Tigris Data

I am bullish on the decentralization of compute. We are going to need 100x more capacity at least if not 1000x and it’s not going to be the big 3 serving all that demand.

If AI agents are going to scale into the millions, we need tools that are tiny, isolated, durable, and safe to leave unattended. That's the niche agent-shell fills. Read more on the blog: tigrisdata.com/blog/agent-she…

This is also great for agents as they are really good at using bash, so it's win/win/win: good for us as a demo, good for you as a user, and good for your endless swarms of agents as you make your life's dreams come true in the form of B2B SaaS apps.

We embedded a Tigris client on our homepage so that if you're in a pinch and need to fork a bucket, we've got your back!

@basic_memory just shipped per-file diff and merge for AI-edited Markdown notes. Pick any prior version, see what changed, apply the lines you want. It's built on Tigris bucket versioning. Every edit becomes a new object version. One ListObjectVersions call returns the full revision history. CodeMirror handles the merge UI. Object versioning has everything git would have (full history, content-addressable IDs) without any of the infrastructure: no commits, no branches, no merge engine on the server. Read how they used Tigris to make it happen👇

Read the full post: tigrisdata.com/blog/case-stud…

Most AI memory locks your context to one platform. Switch tools, lose everything. @basic_memory flips it: your knowledge graph as plain Markdown in your own bucket that you can edit directly. Any MCP tool — Claude, ChatGPT, Cursor — reads and writes the same files. New case study on how a four-person team built it on Tigris. 👇

We caught the disco bug 🪩✨

Our next South Bay Systems meetup is on May 26! This time, we're covering one of my favorite topics: databases, and how to use them to make better architectural decisions and build reliable systems. We have two great talks lined up: - "Building a Distributed Persistent Queue on FoundationDB": @HimankChaudhary will walk through how the queuing infrastructure at @TigrisData was designed and implemented. - "Decisions, Principles, and Lessons from a Year of Teaching MySQL New Tricks": Steve Schirripa will share lessons and challenges around extending relational database systems at @VillageSQL. Food and drinks will be provided courtesy of our hosts at @PingCAP. Registration link below.

1. Main bucket stays clean 2. New docs land in a fork 3. Sketchy → quarantine 4. Clean → promote He calls it "The Immutable Agent" 👇 tigrisdata.com/blog/immutable…

Prompt injection via the knowledge base is the agent attack surface nobody's talking about. David Myriel built a TypeScript agent on @mastra using Tigris bucket forking as a last line of defense:

Think of it as database transactions or PR review, but automated and applied to agent writes. Check out how we built this on Tigris and @mastra (for request-scoped context): t.ly/fIbfl

The Immutable Agent is a pattern for protecting agents that write back to shared storage: t.ly/fIbfl 1. In this build, every agent run executes against a fork of your storage bucket, not the live one. 2. After the run, a validator checks the fork. If it passes, it merges into production. If it fails, it gets quarantined for forensics, and production is untouched.

random fact: you don't need a full vm for forking @TigrisData has support for it inside of their buckets.

Wake up to a fresh LLM Wiki of everything from yesterday. One Slack link, and satisfy your FOMO. We built this on Karpathy's pattern. Runs nightly, costs $0.23/run: tigrisdata.com/blog/self-upda…

Check out agent workspaces on agent-kit: tigrisdata.com/docs/ai/agent-…

Make a bucket per agent, with access locked down for only that agent. On other storage, this would be a dozen API calls. Tigris does it in one.