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I've been doing an implementation of DBSP, which is a way of doing incremental computation built up from some basic concepts from digital signal processing. While I use LLMs in my daily work to ask it questions, spitball with it, and do some basic stuff, I hardly ever
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It's been two or three weeks since the last lab note. I've been on vacation with my family for two weeks, and then caught a cold this last week when I got back. I've been doing two things: * Continuing the implementation of DBSP * Trying
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Had bunch of distractions like doing taxes this week, so I fell behind with the weekly update. However there's been progress these past two weeks. * Generated Deep Research report on the strengths and weaknesses of different notebooks * Learned about E-graphs and Interaction Nets * Read papers on Incremental Lambda
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Wrapped type computing Despite resisting the draw for writing an interpreter/compiler, I did think about it some more, and find it almost inevitable, given the properties and constraints that I have. I went back to the three things I want the notebook to do: reactivity, collaboration, and observability. Under
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I bit the bullet and went towards re-doing the effects system implementation with the native async/await, to see if I can get a baseline implementation. I was successful, but not without roadblocks along the way. This version is much simpler and handicapped than what I wanted originally, but I
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These weeks are passing by faster and faster. I too would like to work faster, but I didn't think vibe coding was going to be it. I work with AI everyday, and I mostly have it do basic debugging or explaining Python minutiae to me. I know what
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The work on the scheduler is to support async effect handlers. It won't do at all, if a network request always blocks the evaluation of the computational graph. So I embarked on it about two weeks ago. Right now, the API looks like this (subject to change). There&
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This was a tough past week. I was working on supporting async handlers, even if the reactive part was synchronous. I didn't want the graph to have to wait on network requests. This turned out to be tough. Also I got food poisoning this past week. The design
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I made good progress on the reactive engine. * Simplified the requirements for define a handler. * Started work on the scheduler * No longer need to handler to ability type checking method, by converting from protocols into abstract base classes. This was one example of where AI lead me astray. I also
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I spent a good part of this week writing and rewriting a blog post, as part of my commitment to publish more this year. I wrote about 10k words, only to keep about 4k of them. Must be the sunk cost fallacy, because it feels bad to delete entire pages
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With the release of OpenAI's Deep Research and its testimonies on Twitter, I finally slapped down $200/month for Pro subscription. Deep research is what I do all the time by hand anyway, I figured I'd get a lot of mileage out of it. The second
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I'd finished up looking into reactivity (with the exception of data type enforced reactivity, like differential dataflow), and I pulled out code with pedagogical sample code implementing signals and autotracking. * iamwilhelm/pedago_signals: A pedagogical implementation of hybrid push-pull signals in Python * iamwilhelm/pedago_autotracking: A pedagogical implementation