#AI

i always copy paste the same prompt to see if it repeats weird stuff tbh i kinda just hit send rn is that normal or am i being weird

I finally got my AI model to recognize patterns in a decent amount of time. It's not perfect but it's a start. I've been working on it for a while now and it's nice to see some progress. Maybe someone can gives me some tips on how to improve it?

so i keep asking chatgpt for new recipe ideas and it keeps giving me weird combinations like avocado + chocolate cake?? tbh i feel like i’m messing up the kitchen vibe rn is this a thing? do other chefs actually rely on ai for cooking? i’m not sure if i’m overthinking it or if it’s just a trend anyone else feel the same??

just tried to use this ai tool to automate some meal prep planning and it keeps suggesting the same 5 recipes over and over.. like whats the point of ai if its just gonna be that limited??

tbh i'm still fuming about the rate limit issues with openai's model.. i was trying to do some devops work and it just kept throttling me anyone else have this problem? seems like a total productivity killer to me imo it's wild that they cant just set the limits higher or something.. is it really that hard to scale??

i just made a simple ai to suggest flour ratios for sourdough but it keeps saying 12% water?? i think it's a bug lol anyone else see this or maybe it's just how ai thinks of hydration?? help???

quick poll rn. i’m trying out ai to figure hydration, autolyse, starter feeds etc. tested three:

• gpt‑4: concise steps, pretty solid ratios
• llama‑2‑13b: sometimes bizarre like 95% humidity lol
• stable diffusion prompt: spits out bread pics but no actual numbers which gave the most on‑point dough advice? vote + drop a comment if you’ve used another model or have tips

i’m curious if ai actually helps in the kitchen or just spams nonsense tbh

I’ve been training a GPT‑4 model on a dataset of honeycomb images and colony health metrics. The results are promising, but I’m stuck at the evaluation stage. Also, I’m looking for a position in an apiary tech startup or a research lab that blends AI with apiculture.

Here’s what I did:

• Collected 12,000 labeled images of honeycomb cells, annotated with health indicators (cancerous, varroa infestation, brood pattern anomalies).
• Preprocessed with ResNet50 as a feature extractor, then fed into a GPT‑4 encoder‑decoder architecture for sequence‑to‑sequence predictions of health scores.
• Used cross‑entropy loss and a custom F1 macro metric for multi‑label classification.
• Fine‑tuned on 80% of the data, validated on 10%, tested on 10%.

Problems:

• Overfitting: training loss drops to 0.02 but validation stays at 0.35.
• Attention maps are noisy; unclear which cells the model focuses on.
• I tried a Vision Transformer (ViT) backbone, but it didn’t improve performance.

What I need:

• Advice on regularization techniques (dropout, weight decay) that work with transformer‑based models.
• Tips for visualizing attention over honeycomb cells.
• Any job openings or research projects that need AI expertise in beekeeping.

If you can help, I’ll gladly share my code or dataset. Also, if you know someone hiring for a data scientist in apiary tech, let me know.

so ive been trying to integrate some basic ai concepts into my papercraft projects rn, like the raspberry pi 4 model b is pretty cool for this stuff but idk how to make it work with my designs lol. been thinking about using some ai poweredmicrocontrollers like the esp32 but not sure where to start, doesnt help that im more of a baking guy than a coding one tbh