Sometimes courts uphold the letter of the law, but say the legislature should change it.
4,743 karma · joined February 26, 2015
Sometimes courts uphold the letter of the law, but say the legislature should change it.
What worked for me: make an absolutely minimal version of the idea (2 hours), and a webpage that describes its promise (9 hours). 500 people looked at it, one thought it was cool - that was enough for me.
Don't do anything out of the ordinary. Do exactly what everyone does, and you won't get hurt. If you enjoy exploring and experimenting, discovering and inventing, creating and wondering, you'll need to train yourself out of that, because thoughts lead to actions.
Think Same.
But we're ridiculously adaptable, tropic to artic.
Well said.
Hiding complexity is harder than handling it. Designing an effective way to expose it for use amounts to (part of) a "programming system" [Brooks].
So it's library writers who hide the complexity for average programmers, with a "programming product": stdlib, open source project or (rarer these days) commercial "engine".
When there is no such library, we get the current state of our art: many projects failing.
In 2000 years, they may say it of "plastic".
A distributable runtime, to a market of 100,000,000+ PS4 (plus PS5's), royalties to creators as publishers, would be an intermediate step.
Bundled with PS5 for early gen content.
Lower birth rates (due to birth control, affluence) is the fundamental change.
mv topo{log,graph}ical.data
It works because it expands to mv topological.data topographical.data
try it with echo instead of mv to see how it expands. Other uses: mv myfile.txt{,.tmp} # add extension
mv mymusic.{mp3,mp4} # change extension
mv myarchive.{zip,jar}It's not that Dreams is revolutionary (it's not, just a step forward), but that you can create with only a console.
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Game makers are a very cool and exciting product category, but also very old, and never seem to really take off.
Making games is hard, even with teams of experts, the best tools money can buy, and inhouse tooling.
Programming - a subset - is also hard. There was once a fad of programs that generate programs, like The Last One https://wikipedia.org/wiki/The_Last_One_(software)
A similar impulse was thinking AI will be easy...
mv topo{lo,raph}ical.data
mv proj/src/{main,test}/com/org/proj/Test.javaDynamic languages don't have an enforced schema. You can do it manually, but a schema is easier, special-purpose, declarative.
Plus, data schema languages are typically far more expressive than programming languages. e.g. java doesn't even have nonnullable (which C had, as non-typedef structs). They're closer to a database schema.
Also, thingo aggressively protected JSON's simplicity, banishing comments because people were using them as parser directives.
To determine what it is, you need to understand the domain. Part of that is knowing not just what it is, but how it generalizes and predicting how it is likely to change.
Of course, this is approaching perfection. One could also cut and paste monkey-like, e.g. instead of looping (without a performance need to unroll loops).
I think the primary problem of giving examples here is similar to teaching software engineering, which needs complex projects solving complex problems - too big for a semester project.
A good schema depends on the problem it's solving.
A secondary problem is similar to code that has sacrificed clarity for performance. The tweaks made for performance are not intrinsic to the logical problem, but are an additional constraint.
For performance on common queries, schema can be flattened or "denomalized". The ability to do so was one of the original motivations for Codd's relational algebra.
> There need be no real danger of it ever becoming a drudge, for any processes that are quite mechanical may be turned over to the machine itself. https://en.m.wikiquote.org/wiki/Alan_Turing
I guess a web can be used as a turing machine tape... though, 3D and finite.
Their big problem is asociability.
OTOH how would you go about assessing his engineering ability? He was accepted into a Stanford PhD program. An ex-employee of spacex was amazed how he absorbed a textbook of rocket science.
Unlike Eistein, he hasn't written any papers himself (AFAIK), so we can't assess his work directly. I would guess his primary role is being a technically and business-aware coach for engineers who actually do the work - like Steve Jobs. But maybe he is a genuine engineering genius himself - but how could we tell?
In difficult projects, it's necessary to improve prerequisite skills before "winning" (completing the project).
But with sufficient layers of prerequisites, the goal becomes distant, the motivation decoupled, until it falls below a threshold.
In addition, for a non-expert, it's difficult to be sure that a prerequisite is really needed, or will be enough, or what the true ROI really is. We don't need precise certainty, but we do need to know well enough.
Do you have any suggestions for getting around this?