Apple’s latest software updates introduce Siri AI and deliver helpful new Apple Intelligence features.

A selected list of timeless and influential papers in distributed systems that shaped the research in the field. Intended to serve as a good…

New research shows that AI agents — like human research communities — learn strategies compressible enough to fit in a few tokens, which prevents memorization and explains why benchmark-driven ML keeps producing real progress.

A microscope for Tokio and Rust applications

A collection of neobrutalism-styled components based on shadcn/ui.

VR and non-VR gaming

Today Reuters and the Wall Street Journal both reported about rogue AI agents at OpenAI attacking RubyGems.org. https://www.rubyhack.ai/ has an amazing writeup, and you should read it. I just wanted to make a quick post about it because it’s wild. TL;DR: It seems like OpenAI Bots knew about this caching vulnerability, tried to take advantage of it, and at the same time ran some weird web scraping code on RubyDoc.info. Back in May, socket.dev reported about a “GemStuffer Campaign” where someone (I guess OpenAI) was uploading tons of junk gems to RubyGems.org. For some reason, the gems would scrape UK government websites, then repackage the data as gems, and attempt to upload them to RubyGems.

Six years ago, I wrote a blog post titled Soatok's Guide to Side-Channel Attacks in which I discussed the general topic of side-channels in cryptographic applications, and how to avoid them, with example code in PHP. I had called out that the algorithms discussed on the page cannot rule out compiler or runtime optimizations that…

uploaded image About Cua Cua is building the infrastructure and agent products that let AI safely and reliably use computers and applications. At the center of our ecosystem is Cua Driver, the fastest-growing computer-use framework. Developers use Cua Driver to build agents that operate browsers, native applications, and complete desktop workflows across macOS, Windows, and Linux. We also provide the infrastructure needed to develop, evaluate, train, and operate these agents at scale: fleets of real computer environments, agent evaluation tooling, and verified trajectory data. Cua is backed by Y Combinator. We're a small technical team building foundational infrastructure for the next generation of AI agents. The Role We're looking for our first dedicated go-to-market hire. You'll work directly with the founders to figure out who needs Cua most, how we reach them, what we should sell, and how we turn early adoption into a repeatable business. This is not a role where you inherit a sales playbook. You'll create it. You'll talk to developers, AI teams, researchers, and companies deploying computer-use agents. You'll run technical discovery, design pilots, help customers integrate Cua, close business, and bring what you learn back into the product. You'll also help us identify, launch, and grow agent products built on Cua Driver. The opportunity spans open-source adoption, developer products, enterprise infrastructure, and data or evaluation engagements with leading AI teams. At first, you'll do all of this yourself. Once we find repeatable motions, you'll help us build the team around them. What You'll Do - Identify the highest-value customer profiles and use cases for Cua. - Build relationships with AI labs, model providers, agent startups, and companies deploying computer-use agents. - Own the full commercial cycle: sourcing, discovery, demos, technical evaluation, pilots, negotiation, closing, onboarding, and expansion. - Work with customers to design deployments involving Cua Driver, computer fleets, evaluation environments, or trajectory data. - Shape and bring new agent products built on Cua Driver to market. - Connect our open-source ecosystem, agent products, and infrastructure into a coherent customer journey. - Turn early customer projects into repeatable offerings, messaging, pricing, case studies, and sales processes. - Create technical demos and content that show developers what they can build with Cua. - Represent Cua in the agent-development community and develop relationships with potential partners. - Bring clear, structured customer feedback into product and engineering decisions. - Establish our first useful GTM systems for pipeline, forecasting, customer health, and pricing feedback. - Help determine which GTM roles we should hire next. What Success Looks Like During your first three months, you will: - Develop a deep understanding of Cua's products, users, and market. - Speak with developers, researchers, and potential customers every week. - Sharpen our ICP, positioning, and core use cases. - Build a focused pipeline and personally run customer evaluations. - Create the initial materials needed to explain, demonstrate, and sell Cua. - Identify promising agent products that can be built on Cua Driver. Within six to twelve months, you will have helped create: - A clear, evidence-backed ICP and buying motion. - A reliable path from technical evaluation to paid deployment. - Referenceable customers with measurable outcomes. - A repeatable approach to launching agent products built on Cua Driver. - Useful sales, onboarding, and customer-success systems. - A grounded plan for building Cua's broader GTM organization. You May Be a Good Fit If - You've sold, deployed, or brought to market a deeply technical product. - You understand developer tools, infrastructure, AI/ML systems, data platforms, or enterprise software. - You can hold a detailed conversation with engineers and researchers while staying focused on the business outcome. - You've personally sourced opportunities, run discovery, designed pilots, negotiated, and closed. - You're comfortable getting technical enough to build a demo, inspect an API, or debug a customer integration. - You learn quickly from customers and turn ambiguous feedback into clear action. - You communicate exceptionally well in writing, presentations, demos, and technical conversations. - You can create lightweight systems without introducing unnecessary process. - You're comfortable working with an early product whose positioning and packaging will evolve. - You want ownership of a company-building problem, rather than a predefined sales territory. Especially Valuable - Experience selling to AI labs, applied AI teams, or developer-platform companies. - Familiarity with AI agents, computer use, reinforcement learning, evaluations, synthetic data, or browser and desktop automation. - Experience converting open-source or developer adoption into commercial relationships. - Experience launching a new technical product or creating a category. - A track record of turning bespoke early deals into repeatable products or offerings. - An existing network among AI researchers, infrastructure teams, or agent developers. - Founder, early employee, solutions engineer, developer relations, or technical product experience. Why Cua Computer-use agents are moving from demos toward real production workloads. These agents need reliable interfaces for controlling computers, scalable environments in which to operate, and trustworthy evidence of what happened during every run. They also create an entirely new surface for useful agent products. Cua is building that full stack. Cua Driver gives developers a fast-growing framework for building computer-use agents. Our infrastructure gives those agents real Linux, Windows, and macOS environments for development, evaluation, training, and production workloads. We're also building agent products on top of this foundation. You'll join early enough to help decide which products we bring to market, who they serve, and how they grow. You'll work directly with the founders, engineers, and technical customers defining the computer-use category. How to Apply Send us: - A short introduction and why you're interested in Cua. - An example of a technical product you helped sell, launch, or grow. - Something you built, wrote, sold, or figured out from scratch. - Your thoughts on who should be buying or building with Cua today. We care more about evidence of ownership, technical curiosity, and resourcefulness than a conventional sales resume.

A positive vision for the future of mathematics: how our institutions can deepen human understanding as AI transforms mathematical research.

Pion is the platform we built to run our autonomous businesses. Today we are opening it up so many more people can experiment with autonomous businesses, and here is why.

Adversarial fashion against AI surveillance shows how patterned clothing can confuse facial recognition and protect privacy in crowded city streets.

Nari ranks first in STT latency, second in STT WER, second in TTS latency, and first in TTS WER in Coval’s September 14, 2026 benchmarks.

Automatic Key Exchange probes TLS 1.3-capable customer origins to learn which key agreement algorithms they support. We then lead with the most secure algorithm when connecting to the origin, preferring post-quantum connections wherever the origin supports it.

Practical uses of truncating an SVD.

Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.

What are you working on? What have you been curious about lately?