lightweight agent orchestration system for Claude Code called the Claude Agents Plugin. It breaks complex development tasks into tracked, parallel sub-agents using hierarchical markdown file trees. The idea is to let you describe a task naturally—like "Build a user auth system with login, signup, and JWT tokens." Claude then automatically scans your codebase, maps out the work, spawns parallel agents, and tracks everything in markdown files. A few architectural details: Context-aware: It reads your existing project before touching anything, clarifies what it will modify versus create, and never overwrites existing code. Dependency management: It builds hierarchical task trees to handle parent-child relationships and detects circular dependencies. Zero dependencies: It’s a single file relying purely on the Python standard library (Python 3.9+). It is MIT licensed. If you are building with Claude Code and want to test out structured agent orchestration, the repository is linked below. https://lnkd.in/gG3fdrmZ #ClaudeCode #Python #OpenSource #AgenticAI #DeveloperTools