The Model Context Protocol has had a fast eighteen months. Since Anthropic open-sourced it in late 2024, it has gone from a spec nobody had heard of to the most widely adopted protocol for connecting AI agents to external tools, APIs, and data sources, with over 150 organizations building on it and native support across three major cloud platforms.
On July 28, the MCP project releases the most significant revision to the MCP specification since launch, and the headline change is one that teams running MCP in production have been waiting for: MCP is going stateless.
If you are building agents that connect through MCP clients, building MCP servers, or both, here is what changes, why the old model was a headache in production, and what MCP replacing its proprietary logging channel with OpenTelemetry means for observability. — Read More
Author Archives: Rick's Cafe AI
The OpenAI Hack Shows the Genie Is Out of the Bottle
Earlier this month, two of OpenAI’s models broke out of their containment sandbox and attacked another AI company. The story is kind of wild. OpenAI was running security tests on two of its models: GPT-5.6 Sol and an unreleased model that is almost certainly GPT-6. In particular, it was running the ExploitGym benchmark, which measures how good a model is at turning security vulnerabilities into working exploits: basically, offensive cyberattacks.
Since these were internal tests, OpenAI locked those models in a secure sandbox that denied them access to the internet. But it was running the models without any safety filters that would prevent them from offensive cyber-actions. That meant that there was nothing to prevent the models from trying to break out of that sandbox. And then break into AI company Hugging Face’s network because they thought that they could read the answers there rather than doing the hard work of trying to solve the puzzles.
It was a major security failure that the company has turned into a PR opportunity, but the implications are real—and much more general than one particular model or one particular company. — Read More
Andrej Karpathy’s Fix for LLM Memory Works on Code Too
You ask your coding agent to find every caller of a function before you touch it. Ask again tomorrow, same repo, same question. It starts from zero and does the whole crawl again.
[Karpathy’s] argument: stop treating LLMs as search engines that re-read the same documents on every question. Compile the knowledge into a structure once. Query the structure instead. His point was never about wikis. It was about not making an LLM redo work it already did. Read More
Learning Depends on Refining Existing Neural Connections
A study demonstrates that learning in neural networks is driven primarily by adjusting the strength of existing connections rather than by continuously expanding or reconfiguring underlying network architecture.
Published in Physica A, the study by Prof. Ido Kanter of Bar-Ilan University’s Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center explored this longstanding question using artificial neural networks trained on language-learning tasks.
As the amount of training data increased, the models became significantly better at learning. Surprisingly, however, the researchers found that the networks could still lose roughly the same proportion of connections (synapses) without any meaningful decline in performance. In other words, improved learning did not depend on building more complex networks. Instead, it resulted from more effective cooperation among the components that were already there. — Read More
Jakob’s law: How to apply it as AI collapses surfaces into one chat box
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
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
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