Two pieces of news pointed in opposite directions this July.
One was the official announcement of Kimi K3: 2.8 trillion total parameters, the largest open model out there, beating Claude Fable 5 and GPT-5.6 Sol on some coding benchmarks (while Moonshot themselves frankly admit it still trails both overall). Make the model bigger, buy more performance. The straightforward direction.
The other went the opposite way. Schema, a reasoning harness, reports solving 98.98% of the ARC-AGI-3 public set with a combination of Claude Opus 4.8 and Fable 5. It doesn’t touch the model weights at all. What it changes is the scaffolding around the model: how observations become a world model, how predictions get tested, and where to start fixing things when they miss. On the same public set, GPT-5.6 Sol alone scores 13.33%; put Schema around it and it reaches 95.35%. Same model, over 7x the score, from outside design alone. And these are the same models I use every day.
A while back, I wrote a post called “Permissions have a range.” Tell an AI “you can push” once, and it will carry that permission into other repos and other tasks on its own. So a permission is bounded along three axes — repository, task, and kind of operation. I logged the failure in an external memory file called mistakes.md, dropped the operations I really cared about into git-layer hooks, built the whole two-layer defense, and closed with “I’ll probably pay this tuition a few more times yet.”
On July 7th (local time), Anthropic announced a five-day extension to the free window for Claude Fable 5. The new deadline is 3:59 PM JST on the 13th. In an earlier post, I wrote about having Fable 5 rebuild my external-brain workflow rules — not an article, not code — before its free window closed. Figuring I might as well use the extension, I picked up where that left off: a much more thorough redesign of my whole Claude Code environment. Once it was done, I had Ultracode (a feature that runs multiple agents in parallel) ask “does this actually work?” — and it turned out a safeguard I’d been running for two weeks and one I’d only just added that day, built at wildly different times, had the exact same kind of hole in them. That interrogation wasn’t free, either.
My last post fell apart when I had Claude Fable 5 read it.
Post 1049 was written by Opus 4.8 and Sonnet 5, and Fable 5’s review came back with 11 findings. The closing asserted “I had misjudged this” with no scene in the body to back it up. The twist was spoiled by bringing up Analyze Requirements right before the reveal. A catchphrase from a discarded draft was still sitting in the conclusion. The summary table had a row the body never touched. Every one of them was the kind of defect you only catch by reading the whole thing straight through.
This week, spec-driven development (SDD) landed as a product and a production case study at the same time. AWS’s Kiro got covered as a system that generates requirements, design, and code in sequence, and Tokio Marine & Nichido was reported to have put it into production, with prototyping time dropping from six months to a single day. It even came with a billing: the third development methodology, after waterfall and agile.
obsidian-import (my homegrown tool that turns external videos and articles into Obsidian notes) had video transcription locked to YouTube only. Every time I fed it a TikTok or Instagram video, it got processed as an article instead, and the friction finally added up enough that I decided to fix it.
Digging in, the YouTube restriction turned out to be a stand-in for a different goal: avoiding Whisper’s heavy load. Both subtitle fetching and description fetching were already site-agnostic yt-dlp calls under the hood. The YouTube check was a single regex at the entry point — replace it with the real condition (a duration cap) and the whole thing could be safely extended.
Honestly, I can’t keep up with how fast AI models are turning over lately. A new one shows up every few days, and whatever was “the best” a moment ago is already last-gen. The incident I mentioned in an earlier post — a model that got cut off just three days after launch — is part of the same story.
In the middle of all that, I noticed something about my own plan: Claude Fable 5 only counts toward my free weekly usage cap until July 7, 2026. After that date it doesn’t stop working — it just switches to pay-per-use. So the question became: what do I spend the free allowance on while it lasts?
I searched “best language for AI.” What came up was Python, R, Julia — one of the rewritten articles even still had a leftover “as of May 2019” buried in the text that nobody bothered to delete.
I read through it and thought: wait, this isn’t the answer I was looking for.
What articles like this actually answer is “if you’re building AI, which language should you use?” Python with its deep ML library ecosystem, R for statistics. The arrow points from language to AI. Call this axis 1.
For a while now, I’ve been syncing my Obsidian vault between my Mac and Android phone with Syncthing. It’s an OSS tool that copies files directly between devices on the same Wi-Fi network, no cloud in between. Thanks to that, the external brain’s vault has already been sitting on my phone for a while.
Right after publishing my previous post, a question popped into my head. If everything’s synced that thoroughly, couldn’t I use the external brain from the Claude app on my phone too?
Last week, a new model got cut off just three days after its public release, and the news framed it as “the blind spot of depending on foreign AI.” I won’t get into the details here, but the headline alone stopped me in my tracks. Right now, I’ve put my entire second brain and workflow on Claude Code. If Claude became unusable tomorrow, would my “brain” go down with it?