Bureaucracy gives you leverage against slop. Review seriously, but limit the time that you spend. This will stall the slop. When the culprit complains, tell your boss "I spend X hours per week on reviews. If you need more throughput, the PRs quality needs to improve."
Writing and reading design documentation can be slower than pair programming. On the other hand, info about code design also belongs into inline documentation or commit messages (in this order of preference), so the effort might not be wasted.
It's not just documentation. Everything that makes programming easier is now suddenly valued, because wasting tokens is obviously worse than wasting your employees' time.
This claim is plain malicious. Of course falling asset prices would be excellent for the median person, since they would be less extremely priced out of everywhere. This is one of the central benefits of a wealth tax.
This is the key point, what is the meaning of "zero knowledge" here? It seems that you need to know something about the implementation, even if it is not the full implementation. Compare this to a zero knowledge proof that you have, say, a factorization gadget, which works by you running the gadget on adversarial input, thus convincing the adversary that you can factor any of their integers. That discloses no implementation details of your factorization gadget, which can be an efficient classical algorithm, a quantum computer, or a phone line to God.
The list of tools that Pythonheads present as a definite solution to their problems changes every year, yet the results are still far behind Rust/Scala/Kotlin/C#.
The "fix everything" button is abolishing zoning laws, and its aggregate cost is negative. Aggregate cost is not the issue preventing problems from being solved.
> on top of a well designed language constructed over past language design experience
While I believe that Chris Lattner is a great compiler designer, his language design record has been less stellar. Swift bidirectional type inference for instance feels like it was implemented because they had a compiler algorithm that they wanted to use, rather than a genuine need, and is just a completely avoidable problem. Trying to make a HPC language that is also Python compatible was doomed from the start. Hopefully the damage from going into this direction will remain limited.
How is it possible to provide a zero knowledge proof that their circuit works for large problem instances if there is no efficient way to run or simulate the circuit with the required instance size?
"I believe that almost anything that has been formalised today in any system could have been formalised in AUTOMATH. Its main drawbacks were its notation, which really was horrible, and its complete lack of automation. Proofs were long and unreadable."
That's like saying that anything that could be programmed today in your modern language of choice could have been programmed 50 years ago in assembly. Technically yes, economically no.
A Bundeswehr worth of equipment is so little nowadays that Bundeswehr itself lost several Bundeswehrs worth of equipment while being at peace for the last few decades.
1. Java is mentioned in their comparison table. They just don't use it much.
2. There is really no reason to include Java in the search for your preferred language, since Kotlin is strictly better along every relevant axis.