🎙️ Future of Coding 73 • Moving Beyond Syntax: Lessons from 20 Years of Blocks Programming in AgentSheets by Alexander Repenning Finally, a title with appropriate length given the duration and depth of its episode. May it swiftly be surpassed. Alexander Repenning created AgentSheets, an environment to help kids develop computational thinking skills. It wrapped an unusual computational model with an even more unusual user interface. The result was divisive. It inspired so many other projects, whilst being rejected at every turn and failing to catch on the way Scratch later did. So in 2017, Repenning published this obit of a paper, Moving Beyond Syntax: Lessons from 20 Years of Blocks Programming in AgentSheets, which covers his findings over the years as AgentSheets evolved and transformed, and gives perspective on block-based programming, programming-by-example, agents / rule / rewrite systems, automata, and more. This is probably the most “normal” episode we’ve done in a while — we stay close to the text and un-clam many a thought-tickling pearl. I’m saying that sincerely now to throw you off our scent the next time we get totally lost in the weeds. I hear a clock ticking.

I haven't listened to the show yet, but in the notes I see "Also couldn't find a good link for TORTIS. :/" Is this the TORTIS from the Logo project? If so, you can find two papers about it at https://el.media.mit.edu/logo-foundation/resources/mit_logo_memos.html

Just from reading the description, I'm amazed I've never heard of it before, either here or elsewhere. Looks like it's come up just once here: https://akkartik.name/archives/foc/thinking-together/1571241687.398100.html#1571296934.435900

@Kartik Agaram I'm glad we did an episode on it then 🙏

I loved y’all’s conversation around computational thinking and programming (active). This is something I think about a lot in the context of array programming, especially APL and BQN. In a lot of ways the experience of programming with APL is similar to using Scratch, where you are combining and recombining a core set of well known pieces to build up to a desired effect, many times you have to build to an output you don’t really actually want, and then have sort of a clean up pass that filters a thing down to what you need. I like APL because it feels like a billion million miles away from the computer. I thinking about it all as sort of transform rules to apply to data. I wonder if a tripping stone for scratch and the things described in the paper is that they mimic too closely things that make you think about the computer while pretending to be obscuring that away, like they need to go harder in one direction or another?

Am I being nerd sniped? I feel targeted. 😅

@Jason Morris Oh, say more!

Well, my FoC project is Blawx, which is a block-based version of a stable model open world constraint logic programming language. It is targeted at representing statutory "language", which has its own rules of interpretation, and so Blawx is using a block interface to support a legal semantics and a logical syntax. It is also designed with explanations for query answers that make the semantics of the execution more visible in the output (not using blocks, but still aimed at semantic "support"). Then, I'm working on different UIs that are designed to facilitate reasoning over imperfect information, so that you can ask questions like "in this hypothetical fact situation, is this additional fact relevant to answering this question, and if so, how?" Which seems like it may be an example of what the author calls pragmatics. Maybe not as directly targeted "for Jason" as the Driver paper, but still... eerily relevant.

Computational thinking: "I'm right here!"

Syntonic computational thinking: "I'm the turtle!" 🐢