OpenAI laid out a new plan on Tuesday to expand access to AI models with advanced cyber capabilities while implementing controls on who can use them.
Why it matters: The roadmap coincides with the release of a new model variant, GPT-5.4-Cyber, designed to assist with defensive cybersecurity tasks and be more permissive for vetted users. — Read More
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8 Tips for Writing Agent Skills
Skills have become one of the most used extension points in agents. They’re flexible, easy to make, and simple to distribute.XXXXBut this flexibility also makes it hard to know what good and what works. What type of skills are worth making? What’s the secret to writing a good skill? When do you share them with others?
I have been using skills extensively with many of them in active use. Here are some tips I’ve learned along the way. — Read More
Org Design in the Age of AI
Strip a company down to first principles and it’s really three things: people, hierarchy, and information flow. We tend to think of hierarchy as being about authority — who reports to whom, who approves what. But that’s the surface. The deeper function of hierarchy is information routing. The org is too large for any single person to see the whole picture, so you install layers of managers to aggregate signals from the front lines, synthesize them, and pass them up — and to translate strategic intent from the top and distribute it down.
Most of the organizational machinery we take for granted exists to solve this problem. Meetings, status updates, steering committees, quarterly business reviews — these are all information-routing mechanisms. They exist because moving knowledge between people is expensive. — Read More
Why Chinese AI Is Suddenly So Good (ft. DeepSeek, SeeDance 2.0)
Microservices at Scale: Engineering Debt and System Complexity
ELT: Elastic Looped Transformers for Visual Generation
We introduce Elastic Looped Transformers (ELT), a highly parameter-efficient class of visual generative models based on a recurrent transformer architecture. While conventional generative models rely on deep stacks of unique transformer layers, our approach employs iterative, weight-shared transformer blocks to drastically reduce parameter counts while maintaining high synthesis quality. To effectively train these models for image and video generation, we propose the idea of Intra-Loop Self Distillation (ILSD), where student configurations (intermediate loops) are distilled from the teacher configuration (maximum training loops) to ensure consistency across the model’s depth in a single training step. Our framework yields a family of elastic models from a single training run, enabling Any-Time inference capability with dynamic trade-offs between computational cost and generation quality, with the same parameter count. ELT significantly shifts the efficiency frontier for visual synthesis. With reduction in parameter count under iso-inference-compute settings, ELT achieves a competitive FID of on class-conditional ImageNet and FVD of on class-conditional UCF-101. — Read More
The Three Enterprise Layers Are Collapsing Into One
For twenty years, enterprise software that processed decisions at scale had a clean three-layer separation. The CRM layer owned the customer touchpoint — above the glass, the intake, the first interaction. Behind it sat the orchestration layer — workflow engines, business rules, approval chains, human queues. Behind that sat the back-office actions: disbursement, fulfillment, settlement, reconciliation. Below the glass.
A loan application entered through the CRM. A workflow engine routed it through underwriting queues, compliance checks, and approval chains. When the process completed, a back-office system disbursed the funds. Three systems. Three vendor contracts. Three integration projects. An entire consulting ecosystem existed to wire them together, and an entire certification industry existed to staff the wiring. — Read More
What is the Application Layer?
Model companies are moving up the stack. Anthropic has grown on the back of Claude Code and competes directly with Cursor. OpenAI bought OpenClaw. Both are forward deploying engineers into enterprises to embed their models into workflows. On the surface, the application layer looks like it’s being subsumed from below.
On a closer look though, I think it’s premature to call the application layer won by the model companies. The more model companies push into applications, the clearer it becomes where they cannot win. But to see why means understanding what an AI application actually is, because it’s not what most people think. — Read More
On Anthropic’s Mythos Preview and Project Glasswing
The cybersecurity industry is obsessing over Anthropic’s new model, Claude Mythos Preview, and its effects on cybersecurity. Anthropic said that it is not releasing it to the general public because of its cyberattack capabilities, and has launched Project Glasswing to run the model against a whole slew of public domain and proprietary software, with the aim of finding and patching all the vulnerabilities before hackers get their hands on the model and exploit them.
… This is very much a PR play by Anthropic—and it worked. Lots of reporters are breathlessly repeating Anthropic’s talking points, without engaging with them critically. OpenAI, presumably pissed that Anthropic’s new model has gotten so much positive press and wanting to grab some of the spotlight for itself, announced its model is just as scary, and won’t be released to the general public, either. — Read More
What hackers talk about when they talk about AI: Early-stage diffusion of a cybercrime innovation
The rapid expansion of artificial intelligence (AI) is raising concerns about its potential to transform cybercrime. Beyond empowering novice offenders, AI stands to intensify the scale and sophistication of attacks by seasoned cybercriminals. This paper examines the evolving relationship between cybercriminals and AI using a unique dataset from a cyber threat intelligence platform. Analyzing more than 160 cybercrime forum conversations collected over seven months, our research reveals how cybercriminals understand AI and discuss how they can exploit its capabilities. Their exchanges reflect growing curiosity about AI’s criminal applications through legal tools and dedicated criminal tools, but also doubts and anxieties about AI’s effectiveness and its effects on their business models and operational security. The study documents attempts to misuse legitimate AI tools and develop bespoke models tailored for illicit purposes. Combining the diffusion of innovation framework with thematic analysis, the paper provides an in-depth view of emerging AI-enabled cybercrime and offers practical insights for law enforcement and policymakers. — Read More
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