Launched this week
Enia Code
Proactive AI that refines code & learns your standards
630 followers
Proactive AI that refines code & learns your standards
630 followers
Most AI coding tools wait for you to ask. Enia Code doesn’t. Enia is a proactive AI coding agent that detects bugs, performance issues, architectural inconsistencies, and refactoring opportunities — as you write code. No prompting. No context re-explaining. No workflow disruption.
I just checked the pricing and I got shocked because it offers so few requests. Maybe I don’t know how it works, but it seems you need to think very well about what you want to achieve and type it, because if not, you will run out fast.
Enia Code
@pierruno Don't worry—Enia’s billing differs from traditional Token-based models。 For example，a single chat interaction consistently consumes only 1 Request, regardless complexity. This ensures your requests won't vanish unexpectedly and lets you focus on coding rather than counting words.
Enia Code
I’m a developer and CEO, and this product started from my own frustration. Over the years, I’ve used countless coding tools that only react after something breaks — after the bug appears, after performance drops, after architecture gets messy. But real development doesn’t work like that. When we code, we’re constantly thinking ahead. We anticipate problems. We refactor before things collapse. I kept asking: why can’t our tools think that way too? That question led us to build Enia Code.
There are already many AI coding tools — copilots, editors, chat-based assistants. Most of them wait for prompts. Enia is different. It’s proactive. It detects bugs, performance risks, architectural inconsistencies, and refactoring opportunities as you write. No constant prompting. No re-explaining context. No switching tabs. It works quietly inside your IDE, adapting to your coding habits and team standards over time. The goal isn’t to replace developers — it’s to protect their flow.
We believe coding tools are evolving from reactive copilots to proactive agents. The next step isn’t just faster autocomplete — it’s intelligent systems that anticipate, learn, and grow with your project. Software complexity is increasing, solo developers are building bigger systems than ever, and “flow” is becoming the most valuable resource. The future of AI coding isn’t about answering questions — it’s about preventing the need to ask them in the first place.
If you have any thoughts, ideas, or feedback, I’d truly love to hear them — feel free to drop a comment and let’s discuss.
Follow Enia Code on X and YouTube:
https://x.com/EniaCode
https://youtube.com/@EniaCode
Happycapy
@jessica_miller_7 Interesting direction. Proactive coding agents feel like the next step beyond prompt based coding tools. Curious how it decides what to fix or suggest in real time.
Enia Code
@jessica_miller_7 @victoria_wu By analyzing your project’s context, we only intervene for high-level requirements—such as critical risks and logical errors—to ensure your workflow remains uninterrupted.
@jessica_miller_7 proactive is what I need. but what model can i use here? can i use skills
Enia Code
@jessica_miller_7 @warren_wu5 Our hybrid architecture auto-selects the best model for each scenario. We’ll be supporting Skills very soon, so stay tuned!
Gemini Code Harvester
@jessica_miller_7
Hey Jessica! Funny we're both launching today 😄
I'm a non-developer who got tired of losing code
in Gemini chats — so I just built a fix (Gemini Code Harvester). Same story, different problem.
Love the proactive angle on Enia, following to see where it goes! 🚀
Product Hunt
PopPop AI Vocal Remover
I’ve been building SaaS tools for the past few years, and one thing that always slows me down is discovering architectural problems way too late. Refactoring a working system is painful. The idea of a tool that signals these issues earlier sounds really valuable. Curious whether Enia detects these patterns based on past projects or just the current repo context.
Enia Code
@charlenechen_123 That’s actually the exact frustration that pushed us to build Enia in the first place. We ran into the same thing a few times — everything looks fine while you’re building, and then a few months later you realize the architecture is fighting you...
Right now most of the signals come from the current repo context (structure, dependencies, patterns in the codebase, etc.), so it’s looking at how the system is evolving while you’re actively working in it.
We’re also exploring how to incorporate longer-term signals from project history over time, but getting the “current repo awareness” right was the first step.
Curious — what kind of architectural problems tend to show up late for you? Dependency cycles, scaling bottlenecks, or something else?
Happycapy
I work on a small startup team where we ship features quickly, and technical debt inevitably creeps in. Having something that proactively points out potential architectural drift could actually save us a lot of cleanup later. Wondering how customizable the rules are for different teams:)
Enia Code
@lyss_luo That’s a very real scenario, Lyss. Fast-moving teams tend to accumulate technical debt before anyone notices 😅Right now the system focuses on detecting structural patterns automatically, but making rules more customizable for different teams and workflows is definitely something we’re exploring.
FastMoss
Interesting concept. Most AI coding tools today are essentially prompt-response systems. You ask → it answers. A proactive model feels more like an actual collaborator. Curious how Enia decides when to surface a suggestion.
Enia Code
@qiwap As I've answered a few mins before, by analyzing your project’s context, we only intervene for high-level requirements—such as critical risks and logical errors—to ensure your workflow remains uninterrupted.
remio - Your Personal ChatGPT
I've been building side projects for a few years, mostly as a solo developer. One pattern I keep seeing is that I only realize architectural problems when the project becomes bigger. At that point it's already painful to refactor. The idea of an AI that proactively signals issues while you're coding is really interesting. Curious how deep the analysis goes — is it mostly file-level reasoning, or does it understand the broader project structure?
Enia Code
@lvyanghuang That’s a very relatable experience, Shake — a lot of architectural issues only become obvious once a project grows.
So we try to go beyond file-level analysis and look at broader structural patterns across the project, so potential issues can surface earlier while the codebase is still evolving.