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funnymunny

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I used git bisect in anger for the first time recently and it felt like magic.

Background: We had two functions in the codebase with identical names and nearly identical implementations, the latter having a subtle bug. Somehow both were imported into a particular python script, but the correct one had always overshadowed the incorrect one - that is, until an unrelated effort to apply code formatting standards to the codebase “fixed” the shadowing problem by removing the import of the correct function. Not exactly mind bending - but, we had looked at the change a few times over in GitHub while debugging and couldn’t find a problem with it - not until we knew for sure that was the commit causing the problem did we find the bug.

We (at my work) are evaluating Iceberg, Hudi, etc. as a next step in scaling up to support larger OLAP workloads beyond what we can currently manage in Redshift and we're leaning towards Iceberg. Does Hudi have a niche that it's especially well suited for?

Apply password protection to anything with a dead-simple app that runs entirely client-side. Encryption is handled by RustCrypto and is executed in your browser as WASM.

How does it work? You enter a password, which Argon2 converts to a 256 bit key. That key is then used to encrypt and decrypt via AES256. As a bonus, all plaintext is compressed prior to encryption.

This app was purpose built to delight its creators. We love all feedback and contributions nonetheless!

Very cool and intuitive; if x0 and x1 are in category A, while x2 is in category B, then the concatenation of x0,x1 is more compressible than the concatenation of x0,x2. It makes sense that this would do better than an out of distribution NN.

Simplicity aside, practicality is unclear. It seems you can’t escape the need to perform at least one compression operation, per class, per inference. Eg classifying X into one of 10 categories requires a minimum of 10 string compressions. Probably more if you want better accuracy.