1) They do not publish rationale of why the world needs yet another protocol / language / framework on the homepage. It is hidden in https://typeschema.org/history

2) In the history page, they confuse strongly typed and statically typed languages. I have a prejudice about people doing this.

3) The biggest challenge about data models is not auto-generated code (that many people would avoid in principle anyway), but compressed, optimized wire serialization. So you START with selecting this for your application (eg. AVRO, CapnProto, MessagePack etc) and then use the schema definition language coming with the serialization tool you've chosen.

LINQ: exists

Splunk query language: exists

KQL: exists

MongoDB query language: exists

PRQL: exists

The real estate listed there is never a bespoke design for you and your family. In some locations, there are plenty of affordable homes so you actually can choose one with the layout nearest to your goals.

In the most cases though, affordable homes are rare so people don't really care about the current layout: they will buy any home and remodel it in according to their tastes.

To estimate the remodeling costs, it is better to work with the bare, empty layout plans, not cluttered with furniture and 3D effects, and having all measures specified.

These features provide for more safety, and at the same time, they reduce productivity by forcing the developer to statically type everything.

The question is then why do we need to transpile to Go, a language with GC and slower than Rust?

If we already agree on super-safe static typing, why not just use Rust? Are there any libraries in Go that are not available or of worse quality in Rust?

You assume that people drive BEV as often and as far as they drive ICE. They don't.

You assume people always charge BEV from the grid. People can also use solar panels.

You assume gasoline forever has to be fossil. It does not need to be.

You forget the carbon footprint of manufacturing of either kind of cars.

You assume all people can afford buying new cars. They don't.

You assume there will be as big second hand market for BEV and it is now for ICE. It won't be, because of the battery degradation.

You assume that current grid can handle simultaneous loading of all BEV. It can't, and you don't include the carbon footprint of grid extension and increase of the generation power in your calculation.

And last but now least, a typical green propaganda trick, you are only calculating one variable and ignoring negative effects in another variables: - people waste more time waiting for charging - people forced to switch to inferious means of transportation - people reducing their leisure travels - a two-class mobility with the lower class of poor people living in apartments without own charging possibility who cannot afford a BEV or are forced spending hours per day charging at public stations, and the higher class of home owners with solar panels, charging their cars for free - higher prices for taxi, delivery services (and therefore all retail prices), facility management, mobile nurses, construction worker and other people who drive more miles per day as one over-night charge can give. - special service vehicles like firefighting trucks, police cars, military vehicles, super-heavy trucks etc. cannot use fragile battery energy only, and need to use gasoline, which becomes very expensive and hard to get, if 99% of other cars are BEV and so all gas stations are closed.

So you should either stop publishing shit or invest a lot more time in your calculation.

In Bayesian approach, you start with some distribution that is a wild guess and doesn't even need to base on any knowledge besides of the basics how money work and that unemployment cannot be 0% or 100%. Each data point will refine your distribution until at some dataset size, it will converge to something estimating the reality.

You might want to watch an amazingly helpful introduction by Richard McElreath here https://www.youtube.com/watch?v=guTdrfycW2Q

This means: merge the PR first, let it go live, use your working students or interns to rerun stuff, wait for a month - if it is still happening, then you have a proof of a problem that needs to be fixed.

Disk space: use your monitoring tool to proactively warn you when the free disk space is below of 20% or is reducing too quickly.

If some other work is blocked by failed thumbnails, this is a logical bug and not the consequence of a message bus. This stuff has been blocked even before the introduction of the message bus anyways.

I am also not sure if the author has valid assumptions about business requirements - thumbnails being not generated doesn't look like a reason to be waked up at night to me.

Also you usually have an intern or two in the support team who can re-run failed jobs and don't need to waste time of your devs or ops or devops for that.

I would also see it as a two-level model. On the global scale, everybody will be negatively affected by the climate change, because its consequences (drought, flooding, but also refuges) will destroy economic cooperation and productivity, so everything will be hard to get and more expensive, and people will be looking for any job.

On the local scale, some land that was cheap and uncomfortable to live before might become wanted and better suitable to live. I think, the places that are barren and/or too cold today might benefit.

1) The CO2 savings are nothing more than a wild guess rather than a scientifically sound estimation. The real numbers might be as low as 600000 tonns. And even these numbers are unsure because they include all those car drivers who have used this ticket only once during the 3-month test period, out of curiosity, and therefore can't be used for reliable prediction of what would happen if this kind of tickets will be introduced permanently.

2) At the same time, we don't know the real costs of this experiment yet. Some sources call it to be 10 billions instead of 2,5 billions.

3) Overall, only 10% of ticket users chose public transport instead of their car. Most of the trips has been generated by people who wouldn't travel at all if the ticket was not so cheap. And on the other hand, there are many news that people who usually commute using public transportation had to fall back to the cars, because the trains were so overcrowded and were frequently cancelled.

More scientific studies are needed to research the REAL effects of this experiment. We shouldn't make any political decisions based on the wishful thinking only.