chaoz_
You want to master your craft, develop "optimal" systems, understand where things are going by utilizing SOTA.
You can call it FOMO, but you get the point.
Validation methods will evolve to accommodate human laziness. Insisting on doing it the hard way is no different than the old-timers who used to claim engineers 'weren't skilled' if they didn't know how to use punch cards.
// Obviously LLMs are non-determenistic etc and it depends on your domain, but your VP's point 100% makes sense if you folks are trying to cook up another demo-CRUD apps to convince investors for another funding round
I don't think they ever going to be able to re-claim large chunk of developers who are now fine with thin VSCode-like + Terminal for non-JVM languages.
Perfect example of how large corp with research capacity failed to navigate their product changes.
I’d imagine 1 year of heavy usage would somehow affect its quality.
Non-VC play (not required until you can raise on your own terms!) and clear differentiation.
If you want to go full-business-evaluation, I would be more worried about someone else implementing same thing with more commission (imo 95% and first to market is good enough).
That would finally be a crypto thing which is backed by value I believe in.
Guess there are limitations on size of the models, but if top-tier models will getting democratized I don’t see a reason not to use this API. The only thing that comes to me is data privacy concerns.
I think batch-evals for non-sensitive data has great PMF here.
There are a quite a few startups created by connecting relevant eBPF/OTel traces e.g. in response to uncaught exceptions (traditional RAG-based bug-fix generation).
Is there any proof of the global telegram issue related to amex links? Sounds like BS
LLMs can't beat the easiest quest in Space Rangers 2
Testing Frontier LLMs on Space Rangers 2 Text Quests
Text and languages contain structured information and encode a lot of real-world complexity (or it's "modelling" that).
Not saying we won't pivot to visual data or world simulations, but he was clearly not the type of person to compete with other LLM research labs, nor did he propose any alternative that could be used to create something interesting for end-users.
Valve can't replicate even part of it, while CS2 game modes are flooded with cheaters. Most people who chase competitiveness (which CS used to be all about – now it's also skins) just install FACEIT directly and ignore 90% of built-in game content.
Maybe Valve just doesn't want to make the game more difficult to install and sacrifice several % of their user base.
Ask HN: Is there value in a "generalized" visual tool for agent workflows?
We build AI agents in both low-code (LangFlow) and high-code environments. Low-code solutions have built-in visual interfaces, but our coded agent networks lack visual tooling for:1. Inspecting complex workflows and decision trees2. Debugging multi-agent interactions3. Testing inputs at different pipeline stages----------------Existing workarounds (like exposing agents as tools in chat UIs)...
While you can definitely read about how some parts of a very complex neural network function, it's very challenging to understand the underlying patterns.
That's why even the people who invented components of these networks still invest in areas like mechanistic interpretability, trying to develop a model of how these systems actually operate. See https://www.transformer-circuits.pub/2022/mech-interp-essay (Chris Olah)
Ask HN: Why mining power is useless for LLM training?
I don't know anything about crypto, but why is it technically so difficult to transform hash function computations into (at least partially) decentralized LLM training? Have there been any attempts to make it at least somewhat useful?