Ruff Replaces Multiple Python Linters with Single Config

Agent Replay Debugger update - now with real agent reasoning Last week I posted v1 of agent-replay-debugger. Since then I've shipped three updates worth sharing. What changed: The original version only showed lifecycle events - pipeline_started, step_passed, step_failed. Useful for status, useless for understanding why the agent did what it did. Now the viewer parses the full transcript: every reasoning block where the agent explains its thinking ("Let me check the existing component structure..."), every tool call with its target (Read src/components/rewards.tsx, Edit src/lib/api.ts, Bash npm test), and the actual user messages that triggered each chain of decisions. A real session now surfaces 56 reasoning events and 138 tool calls - the complete decision chain, not just the outcome. New viewer features:  - Consecutive reasoning events collapse into groups (click to expand) so you're not scrolling past 50 "I'll now..." blocks  - DAG nodes show duration and tool count, and clicking one filters the timeline to just that span  - Automatic PII redaction: emails, API keys, JWTs, tokens, user paths - all scrubbed before any data reaches the viewer Second adapter: Claude Code. Point ARD at any Claude Code session .jsonl and get the full interactive timeline. Two frameworks supported now, more coming. Still zero runtime dependencies. Still one command: ard view ./session.jsonl Try the live demo (real session, auto-scrubbed): https://lnkd.in/gRFB7uWf Code: https://lnkd.in/gPTUt4ue #buildInPublic #AIAgents #LLM #python #openSource #devTools #AIEngineering

Day 18: Why the "How" matters more than the "What" 🏗️ Today’s #30DaysOfCode challenge was a classic: The Palindrome Checker. While most would reach for a simple .reverse() method, I took a deeper dive into Linear Data Structures using JavaScript to implement a dual-stream logic: Stacks (LIFO): To handle the reverse flow of data. Queues (FIFO): To maintain the original sequence. The Engineering Takeaway: By comparing the "pop" of a stack against the "dequeue" of a queue, you aren't just checking a string—you’re managing data flow efficiency. As I scale my work in Data Engineering, these fundamentals are the difference between a script that just "works" and a system that is built to scale. Shoutout to my AI collaborator #Gemini for the technical sounding board as I bridge these concepts across my Java and JS projects. #DataEngineering #JavaScript #Algorithms #DataStructures #30DaysOfCode #TechCareer

My first version of the Task Manager CLI was embarrassing. Everything in one file. No error handling. Variables named x, temp, data2. It worked — until it didn't. And when it broke, I had no idea where to start debugging. That moment of staring at my own code and not understanding it was one of the most important moments in my development as an engineer. I refactored the entire project. Broke it into modules. Added structured exception handling. Rewrote the validation logic. Debugging effort dropped by 40%. More importantly, I could actually read my own code. The lesson wasn't technical. It was about ego. Bad code is often the result of being in a hurry to "finish" — instead of being willing to do it right. I now build slower and ship cleaner. And I'm better for it. Have you ever looked back at old code and learned something important about yourself? #Refactoring #CleanCode #Python #SoftwareEngineering #LessonsLearned

Every safety-critical team traces requirements to code. Almost none of them can prove it without a spreadsheet. ubCode 0.29 changes that. One comment in your source code creates a live, bidirectional link between a requirement and its implementation: # @Function Bar, IMPL_4, impl, [SPEC_1] That single line becomes a traceable need object. Linked to its spec. Validated in real time. Hoverable. Navigable. Diffable in every pull request. No spreadsheet. No manual mapping. No "I think this function covers that requirement." What you get in VS Code: → Real-time diagnostics on malformed markers → Hover cards with type, ID, title, and link targets → Find References from code to every document that backlinks to it → Autocomplete for project configuration → Incremental Rust-based parsing — only changed files are re-processed Works in C++, Python, Rust, C#, and YAML. Huge shoutout to Chris Sewell for the integration in ubCode and Patrick Dahlke and Jui-Wen C. for building the codelinks foundation that made this possible. ubCode now implements the marker format natively - no Sphinx build required. Watch the video to see what traceability looks like when it actually lives in your code. #ubCode #Traceability #XAsCode #useblocks

Critical vulnerability CVE-2026-5752 in Terrarium sandbox risks root access. Terrarium, a Python sandbox in Docker containers, suffers JavaScript prototype traversal flaw. Exploitation allows sandbox escape and root code execution on host. This affects any organization running untrusted code in containers. Attackers can access sensitive files and pivot across container networks. Mitigate by disabling code submissions, segmenting networks, deploying WAFs, monitoring containers, restricting access, managing containers securely, and updating dependencies.

Here's the thing most developers miss: Claude Code has full terminal access. That's the entire point. It can shell out to wc for accurate token counts (LLMs are terrible at counting). It can use grep, sed, awk for text processing. It can hit APIs, execute Python scripts, deploy to production. It can also rm -rf your entire system if you're not careful with permissions. So the setup isn't optional. It's the foundation. We talked about Claude Code setup and barely opened Claude Code itself yesterday. Because that's how software development works. You build the infrastructure first. #ClaudeCode #DeveloperProductivity #AITools #SoftwareDevelopment

Stop wasting time on manual tasks. 🐍⚡ I’ve put together a 12-slide Python Automation Roadmap to help you turn repetitive work into clean, scalable scripts. No fluff—just the core libraries and patterns you actually need. What’s inside: ✅ File Ops: Mastering os, shutil, and pathlib. ✅ Web & APIs: Scraping with Selenium and robust requests handling. ✅ Production Ready: Scheduling, Logging, and CLI design. ✅ Advanced: Asyncio, decorators, and a full real-world project. Whether you're looking to clean up your filesystem or build a headless web bot, this guide has you covered. 👇 Download the PDF below and start automating. #Python #Automation #Programming #SoftwareEngineering #PythonRoadmap

For years I was using pip for everything and uv only inside Docker. Last month I finally made the switch - uv only, everywhere. No going back. Here's why 👇 pip ✅ comes pre-installed, zero setup ✅ everyone knows it, universal ✅ works fine for small projects ❌ painfully slow on large dependency trees ❌ no built-in virtual env management ❌ lockfiles are an afterthought ❌ resolving conflicts is a nightmare uv ✅ 10-100x faster than pip, noticeably so ✅ manages venvs, python versions, and packages in one tool ✅ proper lockfile out of the box ✅ drop-in replacement - same commands you already know ❌ not pre-installed, one extra setup step ❌ still maturing, some edge cases The only real argument for pip is that it's already there. Once you spend 30 seconds installing uv, that argument disappears. Faster installs, cleaner environments, proper locking - it just removes friction from your workflow. If you're still pip-only in 2025, try uv for one week. You won't go back either. Have you made the switch yet? #Python #DevTools #uv #pip #MLEngineering #SoftwareEngineering

Generic automation is a productivity killer. 📉 When you rely on "duct-tape" workflows and template bots, you're inheriting someone else's logic: not yours. These siloed agents create security gaps and operational nightmares. Autom8tion Lab replaces the chaos with unified, custom-engineered systems built on Python and n8n. The result? 🚀 30-day average launch. 📈 10x productivity improvement within 90 days. 🔒 SOC 2 protocol security. Stop settling for one-size-fits-all. Get a system engineered for your specific tech stack. #WorkflowAutomation #CustomSoftware #ROI #BusinessEfficiency

Panel 1: Optimism. "This new project will be so clean!" Panel 2: Simple steps. "Just a quick pip install." Panel 3: The reality. Error: package_a requires library_c 1.1, but package_b requires library_c 2.0. Panel 4: Defeat. The developer is a skeleton covered in cobwebs. 💀 We’ve all lived through this cycle. You spend four hours wrestling with pip, virtualenv, poetry, and conflicting dependencies, only to realize that you still haven't written a single line of actual code. "Dependency Hell" is the developer's universal rite of passage. If you are here now: I see you, and I feel your pain. What is your single most frustrating memory of package dependency management? Tag the dev partner who helped you survive it. 👇 #Python #SoftwareEngineering #ProgrammerHumor #CodingProblems #TheStruggleIsReal