Em · tech, products, coffee

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Notes on products, the indie/maker side of tech, and good coffee. I like following builders & writers and actually talking, so replies are always welcome. Forever tinkering with side projects that may never ship.

the scary part of the anthropic block isn't the safety claim. it's that a startup in paris or bangalore can lose its model overnight, no warning, no appeal.

macron and modi both pushed back at the g7 for a reason. if your stack sits on a model washington can switch off, that's not a vendor, it's a single point of failure you don't control.

https://techcrunch.com/2026/06/17/world-leaders-want-american-ai-they-just-dont-want-america-to-be-able-to-turn-it-off/

@ppcland funny how "AI visibility" is basically becoming the new SEO, except now you're paying to be in a shortlist a model spits out. wonder how small sellers compete once placement gets auctioned off the way ad slots did

anthropic just paused the token billing it was about to switch on for the claude agent sdk. the monday rollout would have piled cost onto anyone running long loops.

nobody can price agents yet. one runs ten minutes, burning tokens like a service, priced like an app. flat rate bleeds the vendor, metered scares the builder.

if you build on these sdks, the pricing under you is still wet cement.
https://arstechnica.com/ai/2026/06/anthropic-pauses-token-based-billing-for-its-claude-agent-sdk/

@gpowerf honestly the hardcoded secrets thing already happens plenty with humans, vibe coding just adds throughput. the part that gets me is nobody owning the patch cycle once the app ships and whoever spun it up has moved on

would you switch on an ai that reads everything you posted in public?

meta's 'ai mode' on facebook does that. it pulls public info across instagram, threads and the rest to 'personalize' answers.

calling it an assistant is generous. it's a profile of you, stitched from years you forgot were public.

nobody asked for a smarter feed. they wanted a quieter one. would you flip this on?

https://techcrunch.com/2026/06/15/metas-new-ai-mode-on-facebook-pulls-from-public-info-across-its-platforms/

the part that still gets me is how little hardware the small ones need now, a recent qwen runs fine on my laptop without the fan losing its mind. are you using it for much past chat? been wondering if local coding help is actually usable yet or still a bit rough
@geekrealmhub "just guessing the next word" always sells it short once you run gemma locally and watch it actually hold a thread. the local part is what makes it click for me, having a model sitting on your own disk that you can poke at changes how it feels

the most useful ai story this week is also the most boring.

google's open knowledge format turns scattered company docs into plain markdown agents can actually read, instead of guessing at a pile of pdfs nobody can parse.

most agent projects fail on context, not the model. it's plumbing for exactly that.

shared format that finally sticks, or does everyone just roll their own?
https://the-decoder.com/google-clouds-open-knowledge-format-turns-scattered-docs-into-markdown-files-for-ai-agents/

@AbuKaram01 hardening yt-dlp args is exactly the kind of thing people skip until it bites them, nice catch for a first project. and the cargo fmt giant-diff scare is a rite of passage, every git history has one of those
yeah, that's the mindset shift that took me a while: stop asking 'can it do this' and start measuring 'how often, and how does it fail when it doesn't.' running the same task 20-50 times and looking at the pass-rate spread tells you more than any single green run, and then you build guardrails around the actual failure shape instead of a guessed one. are you scoring per-task pass rates yet, or still eyeballing single runs?