There’s an extension for that https://github.com/ywelsch/duckdb-psql

Also https://github.com/ywelsch/duckdb-prql (by the same author!)

I got laid off from a job and did some consulting on the side for a few months. The break helped my creativity tremendously

There have been other periods of massive layoffs. (I’ve personally witnessed 2001 and 2008) Weren’t workers disillusioned with full time work back then or has it become easier and more acceptable to freelance in the last decade? Was it perhaps the callousness of the layoffs that ignited these emotions?

> I can't decide if it would be better or worse if it stuck more closely to SQL keywords. You use "from" and "select", but not "where", "order by", "group by". There's some danger of it being in an uncanny valley of SQLish, but I'm pretty sure I'd prefer just using those terms verbatim (including the space in "order by"... that style is less common in modern languages but it's not really that much harder to parse)

I agree 100% here. As a SQL veteran, it would make the transition a lot easier if you used common SQL keywords like group by, order by, limit, etc. e.g.

    from employees
    where country = "USA"
    derive [
      gross_salary: salary + payroll_tax,
      gross_cost:   gross_salary + benefits_cost
    ]           
    where gross_cost > 0
    group by:[title, country] [
        average salary,
        sum     salary,
        average gross_salary,
        sum     gross_salary,
        average gross_cost,
        sum_gross_cost: sum gross_cost,
        count,
    ]
    order by:sum_gross_cost
    where count > 200
    limit 20

That’s because metrics are great for optimization and fine tuning features but terrible for innovation

One reason might be that great innovators don't naturally gravitate towards middle management (or management consulting), so the metrics are flawed out of the gate.

We don't have the tools to measure the complex dynamics and factors that result in innovation.

Do you have evidence for this claim? I think metrics are absolutely invaluable for innovation.

What is innovation but throwing 100 things at the wall and seeing which two of them stick? And to quickly get a sense of which things have stuck to the wall, metrics are the only reliable way.

It probably depends on the type of innovation. If you are trying to do things differently, metrics are invaluable — most modern internal innovation teams use metrics to build and manage an idea funnel.

However, if your innovation strategy is to do different things, you will likely be focused on market differentiation, customer development etc. Common practice is to refer to AARRR metrics for this, but it’s not like metrics themselves are the main focus.

Anecdata: on a couple of projects our metrics have told us that do things differently will not get us to target, forcing hard choices to go do different things to get there. But it is usually an uncomfortable fight to get the boat turned around, especially if people have already invested a lot of money and ego in the earlier decision tree.

Also purely anecdotal, but I think it takes a different skillset to pivot correctly, which execs who work in a corporate environment focused on doing things differently often lack. That’s where external consultants usually get involved.

I agree that the 40h workweek is not technically “designed” per se but rather it’s the best confluence of factors such as keeping competition at bay, keeping society productive, making people feel useful, making life purposeful for many, etc. It’s the current local minima. If we could work 60 or 80 hours and still get the same benefits, we’d do so. However, nobody has experimented with working less while others work more because competition will eat you up.

The comment of Tyler Heaps gives a clue:

“If you look at each one and how they’ve occurred, many have come from “dangerous areas” on the pitch. At AS Monaco over the last season, we looked at where and how goals were most often scored and key areas to shoot/cross from to apply in our game model.”

With teams applying more analytics to soccer there are more cases of high goal probability crosses and defenders’ attempts to clear these are often futile. A great example was England’s equalizer yesterday that technically was an OG but realistically the defender had no chance to clear it and Sterling was right there to tap it in.

The guide is ok, but I will say that nothing works better than having a problem you’re trying to solve. The rest comes naturally. I taught myself data mining (now called machine learning or data science) in a matter of months because I was trying to build a lead scoring model.

Might work wonders in being applied to air filters or disinfecting large spaces. Not sure if you could run this continuously especially when humans are around.

If you don't have humans to worry about, I would think UVC light would always be the more effective solution.

Light is easily shadowed. Ultrasound will propagate everywhere.

UV light also damages objects.

Completely agree. Most business data is noise and most of the signals are already discovered as simple rules and heuristics. On the other hand, if you have a strong signal in your data, even a simple algorithm like linear/logistic regression will be able to help. What I’ll call “signal hunting” is probably the best use of DS resources and also the hardest thing to do.

I’ve done my share of experiments with ML/AI and where I’ve seen the most interesting value has been NLP applications (such as categorizing customer comments or assigning categories to products based in description) and finding “factors that influence behavior x” which then can be turned into either a model or a few simple rules.