This looks very good from the screenshots. I will try this as quickly as I can. I have been building with egui and have tried Iced, Slint and Makepad.
I built (agentic coded) a stocks viewer app for Indian stock market data: https://github.com/brainless/Indistocks. It was a fantastic experience as to how easily I could build a GUI app.
My main product also uses egui: https://github.com/brainless/nocodo. It used to have a web app frontend and I moved to desktop app after the experiment with Indistocks. The experience has been really good, also coded with agents.
Desktop apps are fun and even on my somewhat old and slow laptop (i5 8th gen, 16 GB RAM, 2GB nvidia dedicated graphics), they are so much faster than web apps on Chrome (on Linux). I want desktop apps to make a big comeback, we could use so many old devices.
It is not easy to support multiple providers at each layer of our tech stack but if we do not then a few players become easier to pick and then they monopolize.
I am trying to stay vendor neutral with my own coding agent (1). To approach this, I created a desktop app that connects to coding agent running on either my own infra or yours (local or your cloud server). Desktop app and coding agent are separate binaries.
If you host on your own infra then you can bring your own AI provider too. Similarly, I want to give choice for git host. Right now I am targetting GitHub but I want to add Gitlab soon after MVP. All this has made the path to my MVP longer but I see a clear long-term aim for myself - we should have choices.
This is built with Rust, egui and SQLite3. The app has a downloader for NSE India reports. These are the daily end of day stock prices. Out of the box the app is really fast, which is expected but still surprises me. I am going to work on improving the stocks chart. I also want to add an AI assisted stocks analyst. Since all the stocks data is on the SQLite3 DB, I should be able to express my stocks screening ideas as plain text and let an LLM generate the SQL and show me in my data grid.
It was really interesting to generate it within 3 days. I had just a few places where I had to copy from app (std) log and paste into my prompt. Most of the time just describing the features was enough. Rust compiler did most of the heavy lifting. I have used a mix of Claude Code and OpenCode (with either GLM 4.5 or Grok Code Fast 1).
I have been generating full-stack web apps. I built and launched https://github.com/brainless/letsorder (https://letsorder.app/). Building full-stack web apps is basically building 2 apps (at a minimum) so desktop apps are way better it seems.
In the long-term, I plan to build and help others generated apps. I am building a vibe coding platform (https://github.com/brainless/nocodo). I have a couple early stage founders I consult for who take my guidance to generate their products (web and mobile apps + backend).
I tried a graph based approach in my previous product (1). I am on a new product now and I came back to SQLite. Initially it was because I just wanted a simple DB to enable creating cross-platform desktop apps.
I realized LLMs are really good at using sqlite3 and SQL statements. So in my current product (2) I am planning to keep all project data in SQLite. I am creating a self-hosted AI coding platform and I debated where to keep project state for LLMs. I thought of JSON/NDJSON files (3) but I am gravitating toward SQLite and figuring out the models at the moment (4).
1. Previous product with a graph data approach https://github.com/pixlie/PixlieAI
2. Current product with SQLite for its own and other projects data: https://github.com/brainless/nocodo
3. Github issue on JSON/NDJSON based data for project state for LLMs: https://github.com/brainless/nocodo/issues/114
4. Github issue on expanding the SQLite approach: https://github.com/brainless/nocodo/issues/141
Still work in progress, but I am heading toward SQLite for LLM state.
My implementation was custom, on top of RocksDB. I found it hard to ask LLM to traverse it. While understanding schema of SQLite or making queries to find information is very easy for LLMs. In most cases schema does not have to be inferred since it is going to be available and this makes the job easier. The graph approach may work well for many use-cases but if we want to store structured information for LLMs then SQLite is really good.
This is terrible lack of planning. I understand the need for the US to be strict about people entering the country. They have a right to choose but this is so mismanaged.
There are so many active H-1B visa holders, now everyone is just anxious. The rules can start for new visa applications. For existing holders, there should be a time period where people can figure out if the employer is even able to pay.
If this stays in effect for existing visa holders and the employers cannot pay in time or wants to change the contract, the individuals and their families are stuck. Plus, employees probably lose their job if employer cannot pay the fees.
The strength of a theory lies in its predictive capacity. I used to think people who said “the cruelty is the point” were hyperbolic, but the predictive capacity of that theory is doing pretty well these days.
I have worked for many US based startups, all remotely. Timezone difference (I am in India) is a big issue unless the company is very well structured to work asynchronously.
Companies hiring top talent may still hire with a $100K additional charge but even at $250K - 400K salaries, this is a lot of additional cost.
Here the author has a passion/side project they have been on for a while. Upgrading the tooling is a great thing. Community may not support this since the niche is too narrow. LLM comes in and helps in the upgrade. This is exactly what we want - software to be custom - for people to solve their unique edge cases.
Yes author is technical but we are lowering the barrier and it will be lowered even more. Semi technical people will be able to solve some simpler edge cases, and so one. More power to everyone.
This is the best thing engineers can do. I moved to building as a solo founder. I am building an LLM enabled coding product and I teach. I'm hosting a session on Claude Code today, 134 guests signed up. I'm gradually planning to make money teaching for a few months while building the product.
I built (agentic coded) a stocks viewer app for Indian stock market data: https://github.com/brainless/Indistocks. It was a fantastic experience as to how easily I could build a GUI app.
My main product also uses egui: https://github.com/brainless/nocodo. It used to have a web app frontend and I moved to desktop app after the experiment with Indistocks. The experience has been really good, also coded with agents.
Desktop apps are fun and even on my somewhat old and slow laptop (i5 8th gen, 16 GB RAM, 2GB nvidia dedicated graphics), they are so much faster than web apps on Chrome (on Linux). I want desktop apps to make a big comeback, we could use so many old devices.