The examples aren't great. The personal planning one for example answers the prompt better without deep research than with (with answers only the Visas point)
I’m struggling with MRF. Model Release Fatigue. It’s a syndrome of constantly context switching new large models. Claude 4, gpt, llama, Gemini 2.5, pro-mini, mistrial.
I fire off the ide switch the model and think oh great this is better. I switch to something that worked before and man, this sucks now.
At this point, the entire AI industry seems to just copy OpenAI for the most part. I cannot help but notice that we have the same services just offered by different companies. The amount of innovation in this build is not that high actually.
The Voxtral release seemed interesting, because it brought back competitive open source audio transcription. I wonder if it was necessary to have an LLM backbone (vs a pure-function model) though, but the approach is interesting.
I think they've buried the lede with their image editing capabilities, which seem to be very good! OpenAI's model will change the whole image while editing messing up details in unrelated areas. This seems to perfectly preserve parts of the image unrelated to your query and selectively apply the edits, which is very impressive! The only downside is the output resolution (the resulting image is 1184px wide even though the input image was much larger).
For a quick test I've uploaded a photo of my home office and asked the following prompt: "Retouch this photo to fix the gray panels at the bottom that are slightly ripped, make them look brand new"
I think it did a fantastic job. The output image quality is ever so slightly worse than the original but that's something they'll improve with time I'm sure.
Can anyone point to a good explanation of how these multi-modal text and image models are set up architecturally.. is there like a shared embedding space? or is it lots of integrations..
Incidentally and veering off topic, I find it extremely annoying that to open both pictures I need to click numerous times to avoid receiving unwanted cookies (even if some are „legitimate“, implying others are not). A further nuisance is from the fact that multiple websites have the same cookies vendor pop-up, suggesting there is a „cookies-as-a-service“ vendor of some sort.
That's because they're leveraging BFL models (almost assuredly Kontext) - it's mentioned in the release notes.
The input image is scaled down to the closest aspect ratio of approximately 1 megapixel.
I ran some experiments with Kontext and added a slider so you can see the before / after of the isolated changes it makes without affecting the entire image.
If you haven't tried OpenAI's deep research feature, you are missing out. I'm not sure of any good alternatives, I've tried Google's, and I'm not impressed.
There is a lot of value to say engineers doing tradeoff studies using these tools as a huge head start.
Every time I need a deep research (1-2x per month) I ask all the providers. OpenAI’s deep research has consistently performed the worst by a significant margin. If you’re just using OpenAI’s deep research, then you’re the one missing out ;-)
All of them create an overly verbose report that reads like typical AI slop. Especially Gemini - it's good that it reads like 200 sources but it creates a page long "report" when you e.g. only want to compare features or prices.
Mistral has been there for quite a while (2 years is "a while" in this field).
They are very good at making smaller models, not the smartest or the most knowledgeable, but you generally get pretty clean results quickly. Also, in my experience, they are less heavy handed than others when it comes to censorship.
Probably not: FUTO uses a slightly tweaked whisper that does not pad all audios to be 30s long as the original whisper does. It's not the end of the world to retrain this on more recent versions but they have not done so when whisper v3 came out, nor whisper v3 turbo. And voxtral has way more capabilities than stt that would then be wasted in a stt only setting imho.
At this point I care far more about an open (and credibly ethically-sourced) data model than open code, open weights or whatever. I wanna use models that can tell me whether or not a resource I’m pointing to is in its training data or not.
Does anyone have a good way for doing high-stakes deep research across a number of models? i.e. send to OpenAI, Anthropic, Gemini then evaluate (perhaps LLM as judge)? Does that yield some performance uplift or make it worse?
I've been trying to use other LLM providers than OpenAI over the past few weeks: Claude, Deepseek, Mistral, local Ollama ...
While Mistral might not have the best LLM performances, their UX is IMO the best, or at least a tie with OpenAI's:
- I never had any UI bug, while these were common with Claude or OpenAI (e.g. a discussion disappearing, LLM crashing mid-answer, long context errors on Claude ...);
- They support most of the features I liked from OpenAI, such as libraries and projects;
- Their app is by far the fastest, thanks to their fast reply feature;
40 comments
[ 1.7 ms ] story [ 62.4 ms ] threadI fire off the ide switch the model and think oh great this is better. I switch to something that worked before and man, this sucks now.
Context switching llm, Model Release Fatigue
For a quick test I've uploaded a photo of my home office and asked the following prompt: "Retouch this photo to fix the gray panels at the bottom that are slightly ripped, make them look brand new"
Input image (rescaled): https://i.imgur.com/t0WCKAu.jpeg
Output image: https://i.imgur.com/xb99lmC.png
I think it did a fantastic job. The output image quality is ever so slightly worse than the original but that's something they'll improve with time I'm sure.
Incidentally and veering off topic, I find it extremely annoying that to open both pictures I need to click numerous times to avoid receiving unwanted cookies (even if some are „legitimate“, implying others are not). A further nuisance is from the fact that multiple websites have the same cookies vendor pop-up, suggesting there is a „cookies-as-a-service“ vendor of some sort.
3 thumbs up. Part of the community which _knows_ what is peak gaming (and natively on elf/linux...)
[1]: https://bfl.ai/models/flux-kontext
The input image is scaled down to the closest aspect ratio of approximately 1 megapixel.
I ran some experiments with Kontext and added a slider so you can see the before / after of the isolated changes it makes without affecting the entire image.
https://specularrealms.com/ai-transcripts/experiments-with-f...
There is a lot of value to say engineers doing tradeoff studies using these tools as a huge head start.
They are very good at making smaller models, not the smartest or the most knowledgeable, but you generally get pretty clean results quickly. Also, in my experience, they are less heavy handed than others when it comes to censorship.
I am a bit disappointed, the headline made me think they offer a voice mode similar to OpenAI.
[0] https://www.youtube.com/watch?v=CEP-xIIfuhs
While Mistral might not have the best LLM performances, their UX is IMO the best, or at least a tie with OpenAI's:
- I never had any UI bug, while these were common with Claude or OpenAI (e.g. a discussion disappearing, LLM crashing mid-answer, long context errors on Claude ...);
- They support most of the features I liked from OpenAI, such as libraries and projects;
- Their app is by far the fastest, thanks to their fast reply feature;
- They allow you to disable web-search.