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jdeaton

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The interesting thing about this comment is that JAX is actually higher-level even than pytorch generally. Since everything is compiled you just express a logcial program and let the compiler (XLA) worry about the rest.

Are you suggesting that XLA would be where this "lower level" approach would reside since it can do more automatic optimization?

JAX has a sub-system called Pallas[1] with a Triton-like programming model and an example implementation of Flash Attention [2]. It is quite fast. On TPUs I've heard that the XLA compiler already emits a flash-attention-like computation graph for a regular JAX implementation of attention so there's no need to have some specialized kernel in that case.

1. https://jax.readthedocs.io/en/latest/pallas/index.html

2. https://github.com/jax-ml/jax/blob/main/jax/experimental/pal...

Its not overwhelming clear that finishing is an important piece of a side project. Real work at your job is the place where you can prioritize finishing over other outcomes. By not forcing yourself to finish everything in your side projects you open yourself up to greater levels of exploration and learning which is, after all, the point of side projects to begin with.

very real toxicity within the geometric algebra community. I can’t do much about

I was hoping the article would be about this instead. OP wondering if you have any elaborations for us to hear.

Helix has been trying to implement a plugins system for like 2 years that should tell you everything you need to know about its future prospects

people unacquainted with biology have a false perception of how low-throughput biology experimentation is. In many ways, it can be. But the underlying physics of microbiology lends itself very well to experiments that could allow one to collect tens-of-thousands, if not millions, of measurements in a singular experiment. It just needs to be cleverly set up.

I think this passage gets to the fundamental rift of disagreement in perspective between those focused purely on computational advances versus innovating in wet lab techniques.

Why? Because years of peoples' careers have been wasted waiting on promises from molecular biologists claiming they will make these "clever" high-throughput experiments work. In my experience, they'll spend months to years concocting a Rube Goldberg machine of chained molecular biology steps, each of which has (at best) a 90% success rate. You don't have to chain many of these together before your "clever" setup has a ~0% probability of successfully gathering data.

I once worked with someone who was doing performance benchmarking of two systems, and made a duel axis chart with the lines right on top of eachother when in fact one system was like 5x faster than the other. it drove me nuts because I didn't even realize the dual axis at first and thought that they literally had identical performance