All the best {API,system,product} designs I've seen are the result of intense collaboration between producer and consumer. So why is this so rare? So many APIs I come across appear to have been designed primarily with the implementor's needs in mind.
The best feedback an AI coding agent can get is a type error.
→ fires before the code runs
→ points at the exact line
→ the agent fixes it itself
Most backend infra is stringly-typed, so the compiler never sees the mistake - we've written about it in our recent article 👇
You can now add Redis to your Encore app with a few lines of TypeScript (or 1 prompt).
When you deploy it via Encore Cloud, it becomes a fully managed ElastiCache / Memorystore instance in your own AWS/GCP.
We love having awesome engineering teams as customers, and the Pallet team is definitely one of the best we've worked with.
In the last year they've completely reworked their product to become a fast-growing AI-based logistics platform for the enterprise.
This meant re-architecting their entire system and launching an event-driven architecture on Google Cloud.
All without touching Terraform.
Check out the full story: https://t.co/V38snNoO1T
When AI helps you ship features in minutes, slow infrastructure becomes insanely expensive.
Not in cloud spend, in lost velocity.
That’s why we’re building Encore Cloud: simple development and infrastructure mangement, for your own AWS/GCP.
Example: You can now move services between GCP Cloud Run instances in one click or spin up new ones instantly.
No code changes. No IaC.
Encore gives you distributed tracing without writing instrumentation code.
Every API call, database query, and pub/sub message traced automatically. Works locally and in production.
"The thing that was hard was getting it into prod because I had to fight all these legacy dashboards and tools." @theo said this in his recent Claude Code video. We already solved this for AWS/GCP at @encoredotdev.
We just released Encore Skills for Claude Code - a plugin that gives @claudeai the context it needs to write Encore apps even faster, and more efficient.
For those who haven't tried Encore yet, this means writing "add an S3 bucket" is essentially enough for Claude to create one and have it show up in your AWS account.
Check it out here: https://t.co/uOC2DtAX5w
Introducing Encore Cloud 2.0: The development platform for the AI era
We believe infrastructure and operations need to keep pace with code generation.
Over the past few years, 100+ engineering teams have adopted Encore in production and ship more efficiently because infrastructure isn't a bottleneck anymore. Databases, queues, IAM roles, networking, gateways, tracing - all automatic, in their AWS or GCP accounts, with production guardrails built in.
Encore Cloud 2.0 takes this foundation further - with enhanced visibility, service catalogs, and MCP integration so both teams and AI agents have the context they need.
Everything deploys as standard cloud resources in your cloud account with just a few lines of Encore code to declare what you need - without infrastructure lock-in.
Read more in our announcement post below.
We created a guide on building secure AI code execution infrastructure using @daytonaio w/ Encore.
Includes:
- Run AI-generated code in isolated sandboxes
- Auto-provisioned PostgreSQL database
- Built-in distributed tracing
- Deploy with git push
Link: https://t.co/MIstXiOJr9
We created a comprehensive guide on setting up production-ready auth with @clerk + Encore.
What you get:
- Complete user management platform
- Protected endpoints
- Auto-provisioned Postgres
- Deploy with git push
- Built-in observability
Link: https://t.co/XD7RegOdLm
We created a comprehensive guide on setting up authentication with @better_auth + @DrizzleORM + Encore.
What you get:
- Secure sessions & password hashing
- Type-safe from DB to API
- Auto-provisioned Postgres
- Deploy with git push
- Built-in tracing & API docs
Link: https://t.co/DEIIwwBrVU
I've rebuilt Apple Notes and deployed it to AWS in less than 30 minutes using @encoredotdev and @cursor_ai.
Crazy times we live in. Happy to take any questions.
(No, it's not Electron, it's a native MacOS app)
We built Debug Mode from decades of engineering experience so the AI debugs like a real developer: reproducing the issue, diagnosing the root cause, applying a fix, and validating the result.
Debug Mode — Launch Week Day 5
Leap debugs like a real developer.
Opens your app in a browser. Tests the functionality. Reads error logs and stack traces. Queries your database. Inspects network requests. Examines your code.
Then fixes it.
Leap now has a built-in Database Explorer — Launch Week Day 2.
Browse tables. Edit records. Run SQL queries. All inside Leap. No more switching between tools.
Read the full announcement 👇
Leap 2.0 is out.
Bigger projects. Smaller cost. Claude 4.5. Web browsing. Reads docs. Auto-build and fix. Image generation. Real backends. Deploy to AWS/GCP. No lock-in. And that's just Day 1.
Comment "LEAP" to claim free credits and start generating production-ready apps.
More launches coming this week, stay tuned.
1 year ago we first spoke with @Groupon,
now they’re now all-in on Encore Cloud⚡️
- 3x faster dev
- 90% shorter time to market
- infra setup: weeks → minutes
case study in comments