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thecleaner

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Math is off. Total number of people detained in 2023 was 270k out of 66million annual visitors. 0.4% chance. Chances of dying in a plane crash are roughly 1 in million (Google tells me so, not sure of the source). The ones I listed are the publicized ones but the story is the same.

Every MLE who didnt study Math really likes to downplay its importance. Yeah you dont need measure theoretic probability, but you need a grasp of Lin Alg to structure your computations better. Remember the normalization that we do in attention ? That has a math justification. So I guess yeah academics did have a role in building LLMs.

I mean computer scientists really do like to pretend like they invented the whole field. Whereas in reality the average OS, compilers, networks class has nothing to do with core ML. But of course are also important and these barbs dont get us anywhere.

YCs success has nothing to do with just YC (not to take credit away) but rather from the fact that the US is a large unified well-regulated market. EU has sensible rules sure but the market isnt unified and the individual countries dont have big enough markets to sustain large numbers of software companies. YC itself is honestly quite open with their knowledge but its just that EU in itself is a pretty terrible place to run a business. Lots if regulation, some good ones.

This is a little bit of a word soup. Its hard to see why the various redesigns were done without a set of requirements. I dont get why you'd trigger Airflow workflows for doing CDC. These things were designed for large scale batch jobs rather than doing CDC on a some Google sheets. Either way without scale numbers its hard to why PG was used or why the shift to BigQuery. Anyways the site uses Hugo, which actually sticks out for me.

Not the essay for me. I actually lost patience midway. The author lost me at "when we are young we dream of meeting our idols." I never gave a shit. I mean my idols were fictional so that obviously helped.

Laravel Cloud 1 year ago

Why are cloud based open source things working now but failed the first time Heroku came around ?

Its not fast without pre-segmentation as they do in WhisperX. It actually has terrible transcription speed. For speedup we have to use Ctranslate2 kernels. The decoding code is also a mess where its hard to plug your own custom language model. Not to mention streaming ASR requires even more tweaks. Whisper Small is very fast and quite inaccurate. If you deploy whisper on a GPU which costs around dollar per hr, you really to ensure that the cost savings are worth it.

Although all of this is from a production lens. For personal use, honestly nothing is as easy to use as Whisper (even works on a laptop).

Their interviews were one of the easiest things I ever did. Pay 150bucks for Leetcode, sort by company, memorize the solution and show up.

Even when solving problems in the real world, nobody sits in a dark room until moment of genius strikes. We always try some tactics others have tried and only then innovate.

I like t-shirt estimates but only when we all agree we known wtf we are talking about. I also really like hacks. They get the job done and not everything needs layers and layers of leaky abstraction. I also quite like easy things like Config fixes, esp if they get escalated. You can leverage these to show impact and get a good rapport going.

I hate meetings without agenda. Honestly send me a message and I'll make sure I reply. Meetings with written agenda are great.

Speech to text using whisper is almost perfect.

This isn't true. On benchmarks whisper is not SOTA. It is said to be noise resistant but it doesn't compare well with Conformer based architectures ever on Librispeech mixed. Definitely not perfect, and it doesn't work for medical transcription.

what they really mean is that product quality doesn’t matter.

But does it matter ? I think the only metric with optimising for is latency. Other stuff is something we do.

Shying away from a tool just because it has a reputation is just as silly as using it for a hype.

Trying to explain this to a team is one of the most frustrating things ever. Most of the time people pick / reject tools because of "feels".

On a related note, I never understood the hype around GraphQL for example.

I know nobody likes doing tech interviews but how has AI killed it ? Anyways you do want to know basics of computer science, it is a helpful thing to know if you ever want to progress beyond CRUD shitshovelling.

Also wtf is inverting a binary tree ? Like doing a "bottom-view". That shit is easy.