pilooch

I feel using these tools make me think a lot actually. It's to the point that the more I'm digging a research or code subject, the answers are so dense that I sometimes need hours to get around why this or that (a good example is designing ML systems, losses, theoretical and statistical stuff).

It might not be deep thinking, but it's definitely like being back on a PhD with an always on advisor that splits your brains every few thoughts.

Yes this should be turned off by default. The daemon is useful when building apps on top of the daemon server, e.g. pi or pi-durable custom harnesses.

For information, I just built a similar rtx 6000 pro 96GB server for a customer, it's ~17k euros, so 17 months rental under their reported monthly basic pricing.

Agreed, AI is of-shoring at home.

Apps may just well go away with llms and agents.

My goto private setup, runs ~50t/sex on a dgx spark with sglang, nvfp4. Excellent model.

Isn't this one mimicking the typesafe ai horror website ?

That's unless the code produced in the future is much more complex than today's.

The risk is they are a magnet for many more talented ML scientists to leave google.

Daily sports is the way, purges the body, rests the mind.

Yes, full ft or lora https://github.com/NVIDIA-NeMo/Automodel/blob/main/docs/guid...

Yes, all my emails gyer sorted out by a finetuned gemma. There are turned into images passes to the model, as multimodal is so practical.

Reminded me of the recent, and excellent, Canadian tv series Empathy, with the main character is found in a garbage can by his adoptive parents.

AlphaEvolve couples map-elites with LLMs. It's an key step in machine learning, in the vein of DQN for reinforcement learning.

AE brings diversity from the genetic algorithms community to large scale optmized deep learning and RL models.

It is a mandatory step for moving forward. The approach is clean and simple, while generic.

The only caveats is the per optimization problem definition of the map élites dimensions. But surely, this will get tackled somehow over the next few years.

If you don't know about map-elites, go look up Jean-Baptiste Mouret' s work and talks, it's both very interesting and universal.

Slides, publications and tech reports, very handy for figures !

It's useful when using prism, and for exploratory research & code.

Revolting and so inevitable though I believe: we're sort of running these already in our minds, we'll be outrun here too.!

Good catch. Cancer treatment scheduling is hard as well as mixes need tombe prepared in advance and cancelles appointments are hard to fill.

Hello! Not commenting on content or functionality. Scheduling in AI is a very dense field. An a past researcher in AI decision making, I got confused by the 'Scheduling solved' slogan. FYI recent AI for scheduling include GNNs and RL applied to NP and P-space problems that plague many industries. A larger scope I believe from vela's (rightful) target, a bit confusing IMO. Good luck with your endeavor, all scheduling problems are beautiful :)

Try Flashback, it's darker but genius as well, maybe more approachable.

Synthetic data for human (machine) learning... We should spend more time outside, we will!

The 'Eclipse' album is a classic.

I like the glasses path, well I do wear glasses, but some elements remain unclear to me:

- are prescription glasses available for display ? I guess not ? - these glasses need to be online, I guess they do so with a phone and bluetooth connection nearby ? So that's the glasses, the band and the phone, oh and the glasses case, seems a lot to carry. - pedestrian navigation seems to be rolled out per city, so it's not like having gmaps available right out of the box.