Tailscale

Blog |companyJuly 21, 2026 Avery Pennarun: Good engineering decisions add up. So do shortcuts. In infrastructure software, users eventually feel both. They may not see the architecture, the incident review, the release process, or the quiet argument someone had to win to avoid a bad abstraction. But they feel the result when the product is reliable, understandable, and doesn’t create extra work. That’s the kind of product Tailscale has tried to build from the beginning.

31 cents seems like a lot to pay just to send “Good evening” to Claude Code, even if there are reasons for it. But $3.29 for a personalized B-minus copy of Rampart, running in a browser? I can see that. Those are the current token costs you might pay Anthropic for its coding assistance. If you pay $20 per month for a Claude Pro or ChatGPT subscription—or your company pays even more per-seat for team subscriptions—you don’t see the costs, unless you go looking for them.

Blog |insightsJuly 16, 2026 This blog post, originally published in April 2024, has been updated to reflect the most modern and convenient ways to install a Tailscale app in Home Assistant and use it for remote access, to accompany a newer video tutorial. You can see the original video here. Home Assistant brings the connected devices in your home into one interface, where you can control, connect, and automate them.

Have you ever asked an LLM in chat what seemed like a simple question, then watched as it reinvented fire, the wheel, and quantum physics to tell you how far Scotiabank Arena is from the Toronto airport? AI agents, like Claude Code, Codex, OpenCode, and OpenClaw, can do that, too. But their users explicitly encourage them to dig deep. Agents can make dozens of LLM requests per task, invoke tools, and pass data to one or more providers. So who sent what data to which model, and where is it logged?

I found myself trying to explain what an agentic workflow actually was to a friend the other day. In that moment, I realized I didn't really know. Agents, harnesses, runtimes, workflows, gateways, memory, tools—there are plenty of buzzwords to go around these days, and not much help for newcomers. AI “agents” sound complicated because people use the same word to mean several different things. Sometimes they mean, simply, a model. Sometimes that model offers a coding assistant, which has some agency.

It’s 11:20 p.m., and the newest engineer on the data team spots something wrong in a database table. They need to take a look, so a colleague points them to a connection string stashed in a shared vault. They paste it, run a SELECT, update the chat, and go to bed. Or did they? The database only shows that postgres connected. Not which engineer. Not why they were there. Just the same shared login six different people use when they need to get in. Any of them could have run a DELETE with no WHERE clause.

Today, we’re adding a new destination for Tailscale log streaming: Azure Blob Storage. We're also improving the setup flow for log streaming. Tailscale log streaming lets teams stream configuration audit logs and network flow logs from their tailnet into the systems they use for storage, compliance, detection, and investigation. With Azure Blob Storage, Tailscale now supports object storage across the major cloud providers: AWS S3, Google Cloud, and Azure.

With the rapid adoption of coding agents and “claw-like” assistants, it’s become clear that transformative AI use, both at home and at work, involves the interconnection of four key components: LLM, interface, sandbox environments, and data.

If you managed to put Tailscale on a jailbroken Kindle before it updated too far ahead, you got something pretty great, even if it wasn't the full Tailscale experience. But good things come to those who wait (or dig around on GitHub). Open-source developers have improved the Tailscale experience on one of the weakest computers you own.

Blog |insightsJune 09, 2026 HP is integrating Tailscale into its Remote System Controller, providing secure access to out-of-band management devices within existing tailnets. Before joining Tailscale, I spent years building and operating infrastructure for large companies, back when “the cloud” mostly meant racks of hardware in datacenters. At one job, a major power event took much of the datacenter offline.