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?
We’re already halfway through 2026, so I went back through everything I bought on Amazon this year. 75 orders total. Lined up like that, most of it turned out to be recurring supplement subscriptions, cat food, and ingredients for cooking at home — 10kg of soybeans, koji, kombu, dried shiitake, natto spores. Even I have to admit it’s a pretty plain lineup.
Once I set aside all the consumables and kept only the things I can genuinely say I’m glad I bought, it came down to four. The prices and categories are all over the place, but each one actually changed something about how I live. Here they are, in order.
This continues the story of switching my external brain’s search to semantic search. Last time, I noticed that keyword search on my vault was missing things, so I added local embeddings via Ollama for semantic search. Now even a vague query like “how did I set up gitleaks again” pulls up the right daily log.
That made pulling specifics much better. But as I kept using it, I noticed a different gap. There was no entry point for grasping the whole picture broadly.
I’d known for a long time that I needed to deal with duplicate and leaked passwords. It was just too tedious, so I kept looking away. When I finally braced myself and opened Chrome’s password checkup, this is what came back:
Compromised passwords: 40
Reused passwords: 132
Weak passwords: 109
329 in total. A number that makes you dizzy just looking at it. For a second I thought, I should’ve just left it alone. And going through the list, some of these were for services I’d already stopped using, or accounts I’d closed ages ago. So what’s the efficient, safe way to handle this? Here’s a record of the day I spent cleaning this up, and what I thought about along the way.