February 2026

Google API Keys Weren’t Secrets. But then Gemini Changed the Rules. Yikes! It turns out Gemini and Google Maps (and other services) share the same API keys… but Google Maps API keys are designed to be public, since they are embedded directly in web pages. Gemini API keys can be used to access private files and make billable API requests, so they absolutely should not be shared. If you don’t understand this it’s very easy to accidentally enable […]

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Fairness in Continual Learning for Large Multimodal Models (LMMs) is an emerging yet underexplored challenge, particularly in the presence of imbalanced data distributions that can lead to biased model updates and suboptimal performance across tasks. While recent continual learning studies have made progress in addressing catastrophic forgetting, the problem of fairness caused the imbalanced data remains largely underexplored. This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or $φ$-DPO) framework for continual learning in LMMs. In particular, […]

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We’ve seen a lot of talk about Hybrid models lately (like Jamba). I just noticed that OpenBMB and NVIDIA are running a performance sprint (SOAR 2026) specifically to benchmark MiniCPM-SALA (Sparse+Linear) on SGLang. The challenge is to optimize sparse operator fusion and KV-cache efficiency for ultra-long context. Since the leaderboard just opened today, I was wondering: from a systems research perspective, do you think this hybrid approach will eventually surpass standard Transformers for inference throughput in production? Has […]

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Cloud cost and system reliability are the same problem viewed through different instruments. Cost anomalies surface bugs, retry storms, and memory leaks before they cause outages — if you’re watching. The fix: embed billing telemetry into your observability stack, enforce resource tagging at the pipeline level, write anomaly-based cost alerts, and treat budget overruns as budget burns the same way you treat SLO violations. Manage them separately and you’ll always be six weeks behind the failure that caused […]

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This study investigates the feasibility and performance of deploying YOLOv11-based elbow fracture detection models on mobile devices for real-time clinical diagnosis. Motivated 3by clinicians’ poor diagnostic accuracy (54.4%) in interpreting elbow radiographs, we 4developed and evaluated a smartphone application capable of performing inference via 5three pathways: photo library analysis, direct camera capture, and live video streaming. Two YOLOv11 models were trained on approximately 1,100 elbow X-ray images using systematic hyperparameter optimisation and deployed in both FP16 and FP32 […]

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If people are only using this a couple of times a week at most, and can’t think of anything to do with it on the average day, it hasn’t changed their life. OpenAI itself admits the problem, talking about a ‘capability gap’ between what the models can do and what people do with them, which seems to me like a way to avoid saying that you don’t have clear product-market fit. Hence, OpenAI’s ad project is partly just […]

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Detecting highly divergent or previously unknown viruses remains a major challenge with important clinical implications. In human sequencing datasets, alignment-based approaches frequently classify many assembled contigs as “unknown” because they lack detectable similarity to reference genomes. In this work, we introduce an ensemble of deep neural networks designed to identify viral sequences across diverse human biospecimens. The proposed framework operates on metagenomic sequence data derived from next-generation sequencing platforms, where biochemical nucleotide incorporation events are transduced into fluorescence-based […]

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Handball performance analysis is still often conducted through manual review of match videos, while automation on broadcast footage remains challenging due to camera motion, strong perspective effects, and frequent occlusions during dense interactions. This study presents a practical and reproducible monocular pipeline for extracting handball analytics from a single broadcast viewpoint. Players are detected per frame, tracked over time, and projected onto a standardized handball court via homography-based camera calibration. The resulting court-referenced trajectories in metric units enable […]

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Falls pose severe risks to vulnerable populations, particularly the elderly and individuals with adverse neurological conditions, necessitating reliable and non-obstructive detection systems. While previous multimodal approaches utilising video and audio have demonstrated strong performance, they face significant limitations regarding sensitivity to environmental noise. This paper presents a robust, video-only fall detection framework that eliminates reliance on acoustic data to enhance universality. We conduct a comprehensive comparative analysis of five Optical Flow (OF) algorithms—Horn-Schunck, Lucas-Kanade (LK), LK-Derivative of Gaussian, […]

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Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. Relational Deep Learning (RDL) methods automate feature engineering via message passing, while classical approaches like Deep Feature Synthesis (DFS) rely on predefined non-parametric aggregators. Despite performance gains, the comparative advantages of RDL over DFS and the design principles for selecting effective architectures remain poorly understood. We present a comprehensive study that unifies RDL and DFS in a […]

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