mrintellectual

Good catch on the disclosure, I edited my original comment to reflect this fact.

On the topic of vector search, Milvus is another great vector database - it's open source and we provide single-line startup scripts via `docker-compose` in addition to installation via apt & yum (https://milvus.io/docs/install_standalone-docker.md). There are also no restrictions on the number of vectors that users can store. Internally, we've successfully scaled Milvus to handle billion+ vectors, while many of our users have stored hundreds of millions of vectors in a production environments as well.

Great article. We used something very similar to help implement simlarity search at Yahoo a couple years back (https://yahooresearch.tumblr.com/post/158115871236/introduci...). We were using a indexing strategy called Locally Optimized Product Quantization, which worked great in terms of query times but required a training procedure which made successive inserts fairly inefficient.

Thankfully, we have a much wider variety of indexing options these days (https://milvus.io/docs/index.md) in addition to powerful vector databases (https://zilliz.com/learn/what-is-vector-database). I'm glad to see the barrier to entry for semantic image retrieval becoming lower and lower as ML infrastructure matures.

[EDIT] Disclosure: I work at Zilliz.

Great article. I've been working in and around this space since 2014, and I think similarity learning, vector search, and embedding management will be a core part of future applications that leverage ML.

I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is a huge part of it, and I'm glad more and more tools like Quaterion are being released to help make this kind of tech ubiquitous.

The list of projects seems to be at least partially based on https://github.com/RunaCapital/awesome-oss-alternatives.

My vote for worst CPU goes to the iAPX 432 (also not on this list).

The crux of the problem is not Luna itself, but the Anchor Protocol (19.5% yield), which enticed users to burn Luna by investing in UST through a, for lack of a better phrase, Ponzi scheme. I get it - people like decentralization. But without some form of regulation, either by the broader crypto community itself or by governments around the world, situations like the LUNA/UST collapse will keep happening.

There are also rumors of a Terra fork: https://agora.terra.money/t/terra-ecosystem-revival-plan/870.... Thanks, but no thanks.

It just seems unlikely that the algorithm would be open-sourced right after a deal for Twitter is agreed upon (but before it actually goes through). I've never seen a buyout of this scale done by an individual, but I imagine the SEC and several other parties will need to be involved.

At the minimum, I would make a private Github repo first, add all relevant commits, and then make it public once there's actually content.

This seems to be a practical joke by a Twitter engineer as opposed to an actual release.

Even as an ML-focused graphics-less GPU, this is great. If this can be prototyped on an FPGA, it would be even better. Using block RAM for shared memory and built-in PCIe and DDR IP blocks should help speed things up considerably.

It unfortunately wouldn't be very cost-effective for training ML models, but it would take things a step closer to actual tape-out (if some organization has the $$$ for it).

> Americans spend an average of 12 and a half hours per day consuming news via the television, Internet, newspapers, magazines, and radio.

This just doesn't make sense. The average American spends the majority of waking hours consuming news?