PastAGI (pastagi.com) is an independent AI publication for builders, engineers, and technical decision-makers who want practical guidance instead of hype. The site publishes hands-on guides, honest tool reviews, engineering deep dives, and real-world use cases, organized into clear sections: Guides, Tools, Engineering, Use Cases, News, and a searchable Archive.
What makes PastAGI different is its commitment to verification. Every review and analysis is backed by reproducible math: serving costs, latency budgets, and evaluation numbers are shown with their working, so readers can check the claims themselves. The editorial line is explicitly anti-hype: benchmarks are audited rather than repeated, vendor pricing is broken down line by line, and bold industry claims are tested against public evidence before they earn a recommendation.
Recent coverage shows the range. "Custom AI Inference Chips Are Eating the GPU Market" examines how purpose-built silicon is reshaping inference economics. "The End of AI Reasoning Transparency: When Chain of Thought Becomes a Summary" investigates what is lost when vendors compress or summarize a model's reasoning traces. "Why AI Agents Leak Sensitive Data (and How to Stop Them)" gives a concrete defense checklist for teams shipping agentic features. "GPT-6 Astra Pricing at $6 an Hour, Audited" rebuilds the vendor's cost math from public pricing pages and shows where the marketing numbers bend. "Prompt Dependency Graphs That Shrink Your Retest Set" introduces a practical method for deciding which prompts to re-run when a model or prompt library changes, cutting regression testing from days to hours. "TontaubeV1 Review With Serving Math" reviews a 2.9-billion-parameter character-level text-to-speech model and publishes the serving arithmetic behind the verdict.
PastAGI is free to read, carries no paywall, and does not gate its archive behind an account or newsletter signup. It is written for practitioners: ML engineers choosing serving stacks, platform teams designing evaluation pipelines, and technical leaders who need to separate durable capability from marketing noise. New analysis is added weekly across the Guides and Engineering sections, and the Tools section tracks the models and infrastructure the editorial team has actually run, not sponsored placements.
The site's audience has grown by word of mouth in engineering communities precisely because it admits uncertainty: when the data is incomplete, the analysis says so, and follow-up pieces revisit earlier conclusions as new evidence lands. Corrections and methodology notes are published openly, which is why teams trust the recommendations that survive review.
For anyone building with large language models, PastAGI offers a rare combination: engineering depth, honest economics, and a track record of publishing the numbers behind every conclusion. It is the rare AI site where the review process is as transparent as the technology it covers.
