841–850 of 1001 posts
It should be drop in semantic search for any text. No need to worry about what models, what database, how the data is processed, dealing with performance concerns. None of that. Just vector search.
I would say Photoshop is awesome but expensive (if you can look past how invasive it is for your machine), Affinity is free but "meh", I'm going for the "awesome and cheap" square of the quadrant. Find it at https://skullrocksoftware.com
Conversions can be anything from going from one page to another or getting a user to submit the form. I am using it on my other website which generates >100k page views per month and it generated 70% valid suggestions which i used to improve the things.
Most engineers running Linux in production aren't kernel developers. Keeping up with kernel changes is hard, and unexpected kernel behavior silently impacting production systems happens more often than it should.
So I wanted a way for those engineers to scan through kernel changes quickly. Not just what lines changed, but what the code actually does, why it matters, and what it means for real systems. Something closer to what art museum docents do.
7,000+ commit analyses and release notes for the 7.0-rc series are available. Release notes for 7.0 stable are in progress.
PlantLab ( https://plantlab.ai ) - AI plant health diagnosis for cannabis. It's an API, not an app [1]. Photo in, structured JSON out - condition, confidence, growth stage, nutrient lockout analysis. The response is for machines. Light burn at 0.92 confidence? Your controller dims the light. Calcium deficiency with excess potassium flagged as the lockout cause? Dosing pump adjusts. I'm a software dev/data nerd, not a…
I've been thinking about similar systems for tissue cultures but I can't seem to find a way to generalize and still get good training data or effective results. Once you lose track of white balance, species, optical clarity and distortion from the vessel, etc... Results decline quite a bit in my experience. It makes it a neat yet fairly useless system outside of itself.
Granted, I have no idea what I'm doing and these could be solvable problems. Certainly much easier to solve by focusing on a single species.
I'm impressed with how well it classifies based on the image examples. A little over a million images is probably what makes it possible. My experiments have been much smaller. Maybe with more material I could overcome those limitations I mentioned, but I have a feeling the multi-species pipeline really drags it down.
Have you found that light temperature no longer skews feedback after so much training data? For me it really matters, causing classification to confuse light sources with actual plant condition (hence the colour card for white balance helping so much)
Building Braindump AI a simple privacy first voice to text PWA that runs entirely in the browser with zero API costs. Speak and it auto sorts into tasks, ideas and reminders. On the side, thinking a lot about digital identity and naming strategy for AI tools especially how founders can build trust signals from day one as things scale toward enterprise.
Would be interested to hear what others are wrestling with on the branding or infrastructure side
I believe anyone can learn to type fast - I think it just takes the right tools to make it interesting enough for the users to use consistently