james2doyle

I just added some credits to my account. You can set a daily $ spend limit as well as add credits without auto-refill

Seems like it. I just added some credits to my account. You can set a daily $ spend limit as well as add credits without auto-refill

Yeah you can see here that tool calling is disabled: https://huggingface.co/inference/models?model=google%2Fgemma...

At least, as of this post

Hmm just tried the google/gemma-4-31B-it through HuggingFace (inference provider seems to be Novita) and function/tool calling was not enabled...

Oh I never noticed that. Good to call out. But that would put it much closer to Minimax M2.7 in terms of price than to the likes of Mimo V2 Pro, and Gemini Flash 3 preview, which are both on the list

None of the Qwen 3.5 models seem present? I’ve heard people are pretty happy with the smaller 3.5 versions. I would be curious to see those too.

I would also be interested to see "KAT-Coder-Pro-V2" as they brag about their benchmarks in these bots as well

What caused the switch? Also, are you still trying to use Claude models in OpenCode?

The only similarity is that they both say "you’re absolutely right" when you point out their obvious mistakes

_hyper-competent collaborator who may completely make things up occasionally and will sometimes give different answers to the same question*_

I’ve used M2.5 in OpenCode using their Zen inference. I found it to be decent. Did not really seem comparable to Opus 4.5 for "quality" output. As in, I often tweaked the output more when using M2.5.

I think the best thing was the speed. If it is going to be wrong, I would prefer it to be wrong quickly.

They have the details up on their site now: https://www.minimax.io/models/text/m27

There is a blog post now: https://mistral.ai/news/leanstral

Mentioned in the 2.5.0 release of the Vibe CLI tool: https://github.com/mistralai/mistral-vibe/releases/tag/v2.5....

> Leanstral > Our first open-source code agent designed for Lean 4, built for formal proof engineering in realistic repositories. 119B parameters with 6.5B active.

Mentioned in the 2.5.0 release of the Vibe CLI tool: https://github.com/mistralai/mistral-vibe/releases/tag/v2.5.... A HuggingFace page is linked for the weights but it returns a 404: https://huggingface.co/mistralai/Leanstral-120B-A6B-2603

This article sure uses a lot of em dashes. I see 9 in the article body.

> You're absolutely right

Bot detected

The blog post includes a video showcasing the improvements. Looks really impressive: https://blog.google/innovation-and-ai/models-and-research/ge...

That is just not true. These AI scrapers are hammering all types of sites and causing their bills to explode.

https://www.pcmag.com/news/wikipedia-faces-flood-of-ai-bots-...

The nature of archives is that they are constantly updated.

Haven’t seen this one, but I settled on this offline-running one: https://github.com/hoomanaskari/mac-dictate-anywhere

I was having the same journey but landed on https://github.com/hoomanaskari/mac-dictate-anywhere

Having used both, the experience of building actual UI with Flutter is a breeze compared to building UI in any game engine. I can imagine that most of the usage of Flutter is leveraging the huge amount of work that was already done to get efficient and capable UIs done with just a stack of widgets.

Makes me wish more apps had feature toggles

Over 60 AI models across 8 categories. The entire benchmark was built and runs inside of n8n and scores models on actual use cases in n8n rather than conversational style or subjective preference.

Smaller models often outperform larger models when it comes to specific tasks. We also found that a model's list price doesn't tell the whole story. One model priced at half the cost of competitors ended up being 10x more expensive in practice because it was so verbose in its outputs.

No single model dominates every category, so use the category filter on the benchmark page to find the best fit for your specific workflow. Whether you're building AI agents, automating data extraction, or generating code, this benchmark helps you make more informed decisions and build more cost-effective solutions.

Yep I had the same experience with Astro a couple years ago. Tried to deploy to Cloudflare and it was not working so ended up with Netlify. Tried again a few months ago and it worked flawlessly. Funny enough, they have since "bought" Astro and so I only expect it to get better

I think this one is better: https://huggingface.co/spaces/ronedgecomb/ml-sharp

I believe if you are using a project workspace (working in a directory with a .sublime-project file) then you can also write plugins right there in the project. But, if you wanted, you can use symlinks. Sublime will follow symlinks. I use this to sync my settings to Dropbox so I can have the same setup on any computer

Maybe try out some of the alternative CLI options? Like https://opencode.ai? I also like https://github.com/charmbracelet/crush and https://github.com/mistralai/mistral-vibe

You can actually use tailwind via the script tag in a plain HTML file. Not for production, but great for whipping up prototypes

I don’t think people realize how easy it is to make Sublime plugins. They can be as simple as a single .py file. No dependencies install step, no meta files, no rituals to go through. Just drop the .py file in the right directory. I’ve used Gemini to build about 3-4 plugins of various complexity and they all work great

I have found MCPs helpful. Recently, I used one to migrate a site from WordPress to Sanity. I pasted in the markdown from the original site and told it to create documents that matched my schemas. This was much quicker and more flexible than whipping up a singular migration tool. The Sanity MCP uses oAuth so I also didn’t need to do anything in order to connect to my protected dataset. Just log in. I’ll definitely be using this method in the future for different migrations.