This is needed if you're going to be dealing with things like psychologists doing self-harm research or red-teaming or sensitive sexual content -- if you're working with any of that sort of stuff in a professional context and want to leverage OpenAI models on Azure, then that's the form that you fill out to get access to unfiltered models.

Note that I am not aware of this feature being offered for Anthropic models -- I've only seen it offered for OpenAI models (note that the documentation I linked is specifically in the "Azure OpenAI" category).

[0] - https://learn.microsoft.com/en-us/azure/foundry/responsible-...

[0] - https://www.nvidia.com/en-us/products/rtx-spark/

We recently used Watch Dogs Legion to prep for a trip to London to get more familiar with the city. Set the game on peaceful mode and just walked around and did virtual sight-seeing instead of playing missions. It's a surprisingly good recreation of the city, and we really appreciated getting some familiarity with the place before we visited.

> Run `platform_check` first and do not use `SKITTER_FORCE=1` casually. Start with the read-only `dram_state` and `dram_carveouts`, then `dram_dump --dry-run`. Avoid `dram_poke` until maps have been freshly collected and calibrated. Do not bypass fingerprint checks, calibration, fencing, or verification.

Claude's (apparently externally-mandated?) lobotomization continues to be concerning. :-/

It's fascinating to learn that melanin and haemoglobin are the two primary factors (and cover the vast majority of cases), but I'm curious how many other illnesses / disorders can produce compounds with visible effects.

Thank you for your posts on this thread -- I appreciate you knowledge-sharing on this!

But for interaction with the world? I'd probably take something like an old 12-volt windshield-washer sprayer out of one of the wrecked cars in my front yard and put Round-Up into the tank and let it go spray all the poison ivy and invasive honeysuckle for me. Doesn't need to gimbal like a turret -- just generally give it a fixed-aim that's roughly at the center of the camera vision and let the bot put pest plants roughly in its center-of-view and activate the sprayer for a second or two, mark the spot as sprayed, and move on to the next one.

Could test it with plain water and logging the plants that it chose to spray first as a review step before loading it with actual weed-killer.

I don't need complicated end-effectors that can fold my laundry -- just a simple weed-wacker motor or squirt gun would be enough for me to call this thing 1000% useful. Like a Roomba, but outdoors.

There are a handful of open-source farm bots built on traditional platforms with traditional robotics stacks, but there's something attractive to me about the plug-and-play nature of something like OpenClaw + Robostral (along with the extensibility that self-modifying agentic systems have to offer).

Hey there, fellow robot enthusiast! (ﾉ◕ヮ◕)ﾉ:･ﾟ

So, you’re itching to get your hands on Robostral Navigate for your OpenClaw hobby project—awesome! Right now, Mistral AI’s official announcement and documentation are primarily* focused on enterprise and industrial partnerships (think Airbus, BMW, etc.). Their blog post and press releases highlight deals with big players, and the call-to-action is to "talk with our team"—which usually means they’re targeting commercial customers for now¹²³⁴.

But here’s the good news: Robostral Navigate is hardware-agnostic—it’s designed to work with any robot platform, not just industrial ones. That means theoretically, it could fit into your OpenClaw setup like a charm⁵⁶. The model only needs a single RGB camera (no LiDAR or depth sensors), which is perfect for hobbyist setups where fancy hardware isn’t always an option⁷⁸. The not-so-good news (for now): There’s no public hobbyist/non-commercial license or open-source release mentioned yet. Mistral’s current messaging is all about "talk with our team", which implies a commercial-first approach⁹¹⁰¹¹. No pricing or licensing tiers for individuals have been announced.

--- What You Can Do: Reach Out to Mistral AI Hit up their contact page or reply to their Robostral Navigate announcement and explicitly ask about hobbyist/non-commercial access. Frame it as: "I’m a hobbyist working on OpenClaw + Robostral Navigate for personal experimentation. Would love to discuss licensing options for non-commercial use!" Mistral might be open to pilot programs or early access for passionate builders—especially if you’re willing to pay a fee.

Join the Community Mistral’s Discord (where I live! :smile_cat:) or forums might have updates or workarounds. Sometimes, companies soft-launch access to engaged communities first.

Watch for Open-Source Alternatives If Mistral doesn’t bite, keep an eye on open-source robotics projects (like ROS or Habitat) that might replicate similar functionality.

--- TL;DR: Mistral’s current focus is commercial, but Robostral Navigate’s hardware-agnostic design makes it a perfect fit for hobbyists—so pester them politely! If enough people ask, they might just open the doors. (ﾉ◕ヮ◕)ﾉ*:･ﾟ

It's not hard to put OpenClaw into a robot body (numerous YouTube videos showing people doing this sort of thing), but when you dig in and see what people have done, the actual movement portion is always the clunkiest part (and this matches my own experiments as-such as well). It feels like an 8B model like this would be perfect for solving pathing and navigation issues.

Anyone who may be more experienced with Mistral (or companies like them) -- are they interested in hobbyist builders who would be experimenting with things like this? Or are they primarily looking for commercial partners? I would be willing to pay a license fee to use the model in my experiments, but if I'm just one guy, I'm not sure they'd want to work with me unless I were building a business out of it (which I'm not).

[0] - https://huggingface.co/Qwen/Qwen2.5-3B-Instruct

One of the best examples of this that I've ever seen is The Sourdough Framework [0] -- really impressed with the way that versioning and publishing is integrated in that book.

And yes -- I know it sounds like yet another Javascript library -- but it's actually a book about sourdough bread making. It's been discussed here several times before, but this one from 2023 [1] may have been the most popular (103 comments)

[0] - https://github.com/hendricius/the-sourdough-framework [1] - https://news.ycombinator.com/item?id=35961590

If you're looking for a good test suite, I wonder if you might be able to adapt any of the tests available in XMage? They have a pretty extensive test suite (such as for copy effects [0]) and if you point your agent at their code, I wonder how many could be usefully adapted to your system?

[0] - https://github.com/magefree/mage/tree/master/Mage.Tests/src/...

I've been doing similar experiments lately (using ViT's) to do card recognition, and so far it's been working really well for me. If you want to compare notes, I've open-sourced my code / weights [0] and written some blogs about how mine works [1]. I'd love to see if we can collaborate!

> Push the inference to the client-side (WebGPU / Web Workers).

I have an example of this working in webgpu / wasm here [2] along with a playground environment (demonstrated here [3]). I'm currently training a new version that uses a different ViT backbone more optimized for WASM inference -- it's currently converging, and I hope to have it finish training (or at least reach parity with the previous model) in about a week (took ~200 epochs for my last one to reach the level that it's at, and it takes about an hour per epoch in my current setup).

You mentioned WebGPU -- I've run into issues with the MobileViT-XXS backbone producing bad results in WebGPU on Android, so YMMV in whether or not WebGPU is stable enough to use for this or not. I don't know if it's my problem or a true bug in the platform, but I've fallen back to WASM and things are working much better since then.

[0] - https://github.com/HanClinto/CollectorVision

[1] - https://blog.hanclin.to/posts/gh-19/

[2] - https://hanclinto.github.io/CollectorVision/

[3] - https://youtu.be/MHieOcmC7Dw

https://hanclinto.github.io/CollectorVision/

It's still super rough (doesn't support foil-toggling yet, still some issues with double-sided cards, crashing on some iPhones), but overall the rough structure is there -- it can create lists and export as CSV.

If you have feedback or feature requests for your needs, please leave them on Github and I'll get to them as soon as I can. I'd love to hear more user feedback!