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Gemini 2.5 Flash

developers.googleblog.com

331–340 of 582 posts

Re: Gemini 2.5 Flash

#331

More great innovation from Google. OpenAI have two major problems. The first is Google's vertically integrated chip pipeline and deep supply chain and operational knowledge when it comes to creating AI chips and putting them into production. They have a massive cost advantage at every step. This translates into more free services, cheaper paid services, more capabilities due to more affordable compute, and far more g…

I haven’t heard this much positive sentiment about Google in a while. Making something freely available really turns public sentiment around.

Re: Gemini 2.5 Flash

#332

Earlier quoted context omitted.

>obsequious Thanks for the new word, I have to look it up. "obedient or attentive to an excessive or servile degree" Apparently it means an AI that mindlessly follow your logic and instructions without reasoning and articulation is not good enough.

Another useful word in this context is “sycophancy,” meaning excessive flattery or insincere agreement. Amanda Askell of Anthropic has used it to describe a trait they try to suppress in Claude: https://youtube.com/watch?v=ugvHCXCOmm4&t=10286

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Re: Gemini 2.5 Flash

#333

Genuine naive question: when it comes to Google HN has generally a negative view of it (pick any random story on Chrome, ads, search, web, working at faang, etc. and this should be obvious from the comments), yet when it comes to AI there is a somewhat notable “cheering effect” for Google to win the AI race that goes beyond a conventional appreciation of a healthy competitive landscape, which may appear as a bit of a…

Gemini is just that good. From my usage it is much smarter than DeepSeek or Claude 3.7 Thinking models.

A lot of Google’s market share across its services comes from the monopoly effects Google has. The quality of Gemini 2.5 is noticeably smarter than its competitors so I see the applause for the quality of the LLM and not for Google.

I think it’s way too early to say anything about who is winning the race. There is still a long way to go; o3 scores highest in Humanity’s Last Exam (https://agi.safe.ai/) at 20%, 2.5 scores 18%.

Re: Gemini 2.5 Flash

#334
post #305

Earlier quoted context omitted.

If the battle was between Altman and Pichai I'd have my doubts. But the battle is between Altman and Hassabis. I recall some advice on investment from Buffett regarding how he invests in the management team.

Sorry but my eyes rolled to the back of my head with this one. This is between two teams with tons of smart contributors, but the difference is one is more flexible and able to take risks vs the other that has many times more researchers and the world's best and most mature infrastructure/tooling. Its not a CEO vs CEO battle

I think it requires a nuanced take but allow me to provide some counter-examples.

The first is CEO pay rates. Another is the highest paid public employees (which tend to be coaches at state schools). This is evidence that the market highly values managers.

Another is systemic failures within enterprises. When Boeing had a few very public plane crashes, a certain narrative suggested that the transition from highly capable engineer managers to financial focus managers contributed to the problem. A similar narrative has been used to explain the decline of Intel.

Consider the return of Steve Jobs to Apple. Or the turn around at Microsoft with Nadella.

All of these are complex cases that don't submit to an easy analysis. Success and failure are definitely multi-factor and rarely can be traced to a single definitive cause.

Perhaps another way to look at it would be: what percentage of the success of highly complex organizations can be attributed to management? To what degree can poor management decisions contribute to the failure of an otherwise capable organization?

How much you choose to weight those factors is entirely up to you.

edit: I was also thinking about the way we think about the advantage of exceptional generals/admirals in military analysis. Or the effect a president can have on the direction of a country.

Re: Gemini 2.5 Flash

#335
post #293

Earlier quoted context omitted.

> An often overlooked feature of the Gemini models is that they can write and execute Python code directly via their API. Could you elaborate? I thought function calling is a common feature among models from different providers

The Gemini API runs the Python code for you as part of your single API call, without you having to handle the tool call request yourself.

This is so much cheaper than re-prompting each tool use.

I wish this was extended to things like: you could give the model an API endpoint that it can call to execute JS code, and the only requirement is that your API has to respond within 5 seconds (maybe less actually).

I wonder if this is what OpenAI is planning to do in the upcoming API update to support tools in o3.

Re: Gemini 2.5 Flash

#336

Earlier quoted context omitted.

100% agree. I had Gemini flash 2 chew through thousands of points of nasty unstructured client data and it did a 'better than human intern' level conversion into clean structured output for about $30 of API usage. I am sold. 2.5 pro experimental is a different league though for coding. I'm leveraging it for massive refactoring now and it is almost magical.

> thousands of points of nasty unstructured client data What I always wonder in these kinds of cases is: What makes you confident the AI actually did a good job since presumably you haven't looked at the thousands of client data yourself? For all you know it made up 50% of the result.

This was solved a hundred years ago.

It's the same problem factories have: they produce a lot of parts, and it's very expensive to put a full operator or more on a machine to do 100% part inspection. And the machines aren't perfect, so we can't just trust that they work.

So starting in the 1920s Walter Shewhart and Edward Deming came up with Statistical Process Control. We accept the quality of the product produced based on the variance we see of samples, and how they measure against upper and lower control limits.

Based on that, we can estimate a "good parts rate" (which later got used in ideas like Six Sigma to describe the probability of bad parts being passed).

The software industry was built on determinism, but now software engineers will need to learn the statistical methods created by engineers who have forever lived in the stochastic world of making physical products.

Re: Gemini 2.5 Flash

#337

Genuine naive question: when it comes to Google HN has generally a negative view of it (pick any random story on Chrome, ads, search, web, working at faang, etc. and this should be obvious from the comments), yet when it comes to AI there is a somewhat notable “cheering effect” for Google to win the AI race that goes beyond a conventional appreciation of a healthy competitive landscape, which may appear as a bit of a…

2.5 Pro is free, and I'm sure there's a lot of people who have just never tried the best models because they don't want to pay for them. So 2.5 Pro probably blows their socks off.

Whereas, if you've been paying for access to the best models from OpenAI and Anthropic all along, 2.5 Pro doesn't feel like such a drastic step-change. But going from free models to 2.5 Pro is a crazy difference. I also think this is why DeepSeek got so much attention so quickly - because it was free.

Re: Gemini 2.5 Flash

#338

Google making Gemini 2.5 Pro (Experimental) free was a big deal. I haven't tried the more expensive OpenAI models so I can't even compare, only to the free models I have used of theirs in the past. Gemini 2.5 Pro is so much of a step up (IME) that I've become sold on Google's models in general. It not only is smarter than me on most of the subjects I engage with it, it also isn't completely obsequious. The model push…

Why is it free / so cheap (I seem to be getting charged a few cents a day using it with aider so not free but still crazy cheap compared to sonnet)

we know how Google makes money

Re: Gemini 2.5 Flash

#339

Earlier quoted context omitted.

Isn't Grok 3 basically real time now?

Not at all. The model weights and training data remain the same, it's just RAG'ing real-time twitter data into its context window when returning results. It's like a worse version of Perplexity.

Why worse? Doesn't Grok also search the web along with Twitter?

Re: Gemini 2.5 Flash

#340

Earlier quoted context omitted.

100% agree. I had Gemini flash 2 chew through thousands of points of nasty unstructured client data and it did a 'better than human intern' level conversion into clean structured output for about $30 of API usage. I am sold. 2.5 pro experimental is a different league though for coding. I'm leveraging it for massive refactoring now and it is almost magical.

what tool are you using 2.5-pro-exp through? Cline? Or the browser directly?

For 2.5 pro exp I've been attaching files into AIStudio in the browser in some cases. In others, I have been using vscode's Gemini Code Assist which I believe recently started using 2.5 Pro. Though at one point I noticed that it was acting noticeably dumber, and over in the corner, sure enough it warned that it had reverted to 2.0 due to heavy traffic.

For the bulk data processing I just used the python API and Jupyter notebooks to build things out, since it was a one-time effort.

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