fizx

You're thinking of cryptographic hashes. There are many kinds of hash (geographic, perceptual, semantic, etc), many of which are designed to only be slightly different.

We also logged everything, and then re-planted with the most profitable trees assuming we'd log it all again.

Who knew that densely planted tall pine trees burn so much?

Hi, Person currently in middle management here. While we'd love to pay our good people more, at a moderately sized company and up, we operate under an unbelievable number of constraints, from HR, finance, etc.

Hi, I started an Elasticsearch hosting company, since sold, and have built products on PG's search and SQLite FTS search.

There are in my mind two reasons to not use PG's search.

1. Elasticsearch allows you to build sophisticated linguistic and feature scoring pipelines to optimize your search quality. This is not a typical use case in PG.

2. Your primary database is usually your scaling bottleneck even without adding a relatively expensive search workload into the mix. A full-text search tends to be around as expensive as a 5% table scan of the related table. Most DBAs don't like large scan workloads.

It's easy to put a block cache in front of the index, and I'm sure they'll get to it sooner or later.

The benefit of using S3 in that case is that unlike e.g. Elastic, your block cache servers don't need replication, and you can tear them down when you're done. You can put them in a true autoscaling group as well.

Workday is also a CRUD app-builder, but for HR professionals.

What other typical company functions have this sort of provider?

Conversely, what other company functions need this sort of provider?

GQL isn't really much like a graphdb (e.g. SPARQL).

The goal of GQL is to gather a useful subset of your microservice data into a single tree for your frontend to render. In a facebook context, a typical query is "give me all the info i need to be able to show my home feed".

The goal of graph dbs is to allow arbitrary, complexm, often cyclic and aggregating queries to be run against a data source expressed as a graph. A typical query is something like "how many of my friend-of-friends like basketball, displayed by location."

As a side effect, I imagine that this will lead to a very high hiring bar.

Without the ability & incentive to hire someone "junior," every interviewer will compare the new hire to their inflated view of themselves.

I mean, at this scale, why aren't you at least running in Heroku so you can have some niceties and not have to scramble when your VPS craps the bed?

> One final tip for asynchronous code reviews: Roundtrips (from teammate to reviewer back to teammate) are expensive, costing up to two working days of a sprint each time.

Um, we're doing it wrong.

Go makes pretty bad WebAssembly. It only embeds in Javascript, and it's at least 2MB for Hello World. You can't really use it in server-side engines like wasmer/wasmtime.

TinyGo is far better, but only supports a subset of Go's stdlib.

I'm playing with TinyGo, and using GPRC communication with the outer engine as a poor substitute for WASM interface types.

https://github.com/fizx/goingo if you want to read the ugly prototype!

Yup, also, as someone who's done sustained 60+ (maybe even a month of 80+), there's very little room for creativity. It works ok for pursuing the solution to that one clear, looming problem.

I much prefer 5hrs/day of focus, arriving at the office refreshed, excited, and creative.

Bikes expensive enough to have a tracking device will largely be carbon fiber.

Oh yeah, I forgot about them. Thanks for the pointer! Still looking for something OSS though :/

It's ~200LOC to implement a Lucene block cache over S3, and because Lucene uses log-structured storage, that block cache will be quite effective.

I can't wait until someone "Snowflake"s Elasticsearch and separates compute from storage.

There's not a great reason for there to be "shards" and "clusters". In 2021, I'd appreciate search running from object storage (e.g. S3), with a dynamic compute layer over the top providing all of the compute--especially with indexing being such a source of variable compute.

Also, why do we have to re-index all of the time? Most textual customization can happen at query time, with only a mild performance hit.

I'm curious if we ever truly get a cloud-native, easy-to-maintain search thing, or if we have to wait for ML to eat search in order to get nextgen tech.

The typical tech lawyer has seen this exact scenario ~20 times. You probably have some leverage, and spending $1-2k to figure out how much and how best to apply it, given the lawyer's experience, makes tons of sense.

Any problems you see are likely on the video players side, not the CDN/hosting side. If you like working on this stuff, there's a job open!

https://boards.greenhouse.io/reddit/jobs/2168814

I assume you mean expensive mistakes! Making cheap mistakes is a great way to learn!

Which platform?

Which platform?

You can take it all the way into the db with OSS if you like https://github.com/FoundationDB/fdb-record-layer/blob/master...

You can whitelist queries!

I split time between a successful side project and early Twitter, both at around 30 hours a week, with spikes up to 50hrs.

While they both netted positive outcomes for me, if I had gone all-in on either one, I think I would have had an order-of-magnitude better outcome. Splitting my attention cost me serious advancement & money at both.

I think we now understand much of the legend of the original damascus steel to have come from trace amounts of vanadium in the ore.

Introducing Gizzard, a framework for creating distributed datastores

https://blog.twitter.com/engineering/en_us/a/2010/introducin...

Four years in the AI space is like two lifetimes.

Decoherence describes the spectrum of behavior between quantum and classical.

As you mention, the probability density converges to multiple outcomes, but then I believe that you're in more of an Everett Many Worlds between the outcomes, rather than a "choice."

In practice, it seems like all of the Many Worlds scenarios statistically converge into one world sooner or later anyways (would love this to be formally shown, but have never seen it, so it's just conjecture from me). For example, if you flip a quantum coin, splitting the universe in two in a Many Worlds Interpretation, who cares? Does the Sun notice? Does someone in a town a mile away?

You've created a small bubble of "two universes," in a Many Worlds sense, but that bubble will pop. Quantum mechanics is ambivalent about the direction of time, so it sure seems like worlds are joining as fast as they're splitting, keeping us statistically around one effective universe.

I'm assuming that it's basically a port of GraphQL+DataLoader, so basically using recursive descent to manage hash joins.

You know how we have that "libraries are better than frameworks" discussion once a month? It sounds like your platform team was a framework.

Ideally, a platform team should give you reliable, self-service components to build upon, like databases, caches, rate-limiters, api-gateways, etc.

If they're framework-ish, they're mandating you build your app in exactly their preferred way in order to receive the benefits.