How to Reduce AI Agent Context and Tool-Call Overhead
A real AI-agent workflow audit shows how task routing, compact state checks, shared validation scripts, and smaller handoffs can reduce context and tool-call overhead.
The RAMGPT library / 7 articles
Step-by-step instructions for useful AI projects and tools.
A real AI-agent workflow audit shows how task routing, compact state checks, shared validation scripts, and smaller handoffs can reduce context and tool-call overhead.
I asked ChatGPT how hard it would be to make a token and ended with one billion RAMG on Base Sepolia, backed by public source and an on-chain receipt.
A Whitby high school student explains how she uses ChatGPT for hints, error analysis, proofs, calculus, vectors, and practice without outsourcing the thinking.
A reproducible guide to training LightGBM on Yahoo Finance data, ranking stocks before each week, buying the first session, and exiting the last.
What I learned hardening an open-source coding agent for enterprise use, including controls for MCP, shell execution, secrets, downloads, and runtime trust.
A simple step-by-step llama.cpp guide for beginners: compile from source, run a GGUF model, use GPU acceleration, and tune the main inference settings.
A practical first-person workflow for using ChatGPT to understand technical subjects, test your reasoning, create practice questions, and verify important answers.