# Large language models (LLMs) Large language models are deep learning models trained on vast amounts of text and code to understand and generate natural-language content, using transformer architectures. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Token Jacking: Cybercriminals Could Be Stealing Your AI Resources DevFeed: [Token Jacking: Cybercriminals Could Be Stealing Your AI Resources]() Original publisher: [Read original article]() Author: Unit 42 Published: 2026-08-06T10:00:49Z Content type: article Language: en Sources: [Unit 42]() Topics: [token jacking](), [ai security](), [API keys](), [Large language models (LLMs)](), [Authentication](), [transfer stations]() Tags: [ai](), [ai-api](), [ai-gateway](), [ai-security](), [api-keys](), [authentication](), [large-language-models-llms](), [malware](), [npm-packages](), [obfuscation](), [security](), [threat-research](), [token-jacking](), [transfer-stations]() ### AI overview The article explains how criminals steal developers' AI API keys and use the resulting tokens to consume costly language-model resources, causing rapid financial losses. It outlines the role of authentication, automated access keys, token-based billing, and weak billing controls, and recommends security hygiene and AI protection measures. ### Source excerpt Discover how attackers hijack AI tokens to fuel gray market transfer stations by stealing developer API keys. The post Token Jacking: Cybercriminals Could Be Stealing Your AI Resources appeared first on Unit 42. ## Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence DevFeed: [Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence]() Original publisher: [Read original article]() Published: 2026-07-30T20:36:36Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Google](), [AI-generated research reports](), [Large language models (LLMs)](), [Hallucination detection](), [LLM evaluation / benchmarking]() Tags: [agents](), [ai](), [autonomous-agents](), [benchmarks](), [evaluation](), [general-science](), [google](), [hallucinations](), [language-models](), [machine-intelligence](), [natural-language-processing](), [research](), [research-prototype]() ### AI overview Google Research introduces the Science One Framework, an experimental autonomous research prototype built around Chain-of-Evidence. It is designed to make AI-generated research verifiable by linking claims to supporting evidence and by auditing papers against their code and evidence. The article reports that the framework eliminates phantom references and produces fully verifiable scores in the described evaluations. ### Source excerpt General Science ## MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing DevFeed: [MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing]() Original publisher: [Read original article]() Author: Department of Aeronautics and Astronautics Published: 2026-07-14T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Development](), [Artificial Intelligence](), [Hardware](), [Software Engineering](), [Large language models (LLMs)]() Tags: [3-d-printing](), [aeronautical-and-astronautical-engineering](), [aerospace](), [ai-and-rapid-prototyping](), [ai-copilots](), [ai-native-engineer](), [aircraft](), [andreea-bobu](), [artificial-intelligence](), [chatgpt](), [classes-and-programs](), [claude](), [contests-and-academic-competitions](), [design](), [design-build-test-cycle](), [education-teaching-academics](), [engineering](), [gas-turbine-aero-engine](), [generative-ai](), [hardware](), [independent-activities-period](), [jet-engines](), [large-language-models-llms](), [lincoln-laboratory](), [logistics](), [machine-learning](), [manufacturing](), [masha-folk](), [mechanical-engineering](), [mit-aeroastro](), [mit-gas-turbine-laboratory](), [mit-iap](), [mit-jarvis-challenge](), [mit-lincoln-laboratory](), [mit-meche](), [mit-motorsports-team](), [mit-parley](), [mit-rocket-team](), [robotics](), [school-of-engineering](), [stem-education](), [students](), [undergraduate](), [zachary-cordero](), [zoltan-spakovszky]() ### AI overview MIT's JARVIS Challenge asked undergraduate teams to design, fabricate, assemble, and test small gas turbine aero engines with AI as their primary engineering partner. The challenge found that AI could accelerate parts of safety-critical hardware engineering, while engineering judgment remained essential and manufacturing was the main rate-limiting step. ### Source excerpt MIT students designed, built, and tested a jet engine with AI copilots, assessing AI's usefulness in developing high-performance aerospace systems. ## Thinking to recall: How reasoning unlocks parametric knowledge in LLMs DevFeed: [Thinking to recall: How reasoning unlocks parametric knowledge in LLMs]() Original publisher: [Read original article]() Published: 2026-06-24T16:51:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Large language models (LLMs)](), [Chain-of-thought](), [Language models](), [LLM evaluation / benchmarking]() Tags: [chain-of-thought](), [generative-ai](), [google](), [large-language-models-llms](), [machine-intelligence](), [natural-language-processing](), [reasoning](), [research]() ### AI overview Google Research examines why generating chain-of-thought reasoning can help large language models recall simple facts that are already encoded in their parametric memory. Controlled experiments identify two mechanisms: latent computation through generated reasoning tokens and factual priming through related facts. ### Source excerpt Generative AI ## What a Raspberry Pi Can (and Can't) Do With AI DevFeed: [What a Raspberry Pi Can (and Can't) Do With AI]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-06-24T11:54:14Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Local AI](), [Large language models (LLMs)](), [object-detection](), [OpenClaw]() Tags: [ai](), [applications](), [computer-vision](), [cpu](), [gpu](), [hardware](), [large-language-models-llms](), [local](), [local-ai](), [object-detection](), [openclaw](), [quick-tips](), [raspberry-pi]() ### AI overview This article explains what Raspberry Pi devices can and cannot do with AI. They can run applications such as object detection, computer vision projects, lightweight AI agents, and some small language models, but limited computing power makes most modern LLMs slow or impractical locally. AI HAT and AI Camera products can improve computer vision workloads but do little for LLM execution. ### Source excerpt AI (artificial intelligence) is a buzzword that has been thrown around a lot these days, and the Raspberry Pi ecosystem is no exception. New use cases have been tested on it, and new products have even been released to accompany this phenomenon. So, what can your Raspberry Pi actually do with AI? A Raspberry Pi... ## Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence DevFeed: [Lumos: Inside Dream11's Leap from Task-Based Models to Foundational Intelligence]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-22T06:40:39Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [foundation-models](), [Sports](), [Machine learning](), [Large language models (LLMs)]() Tags: [analysis](), [competition](), [context](), [dream11](), [fragmentation](), [incremental](), [llm](), [ml](), [models](), [notifications](), [personalisation](), [scale](), [sports](), [systems](), [tech]() ### AI overview Dream11 describes Lumos, a foundation model for personalisation that connects user behaviour, context, and changing interests across sports experiences. The article reports a 2.5% lift in ROC AUC and a 4.6% reduction in MAPE across key tasks, while replacing dozens of task-specific systems with a single scalable foundation. ### Source excerpt By Dhruv Nigam At Dream11, our mission to 'make every match more exciting' starts with a simple truth: every fan experiences sport differently. Some users show up for marquee matches, while others engage consistently across the season. Some enjoy deep analysis; others come for emotion, banter, and shared moments. Even how fans prefer to be spoken to -- through in-app communication or notifications -- varies, from playful and expressive to direct and informational. In sports, context changes everything. A quiet weekday feels very different from the eve of a knockout match, and behaviour shifts with formats, rivalries, and the stage of competition. Personalisation at Dream11 therefore goes beyond surface-level customisation -- it's about understanding fans in motion and how their interests evolve. We've long recognised this challenge, but understanding and acting on these signals across millions of users, each with their own patterns and preferences, is far from easy. Over time, it became clear that small, incremental ML enhancements wouldn't get us where we needed to go. To stay truly user-first, we needed a system that could connect behaviour, context, and past, present, and future moments, all at once. That realisation led us to a ground-up rethink of how we build models at Dream11, and eventually, to Lumos -- our foundation model for personalisation. Lumos helped deliver a 2.5% lift in ROC AUC (Area Under the Receiver Operating Characteristic Curve) and a 4.6% reduction in MAPE (mean absolute percentage error) across key tasks, significantly improving personalisation, while replacing dozens of task-specific systems with a single, scalable foundation.The Problem: When Task-Based Models Stop Scaling For a long time, our personalisation stack relied on 50+ small, specialised models, each designed to understand a narrow aspect of user behaviour. Some models focused on sports affinity, others on language preferences or communication style. While these were effective in iso ## Toward provably private insights into AI use DevFeed: [Toward provably private insights into AI use]() Original publisher: [Read original article]() Published: 2025-10-30T10:56:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [Confidential Computing](), [Google](), [trusted-execution-environment](), [data-processing](), [Large language models (LLMs)](), [gemma](), [Open Source]() Tags: [ai](), [confidential-computing](), [data-processing](), [gemma](), [generative-ai](), [google](), [large-language-models-llms](), [mobile-systems](), [on-device](), [open-source](), [research](), [security-privacy-and-abuse-prevention](), [software-systems-engineering]() ### AI overview Google Research introduces provably private insights, a system that combines large language models, differential privacy, and trusted execution environments to analyze aggregate patterns in on-device generative AI use without exposing individual data. ### Source excerpt Generative AI ## 📚 3LM: A Benchmark for Arabic LLMs in STEM and Code DevFeed: [📚 3LM: A Benchmark for Arabic LLMs in STEM and Code]() Original publisher: [Read original article]() Author: Basma Boussaha; Leen AlQadi; Mughaira; Shaikha Alsuwaidi; Giulia Campesan; Ahmed Alzubaidi; Mohammed Alyafeai; Hakim Hacid Published: 2025-08-01T14:25:21Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [LLM evaluation / benchmarking](), [Large language models (LLMs)](), [datasets](), [Code generation]() Tags: [benchmark](), [code](), [code-generation](), [datasets](), [llms](), [ocr](), [quality-assurance](), [stem]() ### AI overview The article introduces 3LM, a benchmark for evaluating Arabic Large Language Models on STEM subjects, structured reasoning, formal logic, and code generation. It combines native educational multiple-choice questions, synthetic high-difficulty STEM questions, and translated code-generation tasks. ### Source excerpt A Blog post by Technology Innovation Institute on Hugging Face ## Falcon-Arabic: A Breakthrough in Arabic Language Models DevFeed: [Falcon-Arabic: A Breakthrough in Arabic Language Models]() Original publisher: [Read original article]() Author: Basma Boussaha; Mohammed Alyafeai; Ahmed Alzubaidi; Leen AlQadi; Younes B; Mike Lubinets; Hakim Hacid; Falcon LLM TII UAE Published: 2025-05-21T06:35:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Large Language Model](), [Large language models (LLMs)](), [Natural language processing](), [long-context](), [Retrieval Augmented Generation (RAG)](), [Benchmark](), [Open Source](), [hugging face]() Tags: [ai](), [arabic](), [artificial-intelligence](), [benchmark](), [blog](), [blog-post](), [hugging-face](), [language-models](), [large-language-models-llms](), [nlp](), [open-source](), [rag](), [technology](), [tokens]() ### AI overview The article introduces Falcon-Arabic, a 7B-parameter multilingual language model built on the Falcon 3 architecture. It is designed for Arabic and English, supports Modern Standard Arabic and regional dialects, handles 32,000-token contexts, and targets tasks including grammar, mathematical reasoning, complex problem solving, content creation, and retrieval-augmented generation. The article presents it as an efficient, accessible, and open-source model that addresses the underrepresentation of Arabic in AI. ### Source excerpt A Blog post by Technology Innovation Institute on Hugging Face ## Introducing the Open Leaderboard for Japanese LLMs! DevFeed: [Introducing the Open Leaderboard for Japanese LLMs!]() Original publisher: [Read original article]() Author: Akim Mousterou; Yusuke Miyao; Namgi Han; Takumi Okamoto; Shigeki Ishida; hysts; Clémentine Fourrier Published: 2024-11-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLM evaluation / benchmarking](), [Large language models (LLMs)](), [hugging face](), [Natural language processing](), [tokenization](), [datasets](), [Open Source]() Tags: [collaboration](), [community](), [datasets](), [evaluation](), [hugging-face](), [leaderboard](), [llms](), [nlp](), [open-source](), [research](), [tokenization]() ### AI overview The article introduces the Open Japanese LLM Leaderboard, an open evaluation platform built by LLM-jp and Hugging Face. It uses more than 20 datasets and a specialized evaluation suite covering 16 tasks to compare Japanese large language models, address the challenges of Japanese NLP, and support transparent, collaborative, open-source research. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Mixed-input matrix multiplication performance optimizations DevFeed: [Mixed-input matrix multiplication performance optimizations]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-01-26T19:56:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Large language models (LLMs)](), [quantization](), [Hardware](), [GPU](), [Nvidia](), [Tensor Cores](), [Open Source](), [Software](), [data type]() Tags: [accelerators](), [ai](), [algorithms](), [ampere](), [compute](), [conversion](), [data](), [deep-learning](), [effective](), [efficiency](), [hardware](), [implementation](), [language-models](), [large-language-models-llms](), [models](), [nvidia](), [optimization](), [performance](), [precision](), [quantization](), [research](), [software](), [tensor-cores]() ### AI overview This Google Research article explains software techniques for mapping mixed-input matrix multiplication onto NVIDIA Ampere hardware. It describes using lower-precision weights with higher-precision inputs, data-type conversion, and layout transformations to support weight-only quantization. The authors report minimal software overhead and performance close to peak hardware capabilities, and state that the techniques were released in the open-source NVIDIA/CUTLASS repository. ### Source excerpt Posted by Manish Gupta, Staff Software Engineer, Google Research AI-driven technologies are weaving themselves into the fabric of our daily routines, with the potential to enhance our access to knowledge and boost our overall productivity. The backbone of these applications lies in large language models (LLMs). LLMs are memory-intensive and typically require specialized hardware accelerators to efficiently deliver tens of exaflops of computing power. This blog post shows how we can start addressing the computational challenges by utilizing memory more effectively. The bulk of an LLM's memory and compute are consumed by weights in matrix multiplication operations. Using narrower data types reduces memory consumption. For example, storing weights in the 8-bit integer (i.e., U8 or S8) data type reduces the memory footprint by 4x relative to single-precision (F32) and 2x relative to half-precision (F16) or bfloat16 (BF16). Furthermore, previous work has shown that LLM models running matrix multiplications with weights in S8 and input in F16 (preserving higher precision of the user-input) is an effective method for increasing the efficiency with acceptable trade-offs in accuracy. This technique is known as weight-only quantization and requires efficient implementation of matrix multiplication with mixed-inputs, e.g., half-precision input multiplied with 8-bits integer. Hardware accelerators, including GPUs, support a fixed set of data types, and thus, mixed-input matrix multiplication requires software transformations to map to the hardware operations. To that end, in this blog we focus on mapping mixed-input matrix multiplication onto the NVIDIA Ampere architecture. We present software techniques addressing data type conversion and layout conformance to map mixed-input matrix multiplication efficiently onto hardware-supported data types and layouts. Our results show that the overhead of additional work in software is minimal and enables performance close to the peak har