O3 Turns Pro

I feed most of my questions/code to 4o, Gemini, o3-pro (in that order). By the time I’ve read through 4o, Gemini is ready. Etc.

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

what/how does that work internally?

I don't think it's necessarily a question of trust, it's a question of cost/benefit, and I can apply this just as much to myself. I have been using a lot more SQL queries lately when I use ChatGPT, because I trust it pretty well to write gnarly queries with subqueries and CASE statements. Things that I wouldn't write myself because it's not worth the time to make the query correct, but ChatGPT can do it in seconds.

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.

Long form research reporting.

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’ve been using it in my job search by handing it stuff like the hn whose hiring threads, giving it a list of criteria i care about, and have it scour those posts for matching jobs, and then chase down all the companies posting and see if they have anything on their corporate site matching my descriptions.

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

Feels like a lot of software engineers I work with (including myself at times).

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.

Some people write in similar ways yea. I've also been accused of writing as an AI.

But we're still human mate.

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

I would say the opposite. Unless the person has a lot of custom instructions going on. Getting sentences like "but usage patterns flipped people finally ran real tasks through it." seem like it would take some amount of work.

yes im agi by the way

Ideally just a few split atoms

Atomic Commits.

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.

ChatGPT is pretty widespread. The only obstacle in the past was the fear that confidential documents might be used for training. OpenAI fixed that with a business account type that guarantees no training.

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

I've tried it with various models. And 4o is really good given that it returns data at least 10 times faster. But if you ask it to fill out a Json document, o3 (or other reasoning models) is still better, more correct and predictable. Or at least, better enough to justify waiting a minute for the API call to return vs 3-5 seconds.

what is people parsing?

P ⊆ NP

To me it's a useful reminder that those tools are nothing but text generating algorithms, optimised to produce compelling answers, irrespective whether they are truthful or not, having no concept of what's factual, and completely missing the ability to give up when asked for impossible or unreasonable answers outside of their training data.

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.

On the other hand I only see downvotes and _never_ an answer on how you are using llms. The anecdotal 8hours to 30 minutes PR sounds great, but in my experience it just won’t happen. How can you set up llm to work autonomously for _hours_? If it is continuous “pair” work I just don’t see the 30 min work solving a beefy PR. In 8 hours coding with a well thought out plan / re-planning, testing one can finish quite interesting stuff. 30 minutes is basically nothing - and this is kinda what you get with an llm in my experience. How do you do it?

I am curious. What are a few examples?

Unless P=NP

The intentional disregard for software quality in your comment is honestly disturbing.

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 would dread having you on my team, let alone using a piece of software that you wrote.

> 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.