{ "source": "https://github.com/pctablet505/pctablet505.github.io/blob/main/PROFILE.md", "format": "https://github.com/pctablet505/pctablet505.github.io/blob/main/docs/PROFILE-FORMAT.md", "identity": { "handle": "pctablet505", "name": "Rahul Kumar", "headline": "Big models, small places, real numbers", "lede": "The model you need rarely fits where it has to run, and the number you are shown is rarely the one that decides. I work on both: vision and sensing on phones, large models on one GPU, and automated trading after costs and tax.", "focus": [ "LLM inference", "Computer vision", "On-device AI", "Automated trading" ], "location": "India", "email": "work.rahul126@gmail.com", "links": [ { "label": "GitHub", "url": "https://github.com/pctablet505", "priority": 1 }, { "label": "LinkedIn", "url": "https://www.linkedin.com/in/pctablet505/", "priority": 1 }, { "label": "DEV", "url": "https://dev.to/pctablet505", "priority": 2 }, { "label": "Medium", "url": "https://medium.com/@pctablet505", "priority": 2 }, { "label": "Hashnode", "url": "https://hashnode.com/@pctablet505", "priority": 2 }, { "label": "Kaggle", "url": "https://www.kaggle.com/pctablet505", "priority": 2 }, { "label": "X", "url": "https://x.com/pctablet505", "priority": 3 } ], "photo": null, "resume": null, "languages": [ "English", "Hindi" ], "proof": [ "glm53-one-gpu.decode", "damaged-barcodes.read-rate", "drop-detection.parameters", "rl-alpha-labs.tiers" ] }, "about": "I make models fit where they have to run, then check what they really do.\n\n### What I work on\n\n#### Large models and fast frameworks\nI serve a language model bigger than my GPU's memory from that one GPU. In open-source Keras, I made the PyTorch backend\nfaster and added one call that turns a trained model into a file a phone can run. See\n[GLM-5.3-Flash on one GPU](/work/glm53-one-gpu/), [Keras on PyTorch](/work/keras-pytorch-speed/) and\n[LiteRT export](/work/keras-litert-export/).\n\n#### On-device vision and sensing\nThese models run on the device itself, with no server to lean on. They read badly damaged barcodes and recognise many\nfaces at once. From touch and motion, they can tell which hand holds a phone, who is using it, and when it is falling.\nSee [damaged barcodes](/work/damaged-barcodes/), [face recognition](/work/face-recognition/) and\n[drop detection](/work/drop-detection/).\n\n#### Projects of my own\n[RL Alpha Labs](/work/rl-alpha-labs/) is automated trading on Indian stocks: an idea stays only if it\nbeats an index fund after costs and tax. [Porchlamp](/work/porchlamp/) ranks open-source projects by what happens to a\nstranger's pull request, not by their stars.\n\n### How I work\n\n- **Measure first.** I profile before changing anything, change one thing at a time, and time the commit before and\n after.\n- **Report the number that decides.** For trading, returns after costs and tax; for speed, the setting that ships, not\n the best run.\n- **One cause per change.** Fixes go upstream as small pull requests, each easy to review and easy to revert.\n- **Write down what failed.** Every closed idea and dead end keeps its reason, so nobody has to find it out twice.\n\nThe lamp in the logo stands for that last habit: a light left on for whoever checks next.", "experience": [ { "id": "google", "title": "Software Engineer, Machine Learning at Google", "priority": 1, "summary": "Now on XProf, the open-source profiling tool for machine learning. Before that, on Keras and KerasHub.", "one_line": "Works on XProf, the open-source ML profiler. Before that, open-source Keras and KerasHub work, including LiteRT export, Llama 3.1 and PyTorch-backend speed.", "org": "Google", "title_field": "Software Engineer, Machine Learning", "location": "India", "start": "2025-02", "end": "present", "skills": [ "Keras", "KerasHub", "LiteRT", "TensorFlow", "PyTorch", "JAX", "profiling", "Python" ], "links": [ { "label": "XProf", "url": "https://github.com/openxla/xprof" } ], "profile_line": 70 }, { "id": "qualcomm", "title": "ML Engineer, Computer Vision at Qualcomm", "priority": 2, "summary": "On-device AI for Snapdragon phones.", "one_line": "Put camera and generative-imaging models on the Snapdragon NPU and DSP.", "org": "Qualcomm", "title_field": "ML Engineer, Computer Vision", "location": "Hyderabad, India", "start": "2024-09", "end": "2025-01", "skills": [ "quantization-aware training", "INT8", "pruning", "Hexagon DSP", "Snapdragon NPU", "TFLite", "C++", "Android" ], "profile_line": 86 }, { "id": "samsung", "title": "Research and Development Engineer at Samsung R&D Institute India", "priority": 1, "summary": "Computer vision, biometrics and on-device AI, plus the apps, portals and servers around them.", "one_line": "Built computer-vision, biometric and motion-sensor systems that run on phones, from damaged-barcode reading to face recognition and drop detection.", "org": "Samsung R&D Institute India", "title_field": "Research and Development Engineer", "location": "Noida, India", "start": "2022-07", "end": "2024-08", "skills": [ "computer vision", "biometrics", "sensor fusion", "TFLite", "OpenCV", "Android", "Kotlin", "Python" ], "profile_line": 100 }, { "id": "samsung-intern", "title": "Intern at Samsung R&D Institute India", "priority": 3, "summary": "Learned Android in Java and Kotlin, and joined full time in July 2022.", "one_line": "Ran quantized image models on Android phones and trained generative image models.", "org": "Samsung R&D Institute India", "title_field": "Intern", "location": "Noida, India", "start": "2022-01", "end": "2022-06", "skills": [ "MobileNet", "EfficientNet", "NNAPI", "TFLite", "CycleGAN", "PyTorch", "Android", "Java", "Kotlin" ], "profile_line": 114 } ], "work": [ { "id": "damaged-barcodes", "title": "Reading damaged barcodes on phones", "priority": 1, "summary": "Knox Capture lets a phone do an enterprise barcode scanner's job. On industrial sites the codes are blurred, partly\ncovered, seen at an angle or shining under lights, and a scanner that reads the wrong number is worse than one that\nreads nothing. I built the part that reads damaged codes on the phone, and knows when to give up.", "parts": [ { "name": "Problem", "md": "Industrial barcodes arrive blurred, partly covered, at an angle or under glare. A plain decoder either fails or\nreturns a wrong code, and a wrong code is the costly failure." }, { "name": "Approach", "md": "- **Data first.** A synthetic-data engine writes random strings that follow each format's rules and draws them as\n barcodes. Then it damages them on purpose: blur, lines, spots, glare, reflections, tears and scratches, each drawn\n from a probability model. Perspective, curvature and lighting make each one a camera view, blended onto photos of\n real objects. 10M+ images in all.\n- **Labels that stay true.** The hardest part: every transform also moves the labels, the bounding box and the\n barcode's outer and inner rectangles. Algebra and trigonometry set how far a code can turn, move or zoom before a\n label leaves the picture.\n- **A chain of small models.** YOLO finds the barcode, Mask R-CNN cuts it out, a U-Net trained pixel to pixel restores\n the damaged bars, a classifier names the format among 10+ 1D formats, then an open-source decoder reads it.\n- **Knowing when to stop.** Every read carries a confidence score. A wrong read costs far more than no read, so a code\n beyond recovery is refused instead of guessed.\n- **On the phone.** Android, the hardware-accelerated camera and TensorFlow Lite, mostly on a Galaxy S21 FE: each model under\n 1 MB, under 100 ms per scan for an undamaged code and up to 350 ms for a damaged one." }, { "name": "Results", "md": "On the damaged synthetic test set, 90%+ of codes were read correctly, where a commercial scanning SDK read 70%. Real\nphotos were checked too, by hand, on a smaller set. Refusing hopeless codes cut wrong reads." } ], "one_line": "Read 90%+ of damaged barcodes correctly on its damaged test set, where a commercial scanning SDK read 70%, with models trained on 10M+ synthetic images.", "org": "samsung", "area": "vision", "status": "built", "start": "2022-07", "end": "2024-08", "skills": [ "YOLO", "Mask R-CNN", "U-Net", "ResNet", "synthetic data", "OpenCV", "TFLite", "Android", "Kotlin", "Python" ], "numbers": [ { "id": "read-rate", "value": "70% → 90%+", "what": "damaged barcodes read correctly on the damaged synthetic test set, a commercial scanning SDK against this reader", "status": "reported", "source": "owner" }, { "id": "synthetic-images", "value": "10M+", "what": "synthetic training images", "status": "reported", "source": "resume" }, { "id": "formats", "value": "10+", "what": "1D barcode formats", "status": "reported", "source": "owner" }, { "id": "scan-clean", "value": "under 100 ms", "what": "per scan of an undamaged code", "status": "reported", "source": "owner" }, { "id": "scan-damaged", "value": "up to 350 ms", "what": "per scan of a damaged code, depending on the damage", "status": "reported", "source": "owner" }, { "id": "model-size", "value": "under 1 MB", "what": "per model, on a Galaxy S21 FE", "status": "reported", "source": "owner" } ], "demo": "barcode-reader", "profile_line": 130 }, { "id": "face-recognition", "title": "Recognising many faces at once on edge devices", "priority": 1, "summary": "Recognises several faces at once in live camera frames, video and photos, on Android, Windows and Linux, for access\ncontrol. I replaced a face detector that missed faces, trained a classifier on a custom set of face embeddings, and moved\nface search from a linear scan to an O(log n) index: 1,200 ms to 87 ms per lookup across 10,000+ registered users,\nat 99.2% accuracy and 30+ FPS.", "parts": [ { "name": "Problem", "md": "Access control needs every face in the frame recognised while people walk past, on modest edge hardware. The detector\nin place missed faces, and searching a growing set of registered people one by one got slower with every person added." }, { "name": "Approach", "md": "- **Find every face.** A different face detector, so the faces the old one missed were found.\n- **Know the person.** A custom set of face embeddings, and a classifier trained on it.\n- **Search in steps, not one by one.** Face search moved from a linear scan to an O(log n) index, so a lookup grows with\n the logarithm of the number of people, not with the number itself.\n- **Run anywhere.** Android, Windows and Linux, with Python, OpenCV and ONNX.\n- **Test on live feeds.** Photos are the easy case. Front and back phone cameras and video each behaved differently,\n so the engine was checked on live camera feeds and recordings, not only on photos.\n- **One engine, many apps.** A desktop app finds every registered face in photos and video, and an Android app does it\n on the live camera. The exam-proctoring platform is one app built on it. I was the engine's only researcher and\n developer." }, { "name": "Results", "md": "99.2% accuracy at 30+ FPS on edge devices, and 1,200 ms to 87 ms per lookup across 10,000+ registered users." } ], "one_line": "A multi-face recognition engine at 99.2% accuracy and 30+ FPS on edge devices, with face search cut from 1,200 ms to 87 ms.", "org": "samsung", "area": "vision", "status": "shipped", "start": "2023-03", "end": "2024-08", "skills": [ "face detection", "face embeddings", "YOLO", "ONNX", "OpenCV", "embedding search", "Python", "Android" ], "numbers": [ { "id": "accuracy", "value": "99.2%", "what": "recognition accuracy on our own test set", "status": "reported", "source": "LinkedIn" }, { "id": "fps", "value": "30+ FPS", "what": "frames per second on edge devices", "status": "reported", "source": "LinkedIn" }, { "id": "lookup", "value": "1,200 ms → 87 ms", "what": "time to search the face index once", "status": "reported", "source": "LinkedIn" }, { "id": "users", "value": "10,000+", "what": "registered users", "status": "reported", "source": "LinkedIn" } ], "demo": "face-attention", "profile_line": 201 }, { "id": "exam-proctoring", "title": "An AI exam-proctoring platform (Satyapan)", "priority": 2, "summary": "An in-house platform for proctored exams: an Android app for candidates, a web portal for examiners, portals for HR and\na central server. Face-recognition sign-in, alerts when someone else appears on camera (with safeguards against people\nwalking past), detection of talking, shuffled questions and options, and a locked-down kiosk mode. It won the MD\nAppreciation Award.", "one_line": "An anti-cheat exam platform used for 2,000+ employees across 100+ tests, with 60% less administrative overhead.", "org": "samsung", "area": "vision", "status": "shipped", "start": "2023-07", "end": "2023-10", "skills": [ "face recognition", "gaze detection", "Android", "Kotlin", "Java", "Python", "Flask", "Django", "SQLite" ], "numbers": [ { "id": "employees", "value": "2,000+", "what": "employees who took exams on it", "status": "reported", "source": "LinkedIn" }, { "id": "tests", "value": "100+", "what": "tests run on it", "status": "reported", "source": "LinkedIn" }, { "id": "overhead", "value": "60%", "what": "less administrative overhead", "status": "reported", "source": "LinkedIn" } ], "related": [ "md-appreciation-award", "attention-detection" ], "profile_line": 259 }, { "id": "attention-detection", "title": "Telling attention from distraction, with emotion recognition", "priority": 2, "summary": "Reads facial expression, eye movement, yawns and head pose, and confirms a signal across several frames before raising\nan alert. Emotion recognition is a feature of the same app. Used in the exam-proctoring platform.", "one_line": "Detects whether a person at a screen is attentive or distracted, at 87% accuracy; checks across frames cut false alarms by 40%.", "org": "samsung", "area": "vision", "status": "shipped", "start": "2023-09", "end": "2023-11", "skills": [ "face landmarks", "head pose", "eye tracking", "OpenCV", "Python", "Kotlin", "Android" ], "numbers": [ { "id": "accuracy", "value": "87%", "what": "attention-monitoring accuracy", "status": "reported", "source": "resume" }, { "id": "false-alarms", "value": "40%", "what": "fewer false positives from multi-frame validation", "status": "reported", "source": "LinkedIn" } ], "related": [ "exam-proctoring" ], "demo": "face-attention", "profile_line": 293 }, { "id": "npu-inpainting", "title": "Generative inpainting on a phone's NPU and DSP", "priority": 2, "summary": "Object removal and image completion, reworked until the models met the phone's latency budget on the NPU and DSP, for\nthe Snapdragon Summit demo.", "one_line": "Redesigned a generative inpainting feature, object removal and image completion, so its models fit NPU and DSP latency budgets for the Snapdragon Summit demo.", "org": "qualcomm", "area": "vision", "status": "demo", "start": "2024-09", "end": "2025-01", "skills": [ "generative inpainting", "Snapdragon NPU", "Hexagon DSP", "quantization", "Android" ], "profile_line": 321 }, { "id": "camera-pipelines", "title": "Camera ML pipelines in C++", "priority": 3, "summary": "Camera pipelines for detection, tracking and segmentation, built in C++ for mobile, and Android demos of GPU, DSP and NPU\ndelegation. Put ML inference inside the native camera stack for a camera assistant you control by voice.", "one_line": "Detection, tracking and segmentation pipelines in C++ with INT8 quantization and pruning, and ML inside the native camera stack for a voice-controlled camera assistant.", "org": "qualcomm", "area": "vision", "status": "built", "start": "2024-09", "end": "2025-01", "skills": [ "C++", "INT8", "pruning", "object tracking", "segmentation", "Android camera stack" ], "profile_line": 336 }, { "id": "document-restoration", "title": "Shadow removal and document clean-up", "priority": 3, "summary": "Research prototypes that clean up photographed documents: shadows lifted, noise removed.", "one_line": "Prototyped shadow removal and noise reduction for document scanning on phones.", "org": "samsung", "area": "vision", "status": "research", "skills": [ "image restoration", "OpenCV", "NumPy" ], "profile_line": 351 }, { "id": "gaze-correction", "title": "Gaze correction for video calls", "priority": 3, "summary": "Moves the eyes toward the camera so a video call feels like eye contact.", "one_line": "A prototype that shifts the eyes toward the camera in video calls, using facial landmarks and geometric transforms.", "org": "samsung-intern", "area": "vision", "status": "prototype", "start": "2022-01", "end": "2022-06", "skills": [ "facial landmarks", "dlib", "OpenCV", "geometric transforms" ], "profile_line": 363 }, { "id": "on-device-classification", "title": "Real-time image models on Android", "priority": 3, "summary": "Quantized models on the phone through NNAPI, at 25+ FPS.", "one_line": "Real-time classification and detection on Android at 25+ FPS with quantized MobileNet and EfficientNet models through NNAPI.", "org": "samsung-intern", "area": "vision", "status": "built", "start": "2022-01", "end": "2022-06", "skills": [ "MobileNet", "EfficientNet", "quantization", "NNAPI", "TFLite", "Android" ], "numbers": [ { "id": "fps", "value": "25+ FPS", "what": "frames per second on the phone", "status": "reported", "source": "LinkedIn" } ], "profile_line": 377 }, { "id": "generative-image-models", "title": "Generative image models", "priority": 3, "summary": "Image-to-image translation, avatar generation and style transfer, then generative face models.", "one_line": "Trained CycleGAN models in PyTorch for image-to-image translation and avatars, deployed with TFLite, and generative face models with StyleGAN and StarGAN.", "org": "samsung-intern", "area": "vision", "status": "built", "start": "2022-01", "end": "2022-06", "skills": [ "CycleGAN", "StyleGAN", "StarGAN", "PyTorch", "TFLite" ], "profile_line": 397 }, { "id": "milap-design", "title": "MILAP: reuniting lost people at mass gatherings", "priority": 3, "summary": "Design research, no code: offline-first so it keeps working when networks fail, an opt-in gallery, and a person who\nchecks every match before anyone is told.", "one_line": "An offline-first design for reuniting lost people at the Kumbh Mela, with face and body re-identification, an opt-in gallery and human-verified matches.", "org": "personal", "area": "vision", "status": "research", "start": "2026-07", "skills": [ "face recognition", "person re-identification", "privacy by design", "system design" ], "profile_line": 411 }, { "id": "shadow-removal", "title": "Shadow-Removal", "priority": 3, "summary": "Classical image processing only: integral images, adaptive thresholds, and an iterative split of shading from\nreflectance.", "one_line": "Removes shadows from document photos with adaptive thresholding and iterative shading and reflectance decomposition, without deep learning.", "org": "personal", "area": "vision", "status": "published", "start": "2022-10", "skills": [ "OpenCV", "NumPy", "image processing" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/Shadow-Removal" } ], "profile_line": 425 }, { "id": "object-detection-tutorial", "title": "Object detection tutorial", "priority": 3, "summary": "Teaching material: the parts of an object detector, built up on a dataset of drawn shapes.", "one_line": "A notebook and a synthetic shape dataset that teach box regression and classification from scratch.", "org": "personal", "area": "vision", "status": "published", "start": "2025-07", "skills": [ "object detection", "synthetic data", "teaching" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/object-detection-tutorial" } ], "profile_line": 441 }, { "id": "behavioural-identification", "title": "Who is holding the phone? Behavioural identification", "priority": 1, "summary": "An Android service that works out in real time whether the phone is in the left or right hand, then combines that with\nswipes, taps, walking pattern and other sensor signals to keep checking who is using the phone. When the pattern looks\nwrong it can restrict access: against theft, or when a colleague or family member picks the phone up. It names the\nhand 90%+ of the time over 30+ seconds of use, and about 87% over shorter windows, without deep learning.", "parts": [ { "name": "Problem", "md": "A phone checks who you are once, at unlock. After that, whoever holds it has the same access: a thief, or a colleague or\nfamily member who picks it up." }, { "name": "Approach", "md": "- **The first signal: the hand.** An Android service tells, in real time, whether the phone is in the left or the right\n hand.\n- **More signals from the same person.** Swipes, taps, walking pattern and other sensor data, collected from several\n users, describe how each person handles the phone.\n- **Keep checking.** Identification runs continuously; when the pattern does not match the owner, access can be\n restricted.\n- **No deep learning.** The models are small enough to run all the time on the phone." }, { "name": "Results", "md": "90%+ accuracy at telling the hand over 30+ seconds of use, and about 87% over shorter windows, without deep learning.\nUsed in a deliberately odd way, to break it, it could still be fooled in some cases." } ], "one_line": "Told which hand holds a phone, right 90%+ of the time over 30+ seconds, and who holds it, without deep learning.", "org": "samsung", "area": "sensing", "status": "shipped", "skills": [ "behavioural biometrics", "sensor fusion", "anomaly detection", "Android" ], "numbers": [ { "id": "hand", "value": "90%+ over 30+ s", "what": "accuracy at telling which hand holds the phone, without deep learning", "status": "reported", "source": "owner" }, { "id": "hand-short", "value": "87%", "what": "the same, about, over shorter windows", "status": "reported", "source": "owner" } ], "demo": "which-hand", "profile_line": 456 }, { "id": "drop-detection", "title": "Phone drop detection in under 22 parameters", "priority": 1, "summary": "Detects a drop, estimates how far the phone fell, and sorts the surface into a hard surface, a soft bed or another\nsurface. It also tells a real drop from a phone tossed in the air and caught. The deep-learning model it replaced was\naccurate but heavy; features from the motion sensors alone and a small statistical model did the job and saved over\n95% of the power, at a small cost in accuracy (97% to 95.2%).", "parts": [ { "name": "Problem", "md": "A drop detector has to listen to the motion sensors all the time, so every bit of compute costs battery. The existing\nmodel had millions of parameters." }, { "name": "Approach", "md": "- **Use the physics.** A falling phone reads close to zero g until it hits something; how long that lasts gives the\n height, and the impact says how hard the surface was.\n- **Engineer the features.** Sensor fusion turns the motion signals, and nothing else, into a handful of features\n that carry those facts.\n- **Not every fall is a drop.** A phone tossed in the air and caught is in free fall too, but a catch slows it gently,\n with no impact spike, so it is not counted.\n- **Keep the model tiny.** Small statistical models on those features, under 22 parameters in all." }, { "name": "Results", "md": "From millions of parameters to under 22, saving over 95% of the power. Accuracy went from 97% to 95.2%: under two\npoints traded for a model small enough to run all the time." } ], "one_line": "Replaced a deep-learning drop detector of millions of parameters with sensor features and a model of under 22, saving over 95% of the power.", "org": "samsung", "area": "sensing", "status": "shipped", "start": "2023-03", "end": "2023-05", "skills": [ "sensor fusion", "feature engineering", "tiny models", "statistical models", "Android", "Kotlin", "Python" ], "numbers": [ { "id": "parameters", "value": "millions → under 22", "what": "model parameters", "status": "reported", "source": "owner" }, { "id": "power", "value": "95%+", "what": "power saved", "status": "reported", "source": "owner" }, { "id": "accuracy", "value": "97% → 95.2%", "what": "accuracy before and after", "status": "reported", "source": "owner" } ], "profile_line": 500 }, { "id": "crash-detection", "title": "Car crash detection with automatic SOS", "priority": 2, "summary": "The phone reads its accelerometer, gyroscope, pressure and sound together. A statistical model with heuristics sorts\nnormal driving, rash driving and the extreme cases that could be crashes, and was tuned against each kind of false\nalarm. Sound and pressure catch an airbag opening; the motion sensors catch a roll, a fall or toppling.\n\nReal crash data is rare, so crashes were simulated with a toy car carrying a phone, and everything that is not a crash\nwas collected for real: rash driving, thrown phones, loud music, films.", "one_line": "A phone that detects a car crash from its sensors and sends an SOS to chosen contacts.", "org": "samsung", "area": "sensing", "status": "built", "start": "2022-11", "end": "2024-08", "skills": [ "sensor fusion", "statistical models", "rare-event data", "Android" ], "profile_line": 550 }, { "id": "glm53-one-gpu", "title": "GLM-5.3-Flash on one GPU", "priority": 1, "summary": "A 181 GiB model does not fit on a 96 GiB GPU, so most of its experts live in host memory and are read over PCIe as\nthe tokens need them. Three changes took decoding from 10.8 to 15.0 tokens/s as shipped, 16.5 tokens/s at peak, on one\nworkstation card.", "parts": [ { "name": "Problem", "md": "GLM-5.3-Flash is a mixture-of-experts model with 320B parameters, 18B of them active per token: each layer has 288\nexperts and uses 8 per token. In 4-bit NVFP4 it is 181 GiB; the card has 96 GiB. Plain offloading works, but every\ntoken waits on expert weights crossing PCIe." }, { "name": "Approach", "md": "- **Read experts in wide blocks.** Narrow, scattered loads across PCIe were the slow part. A gather kernel, a small GPU\n program that reads wide and contiguous, took decoding to 13.5 tokens/s.\n- **Keep the hot experts on the card, not the first ones.** In each layer, 35% of the experts get 74% of the picks. So\n more of the model moved off the card (105 → 125 GiB in host memory) and the freed memory became a cache of the\n most-used experts: 16.5 tokens/s at peak. The shipped setup caches fewer experts per layer (54 instead of 66) to\n leave room for long prompts: 15.0 tokens/s.\n- **Read long prompts in groups.** A long prompt touches nearly every expert; staging them 64 at a time took prompt\n reading from 614 to 1,750 tokens/s." }, { "name": "Results", "md": "Decoding went from 10.8 to 15.0 tokens/s as shipped, and 16.5 tokens/s at peak; prompt reading from 614 to 1,750\ntokens/s. Most numbers are single runs on one machine, with about ±1 token/s of spread." }, { "name": "What did not work", "md": "- Using fewer experts per token reached 20.48 tokens/s, but the output got worse, so it is off.\n- Eight bugs stood between a fresh checkout and correct output. One gave no error at all: the model repeated the same\n token forever.\n- One known output defect is not fixed. It affects every NVFP4 mixture-of-experts checkpoint made with the same\n quantization tool, not only this one." } ], "one_line": "Served a 181 GiB mixture-of-experts LLM from one 96 GiB GPU and took decoding from 10.8 to 15.0 tokens/s shipped, 16.5 at peak.", "org": "personal", "area": "performance", "status": "published", "date": "2026-09-02", "skills": [ "LLM inference", "mixture of experts", "CUDA kernels", "expert caching", "vLLM", "NVFP4" ], "numbers": [ { "id": "fit", "value": "181 GiB model, 96 GiB GPU", "what": "model size against GPU memory", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "decode", "value": "10.8 → 15.0 tokens/s", "what": "decoding speed, from plain offloading to the shipped setup", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "peak", "value": "16.5 tokens/s", "what": "decoding at peak", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "gather", "value": "13.5 tokens/s", "what": "decoding with the gather kernel alone (13.53)", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "skew", "value": "35% → 74%", "what": "share of experts that get this share of the router's picks", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "prefill", "value": "614 → 1,750 tokens/s", "what": "prompt reading, about (1,771 and 1,729 logged)", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-26" }, { "id": "offload", "value": "105 → 125 GiB", "what": "model weights kept in host memory, before and after making room for the hot-expert cache", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-27" }, { "id": "fewer-experts", "value": "20.48 tokens/s", "what": "decoding with fewer experts per token, which hurt the output and is off", "status": "checked", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-27" } ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/glm53-flash-single-gpu" }, { "label": "The eight bugs", "url": "https://github.com/pctablet505/glm53-flash-single-gpu/blob/main/RUNNING-GLM53-ON-ONE-GPU.md" } ], "related": [ "glm53-hot-expert-cache", "note-glm53-eight-bugs", "servedeck" ], "chart": { "title": "Decode speed, tokens per second", "source": "https://github.com/pctablet505/glm53-flash-single-gpu", "as_of": "2026-10-27", "note": "Most are single runs, with about ±1 token/s of spread. Using fewer experts per token hurt the output, so it is off.", "bars": [ { "label": "Plain offloading", "value": 10.77 }, { "label": "+ gather kernel", "value": 13.53 }, { "label": "+ hot-expert cache, peak", "value": 16.5 }, { "label": "As shipped", "value": 14.99 }, { "label": "Fewer experts (not used)", "value": 20.48, "off": true } ] }, "profile_line": 569 }, { "id": "keras-litert-export", "title": "One call from a Keras model to a phone: LiteRT export", "priority": 1, "summary": "One call turns a trained Keras or KerasHub model into a `.tflite` file a phone can run, first for the TensorFlow\nbackend and then for models trained on PyTorch. I wrote the keras.io guide for it.", "parts": [ { "name": "Problem", "md": "Keras had no single call that produced a LiteRT file for a phone: conversion went through separate TensorFlow tooling,\nand models trained on the PyTorch backend had no Keras path at all." }, { "name": "Approach", "md": "- `model.export(\"model.tflite\", format=\"litert\")` in Keras ([keras#21674](https://github.com/keras-team/keras/pull/21674), merged December 2025), and the same for KerasHub models ([keras-hub#2405](https://github.com/keras-team/keras-hub/pull/2405), January 2026).\n- The same call for models trained on the PyTorch backend ([keras#22758](https://github.com/keras-team/keras/pull/22758), May 2026).\n- Fixes to the KerasHub layers that blocked export, one model family at a time.\n- The keras.io guide, [Exporting Keras models to LiteRT](https://keras.io/guides/litert_export/), merged July 2026." }, { "name": "Results", "md": "The export path is merged in Keras and KerasHub and documented on keras.io. An Android app of mine walks the whole path,\nfrom one export call to models running on a phone with no server." }, { "name": "What did not work", "md": "A one-call export of KerasHub LLMs to LiteRT-LM bundles was built and proposed upstream, but not merged." } ], "one_line": "Built the LiteRT export path in Keras and KerasHub: a trained model to a phone-ready file in one call, on TensorFlow and PyTorch.", "org": "google", "area": "performance", "status": "merged", "start": "2025-12", "end": "2026-07", "skills": [ "Keras", "KerasHub", "LiteRT", "TFLite", "TensorFlow", "PyTorch", "model export" ], "links": [ { "label": "keras#21674", "url": "https://github.com/keras-team/keras/pull/21674" }, { "label": "keras-hub#2405", "url": "https://github.com/keras-team/keras-hub/pull/2405" }, { "label": "keras#22758, PyTorch backend", "url": "https://github.com/keras-team/keras/pull/22758" } ], "related": [ "keras-io-litert-guide", "keras-ondevice-android", "kerashub-litert-compat" ], "profile_line": 674 }, { "id": "snapdragon-speed", "title": "3x faster models on the Snapdragon NPU and DSP", "priority": 2, "summary": "Models tuned for the phone's NPU and DSP ran 3x faster, and quantization-aware training cut the memory bandwidth of\nhigh-fidelity image-processing models by 75%.", "one_line": "Made deep-learning models run 3x faster on the Snapdragon NPU and DSP, and cut memory bandwidth 75% with quantization-aware training.", "org": "qualcomm", "area": "performance", "status": "built", "start": "2024-09", "end": "2025-01", "skills": [ "Snapdragon NPU", "Hexagon DSP", "quantization-aware training", "INT8", "TFLite", "C++" ], "numbers": [ { "id": "speed", "value": "3x", "what": "faster inference on the NPU and DSP", "status": "reported", "source": "LinkedIn" }, { "id": "bandwidth", "value": "75%", "what": "less memory bandwidth for image-processing models", "status": "reported", "source": "LinkedIn" } ], "profile_line": 711 }, { "id": "keras-pytorch-speed", "title": "Keras on PyTorch: compiled generation 11.6x faster", "priority": 1, "summary": "Keras runs on PyTorch, but on small models it ran about 9x slower than the same model in plain PyTorch. I traced the\ncost to Python work Keras repeated on every call, opened [keras#22561](https://github.com/keras-team/keras/issues/22561)\nwith the root causes, and landed the fixes as small pull requests, one cause each. Each was timed on the commit just\nbefore and just after its merge.", "parts": [ { "name": "Problem", "md": "Most of each call went to Keras's own Python: building traceback details that were only needed when an op failed,\nre-setting layer state, checking dtypes and inputs. `torch.compile` could not help either. Graph breaks split every\nforward pass and compiled steps kept recompiling, so compiling a Keras model made generation slower than running it\neagerly." }, { "name": "Approach", "md": "- Counted every Python call with cProfile and every `torch.compile` graph break with `torch._dynamo.explain`, on the\n issue's two models: a small CNN and a two-layer transformer language model.\n- Split one large fix into small pull requests, one root cause each, each with tests and a before-and-after benchmark.\n- Three merges made `torch.compile` work with Keras: one removed the graph break in attention, one made the PyTorch\n tree code traceable, and one stopped compiled steps from recompiling.\n- Most fixes changed shared code (`Layer.__call__`, op tracebacks, dtype handling, input checks), so the JAX,\n TensorFlow and NumPy backends gained too." }, { "name": "Results", "md": "By the progress report of October 18, 15 pull requests were merged. Between Keras master on July 18 and on October 15,\ncompiled 32-token generation got 11.6x faster and eager generation 1.21x faster. Graph breaks per forward pass fell from 20 to 0, and Python calls per forward\npass fell 12 to 23% on all four backends. The\n[progress report](https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051) times every pull request on\nits own; its interactive version on DEV is under Links." }, { "name": "What did not work", "md": "The eager gap did not close: eager Keras on PyTorch still takes 8.45x the time of plain PyTorch on this benchmark, so\nthe issue stays open. When the report was posted, two more fixes were in review. Training, CPU timing and larger models\nwere not measured." } ], "one_line": "Traced why Keras on PyTorch ran about 9x slower than plain PyTorch, and merged the fixes upstream, one root cause each.", "org": "google", "area": "performance", "status": "merged", "start": "2026-07", "end": "2026-10", "skills": [ "performance analysis", "PyTorch", "torch.compile", "JAX", "Keras", "cProfile", "benchmarking", "Python" ], "numbers": [ { "id": "compiled", "value": "11.6x", "what": "faster 32-token LLM generation under torch.compile, Keras master on July 18 against October 15 (87.8 → 7.54 ms per call)", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "as_of": "2026-10-28" }, { "id": "breaks", "value": "20 → 0", "what": "torch.compile graph breaks per forward pass (CNN 9 → 0, LLM 11 → 0)", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "as_of": "2026-10-28" }, { "id": "calls", "value": "−12 to −23%", "what": "Python function calls per forward pass, on all four Keras backends", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "as_of": "2026-10-28" }, { "id": "eager", "value": "1.21x", "what": "faster 32-token generation in eager mode over the same months (59.8 → 49.5 ms per call)", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "as_of": "2026-10-28" }, { "id": "gap", "value": "8.45x", "what": "the time eager Keras on PyTorch still takes against plain PyTorch on the same benchmark (10.2x in July)", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "as_of": "2026-10-28" }, { "id": "slowdown", "value": "9x", "what": "slower than plain PyTorch on small models in eager mode, about, when the issue was opened", "status": "checked", "source": "https://github.com/keras-team/keras/issues/22561", "as_of": "2026-10-28" } ], "links": [ { "label": "The progress report, keras#22561", "url": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051" }, { "label": "Interactive version, on DEV", "url": "https://dev.to/pctablet505/keras-22561-speedups-compiled-keras-on-pytorch-now-generates-text-116x-faster-595m" }, { "label": "The issue, keras#22561", "url": "https://github.com/keras-team/keras/issues/22561" } ], "related": [ "keras-22561-speedups" ], "report": { "title": "the progress update on keras#22561", "source": "https://github.com/keras-team/keras/issues/22561#issuecomment-6247116051", "date": "2026-10-18", "dir": "reports/keras-22561", "charts": [ { "file": "tiles.png", "alt": "Headline numbers, Keras master on July 18 against October 15: compiled and eager generation, Python calls per forward, graph breaks" }, { "file": "flips.png", "alt": "Three merges that each made one compiled Keras-on-PyTorch workload several times faster" }, { "file": "waterfall.png", "alt": "Compiled 32-token generation from July to October, one bar per pull request, the rest of master as one grey bar" }, { "file": "throughput.png", "alt": "Tokens per second for 32-token generation, July against October, Keras on PyTorch and JAX, with the plain frameworks for reference" }, { "file": "backends.png", "alt": "Python function calls per LLM forward on PyTorch, JAX, TensorFlow and NumPy, July against October and at every commit" }, { "file": "timeline.png", "alt": "Compiled 32-token generation at every commit from July to October, and torch.compile graph breaks per forward" }, { "file": "jax.png", "alt": "Keras on JAX, eager and under jax.jit, 32-token generation at every commit" } ] }, "profile_line": 737 }, { "id": "xprof-ui-tests", "title": "End-to-end UI tests for XProf", "priority": 2, "summary": "XProf is the open-source profiling tool for machine learning. I built its browser test suite: Playwright tests that\nwalk seven real profiling journeys, page checks that flag error banners and loading screens that never clear, and a\nside-by-side engine that compares the screenshots, page structure and network calls of two builds and turns the\ndifferences into a report to approve. It runs in XProf's continuous integration, and was extended to catch eight\nfrontend regressions in routing, state, chart rendering and responsive layout.", "one_line": "Wrote the end-to-end UI test suite for XProf, the open-source ML profiler, with 107 browser tests and a side-by-side check of two builds.", "org": "google", "area": "performance", "status": "shipped", "start": "2026-08", "end": "present", "skills": [ "Playwright", "pytest", "Python", "visual regression testing", "continuous integration" ], "numbers": [ { "id": "tests", "value": "107", "what": "test functions in the suite", "status": "checked", "source": "https://github.com/openxla/xprof/tree/master/tests/ui", "as_of": "2026-10-27" }, { "id": "journeys", "value": "7", "what": "scripted user journeys through the profiler", "status": "checked", "source": "https://github.com/openxla/xprof/blob/master/tests/ui/journeys/diagnostic_journeys.json", "as_of": "2026-10-27" }, { "id": "regressions", "value": "8", "what": "frontend regressions the suite was extended to catch", "status": "checked", "source": "https://github.com/openxla/xprof/commit/12e29a018ac1a2591a67e38708386d70548f49d3", "as_of": "2026-10-27" } ], "links": [ { "label": "The suite", "url": "https://github.com/openxla/xprof/tree/master/tests/ui" }, { "label": "Its history", "url": "https://github.com/openxla/xprof/commits/master/tests/ui" } ], "profile_line": 836 }, { "id": "llama-kerashub", "title": "Llama 3.1 in KerasHub, and Gemma 3 fixes", "priority": 2, "summary": "Llama 3.1 landed in KerasHub in April 2025; Gemma 3 fixes followed for JAX index types and a preprocessor crash.", "one_line": "Implemented Llama 3.1 in KerasHub and fixed Gemma 3 bugs, including a preprocessor crash across Gemma 3, 3n and 4.", "org": "google", "area": "performance", "status": "merged", "start": "2025-04", "end": "2026-07", "skills": [ "LLMs", "KerasHub", "JAX", "Keras" ], "links": [ { "label": "keras-hub#2132, Llama 3.1", "url": "https://github.com/keras-team/keras-hub/pull/2132" }, { "label": "keras-hub#2773, Gemma preprocessor", "url": "https://github.com/keras-team/keras-hub/pull/2773" } ], "profile_line": 876 }, { "id": "benchmarking-system", "title": "A benchmarking system for Keras and KerasHub", "priority": 2, "one_line": "Built the benchmarking system for all Keras and KerasHub models: automated weekly runs across GPU and TPU, the TensorFlow, JAX and PyTorch backends, presets and batch sizes, to catch performance regressions.", "org": "google", "area": "performance", "status": "built", "skills": [ "benchmarking", "Keras", "KerasHub", "GPU", "TPU" ], "wording": "exact", "profile_line": 893 }, { "id": "keras3-migration", "title": "A Keras 3 migration", "priority": 3, "one_line": "Led the migration of a major internal customer from legacy Keras to Keras 3, with backward compatibility and performance parity.", "org": "google", "area": "performance", "status": "built", "skills": [ "Keras 3", "migration" ], "wording": "exact", "profile_line": 905 }, { "id": "kerashub-litert-compat", "title": "Making KerasHub models exportable", "priority": 3, "summary": "ESM attention, the PARSeq decoder, quantized reversible embeddings, Flux tensor shapes and non-max suppression, each\nmade exportable.", "one_line": "Fixed the layers that kept KerasHub models such as ESM, PARSeq and Flux from exporting to LiteRT.", "org": "google", "area": "performance", "status": "merged", "skills": [ "KerasHub", "LiteRT", "TFLite", "quantization" ], "related": [ "keras-litert-export" ], "profile_line": 917 }, { "id": "keras-fixes", "title": "Keras correctness fixes", "priority": 3, "summary": "Bugs in attention on TPU, saving and loading nested layers, masks, image ops and ONNX export, each fixed with a test.", "one_line": "Fixed TPU attention, N-D attention inputs, nested weight saving, masks, call-context leaks and ONNX export of models over 1.5 GB.", "org": "google", "area": "performance", "status": "merged", "start": "2025-06", "end": "2026-10", "skills": [ "Keras", "TPU", "ONNX", "OpenVINO", "PyTorch" ], "numbers": [ { "id": "onnx-size", "value": "1.5 GB", "what": "model size above which ONNX export now switches to large-model mode", "status": "checked", "source": "https://github.com/keras-team/keras/pull/22825", "as_of": "2026-10-27" } ], "links": [ { "label": "keras#22361, Attention N-D inputs", "url": "https://github.com/keras-team/keras/pull/22361" }, { "label": "keras#22825, ONNX large models", "url": "https://github.com/keras-team/keras/pull/22825" } ], "profile_line": 931 }, { "id": "exynos-npu", "title": "Faster Exynos NPU inference", "priority": 3, "one_line": "Optimized models for the Exynos NPU with TensorFlow Lite, for 2.5x faster inference.", "org": "samsung", "area": "performance", "status": "built", "skills": [ "Exynos NPU", "TFLite", "Android" ], "numbers": [ { "id": "speed", "value": "2.5x", "what": "faster inference", "status": "reported", "source": "LinkedIn" } ], "profile_line": 955 }, { "id": "litert-lm-export", "title": "LiteRT-LM export for KerasHub LLMs", "priority": 3, "summary": "Built and proposed as a series of pull requests; not merged.", "one_line": "Built one call from a KerasHub LLM to a LiteRT-LM bundle with tokenizer and prefill and decode signatures; proposed upstream, not merged.", "org": "google", "area": "performance", "status": "proposed", "skills": [ "LiteRT-LM", "KerasHub", "LLMs", "Gemma" ], "profile_line": 972 }, { "id": "layout-maps", "title": "Model-parallel layout maps for KerasHub LLMs", "priority": 3, "summary": "Designed, with an offline check of real preset configs against mesh shapes; proposed as pull requests and not merged.", "one_line": "Designed model-parallel layout maps for KerasHub LLMs, from Gemma and Qwen to Llama and Mixtral; proposed upstream, not merged.", "org": "google", "area": "performance", "status": "proposed", "start": "2026-07", "skills": [ "model parallelism", "distributed training", "KerasHub", "JAX" ], "profile_line": 984 }, { "id": "keras-ondevice-android", "title": "Keras on an Android phone", "priority": 2, "summary": "Classification, detection, segmentation, depth and two Gemma screens, all on the phone with no server, plus a way to\ncheck the phone's answer against Python and the export traps worth knowing.", "one_line": "An Android app and guide that run KerasHub models on the phone with LiteRT, from one export call to six working screens.", "org": "personal", "area": "performance", "status": "published", "start": "2026-07", "skills": [ "KerasHub", "LiteRT", "Android", "Kotlin", "model export" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/keras-ondevice-android" } ], "related": [ "keras-litert-export", "keras-tflite-android-pitfalls", "android-ml-tests", "keras-tflite-11-cases" ], "profile_line": 997 }, { "id": "gemma-android-demos", "title": "Gemma on Android", "priority": 3, "one_line": "Gemma running fully on an Android phone with TFLite, LiteRT and LiteRT-LM.", "org": "personal", "area": "performance", "status": "published", "skills": [ "Gemma", "LiteRT", "LiteRT-LM", "TFLite", "Android" ], "links": [ { "label": "gemma-tflite-android-demo", "url": "https://github.com/pctablet505/gemma-tflite-android-demo" }, { "label": "litert-demo", "url": "https://github.com/pctablet505/litert-demo" } ], "profile_line": 1014 }, { "id": "rl-alpha-labs", "title": "RL Alpha Labs", "priority": 1, "summary": "Every idea, from reinforcement-learning agents in JAX to simple rules, is tested on point-in-time NSE data with delisted\ncompanies kept in, and counts only if it beats an index fund after real costs and tax. At least 12 are closed, each\nwith its reason. Even a self-held basket of 300 stocks lost 2.14% a year to costs and tax, and the index fund came out\nahead in 69 of 70 tiers of capital and breadth. Paper trading only; no live money.", "parts": [ { "name": "Problem", "md": "Trading ideas that look good in a backtest usually lose to an index fund once real costs, tax and dead companies are\ncounted. The question the lab asks of every idea is whether it still wins after all of that." }, { "name": "Approach", "md": "- **Honest data.** Point-in-time NSE data, with delisted companies kept in, so the past is not filtered by who survived.\n- **Honest costs.** Every statutory charge, depository fee, spread and the tax wedge, written down in the open.\n- **One bar.** An idea counts only if it beats an index fund after costs and tax.\n- **Ideas of every size.** From reinforcement-learning agents trained in JAX to simple rules." }, { "name": "Results", "md": "At least 12 ideas are closed, each with its reason. Holding a 300-stock basket directly lost 2.14% a year to costs and\ntax over twenty years, and the index fund came out ahead in 69 of 70 tiers of capital and breadth." }, { "name": "What did not work", "md": "Every closed idea is a thing that did not work, written down with why. Backtest traps fooled the lab or nearly did;\neach one is listed with the check that now catches it." } ], "one_line": "Automated trading on Indian stocks, where a strategy counts only if it beats an index fund after real costs and tax.", "org": "personal", "area": "projects", "status": "research", "start": "2025-05", "end": "present", "skills": [ "quantitative research", "reinforcement learning", "JAX", "statistics", "backtesting", "Python" ], "numbers": [ { "id": "closed", "value": "12+", "what": "trading ideas tested and closed, each with its reason", "status": "checked", "source": "https://rlalphalabs.com/notes/ideas-we-closed/", "as_of": "2026-10-28" }, { "id": "basket-cost", "value": "2.14%", "what": "yearly loss to rebalancing costs and tax for a self-held 300-stock basket, over 20.73 years", "status": "checked", "source": "https://rlalphalabs.com/", "as_of": "2026-10-27" }, { "id": "tiers", "value": "69 of 70", "what": "capital-and-breadth tiers in which an index fund beat holding the stocks directly, after costs and tax", "status": "checked", "source": "https://rlalphalabs.com/", "as_of": "2026-10-27" } ], "links": [ { "label": "rlalphalabs.com", "url": "https://rlalphalabs.com/" }, { "label": "Ideas we closed", "url": "https://rlalphalabs.com/notes/ideas-we-closed/" } ], "related": [ "servedeck", "flakehound" ], "chart": { "title": "₹1 lakh held from 2006, after costs and tax", "source": "https://rlalphalabs.com/", "as_of": "2026-10-28", "note": "Each line grows at the yearly rate the study reports for it; the real paths were bumpier. The basket's band spans its capital tiers.", "growth": { "from": 2006, "years": 20.73, "start": "₹1 lakh", "lines": [ { "label": "NIFTY 500 index fund", "rate": 11.67 }, { "label": "Self-held 300-stock basket", "rate": 10.48, "high": 10.93 } ] } }, "closed": { "source": "https://rlalphalabs.com/notes/ideas-we-closed/", "as_of": "2026-10-28", "ideas": [ { "idea": "A hand-picked basket of large stocks instead of an index fund", "why": "costs and taxes" }, { "idea": "Intraday chart-pattern setups", "why": "costs and taxes" }, { "idea": "Order-book signals over seconds to minutes", "why": "costs and taxes" }, { "idea": "Trading other public filing feeds on their own", "why": "costs and taxes" }, { "idea": "Reinforcement-learning agents trading on daily prices", "why": "no edge, or only luck" }, { "idea": "A bigger machine-learning model to rank stocks", "why": "no edge, or only luck" }, { "idea": "Buying after a big news-driven jump", "why": "no edge, or only luck" }, { "idea": "Commodity price shocks to predict company results", "why": "no edge, or only luck" }, { "idea": "Writing options on the index for income", "why": "no edge, or only luck" }, { "idea": "Buying stocks announced for addition to a major index", "why": "the market got there first" }, { "idea": "An AI model reading company filings to forecast earnings surprises", "why": "AI that fell short of the bar" }, { "idea": "An AI model mapping which companies supply which, from annual reports", "why": "AI that fell short of the bar" } ] }, "profile_line": 1028 }, { "id": "porchlamp", "title": "Porchlamp", "priority": 2, "summary": "A side project built for fun, out of a struggle every outside contributor knows: a pull request nobody answers. Its\n27 October 2026 snapshot followed 1,442,863 pull requests from 227,260 outside contributors to 11,000 projects. In that\nsnapshot, 13.6% of active projects left most outside pull requests with no human reply for two weeks, and stars barely\npredicted which ones.", "one_line": "Shows which open-source projects reply to, review and merge a stranger's pull request, so contributors spend their effort where it will land.", "org": "personal", "area": "projects", "status": "live", "start": "2026-04", "end": "present", "skills": [ "data pipelines", "GitHub API", "statistics", "static sites", "Android" ], "numbers": [ { "id": "prs", "value": "1,442,863", "what": "pull requests followed, from 227,260 outside contributors, 25 April to 22 October 2026", "status": "checked", "source": "https://porchlamp.org/black-holes/", "as_of": "2026-10-27" }, { "id": "contributors", "value": "227,260", "what": "outside contributors", "status": "checked", "source": "https://porchlamp.org/black-holes/", "as_of": "2026-10-27" }, { "id": "projects", "value": "11,000", "what": "GitHub projects followed", "status": "checked", "source": "https://porchlamp.org/black-holes/", "as_of": "2026-10-27" }, { "id": "silent", "value": "13.6%", "what": "active projects (567 of 4,169) that left most outside pull requests with no human reply for two weeks", "status": "checked", "source": "https://porchlamp.org/black-holes/", "as_of": "2026-10-27" } ], "links": [ { "label": "porchlamp.org", "url": "https://porchlamp.org/" } ], "profile_line": 1114 }, { "id": "servedeck", "title": "Servedeck", "priority": 2, "summary": "Born as a tool for RL Alpha Labs: systemd units, one `/v1` gateway and a dashboard for native vLLM, and it restarts a\nmodel whose engine died, which vLLM's exit code would otherwise hide.", "one_line": "Runs local LLMs on one GPU workstation, switching models with one command behind one OpenAI-compatible endpoint, with a live dashboard.", "org": "personal", "area": "projects", "status": "live", "start": "2026-08", "end": "present", "skills": [ "vLLM", "systemd", "LLM serving", "Linux" ], "links": [ { "label": "Docs", "url": "https://servedeck.rlalphalabs.com/" } ], "related": [ "rl-alpha-labs", "glm53-one-gpu" ], "profile_line": 1158 }, { "id": "flakehound", "title": "flakehound", "priority": 3, "summary": "Born as a tool for RL Alpha Labs. Alpha.", "one_line": "Static checks that find flaky pytest tests by their pattern, with rules for JAX, PyTorch and NumPy.", "org": "personal", "area": "projects", "status": "published", "start": "2026-07", "skills": [ "pytest", "static analysis", "Python" ], "numbers": [ { "id": "rules", "value": "17", "what": "rules", "status": "checked", "source": "https://github.com/pctablet505/flakehound", "as_of": "2026-10-27" } ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/flakehound" } ], "related": [ "rl-alpha-labs" ], "profile_line": 1176 }, { "id": "ats-optimizer", "title": "ATS optimizer", "priority": 3, "summary": "Private code; the idea: public job-board feeds for discovery, a knowledge vault that records where every fact came from,\nresumes built only from facts in the vault, a check against applicant-tracking systems, and form filling with a\ngrounded LLM fallback. Runs as a service.", "one_line": "A job-application pipeline that finds openings, builds each resume by selecting facts rather than inventing them, and fills forms with a browser robot.", "org": "personal", "area": "projects", "status": "live", "start": "2026-02", "skills": [ "Python", "Playwright", "LLMs", "systemd" ], "profile_line": 1199 }, { "id": "hostops", "title": "hostops", "priority": 3, "one_line": "Drops a GPU's power limit the second the UPS reports battery, never fails open, and logs every mains cut.", "org": "personal", "area": "projects", "status": "published", "start": "2026-10", "skills": [ "Linux", "systemd", "SQLite", "power management" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/hostops" } ], "profile_line": 1214 }, { "id": "agent-workflow", "title": "A multi-agent engineering workflow on one workstation", "priority": 3, "one_line": "A shared test scheduler, guard hooks and a context hub that let many coding agents work safely on one workstation.", "org": "personal", "area": "projects", "status": "live", "skills": [ "developer tooling", "Python", "agents" ], "profile_line": 1228 }, { "id": "daily-tracker", "title": "Daily Tracker", "priority": 3, "one_line": "An offline Flutter habit tracker for Android with checksum-verified updates.", "org": "personal", "area": "projects", "status": "published", "start": "2026-06", "skills": [ "Flutter", "Dart", "Android" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/daily_tracker" } ], "profile_line": 1239 }, { "id": "ml-guide", "title": "ML-Guide", "priority": 3, "one_line": "An ordered route through AI and ML courses, in four stages.", "org": "personal", "area": "projects", "status": "published", "start": "2025-03", "skills": [ "teaching" ], "links": [ { "label": "Repository", "url": "https://github.com/pctablet505/ML-Guide" } ], "profile_line": 1253 }, { "id": "data-structures", "title": "Data structures in Python", "priority": 3, "one_line": "A van Emde Boas tree with O(u) and O(n log log u) space versions, plus balanced trees, treaps, segment and Fenwick trees and skip lists.", "org": "personal", "area": "projects", "status": "published", "start": "2020", "skills": [ "algorithms", "data structures", "Python" ], "links": [ { "label": "van-Emde-Boas-Tree", "url": "https://github.com/pctablet505/van-Emde-Boas-Tree" }, { "label": "Data-Structures", "url": "https://github.com/pctablet505/Data-Structures" } ], "profile_line": 1267 }, { "id": "coursework", "title": "Coursework projects", "priority": 3, "one_line": "Projects from MIT's machine learning course, CS50 AI and CS188, including Pac-Man search and reinforcement learning.", "org": "personal", "area": "projects", "status": "published", "skills": [ "machine learning", "search", "reinforcement learning" ], "links": [ { "label": "Machine-Learning", "url": "https://github.com/pctablet505/Machine-Learning" }, { "label": "Artificial-Intelligence", "url": 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#22561 speedups: compiled Keras on PyTorch now generates text 11.6x faster", "priority": 1, "date": "2026-10-16", "related": [ "keras-pytorch-speed" ], "url": "https://dev.to/pctablet505/keras-22561-speedups-compiled-keras-on-pytorch-now-generates-text-116x-faster-595m", "venue": "DEV", "profile_line": 1366 }, { "id": "glm53-hot-expert-cache", "title": "A hot-expert cache made a 181 GiB model faster on one 96 GiB GPU", "priority": 1, "date": "2026-10-22", "related": [ "glm53-one-gpu" ], "url": "https://dev.to/pctablet505/a-hot-expert-cache-made-a-181-gib-model-faster-on-one-96-gib-gpu-4b03", "venue": "DEV", "profile_line": 1376 }, { "id": "keras-tflite-11-cases", "title": "Why a Keras model fails to convert to TFLite: 11 known cases, 5 traps", "priority": 2, "date": "2026-10-22", "links": [ { "label": "Also on Medium", "url": "https://medium.com/@pctablet505/kerashub-expects-11-of-its-own-models-to-fail-this-export-here-is-why-ac5f3f02a7fe" } ], "related": [ "keras-ondevice-android" ], "url": "https://dev.to/pctablet505/why-a-keras-model-fails-to-convert-to-tflite-11-known-cases-5-traps-5bb0", "venue": "DEV", "profile_line": 1386 }, { "id": "keras-tflite-android-pitfalls", "title": "Keras to TFLite on Android: wrong normalization and a backwards top-5", "priority": 2, "date": "2026-10-22", "links": [ { "label": "Also on Medium", "url": "https://medium.com/@pctablet505/the-keras-export-took-one-line-my-android-app-got-the-pixels-and-top-5-wrong-487a543361ee" } ], "related": [ "keras-ondevice-android" ], "url": "https://dev.to/pctablet505/keras-to-tflite-on-android-wrong-normalization-and-a-backwards-top-5-28m2", "venue": "DEV", "profile_line": 1398 }, { "id": "android-ml-tests", "title": "Two Android ML tests that could never fail, and a TFLite check that can", "priority": 2, "date": "2026-10-22", "related": [ "keras-ondevice-android" ], "url": "https://dev.to/pctablet505/two-android-ml-tests-that-could-never-fail-and-a-tflite-check-that-can-3bm8", "venue": "DEV", "profile_line": 1410 }, { "id": "porchlamp-python-java", "title": "Python projects are twice as likely as Java ones to leave most outside pull requests unanswered", "priority": 3, "date": "2026-10-23", "related": [ "porchlamp" ], "url": "https://porchlamp.hashnode.dev/python-projects-are-twice-as-likely-as-java-ones-to-leave-most-outside-pull-requests-unanswered", "venue": "Hashnode", "profile_line": 1420 } ], "notes": [ { "id": "note-glm53-eight-bugs", "title": "The eight bugs between a fresh checkout and correct output", "priority": 1, "summary": "Every bug met on the way to running GLM-5.3-Flash correctly on one GPU, on one page, including the silent one.", "related": [ "glm53-one-gpu" ], "url": "https://github.com/pctablet505/glm53-flash-single-gpu/blob/main/FINDINGS.md", "venue": "GitHub", "profile_line": 1432 }, { "id": "note-glm53-optimizing", "title": "Optimizing GLM-5.3-Flash on one GPU", "priority": 2, "summary": "What made decoding faster, what did not, and how each change 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