What if your code could think? That's LangChain. LangChain is a framework that lets you build apps powered by LLMs (like GPT or Claude) - with memory, tools, and logic. Here's how simple it is to build a chatbot with memory in Python: from langchain_openai import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationChain llm = ChatOpenAI(model="gpt-4") memory = ConversationBufferMemory() chain = ConversationChain(llm=llm, memory=memory) chain.predict(input="My name is Virat") chain.predict(input="What's my name?") # → "Your name is Virat." Without memory → every message is a fresh conversation. With memory → the model remembers context across turns. LangChain also lets you: 🔹 Connect LLMs to your own documents (RAG) 🔹 Give the model tools — search, calculator, APIs 🔹 Build multi-step AI agents that reason and act 🔹 Chain prompts together for complex workflows #LangChain #Python #LLM #MachineLearning #BackendDevelopment #LearningInPublic #Java #SpringBoot #AI

AI Tools Every Developer Should Know From APIs to Production. Another interesting episode from Sateesh Tech Talk, breaking down the end-to-end AI stack for building real, production ready AI apps using familiar software engineering patterns. 🎥 https://lnkd.in/g-YqcHQj You’ll learn: • How requests flow through APIs, RAG, LLMs, tools, and production systems • Upper vs. lower layers of the AI stack • Implementing the same architecture in Java and Python • Why RAG, tools, and observability matter If you can build APIs or microservices, you already have the skills to build AI systems.

"The West forgot how to code" is going viral. The thesis is that AI assisted devs ship faster but understand nothing, and the next generation will be illiterate at the layer that matters. The reality is this panic happens every decade. Assembly devs said C devs were illiterate. C devs said Python devs were illiterate. The abstraction layer moves up. The people who understand the current layer are always the ones building the future.

#Claude Code source code leaked yesterday… and things escalated fast. 🦞 Within hours: A Python version (“#Claw Code”) hit 50k #GitHub stars in ~2 hours. Yes. 2 hours. What happened? • The mistake: #Anthropic shipped a source map in an npm package • The result: ~500k lines of code exposed • The twist: devs used AI to rewrite it in #Python (and even #Rust) almost instantly Let that sink in. We’re now using #AI to reverse-engineer AI, that was built to help us write code. The loop is closed. At this point, serious question: Is proprietary software still defensible… if a global dev #community can rebuild your #product in a few hours?

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

The next generation of data infrastructure won't be built for analysts. It'll be built for agents. And honestly, most data platforms aren't ready for that yet. They were designed for humans- people who slow down, double-check, and ask for sign-off before anything hits production. Agents work differently. They move fast, they iterate, and they need infrastructure that can keep up without breaking things. That means Python-native, isolated by default, atomic merges, instant rollbacks. Not manual guardrails. On April 21 at 9am PT, we're showing what that looks like in practice- live, in #Python, with our friends at dltHub. Looking forward to seeing you there :)) Register: https://lnkd.in/eqVQ5C5n

In 2026, "knowing Python" is the baseline. The real premium is on developers who can orchestrate. If you're looking to level up your stack this quarter, look past the hype and master these three libraries: 1)LangChain/LlamaIndex: For building real-world RAG pipelines and autonomous agents. 2)Pydantic v2: Essential for type-safe data validation in AI APIs. It’s powered by Rust now, making it blazing fast. 3)FastAPI: Still the king of modern backend, but the real power is how it integrates with asynchronous AI workflows.

Python is too slow for the backend. 🥱 This was a valid take in 2023. In 2026? It’s a misunderstanding of how the Agentic Economy actually works. Despite the rise of high-performance languages, Python remains the undisputed king of the backend for AI-native systems. If you want to know why the world’s most advanced Sovereign AI architectures are still built on Python, here are the three non-negotiable reasons: 🚀 1. The "No-GIL" Revolution With the final removal of the Global Interpreter Lock (GIL), Python finally unlocked true multi-core concurrency. We can now run complex Agentic Orchestration and heavy data processing in a single process without the "performance tax" we used to pay. It’s no longer just a "scripting language"; it’s a high-velocity engine. 🧠 2. The "Gravity" of the Ecosystem Every breakthrough from Llama 4 to the latest MCP (Model Context Protocol) servers drops in Python first. When you’re building in a field that moves this fast, "Developer Velocity" is more important than raw execution speed. In the time it takes to write a memory-safe wrapper in another language, a Python dev has already shipped a self-correcting agent to production. 🔗 3. The Ultimate "Glue" for Hybrid Systems Modern backends aren't monolithic. We use Rust for the heavy math and C++ for the kernel, but Python is the connective tissue. It’s the language of LangGraph, PyTorch, and FastAPI. It allows us to orchestrate a "Polyglot Architecture" where we get 100% of the performance with 0% of the boilerplate. The 2026 Reality: We don't use Python because it’s the fastest. We use it because it’s the smartest. It allows us to spend less time fighting the compiler and more time architecting the intelligence. Are you still optimizing for nanoseconds, or are you optimizing for orchestration? Let’s talk about the 2026 stack below. 👇 #Python #BackendEngineering #AgenticAI #SoftwareArchitecture #2026TechTrends #MLOps #SystemDesign #DeveloperVelocity

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

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