One app, two platforms, four programming languages.
The things that look the simplest are often the hardest to build. @raycast is one of them.
Here's a technical deep dive on how we built v2 👉 https://t.co/JP9nVNzbqB
It's so hard to describe the vibe difference between Opus 4.7 and GPT 5.5 (for coding)
GPT is smarter and can unblock you, but it gets stuck in stupid ways and strangles itself with context sometimes.
Opus will go down the most insane paths and refuse to acknowledge obvious answers, but it understands intent better and has more taste.
Whenever I use one for more than an hour, I always reach to the other to "clean up".
Best part? All of this changes every few weeks 🙃
My dear front-end developers (and anyone who’s interested in the future of interfaces):
I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept):
Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow
TypeScript 6.0 is now available!
This release brings better type-checking for methods, new standard library features, new module features for Node.js, and more!
But most important, this release brings us one step closer to the upcoming native-speed 7.0!
https://t.co/hon0RU1L5B
I think we have lost some sense of judgment and moderation when it comes to product building currently.
The moment you turn something into a universally celebrated metric, whether that is token burn, prototype count, or percentage of agent-written code, you start losing sight of what actually matters.
I have felt the same way for a long time about overusing data and A/B testing to build products. The moment you reduce product quality or productivity to a metric, you stop shipping value and start shipping numbers.
A lot of what people are doing with AI makes directional sense. The missing piece is counterbalance:
1. AI should help engineers build better products. Leaderboards and adoption metrics can be useful as directional signals. They do not tell you what is being built, whether it is good, or whether it should exist at all.
2. Users do not care what percentage of your code was written by agents. They care about the outcome. Faster output is useful. Like usually, faster doesn't seem to add to quality, clarity, or stability of products. Power to build should not become an excuse to lower quality bars.
3. LLM-generated prototypes can feel like late-night whiteboarding sessions. They look exciting in the moment and feel productive very quickly. Then a few days later you realize the idea was shallow, distracting, or simply wrong. The same trap shows up in jumping straight to code and solutions more broadly. You may just be building the wrong thing more efficiently. Prototyping has its place. So do clear thinking, good design, and a real understanding of the user’s problem. In terms of activities or momentum, the main quest and the side quest can both feel productive but only one actually moves the mission forward.
4. Adding more to products is still dangerous as ever even if time or effort to add it has gone down. Every addition creates complexity, maintenance cost, and user confusion. New features should be pushed back unless they clearly show it should exist and how it improves the product.
5. Not everything needs to be an agent shaped. A simple scheduled task does not need a full LLM sandbox. Making something agentic because it feels current or impressive does not make it right-sized, correct, or effective.
The core ideas are:
- even if you can, maybe you should not.
- more power we have to build should not reduce our need to think, it should increase it.
it's been probably a month since i've used any claude models in opencode
kimi/glm for most tasks since they're fast af
and then gpt for everything big that i want to background
great pairing