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

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

#552

Post by Apanatshka »

I'm implementing different parsing algorithms (for programming language text to syntax tree purposes). There are so many papers published on different algorithms... I'm just slowly reading it all, and implementing the algorithms and improvement ideas, sometimes combining ones from different papers. At the moment I'm constraining myself to LR-based algorithms to not drown even more in the sea of publications.

I have a recursive ascent code generator with a bunch of optimisations that I wrote about [1,2]; it's a linear-time parser for LR(1) with reduced overhead. I have an RNGLR implementation (a polynomial-time parser for any context-free grammar), that's still a table-based interpreter like more LR-based parsers out there. I've extended that implementation with special code to handle cycles more efficiently. Some day, I'll take some time to write a paper on that and publish it. Currently, I'm trying to combine the two ideas and create a generalised recursive ascent code generator. If I succeed I'll write another blog post again, it's been a year since the last one...

[1]: https://blog.jeffsmits.net/optimising-recursive-ascent/ [2]: https://blog.jeffsmits.net/optimising-recursive-ascent-part-...

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

#553

Post by lucasgerads »

I am building a dev envirnmont to use Claude Code (or other agentic coding tools) for electronics development. I give Claude access to my lab equipment like my scope. That way I can let claude programm an MCU and at the same time verify that it creates the right output. Also, it can correlate spice simulations with the actual measurements. Which is often tedious to do "manuall" and verify how good my spice simulations are. I am going to record a longer demo, but here is very short video I made about it: https://lucasgerads.com/blog/lecroy-mcp-tutorial/

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

#555

Post by danfrost »

A couple of things: 1. data management for music publishers. This came out of nowhere but has got me back into coding, deploying, test. And I've got real lessons using AI-generated code that I wouldn't have got just reading other ppl's blog posts! We've been mapping all the data formats publishers work with, how to organise them better and build a suite of apps around it.

2. the other projects it the framework. Aside from the product itself, I've ended up with a really nice framework (FE and BE) and playbook for copilot to follow. I've hit multiple problems with AI generated code and had to rework it like I have for junior devs! But now, the framework focuses the work and stops the slop!

I want to build out all the product-dev-helper tools I've wanted in the past. I've already got a lovely schema-UI system, UI components which are data-aware and the basis of some low-ish-code tools. I've also nearly got a "run tests and fix" local LLM which saves tokens.

Really enjoying this.

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

#556

Post by shikaan »

A native application (Windows, Linux, MacOS) for music transcription.

It comes with time stretch and pitch shift as most of these softwares do, but it allows you to save loop regions and take notes. It's designed to be a practice session tool.

I'm doing it from first principles, and having fun writing GPU code, platform shims, and squeeze every ms I can to make it fast and smooth.

I will be looking for testers soon. If anybody is interested, hit me up.

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

#557

Post by seyz »

I'm working on RootCX (https://github.com/RootCX/RootCX), a platform for building and shipping internal apps and AI agents in production.

Think of it like "Claude code on Supabase", but for internal apps and AI agents.

I got tired of choosing the deployment platform, wiring up Postgres, SSO (OIDC), RBAC, audit logs, secret vaults, integrations/tools/MCP, ... from scratch every time I needed an internal tool.

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

#560

Post by abbadadda »

ComputerPoker.ai is a website where users can play simulated poker tournaments against GTO Bots to learn GTO poker strategy in a fun and low-risk environment.

My motivation for creating CompterPoker.ai was feeling a bit overwhelmed by some of the professional poker tools out there for learning GTO play. For some tools, learning how to simply operate the tool itself felt like a second job. With ComputerPoker.ai players can play against bots themselves simulating GTO play to learn what it "feels like" to play GTO vs. GTO opponents without having to turn any knobs or dials (feedback is real-time as you play).

The Beta tester code for HN Users is: HackerNews2026. All feedback is welcome! Please send suggestions for improvement or bugs to contact@computerpoker.ai or alternatively leave a comment below. Any questions I will do my best to answer.

As for the product offering the website is designed to teach players how to play optimal poker strategy (GTO) in simulated Texas Hold 'Em poker tournaments. Our value proposition is that if you can consistently beat the bots then you will fare well in live poker tournaments (of course adjusting for your opponents' play).

In addition to GTO pre-flop quizzes and pre-flop charts, users have the ability to simulate poker tournaments from start-to-finish and get feedback on their decisions _in real-time_ in a fun and low-risk environment.

For those interested the tech stack is Django deployed on AWS via Terraform and SaltStack, the database uses a Postgres RDS backend, and the frontend uses HTMX with WebSockets via Django Channels and Redis (Nginx serving as reverse proxy with CloudFlare DNS and SSL). During the project I used Claude Code to aid with various boilerplate aspects of the code base including building out the repos for Terraform and SaltSack and of course speeding up Django development.

Users are graded pre-flop based on the covered pre-flop scenarios (two-ways only for now). Post-flop users are graded on a residual MLP PyTorch model. We have built an in-house solver in Rust using the discontented CFR++ algorithm. The PyTorch model approximates GTO play post-flop (again only two-ways currently) based on training data with raises, EV, and realistic ranges for OOP and IP players. Because the post-flop decisions are based on a model that will always be a work in progress I refer to these decisions as GTOA (or "GTO Approximate").

Version 8 of the PyTorch model is the first one that I am happy with and actually find it quite difficult to play against. If you manage to beat the bots please do let me know how many tries it took! For those curious the PyTorch params for the most recent run are below (I trained on a gaming PC via Linux WSL2 using an AMD GPU).

The website is live in Beta mode as I gather feedback on how things are structured and work out any bugs/kinks. If you have any suggestions for improvements I’d love to hear them. Subscriptions are live so if anyone wanted to test the Stripe payment processing flow I certainly wouldn’t mind! ;-)

p.s. This is a side gig for me. I am currently looking for full-time work either fully remote or on-site based in London, UK (this LLC that runs ComputerPoker.ai operates out of USA but I am based full-time in the UK and authorized to work in both UK and USA). If you or someone you know is looking for a SRE with strong software engineering skills please let me know!

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