Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.

Comments URL: https://news.ycombinator.com/item?id=37734673

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Comments URL: https://news.ycombinator.com/item?id=37618382

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Comments URL: https://news.ycombinator.com/item?id=37560084

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> How well suited are their syntactic choices to the community they're targeting?

Also, "best" practices. One could reduce the pain of the other, but by no means is the right solution to a deeper problem at hand. For example, if one has very deep and complex nesting for configs, TOML "may be a lot nicer" compared to YAML, but that doesn't mean using TOML will make all the config parsing problems go away. It just mask away code smell. Maybe time to check if they're overcomplicating configurations in general.

Comments URL: https://news.ycombinator.com/item?id=37493233

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Yup, and the higher the beat rate, the "smoother" it looks. Grand Seiko Hi-Beats and Zenith El Primeros come to mind. There's a good Hodinkee article describing the tradeoffs of different beat rates [1].

[1] - https://www.hodinkee.com/articles/watchs-frequency-hz-vph-me...

[1] - https://en.wikipedia.org/wiki/Peranakans

> Whatever magical market forces that might change how funding works, they don’t seem to be at play.

I think money is scarcer these days, and founders who are constantly being burned by VCs will think twice about riding the big VC train in the coming years. As a founder myself who work crazy hours getting the business going, it's painful / bad optics to see full time VCs doing weekend Vegas trips, browse art galleries on weekday afternoons and fine dining every couple of days, knowing I've sold a chunk of my / my team's hard earned equity on that bs. Of course there are great VCs, and some businesses needing to be VC backed, but oh boy, are there really bad apples out there.

> alluding to with respect to the ONNX being an Azure on ramp

Yeah sorry, should've worded it better in the original article. ONNX is one of the industry's first attempt at standardization of ops across various deep learning frameworks. Here's a way to think about it: Like Google, Microsoft has a cloud business. Unlike Google, it doesn't have a deep learning framework (Tensorflow) and is a significant threat if the cloud players continue to vertically integrate their ML stack. So ONNX makes sense, ie. you, the ML engineer should convert to ONNX so now at least you can run it on multi cloud providers!

Tangential to this, there's also a rise in heterogenous environments, and picking the "best" is non-trivial. A lot of serving / inference hosting companies capitalize on this, and ONNXRuntime is sort of Microsoft (Azure)'s answer: "give me an ONNX or PyTorch (which we will subsequently convert to ONNX), and I'll show you how easy it is to deploy it to Azure". The key nuance here is how much user tooling friction there is to deploying models, and it's in the best interest of these cloud providers to sometimes use their open source initiatives as on-ramps to their larger cash generating products / services.

Comments URL: https://news.ycombinator.com/item?id=37269211

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Comments URL: https://news.ycombinator.com/item?id=37254454

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This is why I still keep pens and physical notebooks on my desk. It's so much faster to flip it open and start writing than opening a 99th browser tab and navigating to an empty page on Notion, all while more desktop notifications start popping up on screen. Another side benefit is the act of writing / drawing out stuff actually helps me remember things better. Sure, my handwriting is atrocious but I rarely go back and read them anyway. ¯\_(ツ)_/¯

Yes, there has been repeated efforts to chip at Nvidia's market share, but there's also a graveyard full of AI accelerator companies that fail to find product market fit due to lack of software toolchain support - and that applies even for older Nvidia GPUs and their compatible toolchains, let alone other players like AMD. This isn't a hit on Nvidia, I'm just saying things move so quickly in the space that even the only-game-in-town is trying to catch up.

Nvidia is also leading by being one or two hardware cycles ahead of their competition. I'm pretty confident AI workloads in enterprise is their next major focus [1]. I think this more than anything else will accelerate AI adoption in enterprise if well executed.

To your point, I think the industry needs to focus more on the toolchains that sit right between the deep learning frameworks (PyTorch, Tensorflow etc.) and hardware vendors (Nvidia, AMD, Intel, ARM, Google TPU etc.) Deep learning compilers will dictate if we allow all AI workloads run on just Nvidia or several other chips.

[1] - https://www.nvidia.com/en-us/data-center/solutions/confident...