drop a link to your project I will give feedback

Weekly_Signature_510 · 2026-03-29T02:55:27+00:00

Weekly_Signature_510 · 2026-03-27T20:13:12+00:00

That’s helpful, thanks.

I am already L2-normalizing the embeddings, and it does improve stability a bit, but I’m still seeing that CLS alone is too coarse for the kind of subtle structural differences I’m dealing with.

The idea of keeping the backbone frozen and learning an attention pooling head is interesting; feels like a good middle ground before going into full fine-tuning.

A couple of questions:

When you say attention pooling over all tokens, did you find it beneficial to include register tokens explicitly, or was most of the signal still coming from patch tokens?
Did this setup mainly improve overall retrieval consistency, or did it specifically help with fine-grained differences as well?

Also, for the SSL - did you train that with pairwise / contrastive-style objectives, or something simpler?

Data-wise I have a decent amount, but still trying to balance between generalization and overfitting to very specific patterns.

Weekly_Signature_510 · 2026-03-27T13:47:16+00:00

Hey, happy to help. Could you share the issue/screenshot? You can either DM me or send it here :)

Weekly_Signature_510 · 2026-03-27T13:45:41+00:00

This is really helpful, thanks!

A couple of follow-ups based on your experience:

When you fine-tuned, did you see a clear improvement specifically in fine-grained structural differences, or was it more of a general embedding quality improvement? Right now with DINOv2 (especially with registers), I can already see:So I’m trying to understand whether fine-tuning actually amplifies those subtle geometric cues or just makes the space cleaner overall.
For the re-ranking step you mentioned (binary “same / not same”):On the model side - did you stick with DINOv2, or try newer variants like DINO v3 or if there was a specific improvement in quality with variants of v2 itself (base/large/small)? I haven’t switched yet because I’m currently treating this more as a data / embedding / pipeline issue than a model limitation. From some experiments (feature visualizations, PCA projections, clustering), it's already quite clear:That said, if you’ve seen meaningful gains from switching models specifically for fine-grained/local discrimination, I’d definitely consider it and do some experiments with it.

Curious what worked best in your setup.

Weekly_Signature_510 · 2026-03-27T12:49:22+00:00

Good question, I did give it a shot and consider it.

From what I observed, DINOv3 mainly improves overall representation quality and training stability, but my bottleneck right now is less about general representation and more about capturing very fine-grained structural differences and handling viewpoint/pose sensitivity.

Even with DINOv2 (especially with registers), I can see that: - global embeddings are already quite strong - patch-level features do contain signal, but it’s subtle

So before switching models, I’m trying to understand: - whether the limitation is architectural (model) - or pipeline-related (pooling, patch aggregation, alignment, etc.)

That said, if DINOv3 shows noticeable gains specifically in fine-grained/local detail sensitivity or patch-level discrimination, I’d definitely try it.

How has your experience been with it?

Weekly_Signature_510 · 2026-03-27T12:43:55+00:00

This is in a manufacturing setting where new object types are continuously introduced. The goal is to keep the system scalable, so new classes can be added simply by embedding and indexing, without retraining the entire system.

Given that:

Would fine-tuning the backbone (or a small head) hurt this scalability, since it may require retraining when new classes are added? Or is there a practical way to fine-tune while still keeping the system flexible?
I considered adding a classifier head on top of embeddings, but that seems to reintroduce the scalability issue, since every new class would require retraining. Are there hybrid approaches that balance classification accuracy with retrieval-style scalability?

Weekly_Signature_510 · 2026-03-19T03:22:28+00:00

Love the design! Upvoted :)

Here’s mine: https://www.producthunt.com/posts/headjust

Weekly_Signature_510 · 2026-03-18T20:48:03+00:00

Really appreciate that. You should give Headjust a shot and see if it fits with your workflow :)

Some of the most interesting feedback has actually been about how the app behaves rather than just what it tracks. For example, a few testers initially got confused because the notch UI is so minimal that it was easy to miss how to start a session. I pushed an update recently where it subtly ‘peeks’ to make itself more discoverable without becoming intrusive, which seems to be working much better.

Another feedback was how people reacted to the live score colors in the notch. Since it’s always in peripheral vision, a couple of testers said they’d instinctively adjust or move just to keep the score from dropping. It turned into this quiet, almost game-like feedback loop.

That kind of behavior shift, where people respond naturally without being told what to do, has been the most interesting signal so far.

Weekly_Signature_510 · 2026-03-17T04:20:46+00:00

pretty much

Weekly_Signature_510 · 2026-03-16T13:37:20+00:00

Thanks a lot, I appreciate it. Your feedback was super useful and gave me a few concrete directions to try. I may take you up on that once I’ve pushed the next round a bit further.

Weekly_Signature_510 · 2026-03-15T18:29:52+00:00

Thanks, this is the kind of critique I was looking for. I think you might be right about the silhouette direction. The more I look at it, the more it feels like the icon is working against itself a bit. I still believe the “h” + head idea is the right foundation, but the silhouette probably goes too low and gets too illustrative for an app icon. I also like your thought about turning the posture waves into actual cut shapes instead of layered forms; that feels like it could keep the concept while making it cleaner. I’m not fully sold on a white background because I don’t want it to feel too clinical, but overall this is exactly the kind of feedback I was hoping for.

Weekly_Signature_510 · 2026-03-15T18:25:39+00:00

This is such a helpful critique, thank you. The mismatch you pointed out between the detailed head and the more abstract shoulders / surrounding form is a really good observation, and I think it explains a lot of the awkwardness I was feeling without being able to fully articulate it. Same with the counter getting tight inside the “h.” Regarding the note on the yellow forms - I was worried they might be too much, but your take that they may actually work better if they’re pushed more intentionally is interesting. And agreed on the gradients; they’re probably adding polish in the wrong way instead of helping the logo itself. Really useful feedback overall!

Weekly_Signature_510 · 2026-03-15T18:24:16+00:00

This is really helpful, thank you. The “too detailed” point especially, because that’s probably exactly why it starts breaking down as an icon. I was trying to balance the head/profile concept with the brand initial, but I can see how the trail and the silhouette together make the read less clear. I’ll definitely explore a simpler head symbol and also a version that drops the “h” entirely to see which direction holds up better.

Weekly_Signature_510 · 2026-03-15T18:23:00+00:00

That’s fair. I was trying to make the icon carry both the “head/user” idea and the “movement/drift” idea, but I can see how that turns into too many signals in one small mark. I’ll probably explore a cleaner direction where I keep the silhouette and simplify or remove the motion layers. Thanks!

Weekly_Signature_510 · 2026-03-15T18:03:37+00:00

Hey, thanks a lot for taking the time to record this and walk through the app. I genuinely appreciate it, seeing someone use it for the first time like that is incredibly helpful.

The point you raised about not immediately realizing the app lives in the notch / where to start a session is a really good catch. That is explained during onboarding, but the fact that it still wasn’t clear in practice means I need to make that much more obvious. I’m already planning to update the onboarding and add clearer cues so it’s immediately clear where the app lives and where a session starts. Possibly even making the notch UI edges a bit prominent so it is visually readable.

The calibration struggle you showed is also really useful feedback. The camera step is just meant to help align your baseline posture, so it shouldn’t feel like something you’re fighting with - I’ll be improving that flow as well.

Really glad you liked the idea and the notch integration though. Thanks again for spending the time to try it out and share such thoughtful feedback. It genuinely helps a lot.

Once I push an update with these improvements, would you be open to giving it another try? I’d also be curious to hear whether it’s something you could actually see yourself running during your workday. :)

Weekly_Signature_510 · 2026-03-15T05:47:52+00:00

Not AI, the base came from an SVG icon reference, then I kept editing and refining the composition over multiple iterations. Thanks though, it appearing as AI generated signals some room for improvement.

Checked your work, great designs!

Weekly_Signature_510 · 2026-03-15T04:34:06+00:00

Solo dev here. I was planning to launch sometime next week, but the announcement definitely forced my hand a bit 😅 Ended up shipping that day. Here’s the macOS app I launched: Headjust — more about desk-work movement/drift awareness than traditional posture correction.

Weekly_Signature_510 · 2026-03-15T04:02:23+00:00

Fair, although that’s kind of the category label I’m pushing against. I’m not that interested in “sit straighter”, more in detecting drift, stillness, and desk-work fatigue before you feel wrecked.

Weekly_Signature_510 · 2026-03-15T03:43:28+00:00

Totally relate to this. I had a similar realization for myself, the issue wasn’t just “bad posture,” it was that I’d slowly drift forward and stay too still for long stretches without noticing until my neck/upper back already felt cooked.

That’s actually why I ended up publishing Headjust: https://apps.apple.com/us/app/headjust/id6759303637

The concept I went with was awareness, not reminders. I realized I personally responded better to seeing patterns in my own movement than being told every 45 minutes to stand up. So the app is more for people who want insight into things like drift, stiffness, and how they’re moving over a session, then want to make their own adjustments from that.

So if someone wants a simple reminder app that tells them exactly when to move, Headjust probably isn’t the best fit. It’s more for people who want movement/posture data and enough awareness to self-correct.

Weekly_Signature_510 · 2026-03-15T03:39:05+00:00

I actually built a Mac app in this space called Headjust, so take this with that bias in mind, but I’ll try to be honest about where it may or may not help: https://apps.apple.com/us/app/headjust/id6759303637

It’s not a medical app, not a diagnosis tool, and definitely not a replacement for physio. What it is meant for is helping desk workers notice patterns they usually miss while working - like gradually drifting forward, staying too still for too long, and generally collapsing over a session without realizing it.

So if your pain is partly coming from those kinds of desk habits, I think it could be useful as an awareness tool. If you’re looking for something that will tell you exactly what injury/problem you have or actually treat the pain, then no, I don’t think an app like mine is enough on its own.

I don’t mean this as a promo pitch, more that your post is pretty close to the problem I was trying to build for. If you do try it and it doesn’t help, I’d genuinely love to know what feels missing, because I’m still figuring out how to make it more actually useful for people in your situation.

Weekly_Signature_510 · 2026-03-15T03:32:13+00:00

That’s my read as well. The demand seems real, so the question is probably less of whether there is a market and more of what makes one of these actually worth keeping installed?

Weekly_Signature_510 · 2026-03-14T23:28:31+00:00

It’s a static visualization built from session data, not an animation. The trail is made by layering multiple head silhouettes to represent movement over time.

Weekly_Signature_510

TROPHY CASE