Overcoming Context Limitations with Large PDF Q&A RAG Assistant

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

One misconfiguration in a GitHub Actions workflow. Fast forward to Friday evening and we’re tracking six throwaway GitHub accounts, 500+ malicious PRs, and an attacker who spent three weeks iterating on payloads before anyone noticed. Always a pleasure digging into these with Rami McCarthy, Benjamin Read and Scott Piper 🔍

I just wrapped up Anthropic's course on the Model Context Protocol (MCP). If you’ve ever built integrations between AI models and external services, you know it usually means writing a lot of custom boilerplate code and manually handling JSON schemas. The most valuable takeaway from this course was seeing how MCP standardizes that entire process. Instead of building one-off connections, MCP shifts the integration burden to a consistent architecture based on three clean primitives: Tools (controlled by the model), Resources (controlled by the app), and Prompts (controlled by the user). Getting hands-on with the Python SDK to build an MCP server—and replacing manual schema writing with simple decorators—showed exactly how much this protocol reduces the friction of connecting AI to real-world data and APIs. A really practical look at how AI infrastructure is maturing and becoming easier to scale. #ArtificialIntelligence #ModelContextProtocol #Anthropic #Python #SoftwareEngineering

Hyperparameter Optimization Machine Learning using xcessiv #machinelearning #datascience #hyperparameteroptimization #xcessiv Xcessiv is a web-based application for quick and scalable hyperparameter tuning and stacked ensembling in Python. Features : Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code Any model following the Scikit-learn API can be used as a base learner Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches Direct integration with TPOT for automated pipeline construction Automated hyperparameter search through Bayesian optimization Easy management and comparison of hundreds of different model-hyperparameter combinations Automatic saving of generated secondary meta-features Stacked ensemble creation in a few clicks Automated ensemble construction through greedy forward model selection Export your stacked ensemble as a standalone Python file to support multiple levels of stacking https://lnkd.in/gX5EEGYW

I just published my first open source Python package — and I want to share what I built and why. It's called paner — a terminal-based PDF analyzer powered by AI. The idea is simple: instead of uploading your documents to some cloud service and hoping they stay private, paner runs entirely on your local machine. You drop a PDF into your terminal, ask questions about it conversationally, and get intelligent answers — all without your files ever leaving your computer. Under the hood it uses: → Groq - for fast AI responses → ChromaDB for local vector storage → Sentence Transformers for embeddings → Python cmd module for the interactive CLI experience This project taught me a lot about RAG (Retrieval Augmented Generation), vector databases, Python packaging, and shipping a real product end to end. You can install it right now with: pip install paner-cli And the full source code is on GitHub: https://lnkd.in/emZZAHvt This is just the beginning. If you try it out, I'd love your feedback. #Python #OpenSource #AI #RAG #BuildingInPublic #SoftwareDevelopment #MachineLearning

I'm learning how to build AI agents from scratch. I just finished Ask My Docs — a CLI agent that answers questions from documents using Python and the Anthropic SDK. Point it at a folder of text files and ask anything in plain English. The agent decides which files to read and returns a structured answer with sources and confidence level. Three things I learned building this: → How LLM tool calling actually works under the hood → Why forcing structured output via tool_use is more reliable than asking the model nicely for JSON → How Pydantic enforces typed responses from an LLM Stack: Python · Anthropic SDK · Pydantic GitHub: https://lnkd.in/ghwRVxgU #BuildInPublic #LearningInPublic #AIAgents #Python #Anthropic

Python heapq module the hidden gem for priority queues: When you need to manage tasks or data by priority, heapq is your go‑to. It provides a heap‑based priority queue implementation using a min‑heap, which means the smallest element always rises to the top. Why it matters: Efficiently handle large datasets. Perfect for scheduling, simulations, or anytime you need quick access to the lowest value. Built into Python no extra installs needed. Remember: heapq is a min‑heap, not a max‑heap. If you need max‑heap behavior, you can invert values or customize logic. At IT Learning AI, we make sure you don’t just learn syntax you understand how to apply it in real projects. Ready to master Python’s powerful modules? Explore tutorials, guides, and hands‑on coding insights at https://itlearning.ai 🔗 Learn. Apply. Grow. With IT Learning AI. #itlearningai #developergrowth #techeducation #codingjourney #learnwithai #pythonprogramming #pythonforbeginners #phyton3 #pythondatastructures #codesmarter #learnpython #advancedpython #pythondevelopers #pythonbasics #ImmutableData #PythonSets #PythonDataStructures

 🚀 Excited to announce Claw Code Agent — our open-source Python reimplementation of the Claude Code agent architecture!  💡 Inspired by the reverse-engineering work shared here:  🔗 https://lnkd.in/dzrrj6da  We took that foundation and built a fully working Python agent from it.  🐍 Pure Python. No Rust. No TypeScript. Just Python.  🔧 Full Agent Capabilities:   ✅ Agentic coding loop with tool calling   ✅ File read/write/edit, grep, glob, shell   ✅ Slash commands & context engine   ✅ Session persistence & resume   ✅ Tiered permission system  ⚡ Works with any OpenAI-compatible API:   🟢 vLLM   🟢 Ollama   🟢 LiteLLM Proxy  🐉 run a full coding agent locally, for free.  📬 We're actively developing this — if you have feature requests, ideas, or want to contribute, we'd love to hear from you. Open an issue or submit a PR!  👉 https://lnkd.in/dmuAAYah  ⭐ Star the repo if you find it useful!  #OpenSource #AI #CodingAgent #Python #LLM #vLLM #Qwen #MachineLearning #DevTools #AIAgents #ClaudeCode

Python bisect module keeping lists sorted the smart way: Normally, when you insert new items into a list, you’d have to sort it again to keep order. That’s inefficient, especially with large datasets. The bisect module solves this by finding the right position for insertion automatically, so your list stays sorted without the overhead of full re‑sorting. Why developers love it: Saves time and memory. Perfect for maintaining ordered sequences like leaderboards, logs, or priority lists. Built‑in and easy to use no extra libraries needed. At IT Learning AI, we break down these hidden gems so you can write cleaner, faster, and more efficient code. Want to master Python’s built‑in modules? Explore tutorials, guides, and practical coding insights at www.itlearningai.ai Learn. Apply. Grow. With IT Learning AI. #itlearningai #pythonprogramming #learnpython #pythontips #pythonbasics #pythonforbeginners #codesmarter #pythonbisect #sortedlists #efficientcoding #pythonperformance #advancedpython #pythondevelopers

In the spirit of World Quantum Day, I wanted to move beyond theory and actually explore how post-quantum cryptographic algorithms behave in practice. So I built a benchmarking system for PQC KEMs using Python and the Open Quantum Safe (liboqs) library, running everything in a reproducible Docker environment. I analyzed algorithms like: -ML-KEM (Kyber) -NTRU across key metrics: -Key generation -Encapsulation & decapsulation -Key and ciphertext sizes It was fascinating to see how the trade-offs between security, performance, and size show up clearly when you measure these systems. I also used OpenAI's Codex as an AI assistant throughout the process — helping with debugging, structuring the pipeline, and speeding up development. 🔗 GitHub: https://lnkd.in/gk7HuwAa #WorldQuantumDay #PostQuantumCryptography #Cryptography #PQC #Python #Docker #AI