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Altman faces the fallout from OpenAI's Pentagon deal

Altman faces the fallout from OpenAI’s Pentagon deal

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Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}. It took less than a week for OpenAI’s Pentagon deal to go from announced to rewritten, and Sam Altman is calling the fallout “really painful.” With protests outside the company’s HQ, mass cancellations, and its detailed contract revisions bringing as many questions as answers, the rushed agreement is turning what was supposed to be a power move into OpenAI’s biggest brand crisis in […]

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Beyond the Transformer Paradigm

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Author(s): Shashwata Bhattacharjee Originally published on Towards AI. The release of Google’s TITANS architecture in late 2024 marks a theoretical inflection point in how we conceptualize machine memory. This isn’t merely another incremental improvement in long-context processing — it’s a fundamental rethinking of what it means for neural networks to learn, remember, and forget. By implementing principles from cognitive neuroscience that have been validated over six decades, TITANS demonstrates that biological memory systems aren’t just inspiration — they’re […]

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I Built an Ontology Firewall for Microsoft Copilot in 48 Hours — Here’s the Production Code

I Built an Ontology Firewall for Microsoft Copilot in 48 Hours — Here’s the Production Code

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Author(s): Pankaj Kumar Originally published on Towards AI. Most Copilot deployments are one bad agent action away from a serious production failure. This is the architecture that prevents it. 📌 GitHub Repository: cloudbadal007/copilot-ontology-firewall The Copilot had read a SharePoint document titled “Pre-Approved Vendor List — Draft” and interpreted “Pre-Approved” as authorization to proceed. The word “Draft” was in the filename. The agent never checked it.This article details the construction of an Ontology Firewall specifically designed for Microsoft Copilot, […]

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Agent Observability and Evaluation: A 2026 Developer’s Guide to Building Reliable AI Agents

Agent Observability and Evaluation: A 2026 Developer’s Guide to Building Reliable AI Agents

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Author(s): Divy Yadav Originally published on Towards AI. Why building agents without this layer is like driving blind. And how to fix it. You know exactly where to look when traditional software malfunctions. line number, stack trace, and error log. You’ll find the culprit in thirty seconds. Photo by authorThis article discusses the importance of agent observability and evaluation in the development of AI agents, emphasizing that, unlike traditional software, agents present unique challenges in debugging due to […]

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Retrieval-Augmented Forecasting of Time-series

Retrieval-Augmented Forecasting of Time-series

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Author(s): DrSwarnenduAI Originally published on Towards AI. RAFT proves that time series forecasting doesn’t need bigger weights — it needs a better library card Here’s the thing about The Cheesecake Factory menu: it’s 21 pages long. New Frontier in Time seriesThe article discusses a novel approach to time series forecasting called RAFT (Retrieval-Augmented Forecasting of Time-series), which posits that instead of relying on models with larger parameter counts to memorize patterns, it’s more effective to implement a retrieval […]

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The OpenClaw Mess: Why Your Autonomous Agent is a Security Suicide Note.

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Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. When 200,000 GitHub stars meet 30,000 exposed instances, it’s time to stop the madness. These 6 Alternatives Might Actually Be Better for You. OpenClaw is the 800-pound gorilla of self-hosted AI assistants with 251K GitHub stars and 23+ channel integrations. But if you’re about to spin it up, stop. NanoClaw gives you container-isolated security with a codebase small enough to actually read. PicoClaw runs on a $10 RISC-V […]

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Love in the Time of Artificial Shadows

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter VI Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter VI By Oscar Wilde ::: “I suppose you have heard the news, Basil?” said Lord Henry that evening as Hallward was shown into a little private room at the Bristol […]

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Evaluating On-Device Gemma 3 and GPT-OSS as Practical Alternatives to Cloud-Based Language Models on a National Medical Board Examination

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Cloud-based large language models (LLMs) have demonstrated near-human performance in medical applications; however, their clinical deployment is constrained by concerns regarding patient privacy, data security, and network dependence. Locally deployable, open-weight LLMs may provide a privacy-preserving alternative for resource-limited or security-sensitive environments. We evaluated two families of locally deployed models, Google Gemma3 (1B, 4B, 12B, and 27B parameters; vision enabled in models since 4B) and GPT-OSS-20B, using 1,200 multiple-choice questions from the Taiwan Pulmonary Specialist Board Examinations (2013–2024), […]

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The Code Council: Orchestrating Heterogeneous Large Language Models for Robust Programming Scaffolding

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Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils […]

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Enhancing Data Privacy in Large Language Models through Private Association Editing

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Large language models (LLMs) require a significant redesign in solutions to preserve privacy in data-intensive applications due to their text-generation capabilities. Indeed, LLMs tend to memorize and emit private information when maliciously prompted. In this paper, we introduce Private Association Editing (PAE) as a novel defense approach for private data leakage. PAE is designed to effectively remove Personally Identifiable Information (PII) without retraining the model. Experimental results demonstrate the effectiveness of PAE with respect to alternative baseline methods. […]

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