That would mean earnings of ~ $1.1T would be required on that investment annually, so maybe on $2T of revenue, capturing 2% of the global GDP - so I'd estimate that GDP would need to go up more like 5-10% to justify this.

[1] https://substackcdn.com/image/fetch/$s_!Gf2t!,f_auto,q_auto:...

Is anyone seeing significant improvements in day-to-day coding use? If so, it would be great to get a glimpse into the use-case (language, application area, complexity) and how the model is "driven"/used.

Comments URL: https://news.ycombinator.com/item?id=46110433

Points: 3

# Comments: 3

when i signed up for billing via cloud console and entered my credit card, i got $300 "free credits".

i haven't thrown a difficult problem at gemini 3 pro it yet, but i'm sure i got to see it in some of the A/B tests in aistudio for a while. i could not tell which model was clearly better, one was always more succinct and i liked its "style" but they usually offered about the same solution.

Comments URL: https://news.ycombinator.com/item?id=45584152

Points: 1

# Comments: 0

Even with our exorbitant energy prices, I pay about 1/3 for the cost of energy per mile (off-peak). I have to go in for service 3 times less often (and just that is worth a pot of gold to me). I no longer need to detour to gas stations once or twice a week as I can just plug in the car at night. And then EVs are also more enjoyable to drive imo.

I think this post comes in light of Europe being seen as curtailing free speech recently. Europe and the US have always had different ideas on the limits of free speech.

[1] https://kellywarnerlaw.com/germany-defamation-laws

https://news.ycombinator.com/item?id=45008733

I ended shoehorned into backend dev in Ruby/Py/Java and don't find it improves my day to day a lot.

Specifically in C, it can bang out complicated but mostly common data-structures without fault where I would surely do one-off errors. I guess since I do C for hobby I tend to solve more interesting and complicated problems like generating a whole array of dynamic C-dispatchers from a UI-library spec in JSON that allows parsing and rendering a UI specified in YAML. Gemini pro even spat out a YAML-dialect parser after a few attempts/fixes.

Maybe it's a function of familiarity and problems you end using the AI for.

What I'm seeing in the suburban example graph in the article, is that the vehicle and hood have gotten way taller... I don't know how hoods/grills this high improve safety - I assume it's mostly the opposite. But they do "look rugged/beefy" - like all trucks and SUVs have to in order to sell - just look at the difference! [1]

"Millions of SUVs, trucks have dangerous front blind zone" [2]

[1] https://static1.hotcarsimages.com/wordpress/wp-content/uploa...

[2] https://www.nbcnews.com/news/us-news/americas-cars-trucks-ar... (or all the other writeups of this report)

I don't know how this program would be structured, but imo this program is not doomed to fail due to underfinancing - of course this being an EU program it surely has other issues.

Not sure if OpenAI has updated O3, but it looks like "pure" o3 (high) has a score of 79.6% in the linked table, "o3 (high) + gpt-4.1" combo has a the highest score of 82.7%.

The previous Gemini 2.5 Pro Preview 05-06 (yea, not current 06-05!) was at 76.9%.

That looks like a pretty nice bump!

But either way, these Aider benchmarks seem to be most useful/trustworthy benchmarks currently and really the only ones I'm paying attention to.

Right now (for me) it's very frequent, depending on the type of project, but in the future it could be less frequent - but at some you've gotta test what you're rolling out. I guess you can use another AI to do that but I don't know...

Anyway, my current workflow is:

1. write detailed specs/prompt,

2. let agent loose,

3. pull down and test... usually something goes wrong.

3.1 converse with and ask agent to fix,

3.2 let agent loose again,

3.3 test again... if something goes wrong again:

3.3.1 ...

Sometimes the Agent gets lost in the fixes but now have a better idea what can go wrong and you can start over with a better initial prompt.

I haven't had a lot of success with pre-discussing (planning, PRDing) implementations, as in it worked, but not much better than directly trying to prompt what I want and takes a lot longer. But I'm not usually doing "normal" stuff as this is purely fun/exploratory side-project stuff and my asks are usually complicated but not complex if that makes sense.

I guess development is always a lot of testing, but this feels different. I click around but don't gain a lot of insight. It feels more shallow. I can write a new prompt and explain what's different but I haven't furthered my understanding much.

Also, not knowing the codebase, you might need a couple attempts at phrasing your ask just the right way. I probably had to ask my agent 5+ times, trying to explain in different ways how translate phone IMU yaw/pitch/roll into translations of the screen projection. Sometimes it's surprisingly hard to explain what you want to happen when you don't know the how it's implemented.