tfehring

For statistical time series forecasting, yes. This is for judgment-based forecasting, a somewhat different problem. It often involves, e.g. estimating the probabilities of one-off future events, which time series forecasting models aren’t suited for.

The problem is the combination and interaction of those things. RSI without misalignment would be great. Misalignment of models with current capabilities is sort of fine - it's not ideal, but it's not an existential threat to humanity, and we can build around their limitations to get them to do useful things in reliable enough ways. The really bad outcomes probably only happen if capabilities keep accelerating and the models remain misaligned.

Relatedly, I think "acceptance" to this program will be easy, bordering on automatic, if you have an enterprise AI budget and purchasing authority.

Right, the point of this from YC's perspective is to identify and pre-qualify the minority of enterprise AI buyers that can move quickly. Even those buyers aren't going to research and inbound to early-stage YC startups very often. The startups aren't intentionally staying quiet, but often they haven't nailed product-market fit and/or messaging yet.

Waymo also lets you pick, but only from a predetermined list of pickup/dropoff locations. Sometimes there's not a spot on the side of the street you'd want, other times it will default to a spot on the other side of the street but you can override.

I'm also at a startup. My workflow is similar but I have Fable 5 xhigh drive the whole thing: it gets Codex CLI installed in its environment with an API key, and it's instructed to delegate ~everything to Codex and review its work, especially for code quality/conciseness. Fable delegates to Sol or Luna (fast mode) xhigh/max depending on the task - I think Luna xhigh on fast mode is basically a Pareto improvement over Sol medium.

> The full model weights will be released by July 27, 2026.

Still sensible to mark proprietary for now though.

Like, buy and set up the physical hardware? I cba with that. Plus the hardware you want for LoRA (the type but especially the quantity) is different than what you want for inference, so either you'd under-spec it and wait forever for fine-tuning runs, or over-spec it and have low utilization most of the time. And even then who knows if it would be good enough to LoRA next year's best open source model. AWS gets great margins for renting out commodity hardware as a service because it built the right abstractions and can serve them efficiently at scale, I think the arguments here are basically the same.