Agent Memory Is Not a Database: Start with an Event Log
I break agent memory into writing, storage, retrieval, assembly, and forgetting, then outline a minimal implementation path that starts with a durable event log.
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I break agent memory into writing, storage, retrieval, assembly, and forgetting, then outline a minimal implementation path that starts with a durable event log.
A Skill teaches an agent how to work, a Tool lets it act, MCP connects it to external capabilities, and a Plugin packages the pieces for installation.
A reading note on abductive reasoning, interactive simulation, and what current world models can and cannot do.
A source-based comparison of Pi, Codex, and OpenCode shows how a minimal coding harness leaves workflow policy and isolation to the user.
A small Korean-language comparison showed why transcript coverage, timestamps, and manual review matter as much as model output.
A video-subtitle pipeline that uses Cloudflare for coordination and a local machine for private, CPU-heavy media work.
A commit-pinned reading of the Matt Pocock Skills repository shows how its invocation rules keep workflow direction with the human while the host retains tools and permissions.
A follow-cam design that combines face recognition with person tracking, appearance, pose, segmentation review, and hybrid framing.
I adapted public ideas from an AI-assisted broadcast workflow into a cautious follow-cam experiment for my bias.
After moving to GPT-5.6, I began choosing smaller agent workflows and paying more attention to the loop that decides what happens next.
Reusable skills are helping me give agents clearer working context, spend less time repeating process, and keep the important decisions reviewable.
Field notes on using Workers, Static Assets, D1, R2, Cron Triggers, and Access to move two private applications beyond a local machine.
Why I moved this personal website from a Notion-backed Next.js system to a smaller Astro site with local Markdown content.
How I moved a personal RAG chatbot to Cloud Run with PostgreSQL, session-aware retrieval, and Vertex AI.
How I moved a local Python data collector to Google Cloud so a teammate could trigger it remotely through Telegram.
I separated transcription, translation, and subtitle rendering to make a personal video translation pipeline easier to debug.
I wrapped my multi-agent chatbot in Docker and FastAPI so I could use it through a simple browser interface.
I added safety checks, retrieval ranking, and response editing to make my personal assistant more reliable.
I built a personal assistant around my Obsidian notes to learn how retrieval, agents, and personal knowledge can work together.
A short fraud-detection project helped me revisit class imbalance, Random Forest, SMOTETomek, and precision-recall metrics.
I moved my AI newsroom from CrewAI to LangGraph to gain clearer state, workflow control, and more consistent output.
I built a small AI newsroom with CrewAI to collect, filter, and summarize the AI news I actually want to read.
I used Google Apps Script and Gemini to classify incoming Gmail messages and make a crowded inbox easier to scan.
I used Python, an Android emulator, and OCR to automate a repetitive in-app card-spinning workflow.
A practical introduction to agent workflows, using a CrewAI newsroom example to explain roles, loops, and trade-offs.
Four practical ways to give language models clearer instructions, better context, and smaller tasks.
四個實用方法:講清楚任務、補充背景、要求檢查,再把大任務拆小。
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