Mashup Learning: Faster Finetuning by Remixing Past Checkpoints

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Finetuning on domain-specific data is a well-established method for enhancing LLM performance on downstream tasks. Training on each dataset produces a new set of model weights, resulting in a multitude of checkpoints saved in-house or on open-source platforms. However, these training artifacts are rarely reused for subsequent experiments despite containing improved model abilities for potentially similar tasks. In this paper, we propose Mashup Learning, a simple method to leverage the outputs of prior training runs to enhance model […]

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The Cognitive Divergence: AI Context Windows, Human Attention Decline, and the Delegation Feedback Loop

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arXiv:2603.26707v1 Announce Type: new Abstract: This paper documents and theorises a self-reinforcing dynamic between two measurable trends: the exponential expansion of large language model (LLM) context windows and the secular contraction of human sustained-attention capacity. We term the resulting asymmetry the Cognitive Divergence. AI context windows have grown from 512 tokens in 2017 to 2,000,000 tokens by 2026 (factor ~3,906; fitted lambda = 0.59/yr; doubling time ~14 months). Over the same period, human Effective Context Span (ECS) — […]

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Six Thoughts On AI Safety

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[Crossposted on lesswrong, see here for prior posts] The following statements seem to be both important for AI safety and are not widely agreed upon. These are my opinions, not those of my employer or colleagues. As is true for anything involving AI, there is significant uncertainty about everything written below. However, for readability, I present these points in their strongest form, without hedges and caveats. That said, it is essential not to be dogmatic, and I am […]

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The 3 RLAIF Approaches: How AI Learns to Align Itself Without Human Labelers

The 3 RLAIF Approaches: How AI Learns to Align Itself Without Human Labelers

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Author(s): TANVEER MUSTAFA Originally published on Towards AI. Understanding AI-Generated Preferences, Constitutional AI Extensions, and Scalable Oversight Training GPT-4 required thousands of human labelers spending months rating AI outputs. Image generated by Author using AIThis article discusses the transformative potential of Reinforcement Learning from AI Feedback (RLAIF), which uses AI to speed up and reduce the costs of alignment tasks that traditionally depended on human labelers, introducing three approaches: AI-generated preferences, constitutional AI extensions, and scalable oversight. The […]

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The AI Regulation War Has Begun. Here’s the First Target.

The AI Regulation War Has Begun. Here’s the First Target.

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Author(s): MohamedAbdelmenem Originally published on Towards AI. New state laws are creating a compliance nightmare. A federal showdown is imminent. Build a “regulation-agnostic” strategy that protects your business no matter who wins. As state AI laws take effect, a federal task force is preparing to sue to erase them, trapping your business in the middle. Here’s how to prepare for the legal clash and build compliance that survives no matter who wins. The coming legal showdown over AI […]

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Your Mac as a Private AI Server: LM Studio + MCP in 30 Lines of Python

Your Mac as a Private AI Server: LM Studio + MCP in 30 Lines of Python

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Running a local LLM and making it available to any device on your network — no cloud required. Update (Feb 2026): The SSE transport has been deprecated in newer versions of the MCP SDK and replaced by streamable-http. The code in this article has been updated to reflect this. If you’re on an older version of the SDK, transport=”sse” still works but you may see a deprecation warning. The Idea What if your Mac could act as a private AI […]

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Real-Time Agentic RAG: Eradicating Context Rot With Spark & Iceberg

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The Late-Night Pager Alert and The Reality of Context Rot Last month I woke up to a pager alert and a heated Slack thread regarding a critical production failure. The polished writing and perfect syntax of our AI agent hid a massive underlying flaw. The agent was hallucinating and making incorrect operational decisions. The large language model was not the problem. The issue was that the underlying knowledge graph the agent relied upon was completely out of sync […]

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Measuring AI Creativity: Study Methods for Comedians & LLMs

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Table of Links Abstract and 1. Introduction Methods Quantitative Results and Creativity Support Index Qualitative Results from Focus Group Discussions Discussion Mitigations and Conclusion and Acknowledgments Ethical Guidance References A. Related Work on Computational Humour, AI and Comedy B. Participant Questionaire C. Focus 2 METHODS Our study was designed to address a challenging problem, with on one hand, limitations of LLMs (stereotypes, inability to distinguish comedic offensiveness from harmful speech, cultural erasure and homogeneisation of content), and on […]

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NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart

NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart

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Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart. Nemotron 3 Nano is a small […]

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AI agent-driven browser automation for enterprise workflow management

AI agent-driven browser automation for enterprise workflow management

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Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks consume approximately 25-30% of worker time, while manual processes create compliance bottlenecks and cross-system data consistency challenges that require continuous human verification. Traditional […]

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