211–220 of 1001 posts
I hacked my old kindle and turned it into a eink dashboard for my daughter’s school! Now planning to enahace it a bit further and make it easy to customise. Here’s a a detailed write up of the process: https://samkhawase.com/blog/hacking-kindle/
Linux skills? Just the basics: cd, ls, mkdir, touch. Nothing too fancy.
As things got more complex, I found myself constantly copy-pasting terminal commands from ChatGPT without really understanding them.
So I built a tiny, offline Linux tutor:
- Runs locally with Phi-2 (2.7B model, textbook training)
- Uses MiniLM embeddings to vectorize Linux textbooks and TLDR examples
- Stores everything in a local ChromaDB vector store
- When I run a command, it fetches relevant knowledge and feeds it into Phi-2 for a clear explanation.
Full build story + repo here: https://www.rafaelviana.io/posts/linux-tutor
While Kubernetes offers power at the cost of complexity, Uncloud focuses on simplicity for common deployment workflows.
Progress from this month:
- Enhanced Docker Compose support: You can now deploy your entire stack from standard Docker Compose files. This includes volumes, environment variables, resource limits, scaling and logging configuration.
- Volume management: Create and manage Docker volumes across your cluster with automatic scheduling based on volume location.
- Context management: CLI command to quickly switch between multiple clusters, e.g. homelab and production one
If you're seeking something between "just Docker" and full Kubernetes for deploying applications on your own infrastructure, I'd love to get your feedback on Uncloud.
I love exploring data, but it always felt clunky to juggle multiple tools, write code/commads, just to import and query a dataset. While there are multiple GUI tools for databases, none are focused on raw data.
TextQuery is the tool I built to solve that. You can import CSV, JSON, and XLSX files and start querying them instantly using SQL. Want to create a chart? Just hop over to the Visualize tab.
I'm also rolling out a major update this week — adding tabs, filters, a redesigned UI, and keyboard shortcuts.
Looking ahead, I’m planning to expand support for more formats (like Parquet and ORC) and data sources (like Postgres and BigQuery), so you can import data from anywhere, and query it right from your desktop. Something like a local data warehouse. With Apple Silicon, the capability of a desktop can make it very cost-efficient compared to something like BigQuery, Snowflake, or Athena.
Happy to hear any feedback!
It only stores (timestamped) floating point values with a series id and uses a B+Tree as the backing data structure. Querying is done with a lisp-like query language.
It works well but copying/pasting back and forth gets old very fast, and it would be better if the process was done inside the word processor. For some reason (various reasons) I still use Office 2003, which doesn't have any AI feature. (It does have a "translate" function but it's awkward to use and not very good.)
So I wrote a macro to send selected text to OpenRouter and replace with the response (with a system prompt that says to only output the translation, otherwise most models start with "Here's the translated text:")
I had never written a macro in vba; I got started with Sonnet and adjusted many parts with the help of StackOverflow (which turns out to have more information about vba than Sonnet...)
And finally it worked; and it turns out to be an incredible boost to translation productivity!
Key features:
• Built-in speaker and LED for alerts
• Preloaded with 66,000+ known speed camera locations (more to come)
• Easy updates via drag-and-drop on your PC
It’s been a fun project to build, and I’m excited to see it help drivers stay away from speeding tickets while keeping their data private.
I’d love to hear your thoughts or suggestions!
I am working on the sunflower plant density estimation problem. The goal is to be able to estimate the germination rate as early as possible. Farmers benefit from such information, because: - there are lots of expenses still to be made (fertilizer, pesticide, salaries), which may not be worth it if germination is under certain threshold - if detected early, there is still time to plant another grain or to fill up the…
Yield prediction is huge indeed, because overshooting your prediction means seller stuff for a lower price. Undershooting means paying for someone's product to make up for the difference. Probably there's quite a bit of matchmaking in between those under and overshooters and someone making a good buck out of that too.
I don't expect to place competitively but I learn a lot from these competitions. I like competitions like this that are connected to physical problems and datasets (though sadly this one is largely simulated), I learn as much about the broader world as I do deep learning. I've always idly wondered how seismology worked, and now I have an excuse to dig into it.
It's also given me a greater appreciation for the "seismology" we perform in our day to day, like knocking on things to see if they're hollow, or (as I learned here a while back) the way battitores test the porosity of cheese by knocking on it with hammers.