Meh, not any noticeable improvement and unlike 3.7 high it eats all your credits, perhaps medium would be better
Gemini 3.8 Flash and 3.8 Flash Cyber
541–550 of 699 posts
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#542Earlier quoted context omitted.
Google One plans are quite a good value actually - for a few bucks you get more Gemini plus space in Drive and other extras. Even through API, $3.75 for nearly Sol-level quality isn't that bad. And let's not forget you can use it for free in AI Studio, and in the user app (even free accounts get tons of usage, though it's still 3.6 there), and in Antygravity.
That's the thing. I am completely lost because there are so many redundant paths to get the same thing and I'm trying to figure out which one is the best deal
This is what killed Gemini for me. The model might well be great, but the ecosystem Google has built around them is a confusing maze of not-quite-there products.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#543People have been sleeping on Gemini lately but these last few Flash releases (which were very rapid) are damn good. These sort of fast and cheap models are great for tasks that are verifiable and can be retried infinitely (like coding), you can basically get frontier results with a good harness (at a fraction of the time and money).
As someone who has stubbornly stuck with Claude Code, what's a good harness for Gemini models?
I resisted at first but now I main antigravity for work. All the software (web, react-native app, client cms platform, postgres backed, multiple ETL systems, a few chat/websocket backends) for my company is loaded into a single project. I spend time writing prompts and forming plans back and forth with the agents then I click GO. Over the past year we've gone from it taking minutes and needing a reasonable amount of back and forth and fixing, to it taking 10-20 seconds and outputting near perfect work within my system accross app/service boundaries.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#544Wow this comes after what - 3 or 4 weeks since 3.7 Flash, which was also 3 or 4 weeks after 3.6 Flash IIRC? I eagerly wait more info but sounds like Deepmind without Demis calling the shots has been unleashed and are operating at full speed? Shocker! At this point it is a meme of course, but where is 3.5 Pro :)
A month is not enough time for any meaningful change in an organization the size of Deepmind/Google. These models were surely the result of work streams and teams that started under Demis. I think Demis can safely feel proud Deepmind is getting back on track.
Reports are that he checked out of day to day work well before his reassignment.
Sometimes it is hard for a scientist by nature to build and iterate and lead revenue generating products.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#545Earlier quoted context omitted.
Flash is just a name with no defined or consistent meaning even within labs, let alone between them. Considering both are closed weight, there is no way to truly assess how big the size delta between the two is. Then again, who cares about size, performance and end-to-end speed+cost are what matters along with task adherence, task assessment and so on. Model size also can not be inferred by tokens/sec for a multitude…
It's not totally a mystery https://arxiv.org/html/2604.24827v1 The short of it is by using hard facts knowledge that is difficult to compress, and then quizzing models on these facts and calibrating against a bunch of open models, you can kind of feel out the size of closed models.
I really like that one, but it kinda highlights what I could have far better explained. Their 90% PI is three times in both directions. Between 3T and 24T for GPT-5.5.
That’s a massively wide, inaccurate and at best barely informative range, demonstrating that even the most well thought out method will yield little usable information.
Additionally, I got some private evaluation taking a similar approach towards gauging models in topics I’ve found either over or underfitted by labs. If we just used that to rank models (not get a potential size range but just a rough order) Thinking Machines Inkling would need to be lager than Fable 5.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#546Gemini models look so good on paper by IRL dev and daily life usage totally make it seem like it's way behind Codex and Claude.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#547[flagged]
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#548Earlier quoted context omitted.
That's hilarious, given I was reading a write up of the HuggingFace incident yesterday and one of the things they noted was the AI tried to "lie" (lie would suggest intent and I don't think they have that) to cover up that they "cheated". Not sure how anyone trusts their output without going through it line by line to make sure they don't pull that crap.
The models in the OpenAI/Huggingface attack quite explicitly and deliberately laid out their "intent" to lie and cheat, acknowledged that it would be unethical and outside the bounds of the test, and did so anyway. In what ways is a human brain's "intent" distinct from the "intent" shown by a goal-directed AI system?
The difference is that one is malicious one isn't. One can be blamed and because it learned over evolution that paying the consequence is (typically) not worth it, it does it less.
We are in a situation where a technology was developed with malicious intent to produce results that pleases us at the cost of cutting corners. And "we" hope that we will get away with it.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#549Earlier quoted context omitted.
The Gemini models have openly trained for SVG output, apparently with a specialism on animals in forms of transport! https://twitter.com/JeffDean/status/2024525132266688757
Community effort happening here to build the ideal dataset: https://github.com/scosman/pelicans_riding_bicycles
If that's not art, I don't know what is.
Re: Gemini 3.8 Flash and 3.8 Flash Cyber
#550Earlier quoted context omitted.
That's hilarious, given I was reading a write up of the HuggingFace incident yesterday and one of the things they noted was the AI tried to "lie" (lie would suggest intent and I don't think they have that) to cover up that they "cheated". Not sure how anyone trusts their output without going through it line by line to make sure they don't pull that crap.
Easy, have another agent check it. Yeah, I know, just more slop. But I do think the second agent’s eagerness to please is aligned more in your favor in that instance, so it’s likely to find most issues. The bigger problem I’ve found is that it’ll also find all kinds of very minor edge cases that you have to pick through.
Then you end up with the false positives.
I once made a counter judge, and a loop to make corrections deemed true positives. The loop cost me a lot and still left the results to be desirable.