“Vibecoding” doesn’t lend itself to easy translation. For now, Chinese speakers call it 氛围编程 fènwéi biānchéng, 氛围 being “atmosphere”/”vibes” and 编程 being coding. This is an awkward expression because 氛围 usually refers to the atmosphere of a space or environment, and doesn’t have the connotation of care-free DIY that “vibe” does in colloquial American English. 氛围编程 sounds nonsensical as a phrase — something like “coding up an atmosphere.”
But we make do, and oftentimes writers simply use the English word. Developers, creatives, and entrepreneurs in China have been creating many interesting coding projects with AI tools over the past year, utilizing not only popular tools by Silicon Valley giants like Cursor and Claude Code, but also domestic models as Chinese AI companies increasingly compete in the coding-agent market.
Tinkering culture has no borders, and companies are cashing in. This is a roundup of reports from Chinese media on how vibecoding is changing the landscape of technology in China. — Read More
Author Archives: Rick's Cafe AI
The First Fully General Computer Action Model
We trained a model on our 11-million-hour video dataset. Our model can explore complex websites, complete multi-action CAD modeling sequences, and drive a car in the real world, all at 30 FPS.
We designed FDM-1, a foundation model for computer use. FDM-1 is trained on videos from a portion of our 11-million-hour screen recording dataset, which we labeled using an inverse dynamics model that we trained. Our video encoder can compress almost 2 hours of 30 FPS video in only 1M tokens. FDM-1 is the first model with the long-context training needed to become a coworker for CAD, finance, engineering, and eventually ML research, and it consistently improves with scale. It trains and infers directly on video instead of screenshots and can learn unsupervised from the entirety of the internet. — Read More
Novel Technique to Detect Cloud Threat Actor Operations
Cloud-based alerting systems often struggle to distinguish between normal cloud activity and targeted malicious operations by known threat actors. The difficulty doesn’t lie in an inability to identify complex alerting operations across thousands of cloud resources or in a failure to follow identity resources, the problem lies in the accurate detection of known persistent threat actor group techniques specifically within cloud environments.
In this research, we hypothesize how a new method of alert analysis could be used to improve detection. Specifically, we look at cloud-based alerting events and their mapping to the MITRE ATT&CK® tactics and techniques they represent. We believe that we can show a correlation between threat actors and the types of techniques they use, which will trigger specific types of alerting events within victim environments. This distinct, detectable pattern could be used to identify when a known threat actor group compromises an organization. — Read More
How I Use Claude Code
I’ve been using Claude Code as my primary development tool for approx 9 months, and the workflow I’ve settled into is radically different from what most people do with AI coding tools. Most developers type a prompt, sometimes use plan mode, fix the errors, repeat. The more terminally online are stitching together ralph loops, mcps, gas towns (remember those?), etc. The results in both cases are a mess that completely falls apart for anything non-trivial.
The workflow I’m going to describe has one core principle: never let Claude write code until you’ve reviewed and approved a written plan. This separation of planning and execution is the single most important thing I do. It prevents wasted effort, keeps me in control of architecture decisions, and produces significantly better results with minimal token usage than jumping straight to code. — Read More
Software stocks crater as independent research piece details potential AI dystopian scenario
Software stocks are getting shellacked as a post published by Citrini Research and Lotus Technology Management managing partner Alap Shah has sharpened attention on the magnitude and breadth of losers from the AI boom.
The piece, titled “The 2028 Global Intelligence Crisis,” is a hypothetical scenario analysis exploring the left-tail risks in two years’ time in a world where there’s an aggressive AI build-out and adoption of AI agents. — Read More
Detecting and preventing distillation attacks
We have identified industrial-scale campaigns by three AI laboratories—DeepSeek, Moonshot, and MiniMax—to illicitly extract Claude’s capabilities to improve their own models. These labs generated over 16 million exchanges with Claude through approximately 24,000 fraudulent accounts, in violation of our terms of service and regional access restrictions
These labs used a technique called “distillation,” which involves training a less capable model on the outputs of a stronger one. Distillation is a widely used and legitimate training method. — Read More
Andrej Karpathy Just Built an Entire GPT in 243 Lines of Python
I’ve read many transformer implementations during my PhD. Dense codebases. Thousands of files. Dependencies stacked on top of dependencies. You open a repo, run pip install -r requirements.txt, and watch 400 packages download before you can even see your model train (than errors , dependency issues … etc.).
Then on February 11, 2026, Andrej Karpathy dropped a single Python file that trains and runs a GPT from scratch. 243 lines. Zero dependencies. — Read More
Stop Renting Intelligence: The Economics of Local LLMs & The Return of Ownership
Recently, local AI assistants have exploded. Tools like OpenClaw now let anyone run powerful AI agents on their own hardware—no cloud subscription required. Many people still don’t understand what this actually means.
Some say big companies are panicking because everyone’s buying Mac minis to run AI themselves. This isn’t entirely true.
What big companies fear isn’t you buying that machine. It’s not even you canceling ChatGPT. What they really fear is this: the way compute power is consumed is changing from continuous payment to one-time ownership. — Read More
Google adds music-generation capabilities to the Gemini app
Google announced on Wednesday that it’s adding a music-generation feature to the Gemini app. The company is using DeepMind’s Lyria 3 music-generation model to power the feature, which is still in beta.
To use the feature, you’ll describe the song you want to create, and the app will generate a track along with lyrics. For instance, you could ask Gemini to create a “comical R&B slow jam about a sock finding its match,” and the app will generate a 30-second track along with cover art made by Nano Banana. — Read More
The Mythical Agent-Month
… Among my inner circle of engineering and data science friends, there is a lot of discussion about how long our competitive edge as humans will last. Will having good ideas (and lots of them) still matter as the agents begin having better ideas themselves? The human-expert-in-the-loop feels essential now to get good results from the agents, but how long will that last until our wildest ideas can be turned into working, tasteful software while we sleep? Will it be a gentle obsolescence where we happily hand off the reins or something else?
For now, I feel needed. I don’t describe the way I work now as “vibe coding” as this sounds like a pejorative “prompt and chill” way of building AI slop software projects. I’ve been building tools like roborev to bring rigor and continuous supervision to my parallel agent sessions, and to heavily scrutinize the work that my agents are doing. With this radical new way of working it is hard not to be contemplative about the future of software engineering.
Probably the book I’ve referenced the most in my career is The Mythical Man-Month by Fred Brooks, whose now-famous Brooks’s Law argues that “adding manpower to a late software project makes it later”. Lately I find myself asking whether the lessons from this book are applicable in this new era of agentic development. Will a talented developer orchestrating a swarm of AI agents be able to build complex software faster and better, and will the short term productivity gains lead to long term project success? Or will we run into the same bottlenecks – scope creep, architectural drift, and coordination overhead – that have plagued software teams for decades? – Read More
Rick's Cafe AI 