Dynamic Prompt Compression System with Learned Importance Scores
A hands‑on guide to building a from‑scratch prompt compression system that learns token importance and truncates context for fixed‑size LLM windows.
Prompt-Engineering
Dynamic Sliding-Window Context Manager for LLM Prompt Histories
A hands‑on guide to building a production‑ready context manager that automatically trims prompt histories using attention‑weighted saliency, keeping your LLM prompts within any context window.
Build a Sliding-Window Prompt Compressor from Scratch in Pure Python
A hands-on guide to building a sliding-window prompt compressor from scratch in pure Python, using a learned importance scorer to evict low-relevance tokens and keep attention focused within a fixed context budget.
Hands‑On Build Guide: Sliding‑Window Context Manager with Attention‑Weighted Relevance Scoring
Learn to build a token‑aware sliding‑window context manager that keeps the most relevant prompt parts within token limits.
Claude Loop Engineering: Designing Agentic Workflows That Actually Converge
Claude loop engineering is the practice of building bounded, observable, tool-using agentic workflows on top of Anthropic’s Claude. This post covers loop anatomy, stop conditions, tool design, and the production patterns that keep agents from drifting, looping forever, or burning budget.