neoneye2
I had not imagined that it would become real so soon.
For this I used gemini-2.0-flash-lite. Doing 150 LLM invocations.
Code is here https://github.com/neoneye/PlanExe
I'm on Discord https://neoneye.github.io/PlanExe-web/discord.html
Here is a crazy plan for a tunnel connecting Spain/Morocco. https://neoneye.github.io/PlanExe-web/20250706_gibraltar_tun...
Type in a prompt, the more details you add the better. The prompt can also be vague and the result is more random.
The generated plan may serve as an initial draft for a plan. Second stage could be to improve on the underdeveloped areas or cut down on the over engineered areas.
The UI is here https://loda-lang.org/edit/?oeis=2487
It can run from commandline for mining.
Implementation https://github.com/loda-lang/loda-rust
It can be tried out here: https://huggingface.co/spaces/neoneye/PlanExe
If you have python skills: https://github.com/neoneye/PlanExe/tree/main
These are zip files, containing mostly json files, some markdown and a csv file. The tasks gets assigned a UUID. Is this a format that makes sense?
I'm using LlamaIndex so it's not tied to a particular LLM provider. I prefer using OpenRouter with the new Gemini 2.0 Flash. And I also like Ollama with Llama3.1 where I can inspect the logs as the code is running.
I'm using Luigi for a DAG representation of the data being exchanged between the many agents.
PlanExe: Planning AI that outputs WBS/SWOT/estimates
The primes are somewhat evenly spaced with this transformation, I'm the author of it.
A342730: a(n) = floor((frac(e * n) + 1) * prime(n+1)).
https://oeis.org/A342730/a342730.png
Instead of e, I have tried other constants such as pi, but it doesn't look as good. I guess there is another constant that makes the distribution look even nicer.
Seeing the examples while having the editor visible. That's a good idea. I haven't explored this direction, since I had my phone (with tiny screen estate) in mind.
Drafts for a such a UI are much welcome. However I'm probably too lazy to code it though.
Humans can try the 800 tasks here. There is no time limit. I recommend not starting with the `expert` tasks, but instead go with the `entry` level puzzles. https://neoneye.github.io/arc/?dataset=ARC
If a model jumps to 100%, that may be a clever program or maybe the program has been trained on the 100 hidden tasks. Fchollet has 100 more hidden tasks, for verifying this.
There are many examples where the test is slightly OOD (out of distribution), so the solver will have to generalize.
Idea for a metric: - Number of pixels that stays the same between input/output. - Histogram changes.
The short story. I needed something that could render thumbnails of tasks, so I could visual debug what was going on in my solver. However I have never gotten around to make the visual inspection tool. After having the thumbnail renderer, mid january 2024, then it eventually turned into what it is now.
Recently Michael Hodel has reverse engineered 400 of the tasks, so more tasks can be generated. Interestingly it can generate python programs that solves the tasks too.
I'm collecting data for how humans are solving ARC tasks, and so far collected 4100 interaction histories (https://github.com/neoneye/ARC-Interactive-History-Dataset). Besides ARC-AGI, there are other ARC like datasets, these can be tried in my editor (https://neoneye.github.io/arc/).
I have made some videos about ARC:
Replaying the interaction histories, and you can see people have different approaches. It's 100ms per interaction. IRL people doesn't solve task that fast. https://www.youtube.com/watch?v=vQt7UZsYooQ
When I'm manually solving an ARC task, it looks like this, and you can see I'm rather slow. https://www.youtube.com/watch?v=PRdFLRpC6dk
What is weird. The way that I implement a solver for a specific ARC task is much different than the way that I would manually solve the puzzle. Having to deal with all kinds of edge cases.
Huge thanks to the team behind the ARC Prize. Well done.
https://neoneye.github.io/arc/about.html
The middle piece that is vertically scrollable. I'm still investigating how to fix it.
I failed miserable in another video when I tried explaining what ARC is, in my own opinion. It seems to be about striking a balance of both being "I can solve this", while not being to hard "I can't possible solve this". I don't think this is the right video format for the landing page. I'm unsure what kind of intro would be helpful. https://www.youtube.com/watch?v=rLGpNcQ5alI
I will investigate the iframe issue. I tried with a fixed width and height, but that was extending over the screen edge on smaller devices, so I wrapped it in a div that grows. Thank you for pointing this out.
Thank you for great feedback.
Show HN: Brain Grid Game – Solve Arc Puzzles and Aid AI Research
In January 2024, I started working on what has now become Brain Grid Game. It began as a JavaScript prototype focusing on rendering thumbnails of puzzle in the Abstraction and Reasoning Corpus (ARC). As the project evolved, I extended it with a UI for solving puzzles.I have a Django backend for collection of user interaction data. This data is crucial as it helps understand how humans does...
When Spanish is selected, maybe spawn the games in spanish mode.
In the Domino Dissection puzzle, it's possible to select the separator between two cells, so the selection gets kind of stuck. Maybe remove the ability to select the separator.
Making an entire unity game for each puzzle, that's no small feat.
Self promotion: I'm also making a puzzle website, https://braingridgame.com/ where users can solve ARC tasks (Abstraction & Reasoning Corpus). And source code is here: https://github.com/neoneye/ARC-Interactive
ARCathon contest announced. New world record of 34% on fchollet's ARC dataset
I ran "brew upgrade" and that downgraded to version 5.4.6.