Woah sounds cool! if there's rabbit holes in the lawn, then it's a field! This robot has 14-inch wheels for toys and fruit, object detection, and some pincers to take toys and new plastic objects away from it's work zone.

It doesn't have a border wire, it's using ultrasound pings, the same technology that drones use to fly in groups. It's also rather tall with multiple cameras at the top.

The car washing option is just a rinse for salt and hubs, a bit like an intense rain storm that hits sideways. So, worth implementing if it can?

Yeah you’ve definitely setup a good representation bias for modeling entire software architectures.

I had a thought a while back that GitHub Copilot is eventually going to offer a feature where they suggest improvements to entire software architectures… and then eventually just write whole projects from a text description alone. I think that the solution for that would be pretty similar to what you’ve done if scaled up and applied that way.

If your plan is to scale up the system for more advanced features, that would be awesome.

Another suggestion is that if you integrated your tool with GitHub, it would be pretty useful for enterprise software development. Most companies are pretty crappy at documentation. Even with good documentation, a chatbot is better than a static document.

Good job!

I think the biggest issue here though is a large part of a bot like this would be traditional programming. The type of AI hardware needed for these tasks in the garden would likely eclipse the bots 3k goal for the foreseeable future.

Robot control is fundamentally a reinforcement learning problem, and that subfield hasn't had the same breakthroughs as self-supervised learning.

This may be changing though - the current state-of-the-art can use self-supervised learning to build a world model, and then run RL inside that world model instead of on the raw input data.

They claim 100x better sample efficiency than traditional model-free RL. Definitely an area of research to watch.

I have used several robot mowers. Even a single application has many unknowns and a lot of the solutions are mechanical rather than ai.

• Rabbit holes are an issue. Some get stuck in them or worse spin their wheels. others reverse out.
• They often cut their own border wire, typically when rabbits dig it up.
• All robots can climb hills but some draw arcs when entering a hill at an angle.
• There are lots of obstacles...wire that tangles the blades, toys, clothes, small children, fallen fruit...

There are robots already for mowing, weeding, gutter cleaning. However each task has very different mechanical requirements so combining weeding with pressure washing would require huge compromises.

It’s also easy to retrieve representative docs for the topics in the tree

Amazing! Let me know if you end up sharing to huggingface!

Awesome! Looks really interesting, when I set up a rust ast parser, ill run it over the code base!

the full functionality has been constrained a bit due to refactor, will be fixed soon.

I apologize for the messy read me, the main idea is that I’m using a GNN layer as an inductive bias to improve the representation power of the sentence embeddings by exponentiating the Adj matrix A^2 then aggregated node features using message passing. Then finally using the topic model to create a topic tree to then feed it back into the system prompt to generate more high quality semantic context. It’s also relatively easy w/ bertopic to combine this with outlier detection/ filters for low quality data removal.

It’s a recurrent neural network in the sense where your feeding the output of the previous step back into the network.

You can also use another repos topic tree to suggest improvements. For example, I can add the deepmind topic tree to comment on where it could add features such as the graph attention network or where code can be converted to jax and then generate it on the fly.

Also yes, I found it very useful for deconstructing complex repos like knot theory which provided me with a lot of insight making it easier to narrow down my research and study to the principal components of the repo.

You just helped a ton in building personal codex models.

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Cannot agree. It is not a deal at all. Such a pricing strategy in the long term is very profitable for its creators. But it does not matter for those who would like to use it, but due to financial situation cannot afford using such APIs for a longer period of time (think about people beyond rich countries). Moreover 1k tokens can be generated in just one small talk in a matter of a few seconds...

Looks interesting. Could be use in my project, https://github.com/ruvnet/daa

Shared to r/aipromptprogramming

This project is interesting. The description however is hard to parse through. I’d suggest going over your README and cleaning up some things.

If I could also suggest a feature, if you could use this to generate UML diagrams that’d be great.

You mention that the code base can improve itself. I don’t see where that functionality is. Do you mean that if a person uses this tool for software analysis, productivity increases?

Thanks for the tip. I'll take it off the list. Often, thorny bushes can invade a field and they are so thorny (i.e. wild prunus trees), it's fairly difficult to work on as a human, so that's what I was thinking of when I put the rotary blade on it. Probably not necessary indeed.

> single error fatality risk, unlimited chaotic cities, 90mph compute time limits, make self driving cars unfeasible compared to multitask garden robot

I was on-board with the project until I saw the robot with a saw/trimmer attachment

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I won't repeat what other comments said but there are interesting architectures like H-Transformer that have lower asymptotic complexity and scale to longer sequences than the original Transformer. It's also worth noting that in practice the MLP cost may actually dominate the self-attention cost or vice versa, depending on the sequence length and model size.

Thanks, I use Windows, but I want to do a dual boot

I'm definitely not qualified to answer a question like that. I'm just a person that managed to get it working on a 6G VRAM GPU. Basically, as far as I understand the more you can run on the GPU, the better. So it really depends on what other stuff you have using your GPU's memory.

Like I mentioned, when I got it working I already had about 1.25G used by other applications and my desktop environment. From my calculations, it should be possible to fit 21, maybe 22 layers onto the GPU as long as nothing else is using it (so basically, you'd have to be in text mode with no desktop environment running).

If you're using Linux and an Nvidia card then you can try install an application called nvtop — it can show stuff like VRAM usage, etc. The way to install it will be specific to your distribution, so I can't help you with that. If you're using Windows or a different OS I can't really help you either.

But anyway, if you can find how much VRAM you have free, you can look at how much of that loading 16 layers uses and calculate how many more you can add before you run out.

That's still not necessarily going to be optimal though. I don't know how stuff like the difference in speed/precision for fp16 vs fp16i8 works or stuff like that. It's not impossible there's some other combination of parameters that would be better in some way than just trying to as much as possible onto the GPU in fp16i8 format. You'd have to ask someone more knowledgeable for a real answer.

ok so "cuda fp16i8 *16 -> cpu fp32" would be the most optimal argument for me?

No problem. fp16i8 uses about half the memory of fp16, so what you had would not only use 4x as much memory but it would try to put everything on the GPU!

Oh it loaded, it was because I wrote "cuda fp32" instead of "cpu fp32" in the second half of the argument. Thanks

> I'm actually using the oobabooga text generation webui on github

I'm not familiar with that. It does seem like it can use RWKV and supports passing strategy though: https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model#setting-a-custom-strategy

Are you already using that flag with the correct parameter?

Yeah quadratic scaling in context length is a problem lol. Hopefully RWKV will come to the rescue.