The memory-augmented AI IDE—spotlighted on Limito for developers who want their AI assistant to remember context across sessions and truly understand their codebase evolution.
Melty tackles one of the most frustrating aspects of AI coding assistants: they forget everything the moment you close them. Featured on Limito as an innovative emerging IDE, Melty introduces persistent memory that carries context across sessions, remembers your architectural decisions, learns your preferences, and gets smarter the more you use it. It's like working with a pair programmer who actually remembers yesterday's conversations about why you chose that particular API design.
What sets Melty apart on Limito: The memory system is genuinely impressive. Melty builds a semantic understanding of your codebase that persists—it remembers which patterns you prefer, what architectural decisions you've made, and why certain code is structured the way it is. When you ask "how should I implement authentication?" weeks after your last session, Melty recalls your previous discussions about security requirements and suggests solutions consistent with your established patterns. Limito users describe it as the difference between outsourcing to a contractor versus training a full-time team member.
The learning aspect goes beyond just remembering facts. Melty observes which code suggestions you accept or reject and adjusts its future recommendations accordingly. Over time, it develops a model of your coding style, preferred libraries, naming conventions, and even your thoughts on contentious debates like tabs versus spaces. The result is AI assistance that feels increasingly personalized rather than generic.
Limito's perspective: Melty represents an important evolution in AI coding tools. Where first-generation AI assistants were essentially stateless functions—useful but forgettable—Melty demonstrates the value of continuity and learning. The Limito community particularly recommends it for long-term projects where establishing and maintaining context is crucial. The tradeoff is complexity: Melty requires more setup and understanding than simpler tools. But for developers working on codebases they'll maintain for months or years, that investment pays dividends in increasingly intelligent, context-aware assistance.