https://anooptube.in/tags/llm/ Recent content in llm on #anooptube Hugo -- gohugo.io en-us Tue, 18 Aug 2026 15:00:45 +0530 https://anooptube.in/blog/post59/ Tue, 18 Aug 2026 15:00:45 +0530 https://anooptube.in/blog/post59/ Every time I open a new tab it’s another SaaS wrapper around someone else’s API key. At some point I got tired of paying per-token for things I could run on hardware already sitting on my desk. So I spent a weekend actually setting up local LLMs properly — not just running one toy demo, but figuring out what hardware you actually need, which models are worth pulling, and where the real bottlenecks are. https://anooptube.in/blog/post58/ Tue, 18 Aug 2026 15:00:34 +0530 https://anooptube.in/blog/post58/ Every AI product I use daily — Claude, ChatGPT, Gemini, Copilot, Cursor — quietly picked up support for the same protocol this year, and I kept running into it before I actually understood what it was. I’d see “MCP server” mentioned in a plugin’s README, or a coworker would say “just wire it up over MCP,” and I’d nod along without a clear mental model of what was actually happening under the hood. https://anooptube.in/blog/post50/ Sat, 15 Aug 2026 19:48:00 +0530 https://anooptube.in/blog/post50/ Every “build an AI agent” tutorial I ran into lately starts with pip install langchain or pip install langgraph. Useful tools, but they hide the actual mechanism. Before reaching for a framework, I wanted to see the loop with my own eyes — so I built the smallest working agent I could, in plain Python. Turns out it’s not much code. The core loop fits in about 60 lines. This post walks through building that loop from scratch: what an agent actually is, what the ReAct pattern means in practice, the full working code, a real trace of it reasoning through a task, the ways it breaks, and when you should stop doing this yourself and reach for a framework.