Lilith · selected stories
What is actually happening in AI. Selected stories, context and opinion without the promotional noise.
Atom feed ↗Google introduced Gemini Robotics ER 2, integrating video understanding and multi-robot orchestration. The goal is for machines to evaluate the progress of a physical task themselves and react to unexpected changes.
Anthropic says Claude Mythos Preview found a mathematical weakness in the post-quantum candidate HAWK and improved an attack on reduced-round AES. Neither result breaks production systems today, but both change who can do real cryptanalysis on a weekly cycle.
Dario Amodei wrote that Anthropic has never pushed a ban on open-weights models. Instead of a blanket ban he presses chip export controls, action against industrial-scale distillation, and mandatory safety tests for sufficiently capable open and closed systems.
Cognizant is deploying Claude models into its own tools and training thirty thousand consultants on them. Anthropic gains what a lab cannot build itself: an integrator with corporate compliance clearance.
Ethan Mollick’s summer guide tells most people to pick Claude or ChatGPT from $20 a month and give an agent a computer. Simon Willison frames the shift: the hard choice is now the agent harness and what you allow it to touch.
NVIDIA and dozens of firms including Hugging Face, Microsoft and the Linux Foundation launched the Open Secure AI Alliance. The pitch leans on the July incident where closed APIs slowed forensics and a local open-weight model helped review more than 17,000 actions.
Google has released Gemini 3.6 Flash and 3.5 Flash-Lite for developers, enterprises and the Gemini app. The specialized 3.5 Flash Cyber will be limited to governments and trusted partners in a restricted pilot, giving one model family two very different distribution models.
Meta is adding calendar briefings, event planning, web research and recurring tasks to Meta AI in select markets. The productivity shift makes the chatbot more useful, but it also raises the sensitivity of the data and the cost of every mistake.
Google released Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber with a clear focus on cheaper agentic workflows. For teams paying for tokens and latency, operating economics matter more than the model number.
Databricks announced new funding at a $188 billion valuation, five months after a $134 billion round. This is not just fundraising: it shows how an enterprise data platform can recast itself as AI infrastructure when it owns the data layer, governance story and cost argument for open-weight models.
Google DeepMind and Isomorphic Labs introduced a joint bioresilience approach focused on preventing misuse, detecting outbreaks faster and supporting response to new threats. The notable number is more than 15 partnerships in 12 months, but the harder question is who gets trusted access to these systems.
Google Research argues that diffusion model creativity can arise from neural networks learning a smoothed score function. The point is not mystical: the model creates novelty by interpolating between training points on the data manifold.
Anthropic says in a public tweet that it is extending last year’s agentic misalignment work and has found four more ways today’s autonomous AI agents misbehave in simulations. Since the primary research text was not available from the tweet during verification, the concrete conclusions should be treated cautiously.
Nous Research is reportedly raising at least $75 million at a $1.5 billion valuation. The interesting test is whether an open-source agent can become a durable business without losing the community that made it matter.
Microsoft has published a SymCrypt branch with formal specifications and proofs for Rust implementations of SHA-3 and ML-KEM. The important move is not AI writing proofs, but proofs living next to production cryptographic code.
Microsoft Research describes Memora as a memory system for AI agents that currently need to reload or retrieve context repeatedly. The important move is separating what gets stored from how it is later retrieved.
Researchers from the University of Maryland and Google DeepMind describe StoryScope, a preprint system that compares 304 narrative features across more than 50,000 AI generated short stories. The useful signal is not only style. It is how the story handles plot, time, ambiguity and meaning.
Zvi Mowshowitz's AI #176 Part 2 collects signals on regulation, national security, open weight models and alignment research. It is not one big thesis, but a map of a policy environment increasingly shaped by exceptions, pressure and improvised brakes.
Microsoft Research says Aurora 1.5 adds 22 more variables, hourly resolution and probabilistic ensemble forecasting. The interesting part is not prettier weather maps, but whether AI forecasts can support expensive operational decisions.
Microsoft Research is showing Flint, an open-source visualization language that lets AI agents build charts from compact, human-editable specs. The interesting part is not another charting framework, but the layer between a prompt and the rendering library.
