The second problem is distribution: it is already hard enough to obtain good enough distribution with software, let alone software + hardware combinations. Even large silicon companies have struggled to get their HW into products across the world. Part of this is due to the actual purchase dynamics and cycle of people who buy chips, many design products and commit to N year production cycles of products built on certain hardware SKUs, meaning you have to both land large deals, and have opportune timing to catch them when they are evening shopping for a new platform. Furthermore the people with existing distribution i.e the Apple, Google, Nvidia, Intel, AMD, Qualcomms of the world already have distribution and their own offerings in this space and will not partner/buy from you.

My framing (which has remained unchanged since 2018) is that for silicon platform to win you have to beat the incumbents (i.e Nvidia) on the 3Ps: Price (really TCO), Performance, and Programmability.

Most hardware accelerators may win on one, but even then it is often theoretical performance because it assumes their existing software can/will work on your chip, which it often doesn't (see AMD and friends).

There are many other threats that come in this form, for example if you have a fixed function accelerator and some part of the model code has to run on CPU the memory traffic/synchronization can completely negate any performance improvements you might offer.

Even many of the existing silicon startups have been struggling with this since them middle of the last decade, the only thing that saved them is the consolidation to Transformers but it is very easy for a new model architecture to come out and require everyone to rework what they have built. This need for flexibility is what has given rise to the design ethos around GPGPU as flexibility in a changing world is a requirement not just a nice to have.

Best of luck, but these things are worth thinking deeply about as when we started in this market we were already aware of many of these things but their importance and gravity in the AI market have only become more important, not less :)

This is understood in the academic literature as well, as people months/years ago were writing papers that a smaller amount of high quality data, is worth more than a large amount of low quality data (which tracks with what you can pick up from an ML 101 education/training).

Siri is sort of a red herring because its built by teams and tech that existed before Apple acquired most of its ML talent and some of its inability to evolve has been due to internal politics not the inability to build tech. iOS 17 is an example of Apple moving towards more deep learning speech/text work. I would bet heavily we will see them catch up with well integrated pieces as they have Money, infra, and already the ability to go wide (i.e all iOS users, again think FaceID).

Stimulants work for a large number of people diagnosed with ADHD with very little negative effects and are safe modulo a few exceptions for long term use.

Some individuals have negative experiences with Stimulant medications but I know from personal experience and from many friends in the ADHD community stimulants have literally been life saving for them.

They don't just reach for them because they are out to get you but they are effective for many people.

Furthermore many people who choose to forgo medication develop lifestyle and substance use issues which negative effects far out weigh low dose stimulants.

As other commenters said they are just a tool you still have to work on interventions, behavior modification, and so on.

At the end of the day in many ways ADHD is a disability (even if sometimes a super power) and you can't just delete it with a prescription.

Even if you forgo meds there are so many ways to boost your attention and quality of life and lots of research on what is effective, treatment can be much more than just medication.

On this part in particular while it can be great to deeply follow your passion with extreme focus; pursuing things regardless of their importance in your overall life and at the cost of other interests, relationships, or responsibilities can be an empty and unfulfilling existence in the end. Furthermore life can be markedly better with the correct interventions and treatment.

People seemingly get offended by even a suggestion because many people have extreme stigma against conditions like ADHD as well as a lot of misinformation from people have very little understanding of the actual traits, diagnostic criteria, treatment and prevalence of it.

I had even stereotypical symptoms as a child but just wasn't tested.

LLVM already has decades of research applied to it to make it produce fast code, it will be incredibly challenging to even match its performance across all the targets it supports let alone improve on it in significant ways. It would be better to spend the time building an optimization pipeline for Zig itself and being more thoughtful about what code you send to LLVM versus trying to replace it wholesale.

I feel like Go already went through a whole saga of this where the community started with "LLVM and SSA are bad and slow", then a few years go by and they end up building their own SSA IR and spending a bunch of time trying to bring compilation time closer to what it was before as it made everything much slower.

Much of this code is not "new" in the sense that much of the OpenXLA effort has been extracting the existing XLA representations and compiler from the TensorFlow codebase so it can be more modularly used by the ecosystem (including PyTorch).

A better frame is TensorFlow exporting its stable representation that many vendors have already built around, more than a "new" standard.

It has improved a lot but still I think Lean has many things going for it including a large focus on programming and using your Lean programs directly (versus extraction, which is roughly a really shitty compiler from Coq to Ocaml or another extracted language).

For example a few other cool blogs:

* https://octoml.ai/blog/write-python-with-blazing-fast-cuda-l...

* https://octoml.ai/blog/collage-automated-integration-of-vari...

* https://octoml.ai/octoml-bert-model-acceleration-on-apple-m1...

I having been writing C++ extensively for research/work/etc the past 5 years. Compared with modern languages it completely deserves the bad rap.

C++ has come a long way, can you write cleaner code than you could 10 years ago? yes. Are there safer abstractions than there were? yes.

The amount of issues I have to deal with in C++ on a day to day basis which are just non-issues in Rust or Go, etc.

Without complaining too much dependency management and tooling are stuck in the stone age.

Every time I get to use Cargo going back to C++ is a nightmare, even Python which has pretty poor tooling around this is a million light years ahead.

Even simple things like having to carefully order headers or deal with obscure cross-platform incompatibility are mostly non-issues in newer languages and the baggage of C and C++ leads to a lot of unnecessary pain that doesn't provide low-level control or performance advantages.

I know Stanford PhDs have even higher stipends to offset the costs of Palo Alto.