AlphaFold - The Most Useful Thing AI Has Ever Done

What Hacker News said

Strong rec molf · 2025-02 · on “The lottery of the snakebite antivenom industry”

> The first snakebite antivenom was made in the mid-1890s and the method has changed little since: snakes are “milked” for their venom, which is injected into horses or sheep and the antibodies that their immune systems then produce are extracted via the animals’ blood. This is expensive, and many people develop allergies to the antibodies. A cool practical application of DeepMind's AlphaFold is that it allows one to "design" proteins because it is now possible to predict how they will fold. [0] This means it is possible to create synthetic protein antibodies that neutralise snake venom, specifically designed for humans [1]. There is a recent Veritasium video [2] that contains a great explan

Recommends AntonyGarand · 2026-07 · on “Google shuts down Nobel Prize winning AlphaFold”

To learn more about AlphaFold and its importance, I would recommend the Veritasium video[0]: AlphaFold - The Most Useful Thing AI Has Ever Done [0]: [this video]

Cites dataviz1000 · 2025-02 · on “Undergraduate shows that searches within hash tables can be much faster”

A similar idea came up in Veritasium's latest video today. Training AI by DeepMind to predict protein folding greatly improved by withholding the most evident information about a protein's primary structure — its linear polypeptide chain — within the The Structure Module step. [0] After asking ChatGPT not to agree with me that your comment and these two different approaches to solving problems are the alike, it concluded there still might be similarities between the two. [0] [this video] [1] [LINK]

Cites avirajkhare · 2025-02 · on “Ask HN: What are you working on? (February 2025)”

Personal projects: - [GitMentor]( [LINK] ) → 300+ users so far - [NurtiLens]( [LINK] ) → 50+ users so far Blog post on GitMentor: [LINK] - Terminal in Notion: [LINK] Dropping Resume Optimizer: [LINK] Made a video building it from scratch in 30 mins—completely freestyle. Accidentally exposed some API keys, so had to rotate them since I don’t know how to edit videos :) Video: [LINK] Conclusion: You steer the LLM, don’t let the LLM steer you. - [LLM Bootcamp]( [LINK] ) → making content Another day, another drop. Not taking this one to production because PineconeDB costs too much for cosine similarity search. Built it anyway, here's the video: [LINK] Codebase: [LINK] YT Answer Finder goes to pro

Cites anon7000 · 2025-03 · on “Microsoft's Relationship with OpenAI Is Not Looking Good”

> everything else: not much of anything achieving real, deep value. Lots of anecdotal use cases like drafting emails. Well, this is what people are missing IMO. Yeah, general purpose chatbots can’t solve advanced problems. However, models and pipelines (using similar technology as chabots) specifically built for certain tasks can achieve really incredible results — like in the area of protein folding ( [this video] ) I think there is a lot of potential, but most companies are just slapping a chatbot (which uses some other company’s models) on their app and calling it a day. Very few are taking a deep look at how ML could actually solve real problems.

Cites anon7000 · 2025-03 · on “Most AI value will come from broad automation, not from R & D”

Is that really true 120 years ago? We have a lot of hindsight bias, but things really weren’t amazing for the average person in the US even one hundred years ago. Today, the average person has affordable access to a lot of things that may have been luxuries, or simply didn’t exist, 100-200 years ago. Running water, electricity, internet, mobile phones, modern sanitation & infrastructure, and any number of tiny cheap devices that improve QoL. And yes, we also have huge problems with wealth disparity and late stage capitalism. The problem is, we should not throw out the baby with the bathwater. Like, we do not have widespread food scarcity problems today. Extremely advanced medical technology