But, this piece is a fluff piece: "underfunded" means a total of around $400 million ($330 million in the initial round, $70 million for Gradium). Compare to Elevenlabs who used a $2 million pre-seed for creating their initial product.

A bunch of other stuff there is disingenuous, like comparing their 7B model to Llama-3 405B (hint: the 7B model is a _lot_ dumber). There's also the outright lie: team of 4 made Moshi, which is corrected _in the same piece_ to 8 if you read enough.

Remote: Yes

Willing to relocate: Not until 2027

Technologies:

ML / DS: PyTorch, CUDA, distributed training & inference, performance profiling/optimization (audio & speech focus, some LLM inference acceleration)

Systems: C/C++/asm, low-level performance work, reliability/scale engineering

Backend / Infra: Python/Java/C# prod services, ETL / data pipelines, k8s (incl. operator work)

Roles: Tech Lead, Research Engineer (training and/or inference of large models)

Résumé/CV: https://www.linkedin.com/in/roeeshenberg/

Email: roee.shenberg@upai.dev

I’m a hands-on engineer who’s spent the last 6 years doing freelance ML + data science, primarily in audio/speech, and before that 10+ years in startups building and scaling production systems.

I’m looking for where research meets real systems: training and/or inference for large models, especially roles that value end-to-end ownership. Open to freelance engagements or full-time roles.

Also, Simplical attention is pretty much what the OP was going for, but the hardware lottery is such that it's gonna be pretty difficult to get competitive in terms of engineering, not that people aren't trying (e.g. https://arxiv.org/pdf/2507.02754)

There was an interesting paper a while back which investigated using unbounded n-gram models as a complement to LLMs: https://arxiv.org/pdf/2401.17377 (I found the implementation to be clever and I'm somewhat surprised it received so little follow-up work)

[1] https://arxiv.org/abs/2412.19437v1

[1] https://en.wikipedia.org/w/index.php?title=Population_transf... [2] https://websites.umich.edu/~gocek/Work/ja/Gocek.Muge.ja.popu...

This is to say, the evidence in this natural experiment points towards piracy reducing sales by a lot.

The actual story, as far as I can tell, is that money that had previously been considered as M2 (=less liquid) is also counted as M1 due to rule changes regarding savings accounts.

To see this is the case, you can plot both together. If in fact, new money was printed, you would expect M2 to have the same jump as M1, as M2 is M1 + more stuff. However, you see a much smaller jump:

https://fred.stlouisfed.org/graph/fredgraph.png?g=1dwhY

The rule-change coincided with COVID relief measures which did include money-printing, but at a much smaller scale than implied.