It’s the progressive jpg download of 2025. You can short circuit after the first model which gives a good enough response.

Example: Pull together a list of the top 20 startups funded in Germany this year, valuation, founder and business model. Estimate which is most likely to want to take on private equity investment from a lower mid market US PE fund, as well as which would be most suitable taking into consideration their business model, founders and market; write an approach letter in english and in german aimed at getting a meeting. make sure that it's culturally appropriate for german startup founders.

I have no idea what the output of this query would be by the way, but it's one I would trust to get right on

* the list of startups

* the letter and its cultural sensitivity

* broad strokes of what the startup is doing

Stuff I'd "trust but verify" would be

* Names of the founders

* Size of company and target market

Stuff I'd double check / keep my own counsel on

* Suitability and why (note that o3 pro is def. better at this than o3 which is already not bad; it has some genuinely novel and good ideas, but often misses things.)

I had an example where o1 really wowed me - something I don't want to post on the internet because I want to use it to test models. In that case I was thinking through a problem where I had made an incorrect mathematical assumption. I explained my reasoning to o1 and it was able to point out the flaw in my reasoning, along with some examples mathematical expressions that disproved my thinking.

The funny thing in this case it basically functioned as a rubber duck. When it started producing a response I had deduced essentially what it told me - but it was pretty nice to see the detailed reasoning with examples that might've taken me a few more minutes to work out. And I never would've produced a little report explaining in detail why I was wrong, I would've just adjusted my thinking. Having the report was helpful.

Then I have it take those matches and try and chase down the hiring manager based on public info.

I did it at first just to see if it was possible, but I am getting direct emails that have been accurate a handful of times and I never would have gotten that on my own

Chat is more of a "conversational" medium, where you dash off short and informal messages and expect a very quick if not immediate response.

Email is more of a "correspondence" medium, where you write longer and sometimes much more formal messages, and expect a response within ~24 hours.

The latter would be the better way to use ultra-compute-heavy/long-delay models like o3-Pro, because you don't expect the flow of a conversation, and it would nudge you to be much more detailed in your prompt. In fact, if it had a larger context window, an email-format o3-Pro would be perfect for lawyers and for certain types of data analysis.

I'm not building anything, but if you'd like to, lmk.

But we're still human mate.

Stop discriminating or actually solve the problem. I've had enough of this attitude.

Short, concise statements that don't necessarily string together sequentially. However, they still aggregate to a holistic, meaningful thought. No that much different that how a lot of code is written.

As far as usage of API for business processes (like document processing) - I can't say.

I put this into Claude.md and need to remind it every other hour. But yeah, you need to jump back every few hours or so.

In essence, they are only adequate in niche situations (like creative writing, marketing, placeholder during iterative design, …) where there's no such social contract and assumption that people operate in good faith and do their best diligence not to deceive others.

Pretending otherwise, not pushing back when LLMs are clearly used outside of those contexts, or dressing them into what they are not (thinking machines, search engines, knowledge archives, …) is doing the work of useful idiots defending tech oligarchs and data thieves against their own interests.

And yeah, I get it, naysayers are annoying. Doesn't mean they are wrong or their voices shouldn't be heard at a time where the legality and ethics of all this are being debated.

If your quality threshold is so low that you can tolerate crashes on invalid input, you will certainly cut corners when building software for others. I wouldn't want to use a piece of software you wrote, let alone have you on my team.

> I don't care very much if my coworkers use an LLM to write code or not, since all the code gets reviewed by someone else

Ah, yes, let's kick the can down the road.

> if the proposer of the change doesn't even bother to check it themselves then they pay the social cost for it

... The side effects of shoddy code are not redeemed by "paying a social cost". They negatively impact your users, and thus the bottom line of your company.

Anytime I do my own “deep” research I like to then throw the same problem at OpenAI and see how well it fares. Often it misses things or gets things subtly wrong. The results look impressive so it’s easy to fool people and I’m not saying the results are useless, I’ve absolutely gotten value out of it, but I don’t love using it for anything I actually care about.