pradn

On the data warehousing side, I think the story looks like this:

1) Cloud data warehouses like Redshift, Snowflake, and BigQuery proved to be quite good at handling very large datasets (petabytes) with very fast querying.

2) Customers of these proprietary solutions didn't want to be locked in. So many are drifting toward Iceberg tables on top of Parquet (columnar) data files.

Another "hidden" motive here is that Cloud object stores give you regional (multi-zonal) redundancy without having to pay extra inter-zonal fees. An OLTP database would likely have to pay this cost, as it likely won't be based purely on object stores - it'll need a fast durable medium (disk), if at least for the WAL or the hot pages. So here we see the topology of Cloud object stores being another reason forcing the split between OLTP and OLAP.

But how does this new world of open OLTP/OLAP technologies look like? Pretty complicated.

1) You'd probably run PostGres as your OLTP DB, as it's the default these days and scales quite well.

2) You'd set up an Iceberg/Parquet system for OLAP, probably on Cloud object stores.

3) Now you need to stream the changes from PostGres to Iceberg/Parquet. The canonical OSS way to do this is to set up a Kafka cluster with Kafka Connect. You use the Debezium CDC connector for Postgres to pull deltas, then write to Iceberg/Parquet using the Iceberg sink connector. This incurs extra compute, memory, network, and disk.

There's so many moving parts here. The ideal is likely a direct Postgres->Iceberg write flow built-into PostGres. The pg_mooncake this company is offering also adds DuckDB-based querying, but that's likely not necessary if you plan to use Iceberg-compatible querying engines anyway.

Ideally, you have one plugin for purely streaming PostGres writes to Iceberg with some defined lag. That would cut out the third bullet above.

Isn't it basically not possible for the input data set list to be listed? It's an open secret all these labs are using immense amounts of copyrighted material.

There's a few efforts at full open data / open weight / open code models, but none of them have gotten to leading-edge performance.

Wait - there's system webviews? On Mac, Windows, and Linux?

Edit: It looks like Tauri uses the following platform webview features.

https://github.com/tauri-apps/wry?tab=readme-ov-file#platfor...

Every company gets a ton of hate on Hacker News. Don't let it bother you too much. But the specific concerns may be a directional signal.

I'm hoping there will always be a good LLM option, for the following reasons:

1) The Pareto frontier of open LLMs will keep expanding. The breakneck pace of open research/development, combined with techniques like distillation will keep the best open LLMs pretty good, if not the best.

2) The cost of inference will keep going down as software and hardware are optimized. At the extreme, we're lookin toward bit-quantized LLMs that run in RAM itself.

These two factors should mean a good open LLM alternative should always exist, one without ulterior motives. Now, will people be able to have the hardware to run it? Or will users just put up with ads to use the best LLM? The latter is likely, but you do have a choice.

It's premature in the sense that you need to care about it before you do profiling, which is the usual advice for perf improvements: "you don't know where the hotspots are, so write your code, profile it, and fix the slow bits".

> Typically, perf isn't a few bad decisions. It's a very large number of independently reasonable decisions that add up to a bad result. If the team loses that discipline for even one moment then it's very very difficult to fix. I wonder if my former team still exists or if they've all been reassigned elsewhere.

This is precisely where the adage "premature optimization is the root of all evil" falls apart. You really do need everyone to care about performance to an obsessive, unreasonable degree to keep the entire, massive system performant. Companies with good engineering leadership understand this. The thousand cuts can come from language, libraries, feature creep, and pure ignorance or carelessness.

I see a good number of these articles, each with their own typographic features. Is there a "gold standard" set of recommendations for making the most beautiful type, on the web?

This is such a meme on Hacker News. How much evidence is there really for this? There might be some, but saying its the primary reason CEOs hire consultants is an even stronger claim that needs justification.

SWE-years are actually used for two things: a) measuring ongoing cost and b) for calculating if an investment will pay off.

There's a few benefits over just using a dollar figure. It's a unit that natural ties time to value. SWE-years is a natural unit for thinking about the time of SWEs. It's also convertible to other units. The conversion factors to other units can change over time, without having to pay attention to the minutiae.

All these factors make it useful for both measuring ongoing cost and making investment tradeoffs.

There's definitely some nuance around how many resources to consume at the client vs the server. Video decoding and ML inference are probably at the extreme end of what you can make a client do.

On the whole, since clients are so constrained, it usually pays to be efficient there - make websites load quickly to increase revenue, require only weak hardware to get more game/app sales, etc. Clients are also untrusted, so there's so many things you can only do on the backend.

Yes, a SWE-year is a common unit of cost.

And there are internal calculators that tell you how much CPU, memory, network etc a SWE-year gets you. Same for other internal units, like the cost of a particular DB.

This allows you to make time/resource tradeoffs. Spending half an engineer’s year to save 0.5 SWE-y of CPU is not a great ROI. But if you get 10 SWE out of it, it’s probably a great idea.

I personally have used it to argue that we shouldn’t spend 2 weeks of engineering time to save a TB of DB disk space. The cost of the disk comes to less than a SWE-hour per year!

It's the responsibility of leadership to set the correct goals and metrics. If leadership doesn't value maintenance, those they lead won't either. You can't blame people for playing to the tune of those above them.

The list price of a house going up is a big part of it.

But I don't think people actually have an idea of how much more a house of the same price costs when interest rates go from 2.5% to 7%. For a million dollar house, that's an extra thousand or two a month.

There's a great paper that collects a long list of anecdotes about computational evolution.

"The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities"

[1] https://direct.mit.edu/artl/article/26/2/274/93255

> Teaching financial concepts in a classroom is like teaching swimming with PowerPoint slides. You can explain the theory of the butterfly stroke all day, but at some point you need to get in the water. And with money, the water is always cold, deep, and full of currents that weren’t in the textbook.

It is totally true that these extra-mathematical factors have a big part to play how we spend and how we save. If you’re in a certain culture that expects a certain level of spending, it’s hard to take yourself out of that.

I still don’t see even adults with a good idea of basic financial things. How much does it cost to buy a house? How does a monthly payment change with interest rates? How do you become eligible for Social Security?

You really need to keep these several-dimensional functions in your head. At the least, a good grasp of algebra is required.

Ah, I see.

Well, you do have to worry about customers using old client libraries / SDKs, even if your whole backend has migrated to a new API.

Many customers don't like to upgrade unless they need to. It can be significant toil for them. So, you do see some tail traffic in the wild that comes from SDKs released years ago. For a service as big as S3, I bet they get traffic from SDKs ever longer than that.

There is some queasy feeling of fake-ness when auto-completing so much code. It feels like you're doing something wrong. But these are all based on my experience coding for half my life. AI-native devs will probably feel differently.

What sort of ingredients keep well in this process? How about mushrooms and potatoes?

I mean is "wait" even the ideal "think more please" phrase? Would you get better results with other phrases like "wait, a second", or "let's double-check everything"? Or domain-dependent, specific instructions for how to do the checking? Or forcing tool-use?

Omg, another fan of "Memory Resource Management in VMware ESX Server"!! It's one of my favorite papers ever - so clever.

I think you do point to a real issue. The "falsehoods programmers believe about X" genre can be either a) actual things a common programmer is likely to believe b) things a common programmer might not be be knowledgeable enough to believe.

This article is closer to category b. But the category a ones are most useful, because they dispel myths one is likely to encounter in real, practical settings. Good examples of category a articles are those about names, times, and addresses.

The distinction is between false knowledge and unknown unknowns, to put it somewhat crudely.

That's a great one, thank you!

An important design consideration for Hydro, it seems, is to be able to define a workflow in a higher level language and then be able to cut them into different binaries.

Is that something Akka / RX offer? My quick thought is that they structure code in one binary.

> these articles are annoying

You’re being quite negative about a well-researched article full of info most have never seen. It’s not a crime to write up details that don’t generally affect most people.

A more generous take would be that this article is of primarily historical interest.

Brand - it's the most powerful first-mover advantage in this space.

ChatGPT is still vastly more popular than other, similar chat bots.

That's a good insight. I had always thought the key to good algorithm / data structure design was to use all the information present in the data set. For example, if you know a list is sorted, you can use binary sort. But perhaps choosing how much of it to omit is key, too. It comes up less often, however. I can't think of a simple example.

> "de facto controlled by CCP"

Where is the evidence for this?

You're correct - that's a fair point.