That said, I'd like this quality with a relatively quick tool using model; I'm not sure what else I'd want to call it "AGI" at that point.
the only example uses I see written about on HN appear to basically be Substack users asking o3 marketing questions and then writing substack posts about it, and a smattering of vague posts about debugging.
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.)
Or you could just cut out the middleman(bot) and just do the search yourself, since you're going to have to anyway to verify what the "AI" wrote. It's just all so stupid that society is rushing towards this iffy-at-best technology when we still need to do the same work anyway to verify it isn't bullshitting us. Ugh, I hate this timeline.
With no deep research - agreed; too recent to believe info is accurately stored in the model weights.
how do you validate all of that is actually correct?
Like how there's a ton of psychics, tarot and palm readers around Wall St.
If OP had suggested that they were just medium-quality nonsense generators I would have just agreed and not replied.
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
Thank you!
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.
With coding using anything is always a hit and miss, so I prefer to have faster models where I can throw away the chat if it turns into an idiot.
Would I wait 15 minutes for a transcription from Python to Rust if I don't know what the result will be? No.
Would I wait 15 minutes if I'd be a mathematician working on some kind of proof? Probably yes.
It’s the progressive jpg download of 2025. You can short circuit after the first model which gives a good enough response.
I feel like we are in awkward phase of: "We know this has severe environmental impact - but we need to know if these tools are actually going to be useful and worth adopting..." - so it seems like just keeping the environmental question at the forefront will be important as things progress.
This is a rhetorical question.
Sure we aren’t capturing every last externality, but optimization of large systems should be pushed toward the creators and operators of those systems. Customers shouldn’t have to validate environmental impact every time they spend 0.05 dollars to use a machine.
I don't really understand the critique of GPT-4 in particular. GPT-4 cost >$100 Million to train. But likely less than 1 billion. Even if they pissed out $100 million in pure greenhouse gases, that'd be a drop in the bucket compared to, say 1/1000 of the US military's contributions
Does that "hundreds" include the cost of training one human to do the work, or enough humans to do the full range of tasks that an LLM can do? It's not like-for-like unless it's the full range of capabilities.
Given the training gets amortised over all uses until the model becomes obsolete (IDK, let's say 9 months?), I'd say details like this do matter — while I want the creation to be climate friendly just in its own right anyway, once it's made, greater or lesser use does very little:
As a rough guess, let's say that any given extra use of a model is roughly equivalent to turning API costs into kWh of electricity. So, at energy cost of $0.1/kWh, GPT-4.1-mini is currently about 62,500 tokens per kWh.
IDK the typical speed of human thought (and it probably doesn't map well to tokens), but for the sake of a rough guide, I think most people reading a book of that length would take something around 3 hours? Which means if the models burn electricity at about 333 W, they equal the performance (speed) of a human, whose biological requirements are on average 100 W… except 100 W is what you get from dividing 2065 kcal by 24h, and humans not only sleep, but object to working all waking hours 7 days a week, so those 3 hours of wall-clock time come with about 9 hours of down-time (40 hour work week/(7 days times 24 hours/day) ~= 1/4), making the requirements for 3 hours work into 12 hours of calories, or the equivalent of 400 W.
But that's for reading a book. Humans could easily spend months writing a book that size, so an AI model good enough to write 62,500 useful tokens could easily be (2 months * 2065 kcal/day = 144 kWh), at $0.1/kWh around $14.4, or $230/megatoken price range, and still more energy efficient than a human doing the same task.
I've not tried o3*, but I have tried o1, and I don't think o1 can write a book-sized artefact that's worth reading. But well architected code isn't a single monolith function with global state like a book can be, you can break everything down usefully and if one piece doesn't fit the style of the rest it isn't the end of the world, so it may be fine for code.
* I need to "verify my organisation", but also I'm a solo nerd right now, not an organisation… if they'd say I'm good, then that verification seems not very important?
Until then, the choice is being made by the entities funding all of this.
I use AI a lot to double check my code via a code review what I've found is
Gemini - really good at contextual reasoning. Doesn't confabulate bugs that don't exist. Is really good at finding issues related to large context. (this method calls this method, and it does it with a value that could be this)
Sonnet/Opus - Seems to be the more creative. More likely to confabulate bugs that don't exist, but also most likely to catch a bug o3 and gemini missed.
o3 - Somewhere in the middle
That's pretty scary.
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.
my setup is claude code in yolo mode with playwright MCP + browser MCP (to do stuff in the logged i firebase web interface) plus search enabled.
the prototype was developed via firebase studio until i reached a dead end there, then i used claude code to rip out firebase genkit and hooked in google-genai, openai, ...
the whole codebase goes into google gemini studio (caus the million token window) to write tickets, more tickets and even more tickets.
claude code then has the job to implemt these tickets (create a detailed tasklist for each ticket first) and then code it until done. end of each tasklist is a working playwright end to end test with verified output.
and atomic commits.
i hooked anydesk to my computer so i can check i at some point to tell to to continue or to read Claude.md again (the meta instructions which basically tells it to not to fallbacks, mock data or cheat in amy other way.)
ever fourth ticket is refactoring for sinplicity and documentation.
the tickets mist be updated before each commit and moved to the do done folder only when 100 tested ok.
so yeah, when i wale up in the morning either magic happend and the tockets are all done. or it got stuck and refactores half the codebase. in that case it works for an hoor to go over all git commits to find out where it went wrong.
what i need are multiple coding agent which challenge each other at crucial points.
I'm sure lots of code is being generated, but I do wonder about the effectiveness ratio of it when I read comments like above. Like there is a sweet spot after initial scaffold where its easier just to express yourself in code?
I would encourage you to try it. It's generally (much) cheaper doing stuff in Aider, but if you're paying a monthly subscription and using it a lot, Claude Code may be cheaper...
But I should note that o3-pro has been getting faster for me lately. At first every damn thing, however simple, took 15+ minutes. Today I got a few answers back within 5 minutes.
In fact, o1-preview has given me more consistently correct results than any other model. But it's being sunset next month so I have to move to o3.
Not strict rational A+B=C, nuance.
As far as usage of API for business processes (like document processing) - I can't say.
You should assume Facebook level morality.
> The problem with o3-pro is that it is slow.
well maybe Arena is not that silly then. poorly argued/organized article.
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 mainly AI invester not AI user, I think profitability is great importance. It has been race to top so far, soon we see race to the bottom.
My primary use cases where I am willing to wait 10-20 minutes for an answer from the "big slow" model (o3-pro) is code reviews of large amounts of code. I have been comparing results on this task from the three models above.
Oddly, I see many cases where each model will surface issues that the other two miss. In previous months when running this test (e.g., Claude 3.7 Sonnet vs o1-pro vs earlier Gemini), that wasn't the case. Back then, the best model (o1-pro) would almost always find all the issues that the other models found. But now it seems they each have their own blindspots (although they are also all better than the previous generation of models).
With that said, I am seeing Claude Opus 4 (w/extended thinking) be distinctly worse at missing problems which o3-pro and Gemini find. It seems fairly consistent that Opus will be the worst out of the three (despite sometimes noticing things the others do not).
Whether o3-pro or Gemini 2.5 Pro is better is less clear. o3-pro will report more issues, but it also has a tendency to confabulate problems. My workflow involves providing the model with a diff of all changes, plus the full contents of the files that were changed. o3-pro seems to have a tendency to imagine and report problems in the files that were not provided to it. It also has an odd new failure mode, which is very consistent: it gets confused by the fact that I provide both the diff and the full file contents. It "sees" parts of the same code twice and will usually report that there has accidentally been some code duplicated. Base o3 does this as well. None of the other models get confused in that way, and I also do not remember seeing that failure mode with o1-pro.
Nevertheless, it seems o3-pro can sometimes find real issues that Gemini 2.5 Pro and Opus 4 cannot more often than vice versa.
Back in the o1-pro days, it was fairly straightforward in my testing for this use case that o1-pro was simply better across the board. Now with o3-pro compared particularly with Gemini 2.5 Pro, it's no longer clear whether the bonus of occasionally finding a problem that Gemini misses is worth the trouble of (1) waiting way longer for an answer and (2) sifting through more false positives.
My other common code-related use case is actually writing code. Here, Claude Code (with Opus 4) is amazing and has replaced all my other use of coding models, including Cursor. I now code almost exclusively by peer programming with Claude Code, allowing it to be the code writer while I oversee and review. The OpenAI competitor to Claude Code, called Codex CLI, feels distinctly undercooked. It has a recurring problem where it seems to "forget" that it is an agent that needs to go ahead and edit files, and it will instead start to offer me suggestions about how I can make the change. It also hallucinates running commands on a regular basis (e.g., I tell it to commit the changes we've done, and outputs that it has done so, but it has not.)
So where will I spend my $200 monthly model budget? Answer: Claude, for nearly unlimited use of Claude Code. For highly complex tasks, I switch to Gemini 2.5 Pro, which is still free in AI Studio. If I can wait 10+ minutes, I may hand it to o3-pro. But once my ChatGPT Pro subscription expires this month, I may either stop using o3-pro altogether, or I may occasionally use it as a second opinion by paying on-demand through the API.
I've found the same thing. That claude is more likely miss a bug than o3 or gemini but more likely to catch something o3 and gemini missed. If I had to pick one model I'd pick o3 or gemini, but if I had to pick a second model I'd pick opus.
It's also seems to have a much higher false positive rate where as gemini seems to have the lowest false positive rate.
Basically o3 and gemini are better, but also more correlated which gives opus a lot of value.
iLoveOncall•6h ago
My solution for this has been to use non-reasoning models, and so far in 90% of the situations I have received the exact same results from both.
jasonjmcghee•6h ago
It tends to output significantly longer and more detailed output. So when you want that kind of thing- works well. Especially if you need up to date stuff or want to find related sources.
joshstrange•6h ago
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.
bcrosby95•5h ago
matwood•3h ago
joshstrange•3h ago