One crop at a time though. A so-so classifier across 50 species is way less useful than a really good one for the thing you're actually growing.

Early on the photography thing was a real problem. Training data was mostly decent shots, then inference would come in as some blurry phone photo under purple LEDs.

Confident misclassifications. The fix wasn't clever - just more data that looks like how people actually take photos of their plants. Messy, badly lit, half the leaf out of frame. Once there was enough of that in the training set the models stopped caring about white balance. About 1.1 million augmented images now and light temperature just isn't a factor. No color card needed.

For tissue culture - I'd bet the multi-species part is what's killing you. I'd pick the single highest-value species, collect a probably-uncomfortable amount of well-labeled data for just that one, and see if things change. Right now you might not be able to tell what's a data problem vs a fundamental limitation, because the generalization overhead masks both.

I'm a software dev/data nerd, not a grower. I got interested because cannabis grow rooms are already full of automation - VPD controllers, pH/EC monitoring, dosing pumps, dimmable lights. But nothing was looking at the plant. Every sensor in the room measures the environment, not whether the plant is actually doing well. I wanted to add the eyes. And this seems to be a bound domain issue (i.e. limited number of issues/conditions/pests vs. all plants everywhere).

ViT-based multi-stage pipeline that verifies it's cannabis, classifies condition or pest, then runs nutrient subclassification if needed. 30 classes, 18ms inference, Go API, ONNX Runtime. Trained on a little over a million images from grower friends. Classification was 80% of the lift. I also shipped a Home Assistant integration - camera takes a scheduled snapshot, PlantLab diagnoses, HA acts on the result. No human involved.

Recently the part that's been the most fun is the autoresearch loop. Between training runs the system looks at its own confusion matrix, finds which classes it's mixing up, audits those training images for bad labels, and tells me what to fix. It's not fully autonomous yet but it's getting there - the model is increasingly debugging its own training data.

Solo project, <100 users, free tier is 3/day.

[1] I built a simple Android app for those who want to just try it out, it's on Google Store. Probably will make one for iOS too as time allows. https://play.google.com/store/apps/details?id=com.plantlab.p...

This looks promising! Is it for text based models only at this time (i.e. no vision/image training)?

https://github.com/bikemazzell/skald-go/

Just speech to text, CLI only, and it can paste into whatever app you have open.

Has anyone been able to set up Qwen3-Coder to run locally in agentic mode (via LM Studio or similar)? So far, I have only seen in work as Chat via Continue plugin. It gives reasonable suggestions, and it is supposed to be able to call tools, just haven't figured out how to make that happen yet.

Examples that failed: - opening hours for POI (restaurants, tourist attractions, etc) - mostly made up - GPS coordinates - produced results that were nearly 100% inaccurate - finding contact info (e.g. phone, email) for specific government or public bodies - nearly 100% inaccurate

The issue with above was mainly not a lack of results but rather fabricated/made up ones. As in: here are the coordinates (that don't correspond to actual locations) or here are the phone numbers of such and such departments (that don't exist), creating more work to try and discover they are nonsensical vs. just giving "no results found" message.

https://github.com/bikemazzell/whatsignal

I'm working on a WhatsApp to Signal relay. I.e.: whenever someone sends a WA message to you, it appears in your Signal. You can reply and it will go back to the original sender.

Why? I'm privacy conscious and don't fancy using a Meta product. But some of my friends/associates/family still insist on WhatsApp only. Running this WhatBridge service on my micro server behind a VPN allows me to communicate without having WhatsApp on my mobile.

Behind the scenes, it connects WAHA (https://github.com/devlikeapro/waha ) and Signal CLI (https://github.com/AsamK/signal-cli). Still early stages, but getting closer to a workable state.