February 2026

I built a simple 2-layer MNIST MLP that trains + runs inference from scratch, only using Apple’s metal-cpp library. The goal was to learn GPU programming “for real” and see what actually moves the needle on Apple Silicon. Not just a highly optimized matmul kernel, but also understanding Metal’s API for buffer residency, command buffer structure, and CPU/GPU synchronization. It was fun (and humbling) to see how much those API-level choices affect performance. Surprisingly I was able to […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The End of CI/CD Pipelines: The Dawn of Agentic DevOps By @davidiyanu [ 10 Min read ] GitHub’s agent fixed my flaky test in 11 minutes. No human wrote code. But when it fails, instead of a stack trace, you get an outcome. Read […]

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This study addresses the challenges faced by large language models in complex reasoning tasks, including semantic drift, logical breaks, and adversarial vulnerability, and proposes an adversarially robust generation paradigm based on self-constructed negative samples. The method builds a unified framework composed of latent representation modeling, internal perturbation generation, contrastive consistency constraints, and semantic stability control, enabling the model to identify potential biases and generate structured negative samples during reasoning, thereby forming a continuous internal correction mechanism. The model […]

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Large language models exhibit context sensitivity—the degree to which conversational history shapes their responses. However, whether this sensitivity varies systematically across conversation positions and task domains remains unexplored. We analyzed 12 models across 30 conversational positions in two domains: philosophical reasoning (open-goal, 4 models) and medical summarization (closed-goal, 8 models), totaling approximately 54,000 responses. We report three findings. First, position 30 (P30) task enablement: at the summarization position, all 8 medical models showed extreme context sensitivity spikes (Z […]

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Antimicrobial resistance (AMR) is a growing global crisis projected to cause 10 million deaths per year by 2050. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provides standardized surveillance data across 44 countries, few studies have applied machine learning to forecast population-level resistance trends from this data. This paper presents a two-component framework for AMR trend forecasting and evidence-grounded policy decision support. We benchmark six models — Naive, Linear Regression, Ridge Regression, XGBoost, LightGBM, and […]

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We introduce the Informational Coherence Index (ICOER), a metric for quantifying coherence in coupled informational systems composed of human agents and large language models (LLMs). We define recursive coupling as a dynamical regime in which coherence-preserving transformations sustain a stable informational signature across iterative feedback cycles while entropic perturbations are naturally suppressed. The metric is operationalized as ICOER(x) = W(S(x)) · e−βS(x) · R(x), where W(S) is a Gaussian entropy weighting, S(x) is Shannon entropy, β is an […]

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One of the main tools in Statistical Process Control (SPC) for monitoring quality is the control chart. Simultaneous multivariate control charts are widely used to monitor shifts in the process mean and variability at the same time. One Shewhart-type simul-taneous multivariate chart is the Max-Half-Mchart, which can detect both small and large shifts in the mean and variability. However, outliers can distort the estimation of process parameters used to set control limits. In addition, outliers can cause two […]

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We present a standardized cross-domain framework for measuring context sensitivity in large language models (LLMs) using the Delta Relational Coherence Index (ΔRCI). Across 25 model-domain runs (14 unique models, 50 trials each, 112,500 total responses), we compare medical (closed-goal) and philosophical (open-goal) reasoning domains using a three-condition protocol (TRUE/COLD/SCRAMBLED). We find that: (1) both domains elicit robust positive context sensitivity (mean ΔRCI: philosophy=0.317, medical=0.351), with medical showing significantly higher sensitivity (U=40, p=0.041); (2) inter-model variance is comparable across […]

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OpenAI and Figma launch a new Codex integration that connects code and design, enabling teams to move between implementation and the Figma canvas to iterate and ship faster.

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