The problem is really that the promise was so big but the management and implementation was so painful, coupled with non-stop updates, coupled with far too big of an implementation surface.

A classic "lets solve 15 problems at once" situation, but none of them really well.

OpenClaw has come so far since its' original launch/craze. I recommend taking a second look if you haven't touched it in a while. If for no other reason than it's just a really fun playground with a LOT of areas to experiment in. Setup a myriad of agents with various models, skills, cron jobs, etc. The control surfaces have come a long way as well.

Hermes is good fun, running that as well but feels like they focused on polish vs features in order to capitalize on the primitive state that OpenClaw was in for its first months.

People that got attracted to the hype of openclaw but couldn't endure the fast pace of breaking changes while they figured out the problem space were well served by going to Hermes.

Lately I've decided to write for ME. What do I want to write about? That has made it a lot easier to get unstuck. That and not looking at the views, likes, etc.

If you are not aware, ACP creates a persistent session for steering rather than using the models directly.

The task is basically predicting pricing and costs.

Apple’s model came out on top—best accuracy in 6 out of 10 cases in the backtest. That surprised me.

It also looks like it might be fast enough to take over the whole job. If I ran this on Sonnet, we’re talking thousands per month. With DeepSeek, it’s more like hundreds.

So far, the other local models I’ve tried on my 64GB M4 Max Studio haven’t been viable - either far too slow or not accurate enough. That said, I haven’t tested a huge range yet.

Weirdly though I'm getting things like this: Apple FM is fast and free but has a hard limitation — it can't process prompts with Spanish/non-English words, which is a dealbreaker for California and Southwest real estate where half the street names are Spanish.