ai-chat
❓This is post is the result of a Socratic conversation I had with GPT-4o about what "syntax represented by the free monad for a functor that provides a signature." meant. After the question and answering, I asked it to write an outline of all that I learned and
I almost never remember what I did in a year. Having written reviews for 2021 and 2023, I've found it helpful to both have a record to look back on as well as a point to review and reflect. I think this should be a habit every quarter,
ai-chat
❓Oleg Kiselyov has a post and corresponding talk, "Having an Effect", about how to represent side effects in a language. While I watched the talk, I conversed with GPT4o about any questions I had. At the end, I had it write a blog post summarizing our conversation, with
lab notes
I'm a little indisposed this week, but I did find some time here and there to do research into effects. I spent the time reading and watching a talk about effects by Oleg Kiselyov. The short version is that side-effects can be formulated as a communication protocol between
lab notes
I'm back, after a little hiatus from writing the notes. Last time, I discovered that I should have been looking more deeply into other reactive models before I embarked on a functional component (React-like) model to model data pipelines. The impedance mismatch should have been indicative that something
lab notes
Last week, I started looking at Signals. When I stepped back and looked at the entire front-end ecosystem, it seemed like many of them outside of React (Solid, Vue, Svelte, Preact), converged on some variation of Signals. There is even a Javascript proposal to include Signals in the language. Given
lab notes
One of my main goals is to make non-CRUD backend pipelines more visible. I didn't think nodes and wires programming was the way to go, but unconsciously, I envisioned just showing the results of a cell next to the cell, like regular computational notebooks. But my underlying assumption
Essays
🔰If CRDTs (Conflict-free Replicated Data Types) are new to you, check out this previous post. There's a simple way to understand CRDTs: It leverages algebra to unmix the inevitable mixing of data when syncing over an unreliable network. Why are networks unreliable? For one, it'd be
lab notes
I've been a bit anxious about the pace of progress these last three weeks. I wanted to strike a balance between making practical progress and having a sound basis. This last week, I struggled with breaking from structured programming. * What do algebraic effects buy you? * What is the
startups
Despite the disdain for "GPT-wrappers," it's currently where the value is accruing in the supply chain of value to the end-user with LLM-driven apps. Unless something changes with the foundational model companies approach the market, the GPT-wrapper application layer is where the value accrues, hence where
lab notes
This past week, I worked through more confusion on my part. * Consolidated the representation of an algebraic effect with the React-like components (Fibers) * Worked out a solution for resuming fibers after getting a result back from from the algebraic effect. (Then block) * Use a data description of all cells (Epx)
lab notes
Last week, I merged the runtime worker into the kernel worker. Originally, I wanted that to run multiple kernels at once, so users can run languages in a mix of cells, or they can run more than one cell in parallel. It was too hard to trace execution over a