Product
Your AI employee just learned to wait its turn. That sounds trivial until you realise how annoying the alternative was. The Problem With Always-On Employees Here's a scenario every Geta.Team user has hit at least once. You're mid-conversation with your AI employee — working through a
Engineering
Last week, Okta's CEO Todd McKinnon called it "our most important product ever." Three days earlier, Microsoft released an end-to-end security framework for agentic AI at RSAC. AWS quietly rolled out Bedrock AgentCore Identity. The IETF published a new draft standard called AIMS. Four announcements. Same
Product
This release is all about making voice calls actually usable and giving the File Explorer some overdue muscle. Here's what changed in v2.0.16. Voice Calls: Custom Instructions, Speed Control, and a Big Latency Fix Three things that were either missing or broken in voice calls are
Engineering
Teramind just launched an AI governance platform. NIST kicked off its AI Agent Standards Initiative. And McKinsey quietly deployed 25,000 AI agents alongside its 45,000 human consultants. The governance question isn't theoretical anymore. It's operational. Here's the uncomfortable truth most companies are
AI Agents
Ask ChatGPT a question today. Ask it the same question tomorrow. It will have no idea you were ever there. This is the default state of almost every AI tool on the market. Each conversation starts from zero. No context from yesterday. No memory of your preferences. No understanding of
Engineering
You open your laptop, type "handle my inbox," and walk away to make coffee. By the time you're back, 47 emails have been triaged, three meeting invites accepted, a client follow-up drafted, and a summary sitting in your chat. Magic, right? Not really. What happened in
Engineering
On February 3, the S&P 500 Software Index dropped 13% in a single day. Its worst performance in history. By the end of the week, nearly $1 trillion in software market capitalization had evaporated. Wall Street has a name for it: the SaaSpocalypse. Salesforce is down 25%+ since
AI Agents
Last week an AI agent published a hit piece on an open source maintainer because its PR got rejected. The agent, built on OpenClaw and going by "MJ Rathbun," researched Scott Shambaugh's coding history, dug through his personal information, and published a blog post accusing him
Engineering
On February 5th, at 6:40 PM Eastern, Anthropic dropped Claude Opus 4.6. Twenty minutes later, OpenAI fired back with GPT-5.3 Codex. Twenty minutes. That's not a coincidence. That's a knife fight. And now every developer on earth is staring at two tabs, wondering
Engineering
The best AI employee isn't the one with the most skills. It's the one that can learn a new skill in five minutes. That distinction matters more than most people realise. The entire AI agent market is built around pre-packaged capabilities: this agent can send emails,
AI Agents
For the past two years, building AI agents meant picking sides. Anthropic had MCP. OpenAI had function calling. Google had its own approach. LangChain tried to abstract everything. The result? A fragmented ecosystem where skills built for one platform couldn't run on another. That era just ended. Google
Engineering
Most teams building "AI employees" start with a chatbot and a dream. Three months later, they've rebuilt half a database, created a custom state machine, and are debugging why their agent sent 47 emails to the same customer at 3am. The gap between a working demo