pinkbeanz

This is neat -- how would you think about evaluating the quality loss as you change to more efficient models? I saw you did an analysis on the number of messages, but wondering if there's more robust methods?

this is neat, where can i find more details on how the automatic routing logic works?

two mediocre companies join forces

Always suspected this was happening, nice to see some data (and an open-source package) behind it. Great work!

Big guy already died once, change didn’t happen then and there’s plenty more to take their place.

It’s common journalistic practice. That’s one of the benefits of reporting in mainstream media, they have pretty high standards for what gets published.

That doesn’t mean they’re always right or there aren’t bad journalists, but if you spend time listening to most journalists explain their reporting they often explain exactly how they verify their stories, what they went through to get sources, and what they were and weren’t able to report.

Journalism is one of the few ways we have in a democracy to hold power to account. What I don’t get is what a lack of faith in this institution does for us except give those in power even more ways to be unaccountable for what they do.

I’m an Iranian, I’m allowed to wish for the collapse of that awful regime. You can save your sanctimony for someone else.

Iranians are willing to die (and are dying) to fight the current government. Inflation and the economy has destroyed their hope of a future for decades. Talk to any Iranian now and they’re all hoping for the end of the regime. It can’t get much worse.

Iran is not Syria, there’s a flourishing academic population, the fundamentalists are the ones in power, plenty of people still remember what life was like pre-1979.

I think you’re confusing giving the company a chance to respond with made-up news. The time a news outlet gives for a response is usually proportional to the severity of what they’re announcing.

Giving a company a chance to respond to some hiring numbers when they likely have multiple anonymous sources who have been credible before is a different bar than asking for comment on something that alleges fraud or illegal activity.

I’m really comparing it to my previous ICE cars, both an Audi sedans and a Mercedes. All built in the last 10-15 years.

100,000 miles for spark plugs is extremely rare. I just pulled up a 2022 A4 and it’s 30k. Transmission fluid is 40k miles. I don’t think it’s a stretch to say that the heat, vibration and weight from an ICE engine wears down parts faster. Add in complexity from turbochargers and there’s even more parts that can fail.

The coolant system on an electric vehicle is extremely simple. There’s no radiator even. An ICE coolant system is far bigger and more complex.

I’ve seen reports of a few hundred dollars per seat per month. I’m sure it varies widely depending on how big the enterprise is.

Forgetting the home vs fast-charger nuance here, important to remember than electric vehicles have no spark plugs, no muffler, no engine oil, and a very simple coolant system.

My Volvo XC40 Recharge has a 2-year/20,000 mile maintenance cycle, the only work it needs is a check of the coolant and brake fluids and replacing cabin air filters.

Having suffered through broken engine mounts, torn exhaust flex pipes, painful spark plug replacements, coolant leaks, pump failures, and countless other issues with ICE engines, the simplicity of electric is a breath of fresh air.

Of course there’s the question of what happens to the battery over the long term but i’ll take that trade over what ai had before any day.

I think that’s still the outstanding question. Tools like Mode and Hex have already started supporting dbt’s metrics layer, but will big players like Looker and Tableau also move to support it? That’s harder to say

That’s right, they’re solving the same problem, although metric/semantic layers have traditionally lived within BI tools. Looker’s LookML is a great example of a metric layer.

There you define the relationships between tables, which dimensions to expose to your users, and how measures are to be aggregated along which dimensions and at what roll ups. Filters, formatting, and other semantic information rounds out what’s possible, making it easier to build complex reporting without having to pre-build large joined tables in the warehouse.

In theory a semantic layer decoupled from BI can be useful too, as downstream systems can all query a metric. Maybe an operational workflow needs access to revenue numbers or a spreadsheet wants a feed of some fresh aggregates.

The real question is will BI tools adopt a semantic layer they don’t own, giving up control over a key part of their input into their visualizations? Time will tell.

After 2 years of slow progress, dbt acquires Transform and their open-source MetricFlow to help build out the semantic layer.

Believe the replacement was because bash 4 was GPL3 licensed, and zsh was not. It wasn’t so much for user benefits than it was for security and licensing.