15 stories
A personal AI agent is useful because it can read your private data, read content written by other people, and act on your behalf. A prompt injection attack depends on those same three abilities.
Two networks carry a model's data between GPUs. Scale-up connects the GPUs inside one rack. Scale-out connects racks to each other. In NVIDIA's 72-GPU rack, scale-up carries 900 GB/s per GPU in each direction and scale-out carries 100 GB/s, a 9x difference.
Google's Agent Development Kit (ADK) gives you a lot more than a way to call a model with tools. This video walks through what's actually in the kit — how agents get their tools, three different ways to orchestrate them, how they talk to agents built outside the framework entirely, and the…
Every tool an AI agent can use has to be described to it first — and that description is re-read at the start of every conversation. Published measurements put a five-service setup at roughly 55,000 tokens before a single request is read.
Confidential AI means running a model on infrastructure that has no ability to read what it's processing. Data has been encrypted at rest and in transit for decades — but computing on it requires decrypting it into host memory first.
Every AI agent tool call is just text, and the program reading it is unforgiving — one bracket out of place and the whole response fails to parse. Prompting your way around that gets you "most of the time," which isn't the same thing as "every time." This deep dive explains constrained decoding:…
AI agents can now answer questions about your company's data in plain English — but on real enterprise schemas, even frontier models got roughly one answer in five right at launch.
Hugging Face disclosed a breach and said it didn't know what had attacked it. Five days later, OpenAI said the attacker was its own models — running an internal cyber-capability evaluation with refusals reduced, and trying to cheat on a benchmark by stealing the answers.
When an AI agent files your expense report or books your travel, some system has to answer a deceptively hard question: who is this, and who said they could?
AI agents don't get better by reading more of the internet — they get better by practicing inside simulated worlds built for exactly that purpose. This deep dive explains how modern agents actually learn: why human training data hit a wall, how the reinforcement learning loop works, the…
The grid didn't run out of electricity. It ran out of time. Connecting a large new load to the power grid can take four to ten years. An AI data center gets built in two to three.
Two different architectures let an AI take control of software: computer-use agents that drive an entire desktop, and browser agents that live inside the web. This deep dive explains how each one perceives the screen, where each wins, and the security problem they share.
Frontier AI models are almost too big to use — a 70B model needs ~140 GB of memory just to hold its weights. So how do these giants end up running on a single GPU, or even on a laptop?
Bigger context windows were supposed to fix everything — just put it all in the prompt and let the model figure it out. In reality, past a certain point, more context makes AI agents worse. This is the story of context engineering: the discipline of deciding what an AI should — and shouldn't — see.
Antigravity 2.0 is Google's agent-building platform — one agent harness you reach through a desktop app, the agy CLI, an SDK, and a single Managed Agents API. This is a builder-level walkthrough of its capabilities and the path from a prototype agent to one you'd trust in production.
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