# efficiently Published articles for efficiently. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A better way to turn 2D designs into 3D models for rapid prototyping DevFeed: [A better way to turn 2D designs into 3D models for rapid prototyping]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-16T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [3D](), [vlm](), [Artificial Intelligence](), [AI Development](), [Computing](), [coding]() Tags: [3d](), [ai](), [ai-models](), [algorithms](), [artificial-intelligence](), [automated](), [computer-aided-design-cad](), [computer-modeling](), [computer-science-and-technology](), [computing](), [design](), [efficiently](), [faez-ahmed](), [generation](), [generative-ai](), [geometric-inference-feedback-tuning-gift](), [giorgio-giannone](), [machine-learning](), [mechanical-engineering](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [performance](), [rapid-prototyping](), [research](), [school-of-engineering](), [systems-design](), [vision-language-models-vlms]() ### AI overview Researchers developed an automated framework that teaches vision-language models to convert 2D designs into more accurate and functional CAD programs while using less computation. The system turns model failures into training data to improve CAD generation and support rapid prototyping. ### Source excerpt Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently. ## Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained DevFeed: [Local LLM Inference : llama.cpp, GGUF, Quantizations and GGML Explained]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-03-03T11:31:04Z Content type: tutorial Language: en Sources: [Neural Bits]() Topics: [llama.cpp](), [ggml](), [Inference](), [Large Language Model](), [Local AI](), [quantization]() Tags: [backend](), [cross-platform](), [efficiently](), [embedded](), [format](), [ggml](), [inference](), [llama-cpp](), [llm](), [local-llm](), [model]() ### AI overview A practical guide to local LLM inference with llama.cpp, explaining how the GGUF model format, GGML backend concepts, quantization, and inference workflows fit together for efficient execution on edge devices. ### Source excerpt Learn how the llama.cpp runtime, GGML backend concepts, and GGUF model format fit together for fast local inference across devices. ## Polynomial Multiplication Using the FFT DevFeed: [Polynomial Multiplication Using the FFT]() Original publisher: [Read original article]() Published: 2022-11-16T08:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [polynomials](), [Algorithm](), [NumPy](), [Mathematics]() Tags: [algorithm](), [arrays](), [efficiently](), [fft](), [fourier-transform](), [mathematics](), [polynomial-interpolation](), [polynomials](), [programming](), [python]() ### AI overview This tutorial explains how to multiply two polynomials efficiently using the Fast Fourier Transform. It contrasts the naive O(n^2) approach with polynomial interpolation, pointwise multiplication, and carefully chosen roots of unity that enable reusable computations. ### Source excerpt Problem: Compute the product of two polynomials efficiently. Solution: import numpy from numpy.fft import fft, ifft def poly_mul(p1, p2): """Multiply two polynomials. p1 and p2 are arrays of coefficients in degree-increasing order. """ deg1 = p1.shape[0] - 1 deg2 = p1.shape[0] - 1 # Would be 2*(deg1 + deg2) + 1, but the next-power-of-2 handles the +1 total_num_pts = 2 * (deg1 + deg2) next_power_of_2 = 1 << (total_num_pts - 1). ## Speed up your Craft CMS Templates with Eager Loading DevFeed: [Speed up your Craft CMS Templates with Eager Loading]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2017-09-30T04:00:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Content Management System](), [Database](), [Code](), [Back end](), [Front end]() Tags: [allows](), [backend](), [cms](), [craft](), [database](), [eager-loading](), [efficiently](), [elements](), [entries](), [fetching](), [frontend](), [insights](), [loading](), [speed](), [templates]() ### AI overview This tutorial explains how Craft CMS stores content as related Elements and how lazy loading can create an n+1 database access pattern. It introduces eager loading as a way to fetch related entries and assets more efficiently in templates. ### Source excerpt Eager-Loading Elements allows you to speed up your Craft CMS templates by fetching entries from the database more efficiently. ## Weak Learning, Boosting, and the AdaBoost algorithm DevFeed: [Weak Learning, Boosting, and the AdaBoost algorithm]() Original publisher: [Read original article]() Published: 2015-05-18T09:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithm](), [Learning]() Tags: [algorithm](), [better](), [boosting](), [classification](), [distribution](), [efficiently](), [function](), [learner](), [learning](), [models](), [number](), [pac-learning](), [re](), [runtime]() ### AI overview This article introduces learning models in the context of Probably Approximately Correct (PAC) learning and compares the usual PAC model with weak PAC learning. It explains that weak learning requires accuracy only slightly better than random guessing and states that weak and strong learning are equivalent. ### Source excerpt When addressing the question of what it means for an algorithm to learn, one can imagine many different models, and there are quite a few. This invariably raises the question of which models are "the same" and which are "different," along with a precise description of how we're comparing models. We've seen one learning model so far, called Probably Approximately Correct (PAC), which espouses the following answer to the learning question: