GenAlphAI is a research-driven publication for senior AI engineers, founders, and technical operators. We don't chase the news cycle — we publish deep, evidence-backed analysis of the systems that actually matter: agentic loops and harnesses, model evaluation, context engineering, and the economics of AI software.
Every flagship article begins with a multi-source deep-research pass, is written by a frontier model under a strict quality harness, and is structured for both human readers and AI answer engines. We cite specifics, name sources, link primary evidence, and flag what's contested.
Our standard
- Evidence over vibes. Specific numbers, named systems, dated sources, direct quotes.
- Depth over volume. Pillars run 2,500–4,000 words; nothing is padding.
- Built to be cited. Claim-first writing, schema.org markup, a clean llms.txt directory.
We currently track 3 published articles, and the catalog grows continuously.