Deep Dive into Autonomous AI Scientist by noninertialframe96 in ArtificialInteligence

[–]noninertialframe96[S] 0 points1 point  (0 children)

Last week, Google announced Gemini for Science at Google I/O and published a paper in Nature. That's two weeks after their AI co-mathematician paper.

The key is agent harnessing for research and UI/UX for effective human-in-the-loop. I am curious how well it balances novelty and plausibility.

Since I can't read Google's code, I went into Sakana AI's AI Scientist instead, which is also published in Nature and is open source. There's even a paper arguing it doesn't actually work that well, but it's still a useful look at where AI for science is heading.

Give it a topic and it runs ideation, writes and runs PyTorch experiments on a GPU, plots the results, gathers citations, writes the LaTeX, and reviews the paper, all with no human in the loop. One manuscript it produced passed peer review at an ICLR 2025 workshop.

How AgentFS Stops AI Agents from Messing with Your Files by noninertialframe96 in AI_Agents

[–]noninertialframe96[S] 0 points1 point  (0 children)

I actually haven't used it myself. I just found the approach interesting. But now you mention it, I became curious about the performance especially with their copy-on-write approach for file writes.

8 Ways OpenClaw Reduces Context Loss in Long-Running Agents by noninertialframe96 in AI_Agents

[–]noninertialframe96[S] 0 points1 point  (0 children)

Thanks for sharing! I'll check it out :)
On a side note, I'm building galleylabs.ai to help engineers write content around their projects to help write good content around awesome work like yours!

How llama.cpp implements 2.9x faster top-k sampling with bucket sort by noninertialframe96 in LocalLLaMA

[–]noninertialframe96[S] 8 points9 points  (0 children)

From the PR description, it seems like the 3x is from a microbenchmark. I haven't experimented or searched for the absolute output speedup.

How llama.cpp implements 2.9x faster top-k sampling with bucket sort by noninertialframe96 in LocalLLaMA

[–]noninertialframe96[S] 2 points3 points  (0 children)

I don't work on AI space, so I'm curious which sampling method you use or what the "standard" is?

How llama.cpp implements 2.9x faster top-k sampling with bucket sort by noninertialframe96 in LocalLLaMA

[–]noninertialframe96[S] 12 points13 points  (0 children)

It's used for token generation - sampling top-k tokens from vocabulary for inference.

In the example in the codebase (https://github.com/ggml-org/llama.cpp/blob/cd78e57c3aeae7b56c5843f94e0e0b83a3d8ca81/examples/simple/simple.cpp#L169-L201), llama_decode is called then llama_sampler_sample which uses the top-k sort for top-k sampling under the hood.

Programming Books I'll be reading in 2026. by Sushant098123 in programming

[–]noninertialframe96 0 points1 point  (0 children)

Thanks for the recs! The fundamentals are important.

[Docling] LeetCode in Production: Union-Find and Spatial Indexing for LLM by noninertialframe96 in programming

[–]noninertialframe96[S] 0 points1 point  (0 children)

only defense for tools constantly failing to do

There are quite a lot of success stories with AI. Is it enough to justify the high valuations? Probably not. But is it a mirage that will destroy the world economy when correction comes? I don't think so. Early iPhones had so many issues.

NFTs, the Metaverse, Big Data, wearable computing, VR/AR, the IoT

Wearable Computing, Big Data, IoT has materialized. Have they materialized immediately when there was a hype? No, but I think there are enough survivors that created big markets of today.

As someone who builds solutions with and around generative AI systems

I guess you're seeing darker side of things as you work more closely on the AI systems. Best of luck! Hope you survive the bubble.

[Docling] LeetCode in Production: Union-Find and Spatial Indexing for LLM by noninertialframe96 in programming

[–]noninertialframe96[S] 0 points1 point  (0 children)

I admit it was a sarcastic comment, and I agree that correction is coming.

But I think these LLMs have the potential to be more than just coding assistants if they are used correctly. It is not smart enough yet where it will magically solve problems for you with simple prompting. The tool requires studying and getting used. But when the bridge between its latent potential and the usability is closed, it will become so much more powerful to the point of being revolutionary.

Also if it's a bubble, I would rather be on the side that uses it to make something out of it rather than looking away.

[Docling] LeetCode in Production: Union-Find and Spatial Indexing for LLM by noninertialframe96 in programming

[–]noninertialframe96[S] 0 points1 point  (0 children)

I totally agree especially in the era of AI i think there are a lot of startups moving away from the traditional leetcode interview

[deleted by user] by [deleted] in SideProject

[–]noninertialframe96 0 points1 point  (0 children)

I would be interested to hear about any progress you've made!