Makes me wonder if the hardware engineers look at software engineers and shrug, “they don’t really know how their software really works.”
Makes me wonder if C programmers look at JS programmers and shrug, “they don’t understand what their programs are actually doing.”
I’m not trying to be disingenuous, but I also don’t see a fundamental difference here. AI lets programmers express intent at a higher level of abstraction than ever before. So high, apparently, that it becomes debatable whether it is programming at all, out whether it takes any skill, out requires education or engineering knowledge any longer.
Yeah, the online Gemini app is not good for long lived conversations that build up a body of decisions. The context window gets too large and things drop.
What I’ve learned is that once you reach that point you’ve got to break that problem down into smaller pieces that the AI can work productively with.
If you’re about to start with Gemini-cli I recommend you look up https://github.com/github/spec-kit. It’s a project out of Microsoft/Github that encodes a rigorous spec-then-implement multi pass workflow. It gets the AI to produce specs, double check the specs for holes and ambiguity, plan out implementation, translate that into small tasks, then check them off as it goes. I don’t use spec-kit all the time, but it taught me that what explicit multi pass prompting can do when the context is held in files on disk, often markdown that I can go in and change as needed. I think it ask basically comes down to enforcing enough structure in the form of codified processes, self checks and/or tests for your code.
Pro tip, tell spec-kit to do TDD in your constitution and the tests will keep it on the rails as you progress. I suspect “vibe coding” can get a bad rap due to lack of testing. With AI coding I think test coverage gets more important.
See https://abseil.io/tips/ for some idea of the kinds of guidance these kinds of teams work to provide, at least at Google. I worked on the “C++ library team” at Google for a number of years.
These roles don’t really have standard titles in the industry, as far as I’m aware. At Google we were part of the larger language/library/toolchain infrastructure org.
Much of what we did was quasi-political … basically coaxing and convincing people to adopt best practices, after first deciding what those practices are. Half of the tips above were probably written by interested people from the engineering org at large and we provided the platform and helped them get it published.
Speaking to the original question, no, there were no teams just manually reading code and looking for mistakes. If buggy code could be detected in an automated way, then we’d do that and attempt to fix it everywhere. Otherwise we’d attempt to educate and get everyone to level up their code review skills.
Some popular streamers have dabbled in OCaml this year, sometimes calling it "the Go of functional programming", which probably set off a small wave of people tinkering with the language. OCaml has also gotten gradually better in recent years in terms of tooling, documentation, standard library, etc.
In my experience learning a bit of OCaml after Rust, and then looking at Haskell, the three aren't all that different in terms of the basics of how ADTs are declared and used, especially for the simpler cases.
> Bad example. Google docs doesn’t use CRDTs but uses OT instead. CRDTs may handle your scenario just fine depending on how they decide to handle this scenario.
The CRDT may pick one or the other replacement word, but who is to say that either choice is correct? Perhaps including both words is correct.
> Then there’s not even a merge conflict...
Agree, this is what CRDTs are all about.
> ...to really worry about.
I think it is important to make clear that CRDTs do not "solve" the merging problem, they merely make it possible to solve in a deterministic way across replicas.
Often, CRDTs do not capture higher level schema invariants, and so a "conflict free" CRDT merge can produce an invalid state for a particular application.
There is also the example above, where at the application level, one particular merge outcome may be preferred over another.
So, it isn't as simple as having nothing to worry about. When using CRDTs, often, there are some pretty subtle things that must be worried about. :-)
I don't agree that a missing "framework" is the whole of the problem. It just isn't that simple.
Sure, people need to use resiliency skills to cope with the stresses of life. Often times, this is an important part of what therapy for depressed people is trying to achieve.
But this isn't to say that there isn't a constellation of causes in recent decades and years that cause the world to be particularly stressful, especially for young people. It also isn't to say that we should dismiss what is occurring in the world today as "the same old stuff" without acknowledging that it may actually have unique properties worth understanding. Off the top of my head: world population is at an all-time high, global warming is becoming increasingly understood, it is increasingly acknowledged that we can no longer simply extract unlimited resources from the earth to solve all problems, the Internet has changed the way the world works that seems to speed everything up: communication, changes within social groups, larger societal shifts, economic change, etc.
> It's much more a matter of whether you want to do something small scale and fun, or whether you want to suck all the joy out of it by applying the same soul crushing constraints we already get paid to do in our day jobs. Bleh.
Amen. And further, what better prepares a programmer to assess the relative costs of implementing a thing vs using a library providing that thing than having attempted an implementation?
Learning by doing is a valid approach, and this can even be called fun.
Definitely interested in how you achieved another 2-10x over the btree approach. I want surprised that btree was as effective as it was, but I’d be curious to know how you squeezed a bit more out of it.
Makes me wonder if C programmers look at JS programmers and shrug, “they don’t understand what their programs are actually doing.”
I’m not trying to be disingenuous, but I also don’t see a fundamental difference here. AI lets programmers express intent at a higher level of abstraction than ever before. So high, apparently, that it becomes debatable whether it is programming at all, out whether it takes any skill, out requires education or engineering knowledge any longer.