Comparisons

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. ...

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. ...

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. ...