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Gemini 2.5 Deep Think

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Re: Gemini 2.5 Deep Think

#201

Post by epolanski »

dataviz1000 wrote: post #81
foundry27 wrote: post #34

I started doing some experimentation with this new Deep Think agent, and after five prompts I reached my daily usage limit. For $250 USD/mo that’s what you’ll be getting folks. It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy. Anecdotally (from my experience) this was the one feature that enthusiasts in the AI community were interested in to justify the exorbitant price of Google’s Ultra subscription. I…

Several years ago I thought a good litmus test for mastery of coding is not finding a solution using internet search nor getting well written questions about esoteric coding problems answered on StackOverflow. For a while, I would post a question and answer my own question after I solved the problem for posterity (or AI bots). I always loved getting the "I've been working on this for 3 days and you saved my life" com…

Your post misses the fact that 99% of programming is repetitive plumbing and that the overwhelming majority of developers, even ivy league graduates, suck at coding and problem solving.

Thus, AI is a great productivity tool if you know how to use it for the overwhelming majority of problems out there. And it's a boost even for those that are not even good at the craft as well.

This whole narrative of "okay but it can't replace me in this or that situation" is honestly between an obvious touche (why would you think AI would replace rather than empower those who know their craft) and stale luddism.

Re: Gemini 2.5 Deep Think

#202

Post by prabhasp »

lostmsu wrote: post #160
qingcharles wrote: post #135

Earlier quoted context omitted.

This feels like the best pelicanbike yet. The singularity might be closer than we imagine. Time for a leaderboard?

Ask and you'll receive: https://pelicans.borg.games/

Lol, can you add a "both of these are terrible" option?

Re: Gemini 2.5 Deep Think

#203

Post by ankitml »

foundry27 wrote: post #34

I started doing some experimentation with this new Deep Think agent, and after five prompts I reached my daily usage limit. For $250 USD/mo that’s what you’ll be getting folks. It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy. Anecdotally (from my experience) this was the one feature that enthusiasts in the AI community were interested in to justify the exorbitant price of Google’s Ultra subscription. I…

I have ultra. Will not be renewing it. Useless, at least have global limits and let people decide how they want to use it. If I have tokens left, why can't I use it for code assist?

Re: Gemini 2.5 Deep Think

#204

Post by sothatsit »

holowoodman wrote: post #122
asaddhamani wrote: post #19

I find it interesting, how OpenAI came out with a $200 plan, Anthropic did $100 and $200, then Gemini ups it to $250, and now Grok is at $300. OpenAI is the only one that says "practically unlimited" and I have never hit any limit on my ChatGPT Pro plan. I hit limits on Claude Max (both plans) several times. Why are these companies not upfront about what the limits are?

Because they want to have their cake and eat it too. A fair pricing model would be token-based, so that a user can see for each query how much they cost, and only pay for what they actually used. But AI companies want a steady stream of income, and they want users to pay as much as possible, while using as little as possible. Therefore they ask for a monthly or even yearly price with an unknown number of tokens inclu…

Personally, I prefer having a fixed, predictable, price rather than paying for usage. There is something psychologically nicer about it to me, and I find myself rationing my usage more when I am using the API (which is effectively what you describe already, just minus the UI).

Re: Gemini 2.5 Deep Think

#206

Post by npinsker »

NitpickLawyer wrote: post #137
jug wrote: post #120

Earlier quoted context omitted.

Easily the best one yet!

Saw one today from gpt5 (via some api trick someone found) that was better than this, let me see if I can find it. Pelican: https://www.reddit.com/media?url=https%3A%2F%2Fpreview.redd.... Longer thread re gpt5: https://old.reddit.com/r/OpenAI/comments/1mettre/gpt5_is_alr...

This is not better. It doesn't even get the chassis of the bike right, let alone forgetting the pelican should probably be attached to it.

Re: Gemini 2.5 Deep Think

#207

Post by 827a »

com2kid wrote: post #193
827a wrote: post #148

Earlier quoted context omitted.

IMO: Context engineering is a fascinating topic because it starts approaching the metaphysical abstract idea of what LLMs even are. If you believe that an LLM is a digital brain, then it follows that their limitation in capabilities today are a result of their limited characteristics (namely: coherent context windows). If we increase context windows (and intelligence), we can simply pack more data into the context, a…

Information in an LLM exists in two places: 1. Embedded in the parameters 2. Within the context window We all talk a lot about #2, but until we get a really good grip on #1, I think we as a field are going to hit a progress wall. The problem is we have not been able to separate out knowledge embedded in parameters with model capability, famously even if you don't want a model to write code, throwing a bunch of code a…

I wonder if the next phase for leveraging LLMs against large sets of contextual, proprietary data (code repositories & knowledge bases come to mind) is going to look more like smaller models highly (and regularly) trained/fine-tuned against that proprietary data (that is maybe delegated tasks by the ultra-sized internet scale omni-brain models)

If I'm asking Sonnet to agentically make this signin button green: does it really matter that it can also write haikus about the japanese landscape? That links back to your point: We don't have a grip, nearly at all, on how much this crosstalk between problem domains matters. Maybe it actually does matter? But certainly most of it doesn't. B

We're so far from the endgame on these technologies. A part of me really feels like we're wasting too much effort and money on training ASI ultra internet scale models. I'm never going to pay $200+/mo for even a much smarter Claude; what I need is a system that knows my company's code like the back of its hand, knows my company's patterns, technologies, and even business (Jira boards, Google docs, etc), and extrapolates from that. That would be worth thousands a month; but what I'm describing isn't going to be solved by a 195 IQ gigabrain, and it also doesn't feel like we're going to get there with context engineering.

Re: Gemini 2.5 Deep Think

#208

Post by Davidzheng »

danenania wrote: post #103
amelius wrote: post #67

Earlier quoted context omitted.

It could be that your problem was too simple to justify the use of Deep Think. But yes, Google should have figured that out and used a less expensive mode of reasoning.

Model routing is deceptively hard though. It has halting problem characteristics: often only the smartest model is smart enough to accurately determine a task's difficulty. And if you need the smartest model to reliably classify the prompt, it's cheaper to just let it handle the prompt directly. This is why model pickers persist despite no one liking them.

but if the less strong model has low false positives you can just route them in order of strength

Re: Gemini 2.5 Deep Think

#209

Post by DanHulton »

epolanski wrote: post #201
dataviz1000 wrote: post #81

Earlier quoted context omitted.

Several years ago I thought a good litmus test for mastery of coding is not finding a solution using internet search nor getting well written questions about esoteric coding problems answered on StackOverflow. For a while, I would post a question and answer my own question after I solved the problem for posterity (or AI bots). I always loved getting the "I've been working on this for 3 days and you saved my life" com…

Your post misses the fact that 99% of programming is repetitive plumbing and that the overwhelming majority of developers, even ivy league graduates, suck at coding and problem solving. Thus, AI is a great productivity tool if you know how to use it for the overwhelming majority of problems out there. And it's a boost even for those that are not even good at the craft as well. This whole narrative of "okay but it can…

> 99% of programming is repetitive plumbing

Even IF that were true (and I'd argue that it is NOT, and it's people who believe that and act that way who produce the tangled messes of spiderweb code that are utterly opaque to public searches and AI analysis -- the supposed "1%"), if even as low as 1% of the code I interacted with was the kind of code that required really deep thought and analysis, it could easily balloon to take up as much time as the other "99%".

Oh, and Ned Ludd was right, by the way. Weavers WERE replaced by the powered loom. It is in the interest of capital to replace you if they are able to, not to complement you, and furthermore, the teeth of capital have gotten sharper over time, and its appetite more voracious.

Re: Gemini 2.5 Deep Think

#210

Post by danenania »

Davidzheng wrote: post #208
danenania wrote: post #103

Earlier quoted context omitted.

Model routing is deceptively hard though. It has halting problem characteristics: often only the smartest model is smart enough to accurately determine a task's difficulty. And if you need the smartest model to reliably classify the prompt, it's cheaper to just let it handle the prompt directly. This is why model pickers persist despite no one liking them.

but if the less strong model has low false positives you can just route them in order of strength

That's a very big "if".
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