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OpenAI alumni launch $100M Zero Shot fund

Image via TechCrunch

Sunday, April 12, 2026

OpenAI alumni launch $100M Zero Shot fund

OpenAI alumni just launched the $100M Zero Shot fund focused on AI automation and robotics, while Anthropic rolled out Managed Agents with a new decoupled architecture designed for reliable long-running tasks.

Top Stories

1
Elorian (ex-Google DeepMind visual AI startup)

Substack

Ex-DeepMind researchers launch Elorian to build advanced visual AI systems, claiming current models lack sophisticated image understanding needed for real-world applications in automotive, architecture, and robotics.

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2
Zero Shot Fund

Substack

Former OpenAI executives have launched Zero Shot, a $100M venture fund that has raised $20M and made early investments in AI automation and robotics startups, leveraging insider knowledge to identify promising opportunities while avoiding overhyped AI sectors.

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3
Claude Introduces Managed Agents

Anthropic

Anthropic's Managed Agents decouples agent architecture into modular, independently-replaceable components (brain, hands, session), enabling more reliable long-horizon AI task execution while reducing latency by up to 90% and improving security and flexibility for enterprise deployments.

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4
How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings

UC Santa Barbara, MIT CSAIL, and MIT-IBM Watson AI Lab

This comprehensive study reveals that LLM agent skills are fragile under realistic conditions: when agents must retrieve from 34k skills rather than receive curated ones, performance degrades significantly and approaches no-skill baselines. Query-specific refinement can recover much of this gap when relevant skills exist, but cannot compensate when they're absent entirely.

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5
ClawArena: Benchmarking AI Agents in Evolving Information Environments

UNC-Chapel Hill, University of California, Santa Cruz, and University of California, Berkeley

ClawArena is a new benchmark evaluating AI agents in realistic, evolving information environments with conflicting sources and implicit user preferences across 64 scenarios. Experiments show model capability matters more than framework design (15.4% vs 9.2% performance range), though self-evolving frameworks can partially close capability gaps.

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