New: We've tightened the Git/GitHub publish workflow so that if your saved repo settings have changed, you'll need to re-validate before a publish push goes through.
One extra step. Zero config drift.
→ Start building at https://t.co/LoFPMqXXKy
#GitHub#DevOps
New: Full visibility into your hosted app lifecycle
EcosystemCode now shows deployed and terminated timestamps for every hosted app.
No more digging through logs or second-guessing what's live.
#AIDeployment
“Works on my machine” is still one of the biggest architectural failures in software engineering.
The real challenge is designing full-stack systems that remain consistent across every deployment target from day one.
Watch the live demo: https://t.co/bDSajk4auN
Most code generators hand you a starting point. We think the first pass should already be close to done.
We've shipped broad improvements across generation pipelines, outputs, and stack targets.
Code is more consistent and better structured.
#Cloud#DevOps#AIInfrastructure
Generated applications now look the way they should on every screen.
We've shipped mobile layout and formatting improvements for generated applications.
The output is now cleaner, more consistent, and better structured for mobile without additional front-end work.
#AISystem
Architecture decisions lose value when they cannot be communicated clearly.
We’ve updated Document Generation to produce sharper specifications, cleaner exports, and better structured handoff documents directly from your UML models.
Version management now gives you clearer tracking directly in Project Settings.
Track changes and roll back with confidence without losing context on where your architecture stands.
See how it works at https://t.co/kK2S8ooY3p
The way backends are being built is changing, and EcosystemCode is evolving with it.
We’ve introduced agentic support patterns for generated backends, giving every system the foundations needed for workflows and multi-step orchestration.
#SoftwareArchitecture#AIEngineer
EcosystemCode lets you align your deployment targets at the architecture stage.
Teams building serious systems now operate in AI and ML workloads that need cloud-native architecture from day one, and growing data sovereignty requirements that shape where systems can run.
Most teams build to a cloud provider and call it architecture.
Then a compliance req changes or a client needs on-prem.
The teams who survive don't pick better infra. They build so it doesn't matter.
👉: https://t.co/mIdK8xj1AD
#Prompting#ArchitectureDrift#SystemDesign
Most teams fix bugs. They don't fix the decisions that caused them.
What if bugs were propagated backwards through a structured dependency network to correct the weights responsible?
That's the difference between patching code and correcting architecture.
But here’s an uncomfortable truth: Most AI-generated codebases don’t have a foundation.
They run, but they don’t hold.
We wrote about what changes when teams stop prompting and start architecting for AI-native systems.
Link in comments 👇
Your $20/mo plan has $2,000 worth of output in it. You just have to stop prompting blind.
Generate the full-stack codebase first. Let the architecture do the work your prompts can't.
https://t.co/LoFPMqXXKy
Stop sending raw descriptions to an LLM.
Build the scaffolding your AI needs to produce production-grade apps - class diagrams, state machines, and 190+ files of full-stack code that actually run.
Try it: https://t.co/LoFPMqXXKy
#DomainEngineering#ClaudeCode#AgenticWorkflows
What's actually in your context window right now?
EcosystemCode puts system architecture in the context, before the first prompt.
#PromptEngineering#ClaudeCode#AIEngineering