AI tools reviewed. Open-source projects explained. Tutorials that actually work.
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DeepSeek V4 in Claude Code: Anthropic-Compatible API with Native Web Search
TL;DR — DeepSeek ships an Anthropic-compatible API endpoint that lets Claude Code treat DeepSeek V4 Pro like a drop-in replacement for Claude Opus. The setup is eight environment variables, and it works — including tool calling, sub-agent spawning, and native web search. At $0.435/M input tokens (permanent price after the initial launch promo), it’s roughly 4–17× cheaper than Claude Opus 4.7. This is a practical guide based on a real setup we run daily. ...
How Claude Code's Agent Architecture Works — and How We Built a Similar System for a Terraria Server
TL;DR — We reverse-engineered Claude Code’s agent architecture from its TypeScript source to understand how it handles security, complex tasks, and tool permissions. Then we applied those patterns to an open-source Terraria AI bridge that lets players talk to an LLM inside the game. Here’s what we found, what we built, and what we learned about practical agent design. Why We Cracked Open Claude Code’s Source Claude Code isn’t just a coding assistant. Under the hood it’s an agent runtime — it spawns sub-agents, manages file permissions, runs bash commands, and decides when to ask the user vs. just doing the thing. We wanted to understand how it works so we could apply the same ideas to a completely different domain: a Terraria game server. ...
n8n vs Dify: One We Adopted, One We Skipped
TL;DR — n8n and Dify often show up together in self-hosted AI evaluations, but they want to own very different layers of your stack. After evaluating both against a custom self-hosted AI setup, we adopted n8n and skipped Dify. The decision came down to one question — “what slice does this want to own, and do I already own that slice?” — and the answer was opposite for the two platforms. This post lays out the framework so you can run the same evaluation on your own stack. ...
Hello, World
Welcome to MyBrew This is the first post. More coming soon. Tool Reviews — hands-on testing Tutorials — step-by-step AI guides Comparisons — side-by-side breakdowns Roundups — curated collections Everything tested. No hype.
Huawei's τ-Scaling Law: A Real Read of the Paper Behind the Hype
TL;DR — Huawei’s τ (Tao) Scaling Law, announced at IEEE ISCAS 2026, reframes Moore’s Law: instead of shrinking transistors, optimize a time constant τ across the entire computing stack. The paper is real, the production data is concrete, but the “first scaling law since Dennard” claim deserves scrutiny. This is mostly a solid 3D-integration engineering paper wrapped in a strategic narrative about how China builds high-performance chips without leading-edge lithography. ...
RAG vs Agents: When to Use Which (With Real Examples from Our Stack)
TL;DR — RAG answers from documents. Agents take actions. Most real systems use both: RAG provides context, agents act on it. The hard part isn’t picking one — it’s knowing which layer of your problem belongs to which pattern. Why This Comparison Matters Right Now Two things happened in the last six months that make this comparison less academic than it used to be. First: coding agents crossed a quality threshold around November 2025. Simon Willison’s five-minute PyCon talk describes it as the moment agents went from “often-work” to “mostly-work” — usable as daily drivers, not just demos. The “best model” title changed hands five times between Anthropic, OpenAI, and Google in a single month. ...