AkitaOnRails Blog

New: I published Akita’s AI Lair, the central page for the tools, skills, and workflow I use to run AI agents across 40+ repositories: ai-usagebar, ghpending, tclock, ai-memory, ai-jail, and my LLM benchmark.

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2026 - October

2026 - September

2026 - August

2026 - July

2026 - June

2026 - May

2026 - April

2026 - March

2026 - February

2026 - January

2025 - September

2025 - August

2025 - June

2025 - May

2025 - April

2026 - October

7 posts

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.

Linux users: always choose BTRFS, not EXT4

Sequel to the NAS disaster article: filesystem choice is a life-or-death decision for your data. The history of ext4 and btrfs, snapshots, subvolumes, reflink deduplication, corruption protection, compression measured in real GB (630 GB free on 2.2 TB), ZFS vs btrfs on NAS, snapshot boot recovery with Limine and MicroOS, and storage hygiene in the AI coding era: Rust, Docker, model caches, agent worktrees and how to automate cleanup.

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.

Are There Better Languages for Programming with LLMs?

DHH converted his Campfire to several languages with AI and started posting that Rust won. Instead of fighting on X, I built a reproducible benchmark: 7 languages, 4 LLM configurations, 112 implementation attempts, and a queue-under-pressure test with Elixir, Rust, Rails and plain Ruby. Result: no language ranking holds up, concise Ruby cost more tokens than Rust, strong typing spared no repair at all, and under load the four stacks tied on throughput. What matters is testing and discipline, not language.

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.

Disaster on My NAS! How I Almost Lost 90 TB of Data

I swapped one drive in my Synology DS1821+ and woke up the next day with two critical drives in an array that only survives losing one. Full post-mortem: why SHR-1 wasn't enough, the warning DSM never sent, the R$ 185 thousand for a QNAP bought in a hurry, and the recovery: parallel NAS-to-NAS rsync, the slow drive holding the array back, the verification of 5.9 million files, the thick provisioning scare, and both NASes healthy in RAID-6.

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

16 posts

Talking a Bit About My AI Skills

Agent skills are just prompts in text files. I keep 25 in my my-skills repo, from pr-audit to fact-check, and they have already closed over a thousand PRs and issues across my projects. The recommendation: don't copy somebody else's skill, build your own.

2026 - August

11 posts

2026 - July

17 posts

2026 - June

10 posts

2026 - May

12 posts

2026 - April

18 posts

Seedance 2.0 Is Finally Out: First Impressions

ByteDance opened Seedance 2.0 to the public today after months of restricted access. I tested audio-driven lip sync with my anime avatar, and a Blender render fed in as a video reference. There's real work you can do here, but it's nowhere near professional production yet. Also: deepfakes just stopped being hypothetical.

2026 - March

13 posts

2026 - February

17 posts

2026 - January

10 posts

2025 - September

12 posts

2025 - August

2 posts

2025 - June

1 post

2025 - May

7 posts

2025 - April

17 posts

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