pama

I am incredibly optimistic on AI, especially for drug discovery. However, the belief that AI drugs will be more likely to pass phase 2 tests than others is either based on the assumption that AI will mostly be used to attack safe indications (me too drugs for targets that already have therapies operating on the same mechanism), or on some belief that perhaps in a distant future the transfer to human biology is better understood by the AI but not documented or shared with humans. Although I think the latter will come true, eventually, people right now tend to forget that because of the extreme cost of human clinical trials, the preclinical candidate has undergone every possible test that regulatory agencies, consultants, the internal company knowledge have suggested. So the failure on a new drug is only related to the “surprise” differences in humans vs all the tests in animals, cells, or in vitro setups that were previously done. Right now AI can help you get to the clinical candidate much faster and with fewer resources than before, but I believe that the translation to success rate improvement has some assumption that is probably not correct in the context of a fixed/novel target. I am open to the possibility that I miss something fundamental and would love feedback.

I love the current naming scheme and I am excited to see the astronomy trend expand in the future. I hope to see asteroids, binary stars, and solar systems at some point soon, as well as neutron stars, black holes, and eventually galaxies, and clusters of galaxies.

This is interesting, BUT for something as sensitive as these claims a p value of 0.03 does not cut it. Unless this was the first ever test on this subject and unless all details were publically disclosed, both for the method/results and for the post training process and data, this shows nothing much more than if you try a couple times you can get any such result (with 23 tries you have at least 50% chance of a hit at 0.03). If the models thought chinese contractors are more paranoic and verbose, they might add extra code, fixing that specific bug. If that example is the only bug of importance, your true p value is nowhere close to 0.03 under any sensible analysis.

Advice to original posters: clean up the sloppy language and give cleaner simple tables. But making it to HN, already got you some larger audience than expected, so good job, and good luck with convincing people to use a “fixed” but dated model :-)

As a tourist in SF about two weeks ago I experienced a bit of superhuman driving from a Waymo. On one of these 30-degree downhill slopes, a long truck was stopped in the middle of the road and to its right and slightly ahead was a parked car. The space between these two was just about right so that a 45-degree-turned Waymo car would barely fit with less than an inch space on either side. This is exactly what it did, without much hesitation. As a human driver, I would have stopped, tried to squeeze back up and find another way, or stop and try to locate the truck driver and ask them to move.

> What does robotaxi offer apart from less taxi-driver jobs?

One more passenger seat (once the wheel is removed); the option for privacy (once the recordings are removed); increased safety (starting now, but even more so in the future); ability to build overnight charging stations in unpopulated areas; once fully adapted you can have reduction or removal of parking spaces, eliminate commercial daytime traffic, etc. Maybe it is the ever optimist in me… I think eliminating a job that led to increased cardiovascular problems and reduced lifespan of fulltime drivers while keeping the human rate of accidents, will be seen as an obvious improvement in human civilization in the future, similar to giving up horse carriages.

No, why would it be? Unless very detailed web-based microphone or camera manipulation is needed, a modern LLM can built a fallback using safer technology instead.

From the post:

“With my OpenCode Go sub of $10/month, DeepSeek is basically unlimited.”

These posts came shortly before the math release by OpenAI. I hope the continued discussion of the goldmines in their release helps clarify a vision for how to proceed.

Loads nothing on iphone lockdown mode fwiw.

Not sure why you say that. All the major AI-generated proofs so far were done by AI without using lean, and then later validated by AI formalizing with lean in a separate process. Do you mean we would not have enough rigorous training data perhaps?

But what about builds that combine 8 of the 5090 with infiniband between boxes? Wouldn't that be comparable to the mac in terms of price and potentially beat it by a lot in terms of performance for the large MoE? I understand the space/heat/noise considerations, but price wise it may still not make as much sense as people think. (Agreed that it is hard to get the NVIDIA hardware and the 6000 pro are priced less competitively).

Not GP, but you start with Kubernetes…

> You need a Kubernetes cluster, ko (brew install ko), a container registry your cluster can pull from, and a reachable Agent Substrate Control API (in-cluster default: api.ate-system.svc.cluster.local:443).

> make deploy AX_IMAGE_REPO=<your-registry>

> This deploys Redis, then builds and deploys the control plane images with ko. Everything lands in the ax-system namespace.

Having worked with people doing bringup of specialized chips, I am awed at how the world has changed.

> When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when production ramps up can be reduced. “All our schedule assumptions are going to be based on the fact we have this capability now,” he says.

How I write with an LLM: for each page “suggest up to 10 word changes to increase clarity.”