#artificial-intelligence

28 posts

2026 - October

5 posts

2026 - September

6 posts

2026 - August

1 post

2026 - July

2 posts

2026 - May

1 post

2026 - April

1 post

2026 - March

4 posts

2026 - February

6 posts

2025 - June

1 post

2025 - April

1 post

2026 - October

5 posts

I Self-Published My Own Book/e-book: Competent Enough

I published my book about AI, for free, in EPUB and PDF: the blog's 131 posts from 2026, almost half a million words, reorganized into 20 chapters, 6 appendices, a glossary and almost 300 references. This text is the technical backstage: the inventory with a hash per post, the contract every agent read before writing, the parallel drafts, the invisible per-paragraph marker that ties the book to the blog, the checker that fails a chapter with an error, what the fact-check caught wrong in my own posts, and the Quarto, Typst and Pandoc pipeline that generates everything. It's a preview: open an issue if you find a mistake.

AI Reverse Engineering: Is Adobe Dead?

In the same week, a dev dropped seven Adobe clones built with Claude, REA passed 100K stars promising to 'reverse engineer anything', and retro gaming got a new port every day. The viral conclusion: no binary is sacred, Adobe is dead, programmers are dead. I explain what each of these things actually does: PhotoCraft is an alpha with 800 open issues, REA is a toolbox that requires the binary and Ghidra, and retro moves fast because an 8 MB ROM turns into 135K lines of C. Rewriting Bun cost US$ 165K with the author and 64 agents; Photoshop doesn't even have an open binary in the parts that matter. Reverse engineering got easier. Magic still doesn't exist.

LLMs + Agents = competent enough

A synthesis of what I've been measuring and writing: LLMs converged. The transformer architecture got near the ceiling, the parameter war is over, and the leap everyone felt in 2025 and 2026 came from the agentic harness (tool calling, prompt caching, long sessions), not from a base intelligence orders of magnitude larger. I retell the timeline from 2019 onward with sources, explain why the agent allows a less dense model, and why that's already competent enough to automate real software engineering.

Which Jev-like AI Decision/Classifier Model Is Best?

I ran my own decision-model benchmark: Jev, Clef, OpenAI Decisions and Microsoft Decision-1 in the cloud, plus eighteen open configurations (Perplexity Decider 27B, Decision 3.0 and Edge, Clef-flash, Kev, Jebadiah, Mapika, Eikos, Laya, Julia, GLiNER, Simple-Jev) on my Strix Halo, across more than 22 thousand classifications with cases in English and Portuguese. The top now has two open-weights models: Perplexity and Decision 3.0 tied at 99.4%, with identical labels on the 1,024 classifications and 100% in Portuguese. Microsoft Decision-1 tied Jev at 97.6% while being the cheapest hosted one. Full table, cost in support emails, gotchas, and where the data converges.

Understanding the Jev Hype: What Is It For?

Jev is a hosted classifier, and the shock of '193x faster' only exists because so many people use a chat LLM as an expensive classifier. I publish Paulo Câmara's essay (LUA Vision) with his charts, explain what a classifier is and why it's the first thing a computer scientist reaches for, compare the open alternatives Clef and Laya, show the public Decision Index ranking, and close with how to think about this kind of tool in your own project.

2026 - September

6 posts

2026 - August

1 post

2026 - July

2 posts

2026 - May

1 post

2026 - April

1 post

2026 - March

4 posts

2026 - February

6 posts

2025 - June

1 post

2025 - April

1 post