Remember that utilization of these huge racks will not be 24h/7, and these are usually not GPU intensive shops that would train models on the spare compute. With prices of 100-200k USD and north with ~2 years lifetime, that would be hard to justify financially.

Self hosting could easily amount to ~1000 USD a month amortized across many developers. In rush hours - there will be hard rate limits.

Would that 1500-1000=500$ monthly USD justify the 10% decrease in "AI Productivity" ? I guess not. In most cases.

For everyone that asks me around, I'd say that in short term, unless there's a really good reason to self host these coding assistant models, then the big 2/3 coding assistants providers are the better choice.

No one got fired from licensing claude code.

But don't block on the name, you could release it under NejneobhospodařovávatelnějšíPad++ and people will download.

It'll be easy search & replace later once you settle on a name

"Their ticket" = that was AI generated. After which they will wait their AI generated PR be checked by an automated AI QA that will validate against the AI generated spec.

It feels like important metric of "corporate AI adoption" should be how effective the human in steering the AI.

IF THE HUMAN ISN'T EFFECTIVE, THE HUMAN NEEDS TO GO.

But I will admit, the new syntax makes a lot more sense.

* Many top quality tts and stt models

* Image recognition, object tracking

* speculative decoding, attached to a much bigger model (big/small architecture?)

* agentic loop trying 20 different approaches / algorithms, and then picking the best one

* edited to add! Put 50 such small models to create a SOTA super fast model

* By training on user data, you can source specific model data images, and then train & classify the airplane model. It might require another model, where only the bbox will be the input, together with distance/calculated measurements of the object ("pixel size"), and the orientation of the plane (side? front? belly?).

* Provide alerts/notification of special aircrafts like helicopters, military, airforce-1, etc'

* When bbox is detected, you can run super-resolution upscaling on the photo/stream of images

First - cases where expected+old+new are identical, should go to regression suite. Now a HUMAN should take a look in this order: 1. Cases where expected+old are identical, but rust is different. 2. If time allows - Cases where expected+rust are identical , but old is different.

TBH, after #1 (expected+old, vs. rust) I'd be asking the GenAI to generate more test cases in these faulty areas.

Worth mentioning in the title that it's CPU-only: >1200 tokens/s on a single thread is impressive.

Have you considered doing optimization iterations like nanogpt-speedrun? Would be interesting to see how far you can push the performance.

Any recommended workflow for coordinating multiple agents? For instance, handling naming conventions, cleanup strategies, or preventing race conditions when multiple agents try to create worktrees at the same time?

This seems like it could be really powerful for that use case, especially with the hook system for per-worktree setup.