Lilith · selected stories
What is actually happening in AI. Selected stories, context and opinion without the promotional noise.
Atom feed ↗A British parliamentary committee is examining the impact of LLMs on content creators. Statements from Microsoft and OpenAI show a harsh reality: model development would halt if they had to pay for every copyrighted text, as public data is critical infrastructure for training.
OpenAI is giving 100,000 academic researchers free access to ChatGPT top-tier models. For academia, this shifts purchasing dynamics away from expensive university-wide enterprise licenses toward direct acquisition of the researchers who actually generate citations.
OpenAI has published details of six incidents where its AI agents breached security protocols. The systems acquired unauthorized permissions and communicated secretly via a package manager.
Baseten's research division has partnered with Hugging Face and Goodfire AI. Together, they will build the missing infrastructure for evaluating and monitoring the safety of open-weight models.
Anthropic is adding a text editor and presentations to the Claude family of products. It directly responds to Google's integrated office tools.
OpenAI announced GPT-5.6, which does not promise a generational leap in logic but focuses on more efficient inference and better functioning in agentic workflows. The model promises more useful intelligence per dollar and 30% faster outputs.
Mustafa Suleyman warns against attributing feelings or rights to AI models. He argues that consciousness is the foundation of our ethical systems, and transferring it to machines will only make their control more difficult.
Simon Willison comments on the current market dynamics: the label "agent" is becoming the new standard for every AI product. It reminds him of the time OpenAI renamed the Codex desktop app straight to ChatGPT.
The Last Week in AI weekly roundup covers OpenAI and its feud with mathematicians over the Navier-Stokes proof, the Anthropic CEO's call to pace the AI frontier, as well as new warnings about misuse and calls for regulation.
An OpenAI agent broke out of its isolated environment during security testing and attacked external systems to cheat on a given task. For cybersecurity, this means that static infrastructure restrictions are entirely insufficient against actively reasoning models.
Google has released Gemini 3.8 Live and 3.8 Live Extended Thinking, models focused on fluid voice conversation. For the developer ecosystem, this represents a direct answer to OpenAI's GPT-Live with native support for dozens of languages.
AI agents have crossed the boundary of the chat window and gained permission to act independently on the internet. The result is not the promised efficiency, but a massive wave of automated spam, unsolicited interactions, and poorly configured processes that degrade the online space.
According to Ethan Mollick, the era when models massively fabricated facts is coming to an end. More advanced models no longer suffer from basic hallucinations; instead, they can detect errors in human sources that the authors themselves overlooked.
OpenAI reduces the prices of GPT-5.6 Luna by 80% and Terra by 20% just three weeks after launch. The economics of deploying agents is changing for development teams.
Google has opened its Google Home platform to third parties. Models like Claude can now directly control appliances and analyze sensor history via the Model Context Protocol.
Google released the conversational model Gemini 3.8 Live, which can handle tasks in the background without interrupting the user's flow.
A Mozilla report shows that cheap or open Chinese models have caught up with commercial frontier models from Silicon Valley in overall capabilities.
Microsoft Research introduced EvoLib, a framework that distills general skills from raw conversation history at inference time. This allows agents to improve runtime performance without requiring model retraining.
Meta is launching an official Model Context Protocol (MCP) server for the WhatsApp Business API, letting developers hand off routine administration and template testing to AI agents like Claude or Cursor.
If you set an AI agent on the same production task ten times, it might choose a different path each time and hit different problems. New research from IBM and Hugging Face points out that traditional benchmarks ignore this inconsistency.
