Posts

Self-Reinforcing Memory Loops in AI Agents: Causes and Fixes

Agent memory can turn model inferences into future evidence. How those loops form, why they matter, and which write-path controls limit them. Self-Reinforcing Memory Loops in AI Agents: Causes and Fixes

OpenSpec Rejected Proposals: A Decision Memory Convention

OpenSpec has no native rejected-proposal state. Learn why, what the maintainers decided, and the decision.md convention that stops agents reopening settled investigations. OpenSpec Rejected Proposals: A Decision Memory Convention

OpenSpec Quickstart: Install, Workflow, and Common Pitfalls

Install OpenSpec, the change-centric spec-driven development CLI. Learn the explore-propose-apply-archive loop, brownfield setup, and where it breaks. OpenSpec Quickstart: Install, Workflow, and Common Pitfalls

How to Migrate from OpenClaw to Hermes Agent Safely

Migrate OpenClaw to Hermes Agent with dry-run checks, precise memory and skill mapping, secret handling, channel cutover, rollback, and manual fixes. How to Migrate from OpenClaw to Hermes Agent Safely

LLM Hosting in 2026: Local, Self-Hosted and Cloud Infrastructure Compared

Complete guide to LLM hosting in 2026. Compare Ollama, llama.cpp, vLLM, TGI, Docker Model Runner, LocalAI and cloud providers. Learn cost, performance, and infrastructure trade-offs. LLM Hosting in 2026: Local, Self-Hosted and Cloud Infrastructure Compared

llama.cpp vs Ollama in 2026: Which Runtime Should You Run?

Compare llama-server and Ollama for local LLM hosting in 2026: GGUF management, APIs, GPU control, KV cache, model lifetime, and migration triggers. llama.cpp vs Ollama in 2026: Which Runtime Should You Run?

ROCm vs Vulkan for AMD Local LLM Hosting: 2026 Guide

Compare AMD ROCm and Vulkan backends for llama.cpp, Ollama, LM Studio, vLLM and TGI, with build commands, verification checks and a practical 2026 verdict. ROCm vs Vulkan for AMD Local LLM Hosting: 2026 Guide

KV Cache on 16 GB GPUs: Making Long Context Actually Fit

Fit 32K to 128K LLM context into 16 GB VRAM by calculating KV cache cost, choosing cache precision, and tuning llama.cpp, vLLM, or Ollama safely. KV Cache on 16 GB GPUs: Making Long Context Actually Fit