SynapseKit API Docs: Thorough and First-Class

I published a new article about something many developers will appreciate: how to recover your Claude Code chat history in VS Code without losing the early context that gets compacted away. The key idea is simple: - Claude Code stores history locally in ~/.claude/projects. - The logs are saved as JSONL files. - Context compaction affects the live session, not the stored history. - A Python CLI can help you retrieve and read the transcripts. If you’ve ever lost a good idea because you clicked “yes” too quickly or forgot to save your prompt trail, this workflow can save you time and frustration. Read the article here: https://lnkd.in/epUjEF6f #ClaudeCode #VSCode #Python #DataScience #AI #DeveloperProductivity #Automation #Substack

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

Everyone is telling non-technical people to learn to code. I think that's the wrong advice. The people moving fastest right now aren't the ones who learned Python. They're the ones who learned to think precisely. Vague input produces vague output. Every time. The bottleneck isn't code anymore. It's clarity. Learn to write precisely. Learn to describe outcomes instead of methods. Learn to give feedback specific enough to act on. That's the skill nobody is putting in the course catalog yet. Who have you seen thrive with AI tools — and what made the difference?

Andrej Karpathy has released MicroGPT, a remarkable ~200-line pure Python implementation of a full GPT model from scratch. Released on February 12, 2026, the script includes everything needed—dataset handling, tokenizer, autograd engine, GPT-2 architecture, Adam optimizer, and complete training plus inference loops—without any external libraries. This minimal codebase continues Karpathy’s tradition of educational “micro” projects like micrograd and nanoGPT, aimed at stripping away frameworks to reveal how large language models truly work. The release is expected to help developers and students gain deeper intuition into transformer fundamentals through hands-on coding rather than relying on black-box tools. 200 lines. No dependencies. From nothing. For anyone who has ever wanted to understand what a large language model actually is — not what it does, but what it is — this file is the answer. https://lnkd.in/dS6Epsn4

A few weeks ago, a friend of mine who's a Math PhD told me he was completely stuck with his research. He's a genius at math, but coding isn't his thing. He was trying to use AI chatbots to help him turn complex formulas from academic PDFs into Python code so he could test his ideas. The problem? They kept hallucinating or just missing the logic in the math notation entirely. He was spending days trying to fix broken code that was supposed to save him time. He said: "I just want to test these ideas without getting stuck in the code every time." That stuck with me. I'm a software engineer, so I built him something. I called it AlgoMath, a specialized agent skill that sits on top of Claude Code and OpenCode. Instead of a generic chatbot, it follows a proper autonomous workflow to make sure the math actually stays accurate: It reads the PDF and pulls out the raw mathematical logic. Breaks it into structured steps. Turns those into clean, executable Python code. Runs it in a sandbox to catch errors. Then explains the results and checks everything against the original paper. A task that used to kill his whole week now takes about 30 seconds. He just tells his terminal agent to use the AlgoMath skill, and he's back to doing actual research. I open-sourced it and kept the setup simple: npm install, a small wizard walks you through the rest, and you're running it in your terminal agent immediately. Check it out: NPM: https://lnkd.in/d2TMKpjj GitHub: https://lnkd.in/dwWACnnH #SoftwareEngineering #AIAgents #ClaudeCode #Python #Math #AlgoMath #OpenSource

I just published oci-genai-python-starters, a small GitHub repo for anyone looking for a simple starting point with OCI Generative AI in Python. It shows the basic setup across different libraries and SDK styles, including the raw OCI SDK, LangChain, LlamaIndex, OpenAI-compatible chat, OpenAI Agents, and the Responses API. The goal is simple: help beginners and junior developers compare approaches, understand the setup faster, and get working examples running with less friction.  #OCI #Python #GenerativeAI #LangChain #LlamaIndex

The bottleneck in AI-assisted coding isn't the model or your prompts. When an agent can't see your notebook state, it guesses. You're relaying error messages and stuck in the (endless) loop at every step. With marimo-pair, coding agents get a live view of your notebook. Variables, errors, UI elements - if you can interact with it, the agent can too. PS: you're also not paying per token to analyze your own CSV files. https://lnkd.in/gcBjKijm #python #AI #datascience #openSource #mlops

3 RAG chunking strategies. 2 cost the same. 1 costs double. Here's why 👇 Building my own RAG pipeline, I hit a wall. Everyone talks about WHAT chunking is. Nobody talks about WHERE the money goes. Here's what I figured out: 💰 The $ only comes from ONE place — the embedding model API call. Splitting → FREE (plain Python) Storage in FAISS/CSV → FREE Embedding API call → costs money Ingestion → ONE TIME cost only Semantic costs 2x because it calls the embedding API twice: → Once to find topic shifts → Once to store vectors in FAISS Rule of thumb: Start with Document Aware. Upgrade to Semantic only when your data has no clear structure. Ingestion cost = one time only. Query cost? That's a whole different story. 🔜 Follow so you don't miss it. Which chunking strategy are you using? Drop it below 👇 #RAG #AIEngineering #DataEngineering #LLM #Python #VectorDatabase #OpenAI #LangChain #Claude

Build a production AI agent in 10 lines of Python. Strands Agents SDK: ```python from strands import Agent from strands.models.bedrock import BedrockModel from strands_tools import calculator, web_search agent = Agent( model=BedrockModel("anthropic.claude-sonnet-4-20250514-v1:0"), tools=[calculator, web_search] ) response = agent("What's the GDP per capita of the top 5 economies?") ``` That's it. Tool calling, conversation management, streaming, multi-turn context is all handled. Why Strands over LangChain for AWS: - Built for Bedrock integration (not retrofitted) - Works with any Bedrock model - ToolSimulator for agent testing (just released) - Strands Evals for evaluation pipelines - Open-source, runs locally For production: pair with Bedrock AgentCore for managed deployment, guardrails, and observability. The stack: Strands (build) → ToolSimulator (test) → Strands Evals (evaluate) → AgentCore (deploy) Full lifecycle. Open-source foundation. Managed deployment when ready. Start: https://strandsagents.com #AWS #AIAgents #Python #Strands #Bedrock