Welcome to my blog about Go, LLM, AI Agents and engineering.
Agent Memory: From Isolated Context to Collaborative Memory
A deep dive into LLM agent memory architecture based on the MemRec paper (ACL 2026): collaborative memory, decoupling memory management from reasoning, Information Bottleneck. Comparison of the TOP-3 production tools — Mem0, Letta (MemGPT), and Zep — across nine axes. Highlighting the gap: research outpaces tooling.
Go Internals: sync.Mutex and sync/atomic — From Data Race to Starvation Mode
Mutex and atomic are not two independent tools — they are one stack of abstractions. I dig through the stack from data race to starvation mode: why x++ is three ARM64 instructions, how CAS became a universal primitive (Herlihy 1991), why Mutex is built on top of atomic CAS, and how Russ Cox’s barging-vs-handoff debate (issue #13086) gave Go its 1ms starvation threshold. With public benchmarks, the bit layout of Mutex state, and a cheatsheet for when to reach for which.
AI Agent Design Patterns. Part 6: Agent Memory in Practice
Deep dive into agent memory mechanics: five memory files, four context assembly strategies, TOOLS.md as a dual-source responsibility, and Dreaming — background consolidation from Anthropic (Auto Dream, Dreams API, KAIROS) and OpenAI (Dreaming V3, 82.8% factual recall). Engineering decisions behind a production platform.
AI Agent Design Patterns. Part 4: Multi-Agent Patterns
Five multi-agent orchestration architectures — Supervisor, Swarm, Debate, Assembly Line, Hierarchical — with Mermaid diagrams, code examples in Eino (Go), and an honest look at limitations. When one agent isn’t enough, and when you’re better off staying solo.
AI Agent Design Patterns. Part 5: Agent Memory Management
Context window ≠ memory. I break down agent amnesia, the academic taxonomy (Du et al., 2026), five approaches to memory — from OpenClaw’s file-based brain to mem0’s managed layer — and how our team solves this in practice.