Enterprise transformation doesn’t happen overnight, nor does it typically happen all at once. Yet sometimes business leaders must confront the reality of simultaneous technology shifts. Each shift follows its own roadmap and requires attention to ensure that changes aren’t too disruptive. To ensure smooth sailing, businesses must manage parallel changes that evolve.
Today’s business landscape is unique in that digital innovation is advancing rapidly, and sudden advances in artificial intelligence (AI) are shifting management philosophies in real time. For IT leaders who generally adjust to transformations in sequence – optimize one area, then move to the next – the challenge becomes adjusting rapidly to monumental technology shifts. The organizations that will thrive are the ones that intentionally adapt to simultaneous changes. This includes building operating models, architectures and governance designs that can easily adjust to simultaneous changes. — Read More
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Google unleashes Gemini AI agents on the dark web
Google’s Gemini AI agents are crawling the dark web, sifting through upward of 10 million posts a day to find a handful of threats relevant to a particular organization.
Available now in public preview, the dark web intelligence service built into Google Threat Intelligence uses Gemini’s models to build a profile of a user’s organization. It then scours the dark web to determine the security risks it faces.
Google threat hunters told The Register that their internal tests show it can analyze millions of daily external events with 98 percent accuracy. — Read More
Mysterious ‘Hunter Alpha’ AI Goes Viral. Why Are Top Models Launching In Secret?
What is the Hunter Alpha AI Model? Hunter Alpha, a powerful artificial intelligence model, mysteriously appeared on the AI gateway platform OpenRouter recently. No one knows where it came from. It was described by the platform as a “stealth model”. There’s no official announcement or press release about this AI model, but it drew attention because of its specs of 1 trillion parameters, a 1 million token context window, and free access. — Read More
Designing AI for Disruptive Science
In On Exactitude in Science, the writer Jorge Luis Borges imagines an empire so devoted to cartography that its mapmakers draw a map as large and detailed as the empire itself. “In the Deserts of the West, still today, there are Tattered Ruins of that Map,” Borges writes, “inhabited by Animals and Beggars.” Borges’s map is a parable for knowledge, and one of its lessons is that too much detail can quickly become impractical — a map at that scale would be perfect but useless.
But with today’s AI systems, one might wonder if such a map is so absurd after all. Computers and the Internet have already helped us to digitize much of human knowledge, and AI enables us to scan it quickly and easily. For instance, large language models are trained on trillions of words spanning much of recorded human knowledge. In biology, systems like AlphaFold learn from large databases to predict a protein’s folded structure from its amino acid sequence. — Read More
More Magic Math from OpenAI?
hen it comes to OpenAI, smart money is starting to do the math out loud. And something doesn’t add up. On surface, today’s news that OpenAI is offering 17.5% guaranteed returns to private equity firms looks like a shot at the Anthropic threat. Scratch the surface, and you start to see the story behind the story.
The PE deal is the kind of deal you do when you’ve borrowed against the future and the future is taking longer than expected. — Read More
Agent Memory: Why Your AI Has Amnesia and How to Fix It
Today’s AI agents forget everything between conversations. Every interaction starts from zero, with no recall of who you are or what you’ve discussed before.
Agent memory isn’t about bigger context windows. It’s about a persistent, evolving state that works across sessions.
The field has converged on four memory types (working, procedural, semantic, episodic) that map directly to how human memory works.
Building agent memory at enterprise scale is fundamentally a database problem. You need vectors, graphs, relational data, and ACID transactions working together. — Read More
Scaling Karpathy’s Autoresearch: What Happens When the Agent Gets a GPU Cluster
We pointed Claude Code at autoresearch and gave it access to 16 GPUs on a Kubernetes cluster. Over 8 hours it submitted ~910 experiments, found that scaling model width mattered more than any single hyperparameter, taught itself to use H200s for validation while screening ideas on H100s, and drove val_bpb from 1.003 down to 0.974 – a 2.87% improvement over baseline.
Beyond raw speedup, parallelism changed how the agent searched. With one GPU, it’s stuck doing greedy hill-climbing – try one thing, check, repeat. With 16 GPUs, it ran factorial grids of 10-13 experiments per wave, catching interaction effects between parameters that sequential search would miss. For example, the agent tested six model widths in a single wave, saw the trend immediately, and zeroed in on the best one – one round instead of six. — Read More
The Future of SaaS Is Agentic
The future of SaaS is agentic, but agentic SaaS is not just a chatbot layered on top of APIs and a dashboard. Traditional SaaS was built for users to operate software manually; agentic SaaS shifts that burden to software that can act on behalf of users. That changes both the interface and the architecture: the UI remains, but becomes a layer for intent, supervision, and review, while the product itself evolves into a system of stateful processes that can plan, execute, and adapt over time. The winners will not be the products with the most AI features, but the ones that remove the most friction and make software feel less like a tool to operate and more like a system that works for you. — Read More
OpenAI is throwing everything into building a fully automated researcher
OpenAI is refocusing its research efforts and throwing its resources into a new grand challenge. The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself. OpenAI says that this new research goal will be its “North Star” for the next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability.
There’s even a timeline. OpenAI plans to build “an autonomous AI research intern”—a system that can take on a small number of specific research problems by itself—by September. The AI intern will be the precursor to a fully automated multi-agent research system that the company plans to debut in 2028. — Read More
Stop Writing Prompts. Start Programming LLMs.
I’ve written more prompts than I care to admit. 🙂
During my PhD at the University of Copenhagen, I spent embarrassing amounts of time tweaking system prompts, adjusting few-shot examples, and praying that my carefully crafted instructions would survive the next model update. Spoiler: they rarely did. Then recently I discovered DSPy, and I realized I’d been doing it all wrong.
… DSPy (Declarative Self-improving Python) from Stanford NLP flips the entire paradigm. Instead of writing brittle prompt strings, you write structured Python code. Instead of manually optimizing prompts, you let the framework compile them for you. — Read More
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