tyre

Sure, but accounting existed before Excel. That doesn't mean excel didn't change things tremendously.

It's because it's opaque. Signing up and seeing a page like `/companies/366` unnecessarily leaks company data. Sometime it's a huge deal, sometimes not; not here to litigate that. But it's trivially avoidable.

Do you care about users knowing when another business was put into the system (e.g. how long they've been with you"? If yes, v4. If no, v7.

v4 is less efficient but more private.

So you have a second, opaque unique identifier for the frontend to fetch? At that point why not just use a single column (UUID)?

You don't have to handle collisions and I don't believe that multiple Redditors have experienced this. If they have, it's an issue with the implementation and not UUIDs.

The math is simply not in there favor, by a number so large we cannot conceive of it in our tiny brains.

UUIDs have other useful properties. For example, they don't expose counts (e.g. how many customers you have.) They make URL / resource guessing impossible.

ids in the form of `[prefix]_[uuid stripped of dashes]` is ideal for most use cases. It should be the default the way Postgres is the default. Any deviation have good reason.

Let's say you have a remote client connect to a url to the database. You stream a download through there. You close the connection when you're finished.

No commands run.

No persistent access.

Lawyers are amazing.

Edit: Yes, based on the comment by dherls, this is how they're getting around it. Streaming replication is not commands run by the attackers (it's your own system doing the exfiltration!)

Not really. You can still put limits on the rate or aggregate dollar amount of refunds or transfers. You can put alerting on spikes of approvals. You can put back pressure that forces manual review. You can have ML models detecting aberrant behavior against historical data.

No one reasonable is saying, “hook up this model to do anything it wants with no other system available.”

Will people fuck it up? Absolutely! But, “the model has to be perfect or it will bankrupt a company” is more cynical than reality.

> Search API finds web pages for a query and returns structured results. It is powered by DeepSeek's in-house search engine.

Looks like they've built (or are building) their own index and return cached results. What you do with that information is up to you. If you want to visit the live page, you'd need to find a way around bot detection.

One reason (not the only one!) people use Anubis is to prevent the endless flood of requests from AI companies. If Deepseek scrapes once every few hours, that helps the website owner with the traffic problem while allowing access.

If they don't want to allow access, my guess is that deepseek's search crawler respects robots.txt but I don't know.

They’re not really. They make the things useful for two classes of users: Mac Studio for people running models locally and Siri backed by Gemini for voice interaction.

They’ll likely embed text models more deeply in the future, but the history of “Apple Intelligence” doesn’t give a lot of hope that they’ll nail it.

So they’re not selling shovels (a precursor to making the thing) but products and services to use it.

I think it was ousted by a fast food chicken app fwiw. Couldn’t tell you why.

Have they fully implemented the Gemini-backed Siri?

I will say that I asked Siri some questions recently that I was pretty sure it would fumble and it answered directly.

“Does brown go with charcoal in clothing?”, for example, didn’t fall back to “I’ve found some web results. I can send them to your iPhone”. It actually answered.

It still has random nonsense regularly, but the “ask a question, get an actual answer” is new to me.

There's another version where a stats professor divides people into two groups. One group flips a coin 50 times and writes on a whiteboard the results. The other comes up with 50 results.

They leave the room and when they come back immediately know which is which. Specifically because of the over-alternation that yours mentioned. A string of HHHHHH is exactly as likely as HTHTHT, but people don't intuit that.

It might not be that someone is more random than an RNG, but that the distribution of numbers in your 190 vs. theirs appears more "random". It's a limited sample size.

If yours happened to contain consecutive digits or common ones (ending in zero, significant numbers like 911 or 42 or 69), that would imply less randomness even if it's actually the result of "real" RNG.

Agreed. It's a fun cultural tidbit but not really a useful benchmark for model capabilities.

It would be great if this could also be a thermostat. For $300 you could get rid of Nest (etc) while adding all of these other use cases.

Do very old (like 5+ years old) iPads cost more than $300? Seems like you wouldn't need constant OS updates for their use case but idk.

Homepods have intercom support.

What's also crazy is that they can detect fire alarms, will alert your iPhone, and then can pipe both sound and voice to the HomePod. It announces that it's going to do so, so it's not a privacy nightmare.

Pretty cool. I didn't know it existed until it happened. (Thankfully nothing bad.)

It's like a smart thermostat, always plugged in.

(Not exactly the same power feed but you know what I mean)

Yeah but there are also network effects of using fitness apps. Every engineer I know switches easily between Codex and Claude Code when one gets better than the other.

Jev has been around for a couple weeks. Cost and performance matter more than anything. Staying with an existing provider (the # of people choosing Jev without already having a frontier API key is probably zero?) is way easier than this.

What the hell are we even talking about.

Models are getting better all the time and either keeping prices or lowering them.

What you could achieve with $10k a year ago vs today is quite different.

What was your background before Erlang? I came from Ruby, so Elixir felt much more like home than Erlang.

Thank you, I lost my mind, briefly, reading that part.

You mean get rid of Postgres and build bespoke database-esque systems for every use case?

If so, then no. It is not time for that.

Maybe you would have. Maybe you wouldn't have. But they have to design these things for the 90% case. People using things that are far outside what's being taught—in the age of AI—are maybe more likely to be cheating than everyone going above and beyond.

Students are human. Many have been taught to focus on one end goal: a grade. They see a mythical program that knows 6000x more than is taught in this 200 level course; of course they want to use it!

I mean people said this all the time, but wouldn’t pay for it (as you said), couldn’t recruit for it, and definitely couldn’t retain them.

But so many teams said they wanted to Raise the Bar to infinity and hire a World Class Team.

They built this brand for hardware, but, personally, I’ve never had this opinion of their software. I mean, being Unix based is better than Windows, but they’ve always been cooking weird UI refreshes or buggy software.

It’s crazy because they have (or had) something like $100bn in cash. Spending 0.5% of that per year on software (development, bug-fixing, performance, and QA) would help tremendously. They’d be insurmountable.

I know “they don’t have to”, but they don’t have to do a lot of things that they do on the hardware side. They choose to.

iirc these aren’t native menus. Firefox builds their own. I found this out when scratching my head why I couldn’t find “Look Up” in the right click menu in Firefox.

I had a fun one where clicks stopped registering sometimes and right clicks didn’t register sometimes and my mouse disappeared randomly.

I thought my machine was broken. Turns out this is a known bug with window managers (????????)

The sporadic nature of it had me question my own sanity.

Because a lot of people employed as engineers can’t build classifiers. They’re able to glue libraries and tools together, build UIs, and write APIs, but don’t have the curiosity or creative problem solving to learn and master a new domain. Even when “master” is scoped to something like this.

On top, most EMs wouldn’t take a risk on an exploration of something “unknown” (to them) and couldn’t get buy-in from a PM.

I say this as an EM. Interview hundreds of people and, while, yes, some people don’t interview well, you might be shocked at the level of creative thinking. Even when “creative” is narrowly scoped to “this is a solved problem in a related domain.

What are the costs compared to an ML model?