"With the advent of the new Automatic Programming systems, we'll soon have no need for programmers." -- written about a predecessor of Fortran.
My own programming career began in 1970, and included an intriguing visit to Stanford's AI center. I've been an avid fan boi of AI ever since. Currently the folks I'm watching are the Thousand Brains Project, an effort to replicate a network of cortical columns and support services.
In any event, I've written code in a variety of languages. My current favorite is Elixir, because it offers fewer booby traps and far more capability than any others I've seen. However, my take on languages is that they let you work at a comfortable level of abstraction.
Still, I'm delighted to have an earnest pair of assistants (ChatGPT and Codex CLI) who are able to discuss ideas and issues in my native language. They can also help me translate my rough notes and speculations into (apparently) working code, using whatever resources are available (eg, languages, libraries). Indeed, one of the hardest things to do is to imagine what they are able to do.
I suspect that there are at least a few nerdy nine year olds who are grabbing onto this. I also suspect that there are folks who will do unfortunate things with this capability. Should be interesting...
The rectangular array is the basic data type for APL. Until fairly recently, the BEAM had no reasonable implementation of this. However, it seems like Nx's tensors might serve...
Breaking lines at logical boundaries in the "source code" makes it easier for me to see the structure of the text. It also gives me the ability to see the text in two different layouts; this often lets me spot errors which would otherwise slip by.
This is sort of a "generative" (aka property-based) approach to testing makefiles. So, I'd like to see a variation that can find a minimal failing subset of the makefile rules, as in QuickCheck:
> In QuickCheck, assertions are written about logical properties that a function should fulfill. Then QuickCheck attempts to generate a test case that falsifies such assertions. Once such a test case is found, QuickCheck tries to reduce it to a minimal failing subset by removing or simplifying input data that are unneeded to make the test fail. -- https://en.wikipedia.org/wiki/QuickCheck
Nicely written article, but it seems to conflate "what it should do" with "how it should do it". So, for example, it talks a lot about optimizations (which feel like implementation decisions).
I'm not saying they aren't important in this DB, but they might not be as relevant in another. For example, Datomic doesn't go this direction, but its ability to avoid read locks on long-running queries might dance around many optimization issues.
FWIW, I like the use of Cypher, which I first encountered in Neo4j. It has always felt pretty intuitive to me, though I don't know all of its limitations and strengths.
Life hack: weigh cartons of eggs (Jumbo and any other sizes), then calculate the cost per ounce (or whatever). Jumbo eggs are often a real bargain (but you may need to adjust recipes).
BASIC, Forth, Logo, Python, and Ruby are all reasonable starter languages for children. Although Python's semantic indentation can be a hassle for blind users, this isn't an insuperable problem. And, given Python's immense popularity, it is likely to be around and very usable for a long time.
The other day I wrote a fan letter to a developer who has been maintaining a popular and useful library for several years. In his reply, he said that this was the first fan letter he had ever received. I think we need to show Open Source developers a lot more love and a lot less snark...
A wonderful vignette. Note how Einstein structured the dialogue and interaction to find out the extent and nature of the problem, then help the listener.
On an only vaguely related topic, I strongly recommend this video by Evelyn Glennie, a deaf percussionist:
This seems like a very cool idea. The concurrency model in Erlang/Elixir/... eliminates the issues of shared mutable state and the supervision trees catch unexpected data-related problems, etc.
FWIW, the Elixir/Phoenix community seems to have settled on LiveView, which performs most of the processing at the server, assisted by some small JS routines which handle updates, etc.
Clay- I strongly encourage you to find a way to make your writings available once again. Many of the things you wrote are as true today as they ever were and the rest are valuable as historical reminders. FWIW, GitHub Pages might well be the simplest and best solution.
My own programming career began in 1970, and included an intriguing visit to Stanford's AI center. I've been an avid fan boi of AI ever since. Currently the folks I'm watching are the Thousand Brains Project, an effort to replicate a network of cortical columns and support services.
In any event, I've written code in a variety of languages. My current favorite is Elixir, because it offers fewer booby traps and far more capability than any others I've seen. However, my take on languages is that they let you work at a comfortable level of abstraction.
Still, I'm delighted to have an earnest pair of assistants (ChatGPT and Codex CLI) who are able to discuss ideas and issues in my native language. They can also help me translate my rough notes and speculations into (apparently) working code, using whatever resources are available (eg, languages, libraries). Indeed, one of the hardest things to do is to imagine what they are able to do.
I suspect that there are at least a few nerdy nine year olds who are grabbing onto this. I also suspect that there are folks who will do unfortunate things with this capability. Should be interesting...