261–270 of 922 posts
For example, you can search "cheese", and it'll show you results for cheese on Google. If you search "!b cheese", it'll search "cheese" but on Bing instead. "@yt !b cheese" will search "site:youtube.com cheese" on Bing.
I built it mainly so I wouldn't blow through my 100 Kagi trial searches quite so quickly.
I am working on HN wrapper which summarize topic discussions and outline main themes of it https://hn-distilled.fly.dev/ Recently added Similar Topics feature which uses Scikit-learn's TF-IDF vectors
I'm still working on Habitat. It's a free and open source, self-hosted social platform for local communities. The plan is for it to be federated, but that's a while off yet. I recently spoke with a Lemmy developer who gave me some advice on making it easy for anyone to host. I was struggling with the mess of supporting both docker and VM hosting. He told me that Lemmy uses ansible provisioning to install docker compo…
I recently made a little tool for people interested in running local LLMs to figure out if their hardware is able to run an LLM in GPU memory. https://canirunthisllm.com/
There are plenty of apps that do outfit tracking, with some basic stats. But they all have a few or more of these shortcomings (from my perspective); unpleasant UI, no cross device syncing, lack of detailed usage statistics (e.g. cost spread over time by garment category), some categories just not supported, pushing a specific lifestyle such as Capsule Closets, or just plain focused on recommending what to wear using some mediocre algorithm that doesn't understand cuts and how different pieces fit together; basically only suitable for capsule collections.
These apps all have a lot of downsides too in common, which I haven't been able to solve either yet; ultimately you must start with an inventory of your clothes, and then work from there. It takes ages to catalog and import your clothes, and I haven't found many existing product that lets you export if you've even done it before. And on top of that, you have to be quite rigorous at tracking what you wear; the more data you have the more insight you can get from your choices.
I finally published on iOS a couple of months ago. No traction, and I don't expect there to be. I won't argue that my offering is better than any of the competition, but I've tried most of them (and wasted colossal amounts of time onboarding onto them) and found none fit my need properly. It's still very much work in progress, but I find myself reaching for it multiple times per week to inform my purchasing habits.
As part of this effort, I have developed a preliminary prototype (named deLLMiter) that I am refining.
This method is founded upon the hypothesis that there exists a correlation between first-order expressions and more complex, hierarchical forms of expression (https://glthr.com/llm-delimiters-and-higher-order-expression...).
With this project, my goal is to improve the security of LLM models.