Platformatic

If your team is stuck on Node 12 and every upgrade conversation ends with "too risky". Join @lucamaraschi and @matteocollina: → Why the Bun Zig→Rust port worked (hint: not the AI) → What AI migration actually requires → Node 12 → 24 in practice streamyard.com/watch/Qyprr9Rs…

The real reason we built Regina is as development tooling for our enterprise features. You want to manage agents at scale — Regina is the building block." @Matteo OSS first. Enterprise foundation underneath. Sequence is intentional. youtube.com/live/i0VuBKeLX…

We Ran DOOM in a Node.js Terminal. Now There's No Excuse for Your Legacy Native Code x.com/i/broadcasts/1…

Four reasons AI agent projects fail after the demo, none of them are the model: → Prompts outside version control → Sessions wiped on every restart → Multi-pod state with no coordination layer → Tool workflows with zero observability Full breakdown: blog.platformatic.dev/introducing-re…

Most AI agent failures in production aren't model failures. They're orchestration failures: no session persistence, no observable tool calls, no lifecycle management. Regina solves this for Node.js teams. blog.platformatic.dev/introducing-re…

Next week on Banter: we ran @DOOM in a Node.js terminal. 35fps. Sound. One binary. No native addons. No compilation. @p_insogna built it to prove a point about FFI that every enterprise team needs to hear. Register now: streamyard.com/watch/bagFGbXA…

Real numbers from a real production deployment of ai-gateway-auditable 👇 Before: losing 15% of logs at peak. Latency doubling on provider slowdowns. After: latency halved. Zero log loss. Full compliance readiness. Here's exactly what changed blog.platformatic.dev/auditable-ai-g…

Putting audit logging on your AI gateway's critical request path is one of the most common production mistakes we see. Every audit write adds latency. Every audit failure risks your user-facing response. Here is how we solve it: blog.platformatic.dev/auditable-ai-g…

🚀 Autoscaling isn’t broken. It’s just too late. By the time HPA or KEDA reacts, users have already felt the spike. What if your infrastructure could predict traffic instead? New Banter episode with @lucamaraschi & @matteocollina 👇 streamyard.com/watch/jtj4vdxg…

📊 The difference with predictive scaling: ✅ 99.47% success rate ✅ 26 ms median latency ✅ No event loop saturation cliff (Compared to 95% / 154 ms for KEDA and 90% / 522 ms for HPA) Stop reacting. Start predicting. Explore White Paper ↓ arxiv.org/abs/2604.19705

Node.js makes this worse. CPU ≠ real load. The event loop can be saturated while the CPU looks “normal”. That’s where latency suddenly explodes. The cliff. Reactive scalers don’t see it coming.

🚀 Kubernetes autoscaling is always late. By the time HPA or KEDA reacts, your app is already overloaded. We built a different approach. Platformatic ICC predicts load trends and scales before the spike hits 👇 blog.platformatic.dev/ahead-of-time-…

This setup is simpler, and faster. Example We split Next.js image optimization into a separate worker, using the SAME codebase. ❌ No extra repo ❌ No duplication ❌ No noisy neighbour issues Better performance + easier scaling, without added complexity blog.platformatic.dev/run-medusa-kub…

Watt runs multiple Node.js apps as worker threads in a single process. That means: 🔨 No manual service wiring 🏎️ Fast internal communication 🔢 One runtime, multiple apps Frontend → backend calls stay inside the process, no external hops. Simpler and faster.

Running @medusajs in production gets complex fast. Frontend, backend, admin, image optimization, routing, Kubernetes… We simplified it with a Watt monorepo: 1️⃣ One repo 1️⃣ One build 1️⃣ One deployable platform Here’s how ↓ blog.platformatic.dev/run-medusa-kub…

Your AI integration works in the demo. In production, you're silently losing 15% of your request logs at peak, and your latency doubles every time a provider slows down. This is the operational wall every AI team hits. Here's how to get past it blog.platformatic.dev/auditable-ai-g…

We went stateful. More ops complexity, but that's exactly what Regina Coordinator handles, so devs don't have to. Here's the full architecture: blog.platformatic.dev/agents-in-prod…

Stateless works for simple request-response agents. But agents that write code, run it, install packages, manage a virtual filesystem? Each step builds on the last. Stateless means serializing the whole environment. That has a cost. blog.platformatic.dev/agents-in-prod…

Hot take: most "production-ready" AI agent frameworks aren't. Stateless architecture doesn't work for agents that build real in-process state. You need stateful session management. Change my mind. blog.platformatic.dev/agents-in-prod…