As the author explains, OT relies on some ordering of system events, and CRDTs don't. That means CRDTs need to be commutative (and probably associative), and OT doesn't.

So, OT is less scalable but more powerful, and CRDTs are more scalable but less powerful (in theory).

It's sort of like comparing Paxos/Raft to Bittorrent.

(I am not an expert on OT.)

I wrote a steganographic tool in this same spirit back in 2011, called Plainsight.

Back then, we didn't have deep learning, and the "Imagenet moment for NLP" had yet to arrive.

My Python code, with examples, is here: https://github.com/rw/plainsight

Unlike the OP, my Plainsight algorithm is 100% invertible by construction, and accepts binary input. (I verified the inversion process with "roundtrip fuzzing", a technique I still use today.)

Plainsight uses each bit of the input message to generate tokens. Bits are used to decide how to traverse a Huffman-style n-gram tree, weighted by frequency. This tree of n-grams is the model used in both the encoding and decoding steps. The drawbacks to my method are that the output 1) can be verbose and 2) does not convince a human that it's plausible, except for short messages.

Stegasuras has orders-of-magnitude better output, and seems to solve the problems I couldn't solve eight years ago. I would venture that their new result has as much to do with advances in language modeling, as it does with the particulars of their encoding and decoding algorithms.

I'll also note that I'm glad these researchers were able to use grant money to do this work. As a non-academic, I applied for an AI Grant to support me in upgrading Plainsight to use deep learning, but I was turned away at the time.

Finally, one of the ideas I picked up back then is that spam can be used to contain secret messages. Send enough gibberish to enough people, with your intended recipient included, and you'll look like a spammer--not a spy:

   $ wget https://spamassassin.apache.org/publiccorpus/20030228_spam.tar.bz2
   $ tar -jxvf 20030228_spam.tar.bz2
   $ cat spam/0* > spam-corpus.txt

   $ echo "The Magic Words are Squeamish Ossifrage" | plainsight -m encipher -f spam-corpus.txt > spam_ciphertext
   
   $ cat spam_ciphertext
   (8.11.6/8.11.6) 3 (Normal) Internet can send e-mails until to transfer 26 10 [127.0.0.1]
   also include address from the most logical, mail business for your Car have a many our
   portals ESMTP Thu, 29 1.0 this letter on internet, ", output is ""   
   deciphering: 100% | 543.84  B/s | Time: 0:00:00
   
   The Magic Words are Squeamish Ossifrage

I designed and built the influxdb-comparisons benchmark suite to be easy to understand for customers. From a technical perspective, it is simulation-based, verifiable, fast, fair, and extensible. In particular, I created the "use-case approach" so that, no matter how technical our benchmark reports got, customers could say to themselves: "I understand this!". For example, in the devops use-case, we generate data and queries from a realistic simulation of telemetry collected from a server fleet. Doing it this way creates benchmarking stories that appeal to a wide variety of both technical and nontechnical customers.

This user-first design of a benchmarking suite was a novel innovation, and was a large factor in the success of the project.

Another aspect of the project is that we tried to do right by the competition. That means that we spoke with experts (sometimes, the creators of the databases themselves) on how to best achieve our goals. In particular, I worked hard to make the Cassandra, Elasticsearch MongoDB, and OpenTSDB benchmarks show their respective databases in the best light possible. Concretely, each database was configured in a way that is 1) featureful, like InfluxDB, 2) fast at writes, 3) fast at reads, and 4) efficient with disk space.

As an example of my diligence in implementing this benchmark suite for InfluxData, I included a mechanism by which the benchmark query results can be verified for correctness across competing databases, to within floating point tolerances. This is important because, when building adapters for drastically different databases, it is easy to introduce bugs that could give a false advantage to one side or the other (e.g. by accidentally throwing data away, or by executing queries that don't range over the whole dataset).

I don't see that TimescaleDB is using the verification functionality I created. I encourage TimescaleDB to run query verification, and write up their benchmarking methods in detail, like I did here: [1].

I think it's great that TimescaleDB is taking these ideas and extending them. At InfluxData, we made the code open-source so that others could build and learn from our work. In that tradition, I hope that the ongoing discussion about how to do excellent benchmarking of time-series databases keeps evolving.

[0] https://github.com/influxdata/influxdb-comparisons (Note that others maintain this project now.)

[1] https://rwinslow.com/rwinslow-benchmark-tech-paper-influxdb-...

- Jeff Goldblum as Dr. Ian Malcolm in Jurassic Park

Comments URL: https://news.ycombinator.com/item?id=16466834

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That is a clever way to make use of a search API like GitHub's. The principled way to do this, though, is to run LDA over all descriptions on GitHub, then use that similarity index to find similar repositories. You could run LDA over code, too.

I'll note that there is a cold start problem with this implementation: using LDA on such a small set of short documents will often lead to uninformative topics with words that are too-specific. You need a big corpus to capture e.g. synonym relationships.

https://en.wikipedia.org/wiki/Polynomial-time_reduction

https://en.wikipedia.org/wiki/Karp%27s_21_NP-complete_proble...

Why did the comment by `TAForObvReasons calling out this apparent censorship get deleted?

B) How has human play style changed since AlphaGo's introduction?

C) What is the answer to the question you most want to be asked?

In general, the infinite hierarchy of infinite sets "exists" because we can define it.