We are building end-to-end accessibility compliance tool[1] that will take care of auditing, remediation, verification and generation of ACR/VPAT.

Because of the well bound nature of the problem space, we are able to unlock a lot of power from LLMs and put together a good end-to-end product that delivers the promise.

Still early days. I know there are lot of folks who care about a11y. I would love to chat and learn from your experience.

[1] https://workback.ai/

My friend and I working on PennyPost, financial analyst that solely focuses on your spending.

It looks at your spending across all your accounts, categorizes, identifies patterns, trends, runs predictions and sends weekly/monthly summary email.

No apps or dashboards, just insight.

Plug in once and forget. Takes 5 minutes to keep track of your spending.

We are still at early stages but you can check it out here

https://pennypost-landing.vercel.app/

Appreciate any feedback you might have!

I am finally making my own blog. I have been only planning for ages. I found that I had a lot to say for the past years working on AI, and I want to record them somewhere. I do not expect a lot of visitors or at all in fact. The blog is going to be just for me to remember stuffs and to keep track of them.

I am using hugo to build suckless static pages. LLM helped me so that I don't need to read all their docs. I haven't finished it yet nor posted a single blog. But there will be one soon.

I’ve been reverse-engineering an obscure childhood game called Entomorph:

https://github.com/entomorph/reverse-engineering

I started the project when ChatGPT 4 was first released, using it as a way to explore what LLMs could actually do. I also find working on it very relaxing, there is something cool about uncovering secrets hidden in code for more than twenty years.

I'm thinking a lot about the ARC-AGI ML benchmarks, especially the "shape" of the dataset and what that says about how it should be solved. I think there's good reasons to believe that deep learning - at least differentiable SGD backprop style - is a bad fit for this specific benchmark, due to the tasks being almost entirely discrete symmetries, and also having so little data to approximate the discrete symmetries with continuous ones (considering deep learning to be the learning of continuous symmetries). I think that a more explicit and discrete approach is the way to go, and it's possible to build something surprisingly general and not heuristic-based even without gradient descent, guided by minimum description length to search for both grid representations and solver functions. I'm looking for teammates for ARC-3 so hit me up if this sounds interesting, I'd love to chat!

I made a viewer on my website to build intuition for my preferred perception algorithm which is entropy filtering + correlation. Pretty neat to check out the heatmaps for random tasks, there is a lot of information inherent in the heatmap about the structure of the task: https://synapsomorphy.com/arc/

I’m working on a performance capture library for Python because I often need to know the performance of backend systems I maintain. I frequently build tooling to capture performance and save it for later analysis. I/O operations get costly when writing lots of data to disk and creating good real-time analytics tools takes a lot of my time. I wanted a library that captures real-time performance analytics from Python backends.

https://github.com/jakeroggenbuck/kronicler

This is why I wrote kronicler to record performance metrics while being fast and simple to implement. I built my own columnar database in Rust to capture and analyze these logs.

To capture logs, `import kronicler` and add `@kronicler.capture` as a decorator to functions in Python. It will then start saving performance metrics to the custom database on disk. You can also use the middleware for FastAPI.

You can then view these performance metrics by adding a route to your server called `/logs` where you return `DB.logs()`. You can paste your hosted URL into the settings of usekronicler.com (the online dashboard) and view your data with a couple charts. View the readme or the website for more details for how to do this.

I'm still working on features like concurrency and other overall improvements. I've added a lot since the last time I shared on HN. I would love some feedback to help shape this product into something useful for you all.

Thanks! - Jake

I wanted to see if I could use generative AI to build a whole business. Not just a digital product or an app — but the entire business from end to end:

(Spoiler: I did manage it and launched in just 75 days from start to first order)

- Three tier corporate structure with manager-managed LLCs and a private WY LLC as manager, complete with a knowledgebase-powered assistant that can write share registries, banking resolutions, meeting minutes, contribution contracts, loans and more

- Supply chain management with proprietary lot tracking that tracks PO line items from production to delivery

- Generated the base for all product images, helped write and research label design and text, wrote SEO titles and product descriptions

- Used Claude Code to build the entire Shopify theme for the site, all collections, product pages, legal pages and a COA database to boot

- Used Claude Code to build a custom Shopify app to integrate lot tracking into the shop so that when lots sell out the next lot is queued for sale and all lot-related metadata is synced to the product variant and displayed on the product page

- used Claude Code to build a super analytics platform that combines the data from GA4, Shopify orders, and Meta business suite into a single feature store where I can wrangle the data to ask/answer any question I can dream of about audience segments, product popularity, what’s working or not, and get insights on what to do next

If you care to check it out, the site is https://cosmicpeptides.com

Currently building a suite of media inspection and encoding tools for video engineers: https://video-commander.com.

Still very much a work in progress, but expecting to release a first version by end of year. Built on Tauri, in case anyone is curious.

I've created various open-source and commercial tools in the multimedia space over the last 10+ years and wanted to put it all together into something more premium.

I'm working on Habitat. It's a free and open source, self-hosted platform for communities to discover and discuss their local area. The plan is for it to be federated. I've recently solved an issue with cron jobs that was driving me mad for ages. I feel that I'm pretty much nearing a first tagged release, but I feel that I need to work on branding and messaging a bit before I do. I can't tell if I'm procrastinating that final push to something that makes it more official or not.

- The idea: https://carlnewton.github.io/posts/location-based-social-net...

- A build update and plan: https://carlnewton.github.io/posts/building-habitat/

- The repository: https://github.com/carlnewton/habitat

- The project board: https://github.com/users/carlnewton/projects/2

I am working on a SQL query engine for multi-dimensional and hierarchical analysis compatible with Apache Spark, ClickHouse, BigQuery, Snowflake, PostgreSQL and DuckDB. GitHub https://github.com/squashql/squashql Website https://www.squashql.io/