Ask HN: What Are You Working On? (April 2026)

I am working on [1] a modernized open (AGPL) stack for interactive tutoring systems. SRS++, with hooks for defining your own pedagogical protocols over knowledge dependency graphs, Elo rating systems, etc, and with an eye toward gracefully differentiable curriculum that can hill-climb in terms of its efficacy.

With this stack, I'm scaffolding several (fingers crossed) commercial learning SaaS products. The first [2] is LettersPractice - a minimalist early literacy app that's family-first, in so far as it presumes an adult supervisor who co-learns strong confidence as a phonetic coach both at and away from the app. Putting considered rails on the parent-child reading experience.

The second set of apps is in music, with some experimental dev right now against piano (via midi devices), flute [3], aural skills, and sightsinging.

[1] https://github.com/patched-network/vue-skuilder , https://patched.network/skuilder

[2] https://letterspractice.com

[3] https://flutor.app/

[dead]

An open course on building high performance LLM inference engine! Hope to finish by the end of April

https://github.com/jmaczan/tiny-vllm

Python DSL for safe quantum programming that compiles to Qiskit, where the type system enforces coherence and ancilla cleanliness

https://github.com/binroot/b01t

Creating interactive pivot tables from large relational tables.

Many people know that a handy data analysis feature in Excel is to create a pivot table from a spreadsheet. But spreadsheets are limited to just a million rows. You can get around this limit by jumping through a bunch of hoops.

My system lets you easily create tables with thousands of columns and hundreds of millions of rows. (Just drop a CSV, Json, or other file on a window to create a table.)

Now you can create a pivot table from it with just a few clicks of the mouse. It is fast (I created a pivot table against an 8.5 million row table of Chicago crime data in less than a second.)

The resulting pivot table is interactive. Each cell (row/column intersection) has all the row keys mapped to it. Double-click on any cell and it will instantly show you all the rows in the original table that were used to calculate the cell. You can then analyze those rows further.

It also works well against much larger tables. I have tested it out against 25M, 50M, 100M, and 200M+ row tables.