rgavuliak

Fingerprinting, without excessive investment doesn't work well for mobile which eats and ever-increasing share of online traffic.

In addition more and more traffic will be agent based.

I don't think Service Model is really hopeful. The society has collapsed and robots are stuck on useless errands.

I have a hard time understanding your opinion. What do you even mean by garbage? There's thousands of legitimate music bands/musicians on these platforms. Naturally you won't care for a majority of it, but an interpret being niche doesn't make them garbage.

Julia is going to overcome Python in usage, any decade now.

I like how this discussion has focused on Netflix where as the point is something different.

There are 2 useful rules to consider when building stuff: * Your user might not necessarily be like you - this applies even more when we tech people consider ourselves to be like our customers - we're often very different. * While in individual experiences your click of a thumbs up button constitutes a yes, and ...; if we're talking 128k likes, the aggregate dulls some of the edges and has interpretative value.

Interesting. I have had mostly the opposite experience. CEO(s) that changed strategy + focus every month and the middle management having to pick up the pieces. Keep people motivated and deliver.

When was this? MS Copilot has gotten a lot better lately IMO.

Databricks on Azure went down too yesterday

Exactly, especially in long trips this usually resulted to a fight among parents.

Fair point, but before business people relied on other people to pull it for them. Sure, it was checked by human, but a technical human often lacked the business context and there's been plenty of instances, where the business person knowing their revenue and sales numbers challenged the analysis of a technical person.

There's a difference between a hyperbole and being tone deaf.

Well paid engineers compare their experience to a labour death camp just confirms how out of touch they are.

Ultimately the management should care about what brings in the most revenue. A feature that brings low revenue but has no fire is something no-one cares about. If you have a ton of revenue and no bugs, you likely can get recognition.

European Roads and US roads are very different. US is much more car friendly.

For me the whole piping felt like making everything less readable and harder to debug compared to a string of commands.

> This has a great SQL-ish API. Python is similar but starts to be a little clunky since it requires you to think about indices:

groupby has an as_index parameter for this very purpose

> Deducting the discount

You focus on doing the subtraction during the group by. Is there any good reason for this? You could either do it as a step before, or after summing up both columns. Putting too many things into one command is not good practice yet you benchmark the language based on how easy it is to do said bad practice

> The value-add that Microsoft brings to Github Copilot is near zero compared to directly buying from Anthropic or OpenAI

Over here in the EU, we need to store sensitive data in an EU server. Anthropic only offers US-hosted version of their models, while G-cloud and Azure has EU based servers.

I would measure data by time to action. If you're not actioning data it's worthless.

I do usually have a beer at the airport, but having more means you have more usage out of the plane bathroom which I can do without.

I like the saying - weeks of coding can save us hours of planning.

Why do you expect an LLM to provide an accurate distance metrics?

I have recently joined a corporation after years in start ups and 99 % of these apply too.

France is all about big government

Because it doesn't seem to work?

I don't think it's about perfect predictions. It's more about going all in on Metaverse and then on AI and backtracking on both. As a manager you need to use your resources wisely, even if they're as big as what Meta has at its disposal.

The other thing - Peter's principle is that people rise until they hit a level where they can't perform anymore. Zuck is up there as high as you can go, maybe no one is really ready to operate at that level? It seems both him and Elon made a lot of bad decisions lately. It doesn't erase their previous good decisions, but possibly some self-reflection is warranted?

Having worked in such companies, switching to that mode requires very different processes.

Interesting point

I think especially for Data Science, a lot of the curriculum got taken over by universities and new university programmes for Data Science were created since then so in a way it became common knowledge for fresh graduates.

> MOOCs never achieved the transformative potential promised during the early hype.

I would disagree, I saw a lot of people, especially in the Data Science field that got up-skilled by back then free Coursera.

> Nobody wants people who can do easy things, people want people who can do hard things.

No, people want people that can provide value regardless of the difficulty. What you're describing is how we end up with not invented here syndrome.