iFeeling Daily
Daily curated AI insights you can't miss.
Applied Computing raises $20M for oil/gas AI foundation model Orbital
Applied Computing raised $20M for Orbital, an AI model that combines sensor, physics, and language data to analyze oil/gas plants in minutes.
Thinking Machines Lab Releases Open-Weight Model Inkling
Thinking Machines Lab released its first open-weight AI model, Inkling, a 975-billion-parameter mixture-of-experts system designed for enterprise customization and fine-tuning via its Tinker platform.
Hack suggests Suno scraped YouTube for training data
Hack of AI music generator Suno reveals alleged scraping of YouTube Music and other sources for training data, also exposing customer data.
OpenAI’s first hardware is reportedly a screenless moving speaker
OpenAI's first hardware device is reportedly a screenless smart speaker that can move, designed as an AI companion and developed with former Apple engineers.
Google Announces 33 Cybersecurity Startups in Gemini Forum
Google's Gemini Startup Forum: Cybersecurity has selected 33 AI-native startups across six focus areas, from agent security to post-quantum cryptography.
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.
AI Worldviews Mapped: Training and Alignment Shape Values More Than Lab Origin
The Economist scored 25 frontier AI models on the World Values Survey, finding lab origin a weaker predictor than training and alignment choices.
From Research to Reality: Benoit Schillings on AI Code and Reasoning
Benoit Schillings, VP of Technology at Google DeepMind, declares the era of syntax generation over, pushing software engineering toward architecture and validation. With self-play and inductive architectures, the focus shifts to guardrails and reasoning, transforming both code generation and scientific discovery.
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.