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Yusuf Chowdury

Yusufchy
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https://aipulsedaily.vionixai.tech/

AI & ML interests

AI agents, open-weight models, machine learning, MLOps, developer tools, AI automation, and AI-assisted publishing.

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repliedto RDTvlokip's post about 2 hours ago
I published a second article about a reader taking apart four of my numbers. He came back a fifth time, and went to the experiment that had never run. He found the one derived threshold in it was built on a sample maximum. He was right, and there was worse. That maximum cannot converge, because the codes I wanted to declare unreachable are themselves inside the null distribution. Its supremum is exactly the value I was excluding. So I ran the whole thing. Seven questions in one day, in a world of 27 referents small enough that the optimum, the null distribution and the gradient are computed rather than estimated. The certificate carrying the project does not survive two agents. A symmetry argument replaces it, with a corollary I did not expect: a free per-object embedding table cancels in advance anything the message structure could contribute. No training trick recovers it. You can check that by reading an architecture, without running it. Eight of my hypotheses died that day. Not one was an arithmetic error. Then I did the literature review, last, and found the argument published in 2021. The real lesson: Shrinking a world until everything is exact removes one class of mistake and leaves untouched the class that was doing the damage. Twenty minutes of searching would have saved a day of deriving. What caught my errors was never the exactness. It was an outside reader, a second route to the same number, and predictions written down before measuring. Code, every number including the ones I would have cut, and sixteen dated refutations 👇 🔗 https://huggingface.co/blog/RDTvlokip/i-made-my-world-small-enough-to-compute-everything 💻 https://github.com/RDTvlokip/RDTRL 📦 https://doi.org/10.5281/zenodo.21726216
repliedto dronefreak's post about 3 hours ago
🚀 Excited to open-source the SeaDronesSee Object Detection Model Zoo on Hugging Face. This release includes: - 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on SeaDronesSee, spanning nano through x-large YOLO variants plus RF-DETR Nano/Small/Medium. - 🌊 Benchmarked on SeaDronesSee's maritime search-and-rescue setting — swimmers, boats, jet skis, life-saving appliances and buoys captured by UAVs over open water, at varying altitudes and non-uniform image resolutions (1080p up to 4K+). - 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility. Headline numbers: - 🏆 Best mAP@50: 83.47% (RF-DETR Medium), 47.49% mAP@50:95, 87.01% precision. - ⚡ Best efficiency tradeoff: YOLOv26s hits 80.14% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~3 points of the top RF-DETR variant, while actually beating YOLOv11x's 74.82% mAP@50 using ~8.6x fewer FLOPs (196.0 GFLOPs). The goal is to make benchmarking and experimenting with maritime UAV perception easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench). Full credit for the underlying dataset goes to Leon Amadeus Varga, Benjamin Kiefer, Martin Messmer, and Andreas Zell (University of Tübingen, WACV 2022) — this release is an unofficial, YOLO-ready reformatting of their work (CC0-licensed), not a new dataset. If you're working on maritime search-and-rescue, UAV perception, autonomous drones, or real-time object detection, I hope these resources are useful. 📦 Dataset: dronefreak/SeaDronesSee 🤖 Model Collection: https://huggingface.co/collections/dronefreak/seadronessee-object-detection-model-zoo-6a7b030a25797e5dd2d70123 Feedback, bug reports, and contributions are always welcome.
repliedto SoulInPsyAbstract's post about 6 hours ago
Meta released Muse Glimmer 30B on Aug 10. We fine-tuned it the next day. Not the full-precision weights directly — the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data — it's that fresh. What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating — confident or hedged, doesn't matter which. Results on 20 held-out claims never seen in training: - base model: 0/20 - tuned: 20/20 Training: 472.5s, loss 0.799 → 0.086. Open-ended test (not multiple choice — the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time. Dataset: https://huggingface.co/datasets/SoulInPsyAbstract/specialist-cd-binary-honesty Adapter: https://huggingface.co/SoulInPsyAbstract/specialist-cd-muse-glimmer-lora Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model — one of them barely moved (base was already close to ceiling on this exact task). More on that soon.
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