Digest

2026-03-09

302 news sources · 5 podcast sources · 350 items considered · 391 items in digest

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AI agents (131)

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News AI agents 17

Microsoft Recommits to AI Agents With New Copilot Features

https://aibusiness.com/rss.xml · aibusiness.com

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News AI agents 17

AI Copilots at Work: Practical Tools, Open-Source Options, and Strategy

https://medium.com/feed/@odsc · odsc.medium.com

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Podcast AI agents 17

AI Agents in Action, Second Edition: What You’ll Learn in Chapter One

Manning Publications · www.youtube.com

Summary:

**Key Learnings:** 1. **Distinguishing Assistants vs. Agents:** Assistants function as proxies that reformulate requests and identify tools, remaining tethered to a linear approval chain. In contrast, agents possess the agency to execute decision loops autonomously without intermediate human sign-off. 2. **Sense-Plan-Act-Learn Loop:** The internal mechanism powering agent autonomy is the recursive Sense-Plan-Act-Learn loop, which allows agents to dynamically revise their plans based on feedback rather than halting execution. 3. **Decoupling Agents from Tools:** The Model Context Protocol (MCP) is a solution for decoupling agents from tightly-coupled tool code, allowing agents to dynamically discover and connect to external capabilities through a standardized interface. 4. **Evolving from "Prompt Tinkerers" to "Agent Architects":** The goal is to build systems where agents can "earn their keep" in production environments by acting independently, rather than remaining in a state of constant user approval. 5. **Shifting from Chatbots to Autonomous Workers:** We are moving beyond the era of the passive chatbot to the era of the autonomous agent, which represents a fundamental architectural shift in how we design intelligent workflows.

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News AI agents 16

Luma Launches AI Agents for Creative Workflows

https://ai-techpark.com/category/ai/feed/ · ai-techpark.com

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News AI agents 16

OpenAI Buying AI Security Startup Promptfoo to Safeguard AI Agents

https://feeds.bloomberg.com/technology/news.rss · www.bloomberg.com

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Podcast AI agents 16

Stop Hardcoding AI Agents w/ Skill.md - Discover KARL

Discover AI · www.youtube.com

Summary:

**Key Learnings:** 1. **Skills**: Skills are reusable file system-based capabilities that package instructions, metadata, and optional scripts/resources to equip AI agents with specialized abilities beyond a generic language model. 2. **Skill Structure**: Skills have three key components - a metadata layer that describes the skill, a workflow instruction layer that contains the reasoning logic, and a runtime execution layer with scripts and programs for deterministic operations. 3. **Skill Execution**: Skills can include executable code, such as Python scripts, that the AI agent can leverage as tools to complete tasks, allowing for more sophisticated and customized agent behavior. 4. **Agent Configuration**: AI agents can be configured with a set of skills that reside in their file system, enabling them to act as specialized experts rather than generic language models. 5. **Anthropic's Approach**: The podcast discusses Anthropic's approach to defining skills, including specific guidelines like ensuring all relevant data is included (e.g., all competitor information, all relevant years) rather than partial or incomplete information.

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News AI agents 15

Andrej Karpathy Open-Sources ‘Autoresearch’: A 630-Line Python Tool Letting AI Agents Run Autonomous ML Experiments on Single GPUs

https://www.marktechpost.com/feed · www.marktechpost.com

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News AI agents 15

This new Claude Code Review tool uses AI agents to check your pull requests for bugs - here's how

https://www.zdnet.com/topic/artificial-intelligence/rss.xml · www.zdnet.com

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News AI agents 15

OpenAI acquires Promptfoo to secure its AI agents

https://techcrunch.com/feed/ · techcrunch.com

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10.

Podcast AI agents 15

How Retail Banks Can Put AI Agents to Work

None · www.bcg.com

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11.

Podcast AI agents 14

The AI Agent Era Begins | Sarvam Models, Karpathy Experiment & Pentagon AI War

AIM Network · www.youtube.com

Summary:

**Key Learnings:** 1. **Sarvam AI Releases Large Language Models**: Sarvam AI has released 30B and 105B open-weight AI models optimized for Indian languages, making them competitive with global systems. 2. **Autonomous Research Agents**: AI researcher Andrej Karpathy demonstrated "AutoResearch" agents that can run experiments and improve their own training code overnight, showcasing the potential for AI to accelerate its own development. 3. **AI and Software Engineering Jobs**: New labor data suggests AI is accelerating software development rather than replacing developers, as engineering jobs are rising again. 4. **India's Semiconductor Milestone**: India has reached a major milestone in the semiconductor race by training 85,000 chip design engineers years ahead of schedule. 5. **The AI Agent Operating System**: The open-source project OpenFang has launched a full Agent Operating System, signaling the next phase of autonomous software and the expanding AI race beyond chatbots.

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26.

Podcast AI agents 11

Operationalizing Customer Service at Scale with Outcome-Driven Agentic AI - with Craig Walker of Dialpad

The AI in Business Podcast · podcast.emerj.com

Summary:

**Key Learnings:** 1. **Operationalizing Customer Service AI at Scale:** Enterprises can leverage "agentic AI" to automate routine customer service tasks like order status and password resets, freeing up human agents to focus on more complex issues and drive better outcomes. 2. **Preparing the Knowledge Base:** Thoroughly cleaning and organizing the knowledge base is a critical first step to effectively deploying AI-powered virtual agents, ensuring they can accurately respond to customer inquiries. 3. **Controlled Pilots and Iterative Improvement:** Enterprises should start with controlled pilots to test and refine their AI-powered customer service, gradually scaling the technology as they measure its impact on key performance metrics. 4. **Real-Time Agent Coaching and Insights:** AI can provide real-time coaching to human agents, monitoring interactions and providing guidance to improve performance. It can also surface insights for managers to identify areas for improvement. 5. **Driving Measurable ROI:** By automating routine tasks and improving agent productivity, AI-powered customer service can deliver significant ROI for enterprises, measured through metrics like reduced call volumes, handle times, and customer satisfaction.

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Podcast AI agents 11

Do Enterprises Really Need Slow-Thinking AI Models? (with Lin Qiao)

Super Data Science: ML & AI Podcast with Jon Krohn · www.youtube.com

Summary:

**Key Learnings:** 1. **Real-time vs. Offline Applications:** Enterprises have two main categories of AI applications - real-time applications that require sub-second response latency, and offline applications that can take hours or days to process and analyze data. These have very different latency requirements. 2. **Customization and Data Quality:** For real-time applications, the customization process is crucial, and it's important to start with small models to test data quality and model performance before scaling up. High-quality data is essential for developing effective AI models. 3. **Trajectory vs. Model Size:** The trajectory of model quality improvement during customization is more important than the final model size for real-time applications. Smaller models can be quickly tuned and iterated on to get the model on the right trajectory before scaling up. 4. **Specialized Models:** For real-time applications, enterprises benefit from using highly specialized AI models that are finely tuned to their specific data and use case, rather than generic "slow-thinking" models. 5. **Cost Savings:** By leveraging open models and specialized AI solutions, enterprises can achieve significant cost savings compared to developing large, complex models in-house or relying on expensive proprietary solutions.

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Podcast AI agents 8

Defending Bank Onboarding Against Evolving Risk with Mary Ann Miller of Prove

AI in Financial Services Podcast · aiandbanking.libsyn.com

Summary:

**Key Learnings:** 1. **Balancing Seamless Onboarding and Fraud Prevention:** Modern enterprise leaders must find a strategic balance between providing a seamless digital onboarding experience and defending against increasingly sophisticated, AI-driven fraud attacks. 2. **High-Assurance Identity Verification:** A robust "welcome mat" strategy centered on high-assurance identity verification enables the safe scaling of high-value services and drives institutional ROI. 3. **Endpoint Bot Protection:** Implementing endpoint bot protection is crucial to counter agentic AI attacks that can bypass traditional security measures. 4. **Intelligent Data Pre-fill:** The transition to intelligent data pre-fill can significantly reduce customer abandonment during the onboarding process. 5. **Tokenized Identity and Frictionless Experience:** Leveraging tokenized identity can create a persistent, frictionless customer experience throughout the customer lifecycle.

Machine Learning Techniques (86)

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22.

Podcast Machine Learning Techniques 12

Superpowers: NEW Spec Toolkit Ends Vibe Coding! 100x Better Than Vibe Coding (Full Tutorial)

WorldofAI · www.youtube.com

Summary:

Unfortunately, I do not have access to the podcast transcript as the link provided returned a 403 Forbidden error. Without the transcript, I am unable to summarize the key learnings from the podcast episode. Please share the complete podcast transcript or provide alternative access, and I'd be happy to extract the most valuable insights for you.

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29.

News Machine Learning Techniques 10

The Fragility Of Moral Judgment In Large Language Models

https://arxiv.org/rss/cs.CL · arxiv.org

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35.

News Machine Learning Techniques 9

Four Tools, Three Machines, One Question

https://pub.towardsai.net/feed · pub.towardsai.net

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37.

News Machine Learning Techniques 9

Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents

https://arxiv.org/rss/cs.LG · arxiv.org

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44.

News Machine Learning Techniques 8

Attention Meets Reachability: Structural Equivalence and Efficiency in Grammar-Constrained LLM Decoding

https://arxiv.org/rss/cs.CL · arxiv.org

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45.

News Machine Learning Techniques 8

Towards Robust Retrieval-Augmented Generation Based on Knowledge Graph: A Comparative Analysis

https://arxiv.org/rss/cs.CL · arxiv.org

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News Machine Learning Techniques 8

Structured Multidimensional Representation Learning for Large Language Models

https://arxiv.org/rss/cs.CL · arxiv.org

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47.

News Machine Learning Techniques 8

VDCook:DIY video data cook your MLLMs

https://arxiv.org/rss/cs.LG · arxiv.org

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57.

News Machine Learning Techniques 7

The malleable mind: context accumulation drives LLM’s belief drift

https://aihub.org/feed?cat=-473 · aihub.org

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58.

News Machine Learning Techniques 7

Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs

https://arxiv.org/rss/cs.CL · arxiv.org

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119.

Podcast Machine Learning Techniques 5

Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 297) [RB]

Data Science at Home · datascienceathome.podbean.com

Summary:

**Key Learnings:** 1. **VortexNet Architecture**: AI researchers are exploring a novel neural network architecture called VortexNet that uses actual whirlpools and fluid dynamics equations to solve deep learning's challenging problems, like vanishing gradients and long-range dependencies. 2. **Vortex Shedding**: The phenomenon of vortex shedding, where eddies are periodically shed from bluff bodies, is a key inspiration behind VortexNet. Researchers believe modeling this process could lead to more robust and efficient neural networks. 3. **Strouhal Number**: The Strouhal number, which describes the oscillating flow patterns around bluff bodies, is a critical parameter used in the VortexNet architecture. Incorporating this fluid dynamics concept could help neural networks better capture temporal and spatial dependencies. 4. **Turbulent Flows**: VortexNet aims to leverage the complex, chaotic behavior of turbulent fluid flows to build neural networks that can handle highly nonlinear and unpredictable data, going beyond the limitations of current deep learning techniques. 5. **Practical Applications**: While still in early research stages, VortexNet and its underlying fluid dynamics principles could eventually lead to breakthroughs in areas like anomaly detection, reinforcement learning, and even weather and climate modeling.

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120.

Podcast Machine Learning Techniques 5

Fairness in PCA-Based Recommenders

Data Skeptic · dataskeptic.com

Summary:

**Key Learnings:** 1. **Algorithmic Fairness:** Even well-intentioned machine learning models like collaborative filtering and PCA can inadvertently create unfairness, particularly for minority and niche user groups, due to biases in the underlying data. 2. **Power Niche Users:** Highly active users with specialized interests generate valuable data that can benefit the entire platform, but are often overlooked by recommender systems optimized for popular content. 3. **Limitations of PCA:** PCA-based collaborative filtering can over-specialize on popular content while neglecting both niche items and failing to properly recommend popular artists to new potential fans. 4. **Solutions for Fairness:** Item-weighted PCA and thoughtful data upweighting strategies can improve both fairness and performance simultaneously, challenging the common assumption that these goals are in tension. 5. **Practical Applications:** The insights from this research have real-world applications at companies like Meta, offering a comprehensive look at the future of personalized recommendations.

Anthropic vs. US government (174)

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25.

News Anthropic vs. US government 11

Microsoft brings Anthropic's Claude Cowork into Copilot to run tasks across Outlook, Teams, and Excel

https://the-decoder.com/feed/ · the-decoder.com

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42.

News Anthropic vs. US government 9

The US airports where you should brace for long security lines as unpaid TSA agents stop showing up for work

https://feeds.businessinsider.com/custom/all · www.businessinsider.com

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Podcast Anthropic vs. US government 9

In graphic detail: How Anthropic’s Pentagon refusal is paying off in downloads, brand trust and enterprise deals

None · digiday.com

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49.

News Anthropic vs. US government 8

Anthropic sues US government over supply chain risk designation

https://www.engadget.com/rss.xml · www.engadget.com

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55.

News Anthropic vs. US government 7

Anthropic Sues Department of Defense Over ‘Supply Chain Risk’ Label

https://rss.nytimes.com/services/xml/rss/nyt/Technology.xml · www.nytimes.com

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News Anthropic vs. US government 7

Anthropic Sues Department of Defense Over Supply-Chain-Risk Designation

https://www.wired.com/feed/tag/ai/latest/rss · www.wired.com

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66.

News Anthropic vs. US government 7

Anthropic Sues US Government Over Supply Chain Risk Label

https://feeds.bloomberg.com/technology/news.rss · www.bloomberg.com

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76.

News Anthropic vs. US government 6

Anthropic sues the Pentagon over being declared a ‘supply chain risk’

https://www.ft.com/artificial-intelligence · www.ft.com

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81.

News Anthropic vs. US government 6

iPad Air M4 review: Still Apple's best overall tablet, with a few caveats

https://www.engadget.com/rss.xml · www.engadget.com

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87.

News Anthropic vs. US government 6

Perhaps not Boring Technology after all

https://simonwillison.net/atom/everything/ · simonwillison.net

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