# Large language models (LLMs) Published articles for Large language models (LLMs). This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Autocomplete to Intelligence: How Language Became the Operating System of AI DevFeed: [From Autocomplete to Intelligence: How Language Became the Operating System of AI]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-09-15T10:17:07Z Content type: opinion Language: en Sources: [Wix Engineering]() Topics: [Artificial Intelligence](), [Language models](), [Large Language Model](), [ChatGPT](), [OpenAI]() Tags: [ai](), [article](), [chatgpt](), [language-models](), [large-language-models-llms](), [openai]() ### AI overview This article examines why language is closely tied to intelligence in both humans and artificial intelligence. It argues that AI became significantly more powerful by learning language exceptionally well, then introduces how large language models acquire intelligence through models that represent the world and reduce uncertainty. ### Source excerpt Introduction Every week brings a new AI model, a new coding assistant, a new agent framework. The pace can feel overwhelming. Yet beneath the churn lies a single, surprisingly simple fact - the fact this whole revolution turns on: AI became so powerful because it learned language exceptionally well. In this article I want to step away from the weekly headlines and ask a more basic question: why is language so tightly bound to intelligence - human intelligence in general, and artificial... ## Bringing custom knowledge to agents with AutoRAG DevFeed: [Bringing custom knowledge to agents with AutoRAG]() Original publisher: [Read original article]() Author: Diego Alvarez Ponce, Diego Garcia Perez Published: 2026-09-11T13:01:50Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Retrieval Augmented Generation (RAG)](), [Model Context Protocol (MCP)](), [Databases](), [Large Language Model](), [AI Development](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-inference](), [artificial-intelligence](), [database](), [developer](), [generative-ai](), [gpu](), [guide](), [how-to](), [large-language-models-llms](), [model-context-protocol-mcp](), [rag](), [red-hat](), [terminal]() ### AI overview This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario. ### Source excerpt The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer. ## Model-agnostic PII detection with LLMs DevFeed: [Model-agnostic PII detection with LLMs]() Original publisher: [Read original article]() Author: Christophe Dupuy Published: 2026-09-10T16:02:16Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat]() Tags: [advanced-300](), [amazon-bedrock](), [benchmark](), [fine-tuning](), [inference](), [large-language-models-llms](), [llms](), [pii](), [security](), [technical-how-to](), [vpc]() ### AI overview A tutorial on building a configurable, model-agnostic PII detector with large language models. It explains prompt-based entity configuration, implementation, benchmarking, and running the detector on custom data. ### Source excerpt A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors. ## How platform engineering 2.0 mitigates AI security and compliance risks DevFeed: [How platform engineering 2.0 mitigates AI security and compliance risks]() Original publisher: [Read original article]() Author: Steven Vaughan-Nichols Published: 2026-09-04T16:46:09Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Security](), [Securing AI](), [Responsibility & Safety]() Tags: [ai-agents](), [compliance](), [infrastructure](), [large-language-models-llms](), [platform-engineering](), [policy](), [secure-by-default](), [security]() ### AI overview The article explains how Platform Engineering 2.0 evolves existing Kubernetes, pipeline, internal developer platform, and process foundations to support production use of LLMs and AI agents. It emphasizes platform-level isolation, governance, policy-as-code, guardrails, and continuous compliance to mitigate AI security, regulatory, and operational risks. ### Source excerpt Discover how the shift from Platform Engineering 1.0 to 2.0 addresses critical AI security and compliance challenges. Learn how native model governance and workload isolation establish a scalable, secure foundation for integrating AI agents and LLMs into production workflows. ## Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America DevFeed: [Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America]() Original publisher: [Read original article]() Author: Reese Lewis and Sara McBroom Published: 2026-09-03T10:00:58Z Content type: article Language: en Sources: [Unit 42]() Topics: [Language models](), [AI Chat](), [Incident response]() Tags: [agentic-ai](), [ai](), [chatgpt](), [cl-cri-1131](), [cl-cri-1163](), [claude-code](), [data](), [financial-sector](), [go](), [large-language-models-llms](), [malware](), [nextchat](), [operations](), [phishing](), [shipping-and-transportation](), [socks5](), [socktz](), [threat-research]() ### AI overview The article examines two ongoing intrusion and data-exfiltration campaigns targeting organizations in Latin America. It describes attackers using commercial large language models, proxy infrastructure, phishing, remote-access malware, and operational tooling. ### Source excerpt Explore how attackers targeting Latin American entities use AI for data exfiltration and how basic OpSec errors allow defenders to disrupt operations. The post Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America appeared first on Unit 42. ## How to Get Started With Ollama DevFeed: [How to Get Started With Ollama]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Language models](), [SDKs]() Tags: [ai](), [apps](), [how-to](), [large-language-models-llms](), [learn](), [local](), [models](), [offline](), [open-source](), [privacy](), [python](), [text-generation](), [tutorial]() ### AI overview A video course on using Ollama and its Python SDK to run local LLMs, generate text, and build offline-capable AI applications. ### Source excerpt Learn how to install Ollama, pull local models, and connect them to your Python code using the chat and text generation interfaces. ## When LLM judges agree, should we believe them? DevFeed: [When LLM judges agree, should we believe them?]() Original publisher: [Read original article]() Author: Krishna Balasubramanian; Sasha Podkopaev Published: 2026-08-26T17:10:40Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Large Language Model](), [Retrieval-Augmented Generation](), [Ising](), [benchmark overfitting machine learning](), [Network](), [AI, ML & Data Engineering]() Tags: [conversational-ai](), [evaluation](), [ising](), [large-language-models-llms](), [llm](), [machine-learning](), [metrics](), [retrieval](), [retrieval-augmented-generation]() ### AI overview The article examines whether agreement among LLM judges is trustworthy when their outputs may be correlated. It presents a dependence-aware aggregation method based on Ising models that discounts shared blind spots and outperforms historical-accuracy-weighted majority voting on three tasks. ### Source excerpt Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. ## How SLED can win the cybersecurity race with agentic AI DevFeed: [How SLED can win the cybersecurity race with agentic AI]() Original publisher: [Read original article]() Author: Bobby Suber Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Security Attacks](), [AI Bots](), [Language models]() Tags: [agentic-ai](), [agentic-soc](), [ai-agents](), [ai-search](), [attacks](), [cybersecurity](), [large-language-models-llms](), [public-sector-education-non-profit-government](), [security]() ### AI overview The article describes how AI-enabled attacks are pressuring state, local, and education security teams, and presents an agentic SOC as a way to accelerate investigations by correlating alerts, gathering context, and preparing response plans. ### Source excerpt AI-accelerated attacks are outpacing SLED security teams. Learn how state, local, and education organizations are closing the gap by fixing their data foundation first. ## Modern App Protection Requires Polymorphism | Guardsquare DevFeed: [Modern App Protection Requires Polymorphism | Guardsquare]() Original publisher: [Read original article]() Author: Jason Cortlund - Technical Marketing Writer Published: 2026-08-18T13:45:43Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Mobile](), [Mobile Security](), [Polymorphism](), [Security](), [Development](), [Artificial Intelligence](), [Large Language Model]() Tags: [agentic-ai](), [ai](), [development](), [dexguard](), [ixguard](), [large-language-models-llms](), [mobile](), [polymorphism](), [protection](), [reverse-engineering](), [security](), [security-research](), [thought-leadership](), [vulnerabilities]() ### AI overview The article argues that mobile app protection should use polymorphism, with protections changing for each application build. It links this approach to the risks created by development speed, AI-generated code, and scalable reverse-engineering attacks. ### Source excerpt According to credit reporting agency Equifax, "...mobile app security is often neglected by developers -- making apps more vulnerable to fraud." The reason for this is quite simple for most organizations: development speed is the dominant priority. In fact, 79% of mobile developers cite time-to-market pressure as the top barrier to stronger protection. ## Build an AI Agent with Real-Time Web Search in JavaScript DevFeed: [Build an AI Agent with Real-Time Web Search in JavaScript]() Original publisher: [Read original article]() Author: Amit Merchant Published: 2026-08-14T00:00:00Z Content type: tutorial Language: en Sources: [Amit Merchant]() Topics: [AI Agent](), [JavaScript](), [real-time](), [Web](), [Large Language Model](), [Google Search]() Tags: [ai-agent](), [google-search](), [javascript](), [large-language-models-llms](), [programming](), [real-time](), [tutorial](), [web]() ### AI overview This tutorial explains how to build a small JavaScript AI agent that decides when it needs web search, retrieves Google search results through SearchApi, and uses those results to formulate an answer. It also explains tool calling and the agent loop. ### Source excerpt Large language models (LLMs) are great at answering questions, but they have an important limitation: they don't inherently have access to what's happening on the web right now. ## Empty shelves or lost keys? Recall is the bottleneck for parametric factuality DevFeed: [Empty shelves or lost keys? Recall is the bottleneck for parametric factuality]() Original publisher: [Read original article]() Published: 2026-08-12T09:51:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [LLM evaluation / benchmarking](), [Large Language Model](), [Benchmark](), [Hallucination detection](), [AI Inference]() Tags: [benchmark](), [classification](), [errors](), [generative-ai](), [google](), [inference](), [large-language-models-llms](), [natural-language-processing](), [post-training](), [research]() ### AI overview This Google Research article argues that many factual errors in frontier large language models arise from recall failures rather than missing encoded knowledge. It presents knowledge profiling, which separates encoding, recall, and recognition, and introduces WikiProfile, a benchmark of 2,150 Wikipedia-derived facts tested through questions targeting these abilities. ### Source excerpt Generative AI ## 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. ## Off-by-1 Labs: Why AI-generated vulnerability patches still require expert human review DevFeed: [Off-by-1 Labs: Why AI-generated vulnerability patches still require expert human review]() Original publisher: [Read original article]() Author: info@1password.com (Keith Hoodlet) Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Large Language Model](), [Vulnerabilities](), [Security](), [LLM evaluation / benchmarking](), [Artificial Intelligence](), [datasets]() Tags: [agents](), [ai](), [data](), [developers](), [large-language-models-llms](), [openai](), [research](), [review](), [security](), [vulnerability]() ### AI overview A 1Password security research study finds that large language models generated vulnerability patches with embedded defects 53.9% of the time across 6,080 patches for six recently disclosed CVEs. Only 26.0% fully resolved the vulnerability without materially changing application behavior, supporting the need for expert human review. ### Source excerpt We studied what happens when Large Language Models (LLMs) generate vulnerability patches for recently disclosed, complex vulnerabilities. Our data shows that LLMs produce Fix-Like Artifacts with Embedded Defects (FLAWED) 53.9% of the time when complex patches are required. By sharing the results of our research, our goal is to provide defenders with the tooling and methodology necessary to improve vulnerability remediation outcomes at scale. Along with this blog, we are releasing our tooling, datasets, and an in-depth research paper to share what we've learned. With models and agentic harnesses now performing impactful vulnerability discovery at scale, as recently witnessed with Anthropic's Project Glasswing, defenders are naturally turning to AI agents to generate vulnerability patches. Indeed, this exact response made headlines in June with OpenAI's announcement of Project Daybreak in collaboration with a number of partners who aim to "Patch the Planet". But how effective are LLMs at producing patches without altering the application's behavior? Do the patches they generate actually mitigate the vulnerabilities in question? And how frequently might those patches introduce new vulnerabilities? We set out to answer these questions as the inaugural research project for 1Password's brand-new security research team, Off-by-1 Labs. The paper's title is Frontier Models' Vulnerability Patches are Often F.L.A.W.E.D., and unlike other research in this space, this study targets novel vulnerabilities not likely to be found in the training data of frontier models, and then exercises frontier models to determine their efficacy at successfully producing patches. Across six recently-disclosed CVEs, we produced 6,080 patches using two frontier, cyber-capable reasoning models. The average success rate for generating a patch that fully resolved the vulnerability (without materially changing application behavior) was just 26.0%. Patches that successfully resolved the vulnerability, b ## Running Ollama Locally with Podman on Fedora Linux DevFeed: [Running Ollama Locally with Podman on Fedora Linux]() Original publisher: [Read original article]() Author: Yazan Monshed Published: 2026-08-05T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Large Language Model](), [Linux](), [Open Source Models & Datasets](), [API](), [cURL](), [GPU]() Tags: [ai-development](), [api](), [curl](), [fedora-project-community](), [gpu](), [large-language-models-llms](), [linux](), [llama](), [nvidia](), [offline](), [ollama](), [open-source](), [podman](), [privacy](), [python](), [terminal](), [using-software]() ### AI overview This article explains how to run Ollama locally in a Podman container on Fedora Linux. It covers installing or verifying Podman, creating persistent storage for model weights, running the Ollama container, downloading Llama 3, optionally enabling Nvidia GPU acceleration, and using Ollama's local REST API with curl. ### Source excerpt Running Large Language Models (LLMs) locally has become increasingly popular for development, privacy, and offline testing. Ollama makes this incredibly straightforward, allowing you to run models like Llama 3 or Mistral directly on your machine. By leveraging Podman on Fedora Linux, you can isolate Ollama inside a container. This approach keeps your host system clean [...] ## GenRec: Towards LLM-Native Recommendation at Netflix DevFeed: [GenRec: Towards LLM-Native Recommendation at Netflix]() Original publisher: [Read original article]() Author: Netflix Technology Blog Published: 2026-07-30T20:10:15Z Content type: article Language: en Sources: [Netflix](), [Netflix TechBlog - Medium]() Topics: [Netflix](), [recommendation systems](), [Large Language Model](), [LLM Techniques](), [personalization](), [vllm]() Tags: [data](), [feature-engineering](), [genai](), [large-language-models](), [large-language-models-llms](), [llm](), [llms](), [netflix](), [personalization](), [ranking](), [recommendation-system](), [vllm]() ### AI overview Netflix presents GenRec, an LLM-backed recommendation ranker trained on Netflix-specific data and objectives. It converts user histories, item metadata, and context into text, uses a catalog-aware scoring head, aligns recommendations with long-term member value and business goals, and runs in prefill-only mode on Netflix's LLM serving stack. In a large-scale A/B test, GenRec improved short- and long-term online metrics while using fewer labeled examples and input signals than a mature production ranker. ### Source excerpt Authors: Ying Li, Arjun Rao, Shradha Sehgal Introduction Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand-crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi-task objectives. This stack has evolved over many years to support diverse content types (movies, series, games, live, podcasts) and product surfaces, but its complexity makes it costly to onboard new use cases: adding a content type or surface can require significant feature engineering, architecture change, infrastructure work, and experimentation. At the same time, large language models (LLMs) are changing how we think about recommendation, as shown by recent work such as PLUM, GLIDE, and OneRec-Think. Their broad world knowledge and strong language understanding make it possible to represent user histories and item metadata directly as text, capture rich relationships in a shared semantic space, and steer recommendations via natural-language prompts. However, off-the-shelf LLMs are still far from production-ready recommenders: they often over-recommend globally popular content, hallucinate out-of-catalog items, ignore business constraints, and provide only limited personalization. To address this, we built GenRec, an LLM-backed recommendation ranker that post-trains an internal foundation LLM on Netflix-specific data and objectives. GenRec shows that an LLM-based ranker can match or exceed a mature production system while relying on far fewer labeled examples and input signals. Figure 1: GenRec pipeline. Raw logs of user history, item metadata, and context are transformed via context engineering into natural-language prompts and fed into the GenRec, which runs on vLLM in prefill-only mode and outputs scores for each catalog item, yielding a recommendation ranking. At a high level, GenRec: Verbalizes user histories, item metadata, and context as text ## Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions DevFeed: [Crowdsourced taxonomy verification: A feedback-driven framework for refining knowledge graph relationships via online search interactions]() Original publisher: [Read original article]() Author: Junpeng Niu Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [AI search](), [AI, ML & Data Engineering](), [Language models]() Tags: [algorithms](), [data](), [engineering](), [graphs](), [knowledge-graph](), [large-language-models-llms](), [llm](), [machine-learning](), [search](), [validation]() ### AI overview The article presents a feedback-driven system for verifying knowledge-graph relationships through live search interactions. It tests unverified parent-child or sibling edges in search suggestions, measures user engagement, and uses greedy exploration to balance data collection with result quality. ### Source excerpt Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG construction and maintenance face three critical challenges: Inaccuracy and hallucination from Large Language Models (LLMs): Automated models often infer relationships based on statistical text co-occurrence rather than semantic reality. For instance, an LLM might incorrectly classify "Pho" as a child of "Italian Noodle Soup" due to linguistic similarity, leading to irrelevant search results. Scalability limits of manual verification: Traditional verification relies on human annotators or domain experts. This approach is slow, expensive, and unable to keep pace with dynamic catalogs containing millions of entities. For example, daily changes in restaurant menus or grocery stock keeping units (SKUs). Error propagation in ranking: Inaccurate graph edges propagate errors downstream. If a parent-child relationship is wrong, query expansion algorithms will retrieve irrelevant items, directly degrading Click-Through Rate (CTR) and user trust. We introduce a feedback-driven verification engine that operationalizes the search interface as a validation environment. Key contributions include: User feedback-driven verification: The system treats unverified graph edges as hypotheses. Instead of accepting them as truth, it tests them against live traffic by injecting them into search suggestions and measuring user engagement. Hierarchical relationship refinement: Unlike systems that only validate entities (nodes), this framework validates structural links (edges). It confirms whether entity A is truly a parent, child, or sibling of entity B, ensuring structural integrity. Adaptive exploration: The system employs a greedy exploration policy. It intelligently balances exploitation by showing known good resul ## Amazon is investing in the Lean Focused Research Organization DevFeed: [Amazon is investing in the Lean Focused Research Organization]() Original publisher: [Read original article]() Author: Byron Cook; Shawn Bice Published: 2026-07-26T08:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai-agents](), [amazon](), [amazon-aurora](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-web-services-aws](), [automated-reasoning](), [aws](), [developer](), [formal-verification](), [generative-ai](), [large-language-models-llms](), [llm](), [programming](), [programming-language](), [research](), [responsible-ai](), [safety](), [software-testing](), [testing](), [trustworthy-ai]() ### AI overview Amazon is making a long-term investment in the Lean Focused Research Organization to advance Lean, a programming language for mathematical correctness proofs. The article highlights Lean-based verification for safer AI agents and AWS systems. ### Source excerpt As AI agents take on higher-stakes decisions, Lean programming language makes it possible to mathematically prove they will behave safely. ## Announcing Capital One's 2026 UIUC AI Awardees DevFeed: [Announcing Capital One's 2026 UIUC AI Awardees]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-21T14:45:44Z Content type: release Language: en Sources: [Capital One Tech]() Topics: [Artificial Intelligence](), [ai safety](), [Generative AI](), [Large Language Model](), [Code generation](), [Cybersecurity]() Tags: [2026](), [academic-research](), [agentic-ai](), [agents](), [ai](), [ai-research](), [ai-safety](), [code-generation](), [cybersecurity](), [generative-ai](), [large-language-models-llms](), [research](), [science]() ### AI overview Capital One announces its 2026-2027 research and fellowship awardees from the University of Illinois. The featured projects address LLM reasoning faithfulness, code reasoning, imaginative LLM agents, and agentic AI safety. ### Source excerpt Meet the University of Illinois researchers and fellows advancing Agentic AI through our academic partnership.Announcing the Center for Generative AI Safety, Knowledge Systems, and Cybersecurity (ASKS) 2026-2027 Capital One Research Awardees from the University of Illinois As we look toward the 2026-2027 academic year, we continue to partner with institutions that lead the global conversation on the future of intelligence. We are thrilled to announce this year's cohort of research and fellowship awardees from the University of Illinois, whose pioneering work addresses the most critical frontier in technology today: Agentic AI. From the way machines "think" and "imagine" to the hardware that powers them and the safety protocols that govern them, these five projects represent Capital One's holistic push toward AI that is not only powerful but also faithful, safe and creative. The 2026-2027 research awardees1. Ensuring Intellectual Honesty Advancing LLM Reasoning Faithfulness without Faithfulness Rewards Faculty: Hao Peng Professor Peng is tackling the "hallucination" problem at its core. By developing methods to ensure large language models (LLMs) follow a logical, faithful reasoning path-without relying on traditional, often biased, reward systems-this work ensures that when an AI gives an answer, the "why" behind it is actually true, a critical component for trustworthy financial applications. TrACE-Contrast: Faithful & Consistent Code Reasoning via Trace-Aware Contrastive Learning Faculty: Talia Ringer and Reyhan Jabaarvand Writing code is one thing; understanding how it executes is another. Professor Ringer's project uses contrastive learning to align a model's code generation with its actual execution "trace." This ensures that AI-generated software is verified and logically sound, which is vital for maintaining the integrity of our core technology systems. 2. Bridging the Gap: Agency & Imagination Creative LLM Agents based on Thinking with Imagination Faculty: H ## 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. ## The "100x Developer" Paradox: Is AI "Brain Fry" Killing the Craft? DevFeed: [The "100x Developer" Paradox: Is AI "Brain Fry" Killing the Craft?]() Original publisher: [Read original article]() Author: Simon Sterne Published: 2026-07-06T12:07:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Artificial Intelligence](), [Large Language Model](), [Code](), [Security]() Tags: [ai](), [ai-tech](), [ai-tools](), [artificial-intelligence](), [automation](), [code](), [coding](), [coding-habits](), [cognitive-load](), [developer](), [developer-burnout](), [developer-experience](), [developers](), [future-of-work](), [github-copilot](), [large-language-models](), [large-language-models-llms](), [mental-fatigue](), [models](), [productivity](), [programming](), [security](), [software-engineering](), [tech-culture](), [tech-industry](), [workflow-optimization]() ### AI overview The article argues that AI-assisted development can increase coding speed while imposing a heavy cognitive burden on developers. It describes mental fatigue, constant context-switching, rigorous review responsibilities, and concerns that dependence on AI may weaken deep-thinking and coding skills. ### Source excerpt AI promised to turn every developer into a 10x machine--but instead, it's quietly frying our brains. We're shipping faster than ever while thinking less, reviewing more, and losing the focus that made us good in the first place. The real cost of AI isn't in the code--it's in what it's doing to our minds. ## Build AI agents with short-term & long-term memory in Redis DevFeed: [Build AI agents with short-term & long-term memory in Redis]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI Bots](), [Language models]() Tags: [ai-agents](), [large-language-models-llms](), [memory](), [redis](), [tech-de](), [vector]() ### AI overview A guide to building AI-agent memory in Redis, covering short-term context, long-term preferences, and semantic retrieval across conversations. ### Source excerpt AI agent memory is the system that lets an agent store, retrieve, and reuse information across interactions instead of starting over on every request. Getting there is one of the trickier parts of building AI agents. Large language models (LLMs) are s... ## Q&A: What is agentic AI today, and what do we want it to be? DevFeed: [Q&A: What is agentic AI today, and what do we want it to be?]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-30T15:30:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [what is agentic AI](), [Artificial Intelligence](), [AI Models]() Tags: [agentic-ai](), [ai](), [ai-agents](), [ai-models](), [algorithms](), [artificial-intelligence](), [chatgpt](), [claude](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [data](), [electrical-engineering-and-computer-science-eecs](), [generative-ai](), [human-computer-interaction](), [interview](), [large-language-models-llms](), [machine-learning](), [mit-schwarzman-college-of-computing](), [phillip-isola](), [school-of-engineering]() ### AI overview This Q&A explains agentic AI as systems that take actions in the physical or digital world, distinguishing them from generative AI systems that primarily produce content. It describes how agents combine foundation models such as Claude with application-specific tools and memory, and identifies limited training data for real-world tasks as a major development challenge. ### Source excerpt Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology. ## 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... [Next page]()