advaith08

5 ms·

1.
by advaith08 1mo ago
we have access to the thinking trace summaries, not the raw traces
2.
by advaith08 1mo ago
yeah, the tricky thing about the experimental loop is that: 1. its very difficult to do it in a reproducible manner (the same experiment done twice often gives different results due to small undocumented changes) 2. its expensive to do at s
3.
by advaith08 1mo ago
thanks for sharing! We think the Experiments-as-code path is not the right approach. the beauty of LLMs is their ability to ingest and reason over unstructured data - they remove the need for formalizing experiments. We tried using declarat
4.
by advaith08 1mo ago
interesting! we haven't really considered these yet. However, we may soon have to - the recurring feedback we hear from industry is that crystallinity is a pipe dream, and that most materials are going to be amorphous (or perhaps quasi
5.
by advaith08 1mo ago
that's true, we've seen examples of many promising startups that are a few years in and stuck because the industry is so risk averse. will explore the other domains you've mentioned!
6.
by advaith08 1mo ago
good points. one of the reasons we picked the semiconductor industry is that its less price sensitive than others-companies are willing to pay if the performance is there. Effort is a different story though, and definitely a tradeoff to kee
7.
by advaith08 1mo ago
yeah we were surprised by how much it does it. Our approach has been retroactive - we monitor the thinking trace, spot reward hacking behavior and then fix things. We haven't faced this issue with Sol though - its been much more well
8.
by advaith08 1mo ago
We're still figuring this out. We'll need some synthesis equipment (think CVD, PVD etc) and characterization (XRD, Raman spectroscopy) tools in-house to validate that we're making the right materials. We're considering d
9.
by advaith08 1mo ago
Cool read, and agree that closing the computation > experimental loop is key!
10.
by advaith08 1mo ago
There’s a variety of computational techniques that help us establish some confidence on the materials. Atomistic simulations can estimate stability and bulk properties of a new material, and we have synthesis experts (min qualification: PhD
11.
Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials(discoveredmaterials.com)
162 points by advaith08 1mo ago|37 comments
12.
Show HN: LLMs Play Monopoly Deal(dealbench.org)
1 points by advaith08 1y ago|0 comments
13.
Vibe Coding with Claude3.7: What's Good, What's Bad and Some Tips(advaithsridhar.blog)
3 points by advaith08 1y ago|0 comments
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Linguistics as a test to measure AI intelligence(advaithsridhar.blog)
1 points by advaith08 2y ago|0 comments
15.
by advaith08 2y ago
The custom instructions to the model say: "Please note that this is similar but not identical to the antArtifact syntax which is used for Artifacts; sorry for the ambiguity." They seem to be apologizing to the model in the system
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"My heart is in the work": Organs required for your CMU education(the-tartan.org)
2 points by advaith08 2y ago|0 comments
17.
by advaith08 2y ago
Agree. I think a more core issue here is that only 1 person needed to be convinced in order to push malware into xz
18.
by advaith08 2y ago
The dehydrate comment is a reference to the three body problem - a great book!
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Ego-Exo4D: A foundational dataset on video learning and perception(ai.meta.com)
1 points by advaith08 3y ago|0 comments
20.
by advaith08 3y ago
fully agree with this. Plus, if you're worried about "promotion competitiveness" souring friendships with people in your team, you can always make friends with people in other departments and meet them during lunch. This does
21.
by advaith08 3y ago
seen a lot of these, but none for Indian languages. Would love to try an Indian language one!
22.
by advaith08 3y ago
I think the point of the comparison with chess is to show what happens when AI becomes vastly better than humans at something. GPT4 is not vastly better than humans at coding, but the author is using chess as an analogy to visualize what th
23.
by advaith08 3y ago
Location: Bay Area, SF Remote: No Willing to relocate: Yes Technologies: Pytorch, Python, NLP, CV, LLMs Résumé/CV: https://drive.google.com/file/d/1Ac_zraUpg4MAojQekE_XSNEfhs4... Email: advaith@cmu.
24.
by advaith08 3y ago
agree with this. in their new values, they literally use the same words that were in the old ones ("unpretentious", "collaboration"). I also dont think this is a change "at the drop of a hat" as the article sug
25.
by advaith08 3y ago
imo they dont have batching because they pack sequences before passing through the model. so a single sequence in a batch on OpenAI might have requests from multiple customers in it
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Ask HN: Are employers focussing more on system design since LLMs have come out?
1 points by advaith08 3y ago|0 comments
27.
by advaith08 3y ago
Oh my apologies, this is Llama 2! Im using Llama 2 7b chat here, not the original Llama
28.
by advaith08 3y ago
yeah I think there are a lot of use cases for an assistant trained on your chat history. Given how privacy sensitive this use case is, I think maybe Apple is the best suited to build something like this? Hope they come out with something co
29.
by advaith08 3y ago
Very cool. I like the introspection bit, I've realised quite a bit about my texting style from talking to Llama too. I think Im also very "type first and think later" on WhatsApp
30.
by advaith08 3y ago
here's how you can export your chat history: https://faq.whatsapp.com/1180414079177245/?cms_platform=andr...

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