softwaredoug

One thing - your expenses will ALWAYS somewhow expand to your income. We see our current income as somehow the steady state long term norm. I would do your absolute best to hide extra income from your day to day self and invisibly invest it in a way you don’t even think about

And when you get a windfall (stock grants, acquisitions, etc) unless it’s truly life changing money where you never have to work again, find a good investor (CFP) to manage so you’re not even thinking about it. Or invest it in some future liability like college expenses, home down payment, retirement, etc.

Leaving this stuff at the discretion of courts will mean a lot of routine filming of police will just get thrown out by right leaning courts.

No because then courts would litigate what “newsworthy” means leaving your 1st amendment rights in the hands of random judges. I can imagine filming ICE doing routine operations being seen as “not newsworthy” by a conservative court.

All I’ll say is… see Digger before it leaves theaters

To me the Jev moment isn't about Jev / Typesafe. It's articles like these. It's the letting a million Jevs bloom.

The bubble will come from a realization that a lot of people can train these models. AI Researchers will become more diffuse, work at more companies, and building a Jev or fine-tuning an LLM isn't some trillion dollar frontier lab exercise, but increasingly just something developers do.

I’ve started to see (and react negatively) to a kind of “Claude design”. Apps, slides, images share the same aesthetic, and even after aggressive prompting it reverts to a the mean.

I even create these things. But that aesthetic has started to imply a kind of throwaway, low value thing

What will be interesting is if models can create more diverse styles than the neutral Claude default we see everywhere

Not the optional war Trump started where 1/4 of the worlds oil is shipped

Why is it a middling product? Most clones don’t approach its performance, and it solves a specific problem well in a way that was awkward and ignored by most frontier labs.

What’s interesting about the Jev moment isn’t just Jev, it’s the unleashing of distillation / fine tuning outside the frontier-adjacent labs. It’s the sudden explosion of a million Jevs.

If being an “AI Researcher” is a ticket to multimillion dollar salary, AI training talent cannot be contained to a handful of companies. It’ll become more common and diffuse. The old advice of not fine tuning, because it’s hard, goes out the window as that knowledge diffuses through the industry.

A similar thing is happening in search. For a long time labs have trained tailored embedding models. And now companies like SID training their own agentic models that are smaller and faster at search than GPT-5.

Probably the same reason people aren’t vibe coding nuclear reactor software. One small mistake has large downside consequences.

To be frank, the obsession with being first, and not making research accessible, has always held academia back

Aren’t there dozens of proofs of the Pythagorean theorem? The goal isn’t to just “prove” but create something well written and intuitive to the average practitioner. And by gaining a deeper understanding we can ask better questions.

Clearly AI can grind on math problems now. It can generate proofs and get immediate feedback.

But what I wonder: can we legitimately grind on Physics or curing cancer? There’s a lot of physical world experimentation that needs to happen to make progress.

The question to me isn’t whether they can rush out an API, but whether a general model can out compete one post trained on the classification task. GPT-6-Luna has to write emails and classify them. Jev only needs to do decisions.

Or does OpenAI start distilling their own models for use cases like decisions?

“Some people exist in this intersection of arbitrary groups I just made! Therefore the whole group is a fraud!”

I think we can only say we are forwarding human knowledge if a human understands the content they’re putting out in the world. I don’t know if that applies in this case, it I think it’s an important bar to clear.

So this is like OpenClaw but managed and first class?

The big takeaway is they announced decision model API for Luna

Well I often forget the rules, so its useful :)

I’m reading The Well of Ascension, the second book of Brandon Sanderson’s Mistborn trilogy

I like how he has the slow, almost clinical way of revealing interesting worlds and systems. It’s not the kind of book that’ll make me cry, but I really like world building with fun enough characters

Yeah we forgot the goal of programming isn’t just to tell the computer what to do, it’s to program the programmer into thinking a deeper understanding of the problem.

Strangely I’d expect urban centers with apartments and street parking to be the problem.

In the US Electric vehicles are easier for those who can park a car next to an outlet they own. We have a lot more suburbia with people owning their own charging.

A lot of the hate is in the same spirit of the infamous hacker news comment discounting Dropbox because you can just setup an FTP server to do the same thing[1]

In this case, its people who have built BERT classifiers for years, pointing out you can just fine-tune some model to classify. Of course that's true, and still have value, but its not exactly turnkey. And Jev is surprisingly high quality at what its built for.

1 - https://news.ycombinator.com/item?id=9224

I don’t know if this is 100% true because there’s value in writing code for education and idea exploration.

I find it saves tokens and reduces slop piled on slop to have a foundational technical grasp of the technology behind your projects. And that means writing code for exploration and scaffolding.

Agents are such rich BS machines that I also don’t trust them. So I frequently debug and dive into my code rather than letting an agent layer brittle fix on top of brittle fix

My lack of stress about AI coding comes from years of being a principal engineer. Where the job isn’t to write 100% of the code, it’s to set technical direction and ensure we’re going in the right direction as a project.

I both write code and use coding agents. Because I find it’s often easier to set technical direction by showing some scaffolding, then later letting the agent take over the details. I waste less time with my agent if I can show it roughly what I want.

Occasionally I get disconnected by what my agent colleague is doing (or it gaslights me) and I have to dive in to right the ship a bit.

It’s not just price and speed, the API is very well designed for classification. Going beyond the current structured outputs.

Absolutely. I write a lot of code in 2026. You've got to be able to reason about multiple abstractions, and writing is thinking. In many situations, that means getting down to the code.

We have copious evidence for behaviors like note taking and writing creates deeper thinking and retention that passively watching / reading

https://www.scientificamerican.com/article/why-writing-by-ha...

> Unfortunately, the manufacturing environment has changed dramatically. The effects of COVID, increasing manufacturing and operating costs, and significant changes in consumer purchasing habits have made it increasingly difficult for a small American manufacturer such as Sherline to maintain the workforce and production levels necessary to remain competitive.

Can’t help but wonder if “operating costs” include tariffs and other policy decisions by the current administration that have driven up costs for everyone.

Is it really “research” - as in expanding human knowledge - if nobody understands it? The point is deepening human understanding, not producing research papers