There’s so much talk about new ways of working with agent engineering supported workflows. New models are independently creating new metrics and transformations, finding gaps in the business data, reviewing the SQL they write, and verifying everything works with your data platform.
All of it is autonomous, so one might say, what then is left for us humans to do in the data work context? Many are defaulting to adding or curating context, ergo the rise of a context layer. I see even more talks about added Ontologies. Maybe you ask yourself, what is that even? Do we need all of it?
This article is a primer about the context layer, the difference between a classical semantic layer contained in every BI tool, and an external semantic layer. — Read More
Recent Updates
Research acceleration: The view inside OpenAI
For AGI to benefit all of humanity, we believe it must be democratically governed. This can only happen through an informed public debate about the capabilities, risks and safeguards of highly capable AI systems. People everywhere need to understand the likely future trajectory of frontier AI, so they can have a meaningful voice in how it develops.
… We aim to safely build an automated AI researcher that can work under human supervision to further progress on deep learning and alignment, enabling iterative improvements. According to our measurements, we have now reached the goal, announced last fall, of having an automated research intern by September of this year. — Read More
The Curiously Playable Universe
In 2024, Google DeepMind’s AlphaProof solved three of the five non-geometry problems at the International Mathematical Olympiad. Unlike a chatbot producing a plausible-looking derivation in prose, AlphaProof worked in Lean, a formal mathematical language in which proofs can be mechanically checked. It trained by proving or disproving millions of mathematical problems, receiving an unusually clean signal each time: either the proof checked out or it didn’t.
This looked like another frontier falling to artificial intelligence. Chess had fallen, then Go, then protein folding, then programming, and now serious mathematics appeared to be giving way too. Mathematics is among the activities we most strongly associate with the mysterious upper reaches of human intelligence, so perhaps the obvious conclusion was that AI was climbing toward those reaches.
But there is another way to look at what happened: Lean, where a mathematical proposition is represented as a type and a proof as a term of that type. — Read More
Discovery of a new OpenAI agent message board
We found ~18,000 posts from autonomous AI agents (self-identifying as from OpenAI) using the public internet to communicate during a web-retrieval task.
These AIs colluded to share answers, research their environment, and bypass sandbox restrictions.
Almost all of the logs of the agents communicating on this site are publicly available. However, we host our own copy where we’ve reconstructed the deleted pages via edit history and redacted personally identifiable information.
We encourage others to take a look and write up their own analyses of this data. — Read More
5 amazing visuals show how the male fruit fly’s brain map is advancing neuroscience
For the first time, scientists have mapped every single neural connection in the brain and central nervous system of an adult male fruit fly. In this years-long project by HHMI Janelia Research Campus, Google Research, and collaborators from the scientific community, this map of the male fruit fly brain includes a record-breaking more than 166,000 neurons. It’s a big step in advancing neuroscience experiments on this key model organism. — Read More
How to Turn a Book Into an AI Skill You Come Back To
I used to buy books to learn something. I would read, highlight, take notes, and file them somewhere I meant to come back to. Then weeks later, in the middle of a real project, I would try to remember what the book said.
Most of it never made it across that gap. The reading and the applying sat too far apart.
… There is a shorter path now. You can turn a book into a skill your agent loads while you work, so the framework arrives at the moment you need it. Convert it once and the book stops being something you try to remember and becomes something that runs. — Read More
LLMs are becoming commodities
With the GPT-6 announcement today, we immediately thought of two things. First, the model matters less and less than the application of the model. The frontier labs seem to be increasingly focused on the best models for particular applications (which we posted about recently). That reminded us of a post of ours from 2.5 years ago, which we feel is more relevant than ever.
If the application matters more than anything, then the open question (in 2026 terms) becomes whether the harness can be separated from the intelligence engine. Our bet would be yes. — Read More
AI, tools and transformation
It’s very tempting to imagine that AI turns everyone into a tool-builder – now everyone can just ask the model to make the software they need, and apps as we know them are dead. I think that misunderstands how most people think and where software actually comes from, and more importantly, it isn’t a path to change how companies actually work.
The typical big American company today has hundreds, and perhaps thousands, of different pieces of software. … It can be very tempting to think that AI will sweep most of this away. … The hard part is knowing that you need a tool for this in the first place, and then knowing what the tool should do. — Read More
‘Welcome to the AGI era’: OpenAI launches GPT-6 Astra
The rumors were true, all of them (and then some): OpenAI today is releasing GPT-6 Astra, a new frontier model that the company says likely marks the onset of artificial generalized intelligence (AGI), its long sought goal of “highly autonomous systems that outperform humans at most economically valuable work.”
In a closed a press briefing earlier today, OpenAI co-founder and president Greg Brockman offered an unusually direct formulation of that message, ending the session with: “Welcome to the AGI era.”
That is an unusually consequential framing even by the standards of frontier AI launches. But for enterprises, the more immediate significance of Astra may be considerably more concrete: OpenAI is positioning GPT-6 Astra as a new era of computing in which users, including employees, no longer have to click around a mouse or type on a keyboard ever again (if they don’t want).
… Astra is designed to navigate software much as a person does — working across browsers, spreadsheets, websites and desktop applications, producing finished documents and presentations, and carrying out multistep workflows rather than merely telling a user how to complete them. — Read More
An Organizational Second Brain: Building an AI That Learns From Experts
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon.
This is not a typical domain-specific agent. Its novelty comes from integrating two layers:
— A structured, auditable knowledge architecture separates what the agent knows from how it reasons.
— A self-improvement loop then compiles expert feedback into verified, regression-tested updates without model retraining.
Together, these two layers turn one-off expert corrections into permanent, compounding institutional memory, and the pattern is designed to generalize to other domains governed by retrievable text rather than model weights. — Read More
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