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Curious to hear what folks are doing with Gemini outside of the coding space and why you chose it. Are you building your app so you can swap the underlying Gen

Curious to hear what folks are doing with Gemini outside of the coding space and why you chose it. Are you building your app so you can swap the underlying GenAI easily? Do you "load balance" your usage across other providers for redundancy or cost savings? What would happen if there was ever some kind of spot market for LLMs?

crowcroft 1y ago
Simple unstructured to structured data transformation. I find Flash and Flash Lite are more consistent than others as well as being really fast and cheap. I could swap to other providers fairly easily, but don't intend to at this point. I don't operate at a large scale.
willidiots 1y ago
Low-latency LLM for my home automation. Anecdotally, Gemini was much quicker than OpenAI in responding to simple commands. In general, when I need "cheap and fast" I choose Gemini.
thimabi 1y ago
In my experience, Gemini 2.5 Pro really shines in some non-coding use cases such as translation and summarization via Canvas. The gigantic context window and large usage limits help in this regard. I also believe Gemini is much better than ChatGPT in generating deep research reports. Google has an edge in web search and it shows. Gemini’s reports draw on a vast number of sources, thus tend to be more accurate. In general, I even prefer its writing style, and I like the possibility of exporting reports to Google Docs. One thing that I don’t like about Gemini is its UI, which is miles behind the competition. Custom instructions, projects, temporary chats… these things either have no equivalent in Gemini or are underdeveloped.
- hnuser123456 1y ago
  If you're a power user, you should probably be using Gemini through AI studio rather than the "basic user" version. That allows you to set system instructions, temperature, structured output, etc. There's also NotebookLM. Google seems to be trying to make a bunch of side projects based on Gemini and seeing what sticks, and the generic gemini app/webchat is just one of those.
  - thimabi 1y ago
    My complaint is that any data within AI Studio can be kept by Google and used for training purposes — even if using the paid tier of the API, as far as I know. Because of that, I end up only using it rarely, when I don’t care about the fate of the data.
    - happyopossum 1y ago
      This is only true for the free tier. Paid Ai Studio users have strong privacy protections.
      - thimabi 1y ago
        Thank you for clarifying that. I’ve researched this once again and confirmed that Google treats all AI Studio usage as private if there’s at least one API project with billing enabled in an account.
      - radicality 1y ago
        Can you elaborate on “paid” ? Because I honestly still have no idea if my usage of AI Studio is used for training purposes. I have google workspace business standard, which comes with some pro AI features. Eg, Gemini chat clearly shows “Pro”, and says something like “chats in your organization won’t be used for training”. On AI Studio it’s not clear at all. I do have some version of paid AI services through Google, but no idea if it applies to AI studio. I did create some dummy Google cloud project which allowed me to generate api key, but afaik I still haven’t authorized any billing method.
- VeejayRampay 1y ago
  for translation you'll still be limited for longer texts by the 65K output limit though I suppose?
  - thimabi 1y ago
    Yes. I haven't had problems with the output limit so far, as I do translations iteratively, over each section of longer texts. What I like the most about translating with Gemini is that its default performance is already good enough, and it can be improved via the one million tokens of the context window. I load to the context my private databases of idiomatic translations, separated by language pairs and subject areas. After doing that, the need for manually reviewing Gemini translations is greatly diminished.
HDThoreaun 1y ago
I tried swapping for my project which involves having the LLM summarize and critique medical research and didn’t have great results. The prompt I found works best with the main LLM I use fucks up the intended format when fed to other LLMs. Thinking about refining prompts for each different llm but haven’t gotten there. My favorite personal use of Gemini right now is basically as a book club. Of course it’s not as good as my real one but I often can’t them to read the books I want and Gemini is always ready when I want to explore themes. It’s often more profound than the book club too and seems a bit less likely to tunnel vision. Before LLMs I found exploring book themes pretty tedious, often I would have to wait a while to find someone who had read it but now I can get into it as soon as I’m done reading.
ttul 1y ago
I can throw a pile of NDAs at it and it neatly pulls out relevant stuff from them within a few seconds. The huge context window and excellent needle in a haystack performance is great for this kind of task.
- spmurrayzzz 1y ago
  The NIAH performance is a misleading indicator for performance on the tasks people really want the long context for. It's great as a smoke/regression test. If you're bad on NIAH, you're not gonna do well on the more holistic evals. But the long context eval they used (MRCR) is limited. It's multi-needle, so that's a start, but its not evaluating long range dependency resolution nor topic modeling, which are the things you actually care about beyond raw retrieval for downstream tasks. Better than nothing, but not great for just throwing a pile of text at it and hoping for the best. Particularly for out-of-distribution token sequences. I do give google some credit though, they didn't try to hide how poorly they did on that eval. But there's a reason you don't see them adding RULER, HELMET, or LongProc to this. The performance is abysmal after ~32k. EDIT: I still love using 2.5 Pro for a ton of different tasks. I just tend to have all my custom agents compress the context aggressively for any long context or long horizon tasks.
  - NitpickLawyer 1y ago
    > The performance is abysmal after ~32k. Huh. We've not seen this in real-world use. 2.5 pro has been the only model where you can throw a bunch of docs into it, give it a "template" document (report, proposal, etc), even some other-project-example stuff, and tell it to gather all relevant context from each file and produce "template", and it does surprisingly well. Couldn't reproduce this with any other top tier model, at this level of quality.
    - spmurrayzzz 1y ago
      We're a G-suite shop so I set aside a ton of time trying to get 2.5 pro to work for us. I'm not entirely unhappy with it, its a highly capable model, but the long context implosion significantly limits it for the majority of task domains. We have long context evals using internal data that are leveraged for this (modeled after longproc specifically) and the performance across the board is pretty bad. Task-wise for us, it's about as real world as it gets, using production data. Summarization, Q&A, coding, reasoning, etc. But I think this is where the in-distribution vs out-of-distribution distinction really carries weight. If the model has seen more instances of your token sequences in training and thus has more stable semantic representations of them in latent space, it would make sense that it would perform better on average. In my case, the public evals align very closely with performance on internal enterprise data. They both tank pretty hard. Notably, this is true for all models after a certain context cliff. The flagship frontier models predictably do the best.
jasoncartwright 1y ago
Web scraping - creating semi-structured data from a wide variety of horrific HTML soups. Absolutely do swap out models sometimes, but Gemini 2.0 Flash is the right price/performance mix for me right now. Will test Gemini 2.5 Flash-Lite tomorrow though.
k8sToGo 1y ago
I use Gemini 2.5 Flash (non thinking) as a thought partner. It helps me organize my thoughts or maybe even give some new input I didn't think of before. I really like to use it also for self reflection where I just input my thoughts and maybe concerns and just see what it has to say.
fastest963 1y ago
Yes, we implemented a separate service internally that interfaces with an LLM and so the callers can be agnostic as to what provider or model is being used. Haven't needed to load balance between models though.
androng 1y ago
I use it for https://toolong.link https://toolong.link Youtube summaries with images because only Gemini has easy access to YouTube and it has a gigantic context window
Dnajsre 1y ago
It basically made a university physics exam for me. It almost one-shot it as well. Just uploaded some exams from previous years together with a latex template and told it to make me a similar one. Worked great. Also made it do the solutions.
jeffbee 1y ago
It's very good at automatically segmenting and recognizing handwritten and badly scanned text. I use it to make spreadsheets out of handwritten petitions.
extr 1y ago
Gemini Flash 2.0 is an absolute workhorse of a model at extremely low cost. It's obviously not going to measure up to frontier models in terms of intelligence but the combination of low cost, extreme speed, and highly reliable structured output generation make it really pleasant to develop with. I'll probably test against 2.5 Lite for an upgrade here.
- wg0 1y ago
  I want to know what use cases you're using if for it it's not confidential.
  - extr 1y ago
    We use it by having a Large Model delegate to Flash 2.0. Let's say you have a big collection of objects and a SOTA model identifies the need to edit some properties of one of them. Rather than have the Large Model perform a tool call or structured output itself (potentially slow/costly at scale), it can create a small summary of the context and change needed. You can then provide this Flash 2.0 and have it generate the full object or diffed object in a safe way using the OpenAPI schema that Gemini accepts. The controlled generation is quite powerful, especially if you create the schema dynamically. You can generate an arbtirarily complex object with full typing, restrict valid values by enum, etc. And it's super fast and cheap and easily parallelizable. Have 100 objects to edit? No problem, send 100 simultaneous flash 2.0 calls. It's google, they can handle it.
bradly 1y ago
I've yet to run out of free image gen credits with Gemini, so I use it for any low-effort image gen like when my kids want to play with it or for testing prompts before committing my o4 tokens for better quality results.
sync 1y ago
I use it extensively for https://lexikon.ai https://lexikon.ai - in particular one part of what Lexikon does involves processing large amounts of images, and the way Google charges for vision is vastly cheaper compared to the big alternatives (OpenAI, Anthropic)
- mrtesthah 1y ago
  Wow, if I knew that someone was using your product on my conversation with them I'd probably have to block them.
  - satvikpendem 1y ago
    I mean I've copy pasted conversations and emails into ChatGPT as well, it often gives good advice on tricky problems (essentially like your own personalized r/AmITheAsshole chat). This service seems to just automate that process.
upcoming-sesame 1y ago
Turn local real estate agents websites to RSS to get new properties on the market before they get uploaded to real estate market place platforms. I give it the HTML, it finds the appropriate selector for the property item and then I use a HTML to RSS tool to publish the feed
tristenharr 1y ago
I’ve found the 2.5 pro to be pretty insane at math. Having a lot of fun doing math that normally I wouldn’t be able to touch. I’ve always been good at math, but it’s one of those things where you have to do a LOT of learning to do anything. Being able to breeze through topics I don’t know with the help of AI and a good CAS + sympy and Mathematica verification lets me chew on problems I have no right to be even thinking about considering my mathematical background. (I did minor in math.. but the kinds of problems I’m chewing on are things people spend lifetimes working on. That I can even poke at the edges of them thanks to Gemini is really neat.)

crowcroft 1y ago

Simple unstructured to structured data transformation. I find Flash and Flash Lite are more consistent than others as well as being really fast and cheap. I could swap to other providers fairly easily, but don't intend to at this point. I don't operate at a large scale.

willidiots 1y ago

Low-latency LLM for my home automation. Anecdotally, Gemini was much quicker than OpenAI in responding to simple commands. In general, when I need "cheap and fast" I choose Gemini.

thimabi 1y ago

In my experience, Gemini 2.5 Pro really shines in some non-coding use cases such as translation and summarization via Canvas. The gigantic context window and large usage limits help in this regard. I also believe Gemini is much better than ChatGPT in generating deep research reports. Google has an edge in web search and it shows. Gemini’s reports draw on a vast number of sources, thus tend to be more accurate. In general, I even prefer its writing style, and I like the possibility of exporting reports to Google Docs. One thing that I don’t like about Gemini is its UI, which is miles behind the competition. Custom instructions, projects, temporary chats… these things either have no equivalent in Gemini or are underdeveloped.

hnuser123456 1y ago
If you're a power user, you should probably be using Gemini through AI studio rather than the "basic user" version. That allows you to set system instructions, temperature, structured output, etc. There's also NotebookLM. Google seems to be trying to make a bunch of side projects based on Gemini and seeing what sticks, and the generic gemini app/webchat is just one of those.
- thimabi 1y ago
  My complaint is that any data within AI Studio can be kept by Google and used for training purposes — even if using the paid tier of the API, as far as I know. Because of that, I end up only using it rarely, when I don’t care about the fate of the data.
  - happyopossum 1y ago
    This is only true for the free tier. Paid Ai Studio users have strong privacy protections.
    - thimabi 1y ago
      Thank you for clarifying that. I’ve researched this once again and confirmed that Google treats all AI Studio usage as private if there’s at least one API project with billing enabled in an account.
    - radicality 1y ago
      Can you elaborate on “paid” ? Because I honestly still have no idea if my usage of AI Studio is used for training purposes. I have google workspace business standard, which comes with some pro AI features. Eg, Gemini chat clearly shows “Pro”, and says something like “chats in your organization won’t be used for training”. On AI Studio it’s not clear at all. I do have some version of paid AI services through Google, but no idea if it applies to AI studio. I did create some dummy Google cloud project which allowed me to generate api key, but afaik I still haven’t authorized any billing method.
VeejayRampay 1y ago
for translation you'll still be limited for longer texts by the 65K output limit though I suppose?
- thimabi 1y ago
  Yes. I haven't had problems with the output limit so far, as I do translations iteratively, over each section of longer texts. What I like the most about translating with Gemini is that its default performance is already good enough, and it can be improved via the one million tokens of the context window. I load to the context my private databases of idiomatic translations, separated by language pairs and subject areas. After doing that, the need for manually reviewing Gemini translations is greatly diminished.

HDThoreaun 1y ago

I tried swapping for my project which involves having the LLM summarize and critique medical research and didn’t have great results. The prompt I found works best with the main LLM I use fucks up the intended format when fed to other LLMs. Thinking about refining prompts for each different llm but haven’t gotten there. My favorite personal use of Gemini right now is basically as a book club. Of course it’s not as good as my real one but I often can’t them to read the books I want and Gemini is always ready when I want to explore themes. It’s often more profound than the book club too and seems a bit less likely to tunnel vision. Before LLMs I found exploring book themes pretty tedious, often I would have to wait a while to find someone who had read it but now I can get into it as soon as I’m done reading.

ttul 1y ago

I can throw a pile of NDAs at it and it neatly pulls out relevant stuff from them within a few seconds. The huge context window and excellent needle in a haystack performance is great for this kind of task.

spmurrayzzz 1y ago
The NIAH performance is a misleading indicator for performance on the tasks people really want the long context for. It's great as a smoke/regression test. If you're bad on NIAH, you're not gonna do well on the more holistic evals. But the long context eval they used (MRCR) is limited. It's multi-needle, so that's a start, but its not evaluating long range dependency resolution nor topic modeling, which are the things you actually care about beyond raw retrieval for downstream tasks. Better than nothing, but not great for just throwing a pile of text at it and hoping for the best. Particularly for out-of-distribution token sequences. I do give google some credit though, they didn't try to hide how poorly they did on that eval. But there's a reason you don't see them adding RULER, HELMET, or LongProc to this. The performance is abysmal after ~32k. EDIT: I still love using 2.5 Pro for a ton of different tasks. I just tend to have all my custom agents compress the context aggressively for any long context or long horizon tasks.
- NitpickLawyer 1y ago
  > The performance is abysmal after ~32k. Huh. We've not seen this in real-world use. 2.5 pro has been the only model where you can throw a bunch of docs into it, give it a "template" document (report, proposal, etc), even some other-project-example stuff, and tell it to gather all relevant context from each file and produce "template", and it does surprisingly well. Couldn't reproduce this with any other top tier model, at this level of quality.
  - spmurrayzzz 1y ago
    We're a G-suite shop so I set aside a ton of time trying to get 2.5 pro to work for us. I'm not entirely unhappy with it, its a highly capable model, but the long context implosion significantly limits it for the majority of task domains. We have long context evals using internal data that are leveraged for this (modeled after longproc specifically) and the performance across the board is pretty bad. Task-wise for us, it's about as real world as it gets, using production data. Summarization, Q&A, coding, reasoning, etc. But I think this is where the in-distribution vs out-of-distribution distinction really carries weight. If the model has seen more instances of your token sequences in training and thus has more stable semantic representations of them in latent space, it would make sense that it would perform better on average. In my case, the public evals align very closely with performance on internal enterprise data. They both tank pretty hard. Notably, this is true for all models after a certain context cliff. The flagship frontier models predictably do the best.

jasoncartwright 1y ago

Web scraping - creating semi-structured data from a wide variety of horrific HTML soups. Absolutely do swap out models sometimes, but Gemini 2.0 Flash is the right price/performance mix for me right now. Will test Gemini 2.5 Flash-Lite tomorrow though.

k8sToGo 1y ago

I use Gemini 2.5 Flash (non thinking) as a thought partner. It helps me organize my thoughts or maybe even give some new input I didn't think of before. I really like to use it also for self reflection where I just input my thoughts and maybe concerns and just see what it has to say.

fastest963 1y ago

Yes, we implemented a separate service internally that interfaces with an LLM and so the callers can be agnostic as to what provider or model is being used. Haven't needed to load balance between models though.

androng 1y ago

I use it for https://toolong.link https://toolong.link Youtube summaries with images because only Gemini has easy access to YouTube and it has a gigantic context window

Dnajsre 1y ago

It basically made a university physics exam for me. It almost one-shot it as well. Just uploaded some exams from previous years together with a latex template and told it to make me a similar one. Worked great. Also made it do the solutions.

jeffbee 1y ago

It's very good at automatically segmenting and recognizing handwritten and badly scanned text. I use it to make spreadsheets out of handwritten petitions.

extr 1y ago

Gemini Flash 2.0 is an absolute workhorse of a model at extremely low cost. It's obviously not going to measure up to frontier models in terms of intelligence but the combination of low cost, extreme speed, and highly reliable structured output generation make it really pleasant to develop with. I'll probably test against 2.5 Lite for an upgrade here.

wg0 1y ago
I want to know what use cases you're using if for it it's not confidential.
- extr 1y ago
  We use it by having a Large Model delegate to Flash 2.0. Let's say you have a big collection of objects and a SOTA model identifies the need to edit some properties of one of them. Rather than have the Large Model perform a tool call or structured output itself (potentially slow/costly at scale), it can create a small summary of the context and change needed. You can then provide this Flash 2.0 and have it generate the full object or diffed object in a safe way using the OpenAPI schema that Gemini accepts. The controlled generation is quite powerful, especially if you create the schema dynamically. You can generate an arbtirarily complex object with full typing, restrict valid values by enum, etc. And it's super fast and cheap and easily parallelizable. Have 100 objects to edit? No problem, send 100 simultaneous flash 2.0 calls. It's google, they can handle it.

bradly 1y ago

I've yet to run out of free image gen credits with Gemini, so I use it for any low-effort image gen like when my kids want to play with it or for testing prompts before committing my o4 tokens for better quality results.

sync 1y ago

I use it extensively for https://lexikon.ai https://lexikon.ai - in particular one part of what Lexikon does involves processing large amounts of images, and the way Google charges for vision is vastly cheaper compared to the big alternatives (OpenAI, Anthropic)

mrtesthah 1y ago
Wow, if I knew that someone was using your product on my conversation with them I'd probably have to block them.
- satvikpendem 1y ago
  I mean I've copy pasted conversations and emails into ChatGPT as well, it often gives good advice on tricky problems (essentially like your own personalized r/AmITheAsshole chat). This service seems to just automate that process.

upcoming-sesame 1y ago

Turn local real estate agents websites to RSS to get new properties on the market before they get uploaded to real estate market place platforms. I give it the HTML, it finds the appropriate selector for the property item and then I use a HTML to RSS tool to publish the feed

tristenharr 1y ago

I’ve found the 2.5 pro to be pretty insane at math. Having a lot of fun doing math that normally I wouldn’t be able to touch. I’ve always been good at math, but it’s one of those things where you have to do a LOT of learning to do anything. Being able to breeze through topics I don’t know with the help of AI and a good CAS + sympy and Mathematica verification lets me chew on problems I have no right to be even thinking about considering my mathematical background. (I did minor in math.. but the kinds of problems I’m chewing on are things people spend lifetimes working on. That I can even poke at the edges of them thanks to Gemini is really neat.)