6 hours 20 minutes ago
In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage.
The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost.
Sana Hassan
9 hours 4 minutes ago
Asif Razzaq
20 hours ago
Sana Hassan
22 hours 9 minutes ago
Asif Razzaq
1 day 6 hours ago
Asif Razzaq
1 day 6 hours ago
Michal Sutter
1 day 21 hours ago
In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step arithmetic word problems. We start from a weak seed prompt, build a deterministic benchmark, and define a structured evaluator that returns actionable feedback. A multi-component setup evolves both the instruction field and the output-format rules together. We then compare the baseline and optimized prompts on a held-out validation set to check whether the gains generalize.
The post Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation appeared first on MarkTechPost.
Sana Hassan
2 days 6 hours ago
Low-code and no-code AI platforms now turn a prompt into a working app, agent, or model. This guide compares 21 tools across app builders, automation, AI agents, and machine learning platforms, each linked to its official site.
The post Best 21 Low-Code and No-Code AI Tools in 2026 appeared first on MarkTechPost.
Michal Sutter
2 days 8 hours ago
Asif Razzaq
2 days 9 hours ago
Sana Hassan
2 days 16 hours ago
Asif Razzaq
3 days 5 hours ago
Michal Sutter
3 days 7 hours ago
Asif Razzaq
3 days 16 hours ago
Sana Hassan
3 days 19 hours ago
Asif Razzaq
4 days 4 hours ago
Asif Razzaq
4 days 5 hours ago
Michal Sutter
4 days 5 hours ago
Sana Hassan
4 days 6 hours ago
Asif Razzaq
4 days 16 hours ago
This tutorial walks through a complete NLP pipeline for research-level mathematics. Using the ResearchMath-14k dataset, we extract field-specific keywords with TF-IDF, generate sentence embeddings, visualize the problem landscape with UMAP, cluster with K-Means, build a semantic search engine, and train a classifier to predict each problem's open status — then surface near-duplicate problems by similarity.
The post Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset appeared first on MarkTechPost.
Sana Hassan
Checked
16 minutes 38 seconds ago
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