Jakob Nielsen wrote his law in 2000, and it has outlasted almost everything else from that era of the web. Many of his commandments still hold, and not because technology stopped moving — it never does — but because people don’t change nearly as fast as their tools.
The implication: users expect your site to work like the ones they already know in the same way we expect automobiles the act a certain way i.e. if the steering wheel is a bunch of levers, I’m not going to want to rent or buy that car.
More and more tasks no longer start at a website or an app — they start in an assistant. The number of interfaces a person touches is falling, and the assistant is becoming the front door to tasks that each used to have their own destination. — Read More
Monthly Archives: July 2026
The Next AI Moat Isn’t a Better Model
A billion machines will become autonomous or intelligent over the next ten years. Cars, trucks, tractors, mining haulers, defense systems, warehouse robots, humanoids—the physical economy will be rebuilt around software that perceives, decides, and acts.
The prevailing assumption about how we get there goes something like this: models keep improving, world models mature, foundation models for robotics arrive, and autonomy falls out the other end. Intelligence is the whole game; scale the intelligence and the machines will follow.
Deployed physical AI is a product of two variables: the capability of the models, and the capacity of the engineering system around them—i.e. how requirements become software, how software gets validated, and how validated systems get deployed, monitored, and improved. The industry has largely poured everything into the first variable while the second sits roughly where it was a decade ago. The contrarian bet, then, isn’t against intelligence. It’s that the next order of magnitude in physical AI comes from making the engineering system as intelligent as the models it carries. — Read More
UX-Context Design: Using UX Knowledge to Inform AI-Generated Design
AI models produce output based on context. Context is everything the model can see when it does the work: your request, plus whatever instructions, standards, examples, and background information come along with it.
As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI. — Read More
Introducing Inkling-Small
Today, we are releasing Inkling-Small, an efficient open-weights model that achieves comparable performance to Inkling at a quarter of its size.
Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort, a context window of up to 1M tokens, and well-rounded performance across a range of benchmarks. — Read More
Zero risk isn’t the job: a CISO’s guide to agentic AI
Security leaders are being asked to approve agentic AI use cases that did not even exist a few months ago. Boards want to know whether any of it is governed, and somewhere in your organization, an employee has already connected an agent to something without telling you.
Saying “no” to these requests produces shadow adoption, which has zero telemetry and generally no off switch. Saying “yes” without controls produces incidents, and the first serious agent incident at your company will set your AI program back.
A CISO’s responsibility in the age of agentic AI is not to achieve zero risk. Instead, our jobs are to make agentic risk legible and bounded. This way, we can deliberately accept what we can manage, so the business moves on our terms instead of around us. — Read More
Control Reliability Engineering (CRE): Applying SRE Principles to Cybersecurity Controls
Security breaches are often not the result of awesome attacker capabilities or the sudden emergence of sophisticated zero-day exploits. Instead, what we usually find are the controls designed to stop the attack were believed to be operational but were actually broken or misconfigured at the moment when they were needed. Sometimes they were never fully in place to meet the security team’s original intent.
So, continuous control monitoring is needed to counter the natural decay that occurs to any type of control, not just security. More than just the monitoring itself, we need to apply a wider operational discipline to design controls that are able to be monitored, to look at root cause analysis in the face of detected failures, and to apply engineering principles to the overall control environment. In short, we need Control Reliability Engineering (CRE) as a direct application of Site Reliability Engineering (SRE) approaches to cybersecurity controls. — Read More
The majority of corporate IT is now off premises for the first time
IT is going remote while sucking up more power.
Most corporate IT is now off-premises for the first time. … Uptime’s Global Data Center Survey 2026 reveals how the industry is managing to adapt to challenging circumstances, with the usual evergreen concerns over rising costs plus staffing and skills shortages. — Read More
Gemini Robotics 2 brings whole body intelligence to robots
For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Now, that vision takes a significant stride forward.
Most robots are pre-programmed or teleoperated for narrow, repetitive task sequences. They lack the ability to truly learn for themselves or adapt to unpredictable environments. Moreover, transferring learned skills from one robot body to another remains incredibly difficult. To take on the hardest problems at scale, robots of every shape and size need AI models giving them the ability to think, act, and interact intelligently to safely complete tasks.
We demonstrated how Gemini’s multimodal understanding could drive real-world action with Gemini Robotics. Today, we are introducing Gemini Robotics 2 – the intelligence layer powering the next generation of truly adaptable robots. As it takes its first literal steps, this major advance unlocks intelligent whole-body control, advanced dexterity, and multi-robot collaboration. — Read More
COG: The Agentic Second Brain That Actually Self-Evolves
Cognition + Obsidian + Git — A self-evolving second brain powered by AI agents, markdown files, and version control. No database, no vendor lock-in — just .md files that think.
COG is an open-source ai agents skill built by Huy Tieu for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT. IT learns your patterns and organizes itself offering custom workflows for braindumps, daily briefs andcompetitive intel. — Read More
Advancing the price-performance frontier with GPT‑5.6
Yesterday, we shared how GPT‑5.6 helped make itself more efficient to run. Today, we’re passing those gains on to customers with lower prices for GPT‑5.6 Luna(opens in a new window) and Terra(opens in a new window) and faster performance with GPT‑5.6 Sol in the API. Together, these updates help customers get more from every dollar they invest in AI and move faster when time matters. — Read More
Rick's Cafe AI 