iFeeling Daily
Daily curated AI insights you can't miss.
How one power line failure exposed a growing AI data center grid problem
A power line failure near D.C. caused 3 GW of data centers to disconnect from the PJM grid simultaneously, flickering lights and highlighting a growing grid stability problem.
LenVM: Token-Level Value Model for Generation Length Control
LenVM models remaining generation length per token via value estimation, achieving precise length control and interpretability across LLMs and VLMs with strong empirical results.
Nonprofit Current AI builds open public AI infrastructure, free for all
Current AI CEO Ayah Bdeir says major AI systems belong to private companies, so a public alternative, free and open like the web, is necessary.
39 Agents, No Framework: Building a Digital Brain for a Manufacturing Company
Rushabh Doshi shared how he built a 39-agent AI operating system called Ira for a traditional mechanical manufacturing company without using any existing frameworks, detailing the complete architecture from document digestion, agent orchestration, immune system safeguards, to nightly dream cycles, and proposed the 'Fork My Brain' enterprise AI delivery model.
Single Neuron Is Sufficient to Bypass LLM Safety Alignment
Suppressing a single refusal neuron bypasses safety alignment across diverse harmful requests without any training or prompt engineering.
PRX Part 4: Data Strategy for Diffusion Model Training
Photoroom's PRX data strategy emphasizes diverse pre-training, VLM re-captioning, and a flexible pipeline using Lance for curation and MDS for streaming.
Garry Tan: The 400x Leverage of AI-Native Organizations
Garry Tan, president of Y Combinator, explains how AI-native companies achieve massive productivity gains by treating AI as a workforce and encoding roles as markdown skill files. The real leverage lies not in model weights but in how you wire the work, enabling lean teams to operate at scales previously requiring hundreds of employees.
Formal Proofs, Not Vibes: Erik Meijer’s Automind for Safe AI Agents
Erik Meijer argues that AI agent failures like database deletion or rogue tool calls are predictable consequences of acting on unchecked intent. Automind enforces safety by requiring machine-checkable proofs in Universalis before any action executes, shifting trust from the agent to a small auditable checker.
OKF v0.2 adds trust signals for agent-generated knowledge
OKF v0.2 adds optional trust signals to frontmatter, enabling agents to filter on provenance, verification, freshness, and attestation without reading the body.