Enforced SDD skills, installed in one command.
Superpowers packages a full spec-driven methodology into installable Claude Skills, enforcing brainstorming, planning, subagent-driven implementation, and strict TDD instead of leaving that structure up to you.
A small coding agent that expects you to shape it.
Pi Coding Agent is a minimal, open-source terminal coding harness that ships with four default tools and leaves most of the behaviour to extensions, skills and your own workflow.
Why your AI stack gets stickier every month.
Every API call feels like a simple transaction - until enough of them accumulate that your fine-tuning data, evaluation harnesses, and tool schemas are all shaped around one vendor, and switching stops being a routing change.
Delegate the noisy work, keep your context clean.
Most Claude Code sessions get slow and cluttered for the same reason: every exploratory grep, every log dump, and every “let me check one more file” stays in the main conversation forever.
When to move from Ollama to vLLM
Ollama is one of the easiest ways to run a local language model, but convenience can conceal the moment when a local experiment becomes a shared inference service that needs better scheduling and observability.
Keep compiled knowledge trustworthy
An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources.
AI GPU comparison across three vendors
The AI hardware landscape has shifted significantly in 2026, with NVIDIA, AMD, and Intel all competing for developers who need GPUs capable of running local large language models and AI inference workloads.
Headless Hermes server with remote desktop access
Running Hermes Agent on a headless server while connecting from a desktop client on another machine requires two server processes and a single client connection.
Protocol security is who may act, not the model.
Prompt injection gets most of the security attention in LLM systems, and it deserves attention, but it is not the whole problem once agents start calling tools and delegating work to other agents.
Long-running A2A tasks outlive chat sessions.
Most AI agent demos still behave like chat completions with extra steps: you send a prompt, wait a few seconds, and get an answer back in one response.
Faster LLM inference without quality loss - a practical guide
A 70B model generates one token per forward pass, and each pass reloads weights from VRAM, computes attention across the context, and synchronizes memory. Between tokens, the GPU sits idle while it waits for sequential dependencies to resolve.
A2A is not dead. It is just not universal.
Google’s Agent2Agent protocol, usually shortened to A2A, had a strange first year.
Reliable polling patterns for AI agents.
Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.
MCP gives agents tools. A2A gives agents peers.
AI agent architecture is starting to split into two layers.
A2A turns agents into network peers.
The A2A Protocol, short for Agent2Agent Protocol, is an open standard for communication between independent AI agent systems.
