Recover Claude Code Chat History in VS Code Without Losing Context

Posted a tiny Python demo: atlas-kvd-demo. It models a bounded state update with: K = carried state  V = outside pressure  Δ = local event nudge Small repo, but the idea matters to me: systems should drift, react, and settle instead of snapping all over the place. https://lnkd.in/e8WFcFPv Not a giant framework.  Not “AI solves everything.”  Just one legible state law, in code. #Python #GitHub #Simulation #StateMachines #AI #IndieDev #BuildInPublic

Wild to see Claude Code, one of the most talked-about AI coding tools right now, go viral like this. v2.1.88 was accidentally shipped with a debug/source map file, exposing a large part of the internal TypeScript codebase. Anthropic said it was a release mistake caused by human error, not a hack, and no customer data was leaked. The craziest part? The internet moved so fast that people were already digging through it and rebuilding ideas around it in Python and Rust. Just shows how one bad release can put your product everywhere in a few hours. checkout python implementation https://lnkd.in/gWEV_XKc from instructkr. #ClaudeCode #Anthropic #AI #SoftwareEngineering

Stop digging through logs. Start fixing the problem. 🛠️ During a technical incident, every second counts. I built the Automated Technical Log Analyzer to handle the heavy lifting. What it does: It takes raw logs from multiple sources and uses native Python logic to strip away the noise. The Brain: I implemented an agentic workflow using GPT-4o-mini to identify root causes and recommend actionable recovery steps. The ROI: This pipeline is designed to reduce Mean Time To Repair (MTTR) by approximately 70%. Check out the repo: https://lnkd.in/dDPyJDNH #Python #DevOps #AI #AgenticWorkflows #GPT4

You don't need LangChain to build AI agents. You need to understand what's inside it. I spent the last month building agents from scratch just Python, Ollama, and the ReAct loop. No frameworks. No abstractions. Nothing I couldn't debug. 6 concepts kept coming back: → Anatomy — the 4 parts of every agent → The loop — how decisions actually happen → Tools — what they really are (not magic) → Memory — the illusion the harness creates → Scaling — what breaks at each stage → Orchestration — when one agent isn't enough I put them in a visual guide. 11 slides. Save it. Open it next time your framework does something weird. Share it with someone building their first agent. This is the reference I wish I had on day 1. 👇 Swipe through. #AIAgents #AIEngineering #LLM #MachineLearning #SoftwareEngineering #Python #BuildInPublic #FirstPrinciples #TechCareers

A few weeks ago I couldn't write a single line of code. Tonight I built a Python script that: → Reads my own Discord log files → Sends them to Claude AI via API → Gets back a real-time status report about my progress The script is called Report.py. It runs locally, pulls data from my Realtime Discord Logger, and uses an LLM to summarize what's happening in plain English. No tutorial. No copy-paste. Just debugging until it worked. This is what Phase 2 looks like — connecting real data to real AI. Full project on GitHub 👇 https://lnkd.in/eCwq_3XZ #AIAutomation #Python #BuildingInPublic #CareerChange

LLM apps are powerful, but they're also hard to debug and expensive to run. Observability shouldn't make that worse. In our latest blog, we break down how the Python OpenTelemetry SDK impacts performance in LLM workloads and what happens when you rethink the stack. If you're building with LLMs and care about performance, this is worth a read 👉 https://lnkd.in/e_aHurKw #honeycomb #OpenTelemetry #AI #LLMs

I built my first AI Smart Study Assistant using Python and Streamlit. It’s a simple project where I tried to understand how AI apps actually work with a clean user interface. ✨ What it can do: 📄 Summarize text (demo mode) 🧠 Explain topics in simple words ❓ Generate quiz questions 🎨 Simple and interactive web UI 🛠️ Tech used: Python, Streamlit While building this, I understood how user input flows into logic and how AI-based applications are structured. Right now this is a demo version, but I designed it in a way that it can be upgraded later with real AI models and a chat interface. Next step for me is to improve this project further and keep building more AI-based applications. Would love feedback or suggestions 🙌#AI #Python #Streamlit #MachineLearning #LearningByDoing #ArtificialIntelligence #TechJourney #WomenInTech #DataScience Microsoft Google OpenAI https://lnkd.in/eA-xwtqG

I used to write a lot of clumsy `if` statements just to group data. Checking if a key existed, then initializing a list, then appending. It felt clunky and repetitive. This simple Python trick lets you group any data points by category without boilerplate code, making your data prep for AI/ML much cleaner. It's perfect for aggregating model results by metric or sorting samples by class. 💡 What's your go-to Python trick for cleaning up data operations? #Python #PythonTips #MachineLearning #DataScience #Coding

𝐋𝐋𝐌𝐬 𝐝𝐨𝐧'𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨 𝐚𝐧𝐲𝐭𝐡𝐢𝐧𝐠.   They generate text. So when an “AI agent” queries a database, sends an email, or runs a command — something else is doing the real work. An orchestration layer most people never see. I wrote a breakdown of what that layer actually is — the 𝐑𝐞𝐀𝐂𝐓 𝐥𝐨𝐨𝐩 behind agent frameworks — built from scratch in ~40 lines of Python, with an interactive stepper to watch a full run end-to-end. Check out my blog post about this subject: → https://lnkd.in/e3K94--D #AIAgents #AgenticAI #LLM #AIEngineering #SoftwareEngineering