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Software Architecture Guide. By Martin Fowler.
It's 1:30 am and I've nothing better to do, so here we go:
* stereo vs. mono: yes, if you have stereo, use stereo. If you're stuck with mono and have some compute to spare, use an ML depth model to get approximate depth. Accuracy at this stage is overrated. I'll all get bundle-adjusted anyway.
* feature tracking: search vs. KLT. Both have upsides and downsides. The best systems use a hybrid. Search is often too expensive and depends on detection, which is often brittle -- but can save your butt in many edge cases. KLT is fast & robust but bare KLT is not very accurate and also drifts over time. I hope to be doing one or more posts on this very topic hopefully soon.
* pose estimation: directionally correct but there's a world of best practices to make this fast & robust. People have written entire PhD theses about this. Topic for another post.
* KF-based map expansion: yes that's best practice. But KF-selection based on "every few meters" is instant game over. Lots of cases and edge cases that need to go into a suitable heuristic.
* CUDA kernels for stereo matching: serious overkill. Matching few hundred features on CPU takes at most a couple milliseconds if implemented right.
* Local BA: 12 KFs is kinda arbitrary. Might work well for KITTY but not generalize.
* Eval on KITTY: that's easy-tier: camera always upright. No pure rotations. Very controlled motion. Very large field of view. Drone datasets are where the rubber meets the road.
* Performance: 9 FPS on RTX 3050. NGL, that is brutally, ludicrously slow. Us old-schoolers did realtime visual SLAM 20 years ago on ~ 1/1000th of the compute budget.
As a designer, I truly dislike moodboards, specifically moodboards that are just full of end work from the same industry (ie. moodboards of UIs from other products).
If you are working on a dashboard and you collect inspiration from other dashboards you are just copying the result, not the thinking.
Better off collecting from nature, editorial, motion, unrelated industries, etc.
sam3.cpp - Meta's SAM 3 in pure C++ with @ggerganov's ggml
- Supports SAM 3.1, 3, 2.1, 2 and EdgeTAM
- FP16, 4-bit quant (EdgeTAM in 15 MB)
- Apple Metal GPU, CUDA, CPU
- Text-prompted: "peach" → every peach
- Single-file C++14
Performance-wise:
- 100ms object detection, segmentation
- Video object segmentation @ 20FPS on M4 Pro with EdgeTAM
https://github.com/PABannier/sam3.cpp
recommended reading.
Signs of vibe coded UI [X Article - 8 minute read]
Claude no longer generates purple gradients. AI's design quality has been improving in general, but there are still plenty of bad patterns in vibe-coded UIs. The article covers: Color (Homogenous goo, overuse of gradients/shadows), Visual Assets (Simple icons in rounded squares, overuse of emojis), Typography (Excessive serif fonts, Glassmorphism), Visual Hierarchy (Excessive nested layers), and Animations (Unnecessary or broken).