The problem with the "if it works who cares what the code looks like" mindset for agentic work is that it assumes the agent has a perfect understanding of "works." Realistically, things are underspecified, agents make bad assumptions, etc.
To be fair, agents are pretty good at unit test coverage. They're pretty bad at designing human experiences (API, CLI flags, etc.), especially cohesive ones for future roadmap plans they may not have visibility into (unless your backlog is perfect and vision fully laid out, which I doubt). They're bad at knowing where performance matters and what type (CPU vs memory tradeoffs). They're bad at where compatibility matters and where it doesn't (and tend to err on the side of preserving it without further guidance). Etc.
Unless you have this ALL specified, you can't possibly claim "it works" without taking a look and thinking about it.
i don’t think people realize how early we still are in the ai cycle even though the major companies are now becoming public.
the models are getting way better but still have gaps. most of the products are still primitive in so many ways. the interfaces are mostly bad. the workflows are barely rebuilt. the hardware layer has barely started. robotics is just the at the very precipice. consumer behavior has not even begun to rewire yet.
there is a long long way to go. what a crazy time to be alive.
The fallacy of this is that more creates more. More hours, more hiring, more something.
And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy.
Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output.
This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work.
I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution.
Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook.
With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it.
My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day.
There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed.
Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work.
I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things.
I sometimes wish we could move the culture more toward a Zen master.
Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.
AI should dramatically increase quality of life and individual freedoms for people around the world.
The OpenAI Foundation is making an initial $250M commitment to measurement, transition support, and new approaches to broadly shared prosperity.
https://t.co/zOD8O94RjQ
It’s 2018 and your coworker just sent you a 400 line pull request.
You get a cup of coffee and sit down to review it.
It’s beautiful. Elegant micro-refactors. Crispy method names.
You catch a few things, but that’s ok. It’s part of the dance. They didn’t consider extensibility on part of their API. Here’s a comment buddy.
They respond in an hour saying they think we should do one piece differently than your comment. Hey let’s jump into a room and figure it out. We can’t just agree to disagree, this code is too important.
The PR merges and goes to prod. You feel a shared sense of ownership and accomplishment.
That night you go to sleep and dream of that code. You can still see the shapes of it on the backs of your eyelids, your IDE syntax highlighting sparking neurons in your reptile brain.
You go to work the next day ready to go. You understand the system. N is your foundation. Time to build n+1.
the idea -> execution pipeline has been reduced to how well you can articulate yourself.
if you already know how to do something, it’s an ask with some odd number of tokens. if you don’t, it’s hours of fiddling instead of months.
the second order effects of what people will build are mad
finally found the right metaphor for this shift in how i use opencode.
i used to treat it like 3D printing, where you build the thing layer by layer and commit to each piece as you go
now it feels more like progressive rendering, you start with a blurry version of the whole thing, then keep making full passes over it, and each pass sharpens the entire shape
doing this with gpt 5.5 and voice prompting is the first time things feel like they're clicking
Introducing GPT-Realtime-2 in the API: our most intelligent voice model yet, bringing GPT-5-class reasoning to voice agents.
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Now available in the API alongside streaming models GPT-Realtime-Translate and GPT-Realtime-Whisper — a new set of audio capabilities for the next generation of voice interfaces.