We went from 'AI will replace programmers' to 'AI will help programmers' to 'AI writes code while other AI reviews it' in about 18 months. At this rate the humans are just providing the electricity.
AlphaEvolve: Gemini-powered coding agent scaling impact across fields
61–70 of 159 posts
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#62A fantastically simple solution to improving algorithms, I wish I had this years ago in activation engineering: https://blog.n.ichol.ai/llm-activation-engineering-an-easy-f... How do I access AlphaEvolve?
This is just a flex post. Be a billion dollar company or get out.
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#63This reminds me of Antirez's "Don't fall into the anti-AI hype" [0] In a sentence: These foundation models are really good at optimizing these extremely high level, extremely well defined problem spaces (ie multiply matrices faster). In Antirez's case, it's "make Redis faster". There have been two reactions: "Oh it would never work for me" and "I have seen months of my life accomplished in an hour", and I think they'…
>I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work I don't believe that anymore, to be honest. Models are starting to get good at ambiguity. Claude Code now asks me when something is ambiguous. Soon, all meetings will be recorded, transcribed and stored in a…
So self chosen total surveillance and transparency so your fav LLM can be better?
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#64I wish that Google would focus on bringing their Gemini 3.x models to GA, and provide enough capacity such that one not constantly has to fight with 429 errors. It often feels like they do not want me to develop applications for corporate clients using their Vertex API. It is just such a shame, given that their models were so great for document analysis etc.
Are you doing it on a free plan? I noticed they serve way more 429s on the free plan.
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#65Earlier quoted context omitted.
I'd say it's a malefactor of: 1. Amazing, you just tweaked 1% efficiency 2. You idiot, you just spent an hour trying to trouble shoot a hallucinated api. On average, it's really hard to tell which ones going to win here.
Its not hard to tell at all, just look at how much it costs to run a 10T param model (especially with parallelized agents). Those costs are not worth the occasional slot machine-eque jackpot you get. For an entity like Google it might be worth it, but that's it. They definitely aren't going to let us use these things for cost they are now for much longer. Imagine going back to 2020 and tell people in 6 years going to…
Whenever you solve any hard problem, you start off by finding a complicated solution, which you then scale down to a simpler solution.
LLMs are a "complicated solution" in the sense that they're expensive. Once you know what they're capable of, you can scale them down to something less expensive. There's usually a way.
Also, an important advantage of LLMs over other approaches is that it's easy to improve them by finding better ways of prompting them. Those prompting strategies can then get hard-coded into the models to make them more efficient. Rinse and repeat. Similarly, you can produce curated data to make them better in certain areas like programming or mathematics.
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#66[deleted]
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#67I would be interested to see how exactly the agent helped. How was it used, where did it lead to the given improvement and in how far would it have taken a human to come to the same solution.
The blog post has many links to papers and preprints discussing this exact question.
The CANOS arxiv link says absolutely nothing about AlphaEvolve, Gemini, or LLMs. It seems to use purely traditional ML models. If AE did in fact write a quick script to test different configurations in order to optimize the results, they don't seem to have bothered to write about it.
I can't read the Nature paper about DeepConsensus, but from the summary, it doesn't really explain what role AE had in improving DC. It would be nice to be able to read about what role it actually played, and whether it used traditional or novel methods of performing it
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#68Earlier quoted context omitted.
Not in the next decade. Won't get funded.
Advanced Machine Intelligence (AMI), a new Paris-based startup cofounded by Meta’s former chief AI scientist Yann LeCun, announced Monday it has raised more than $1 billion to develop AI world models. LeCun argues that most human reasoning is grounded in the physical world, not language, and that AI world models are necessary to develop true human-level intelligence. “The idea that you’re going to extend the capabili…
Now check how much OpenAI got in their last funding round, and you have your answer.
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#69Earlier quoted context omitted.
The money will go to LLMs.
Why not both? You don’t need 1trillion allocated before you have a proof of concept to demonstrate your non-LLM model, and once you have a PoC you will definitely have the larger investors interested
You will need 100s of billions to make a viable POC.
Re: AlphaEvolve: Gemini-powered coding agent scaling impact across fields
#70I wish that Google would focus on bringing their Gemini 3.x models to GA, and provide enough capacity such that one not constantly has to fight with 429 errors. It often feels like they do not want me to develop applications for corporate clients using their Vertex API. It is just such a shame, given that their models were so great for document analysis etc.
Are you doing it on a free plan? I noticed they serve way more 429s on the free plan.
No, for clients we use paid Vertex AI accounts. We often need to host workloads in an EU region, which rules out “global” models (and probably better capacity).
In the past, we used a wrapper that round-robined across multiple projects to get enough quota. Luckily, many of our workloads are workflow-style tasks, so we can simply keep retrying on 429s.
Fun fact: for one of their services, I think it was Stitch, I noticed that my paid key kept hitting quota, while the free worked fine. That blew my mind.