Most AI tools today are just chat interfaces.
You open them → type → get a response → close them.
That’s not an assistant.
So I started building VISAR Edge — a system-level AI assistant that’s always present, not something you have to “open.”
🧠 The Core Idea
Instead of:
«User → Prompt → Response»
I’m building:
«Environment → Context → AI → Action»
⚙️ Current Architecture
- Python (core system)
- PySide6 (UI layer experiments)
- SpeechRecognition (voice input)
- Gemini API (LLM)
- System hooks (clipboard monitoring, active window tracking)
- Chroma Vector DB,many more...
🚀 What It Can Do (So Far)
- Floating UI overlay (works across apps)
- Voice-triggered interactions
- Clipboard-aware responses
- Modular feature system (plug-and-play architecture)
⚠️ What’s Harder Than Expected
- Real-Time Context Awareness
Capturing meaningful context without overwhelming the system is tricky.
Too little → useless assistant
Too much → performance nightmare
- Latency
Cloud APIs are powerful but slow for an “always-on” experience.
This is pushing me toward:
- Local models
- Hybrid inference (local + cloud fallback)
- System Design > AI
Big realization:
«This is more of a systems engineering problem than an AI problem.»
Managing:
- State
- Memory
- Context flow
- Event triggers
…is harder than calling an LLM.
🧩 What I’m Exploring Next
- Local LLM integration (7B–13B range)
- Vector memory systems
- Event-driven architecture
- Background processing optimization
❓ Where I Need Feedback
If you’ve built anything similar, I’d love your thoughts:
- Is Python a bad long-term choice for this?
- Best way to handle continuous context streams?
- How would you design memory for this kind of system?
🔗 Full Project Context
I’ve documented the vision, UI, and direction here:
👉 https://www.linkedin.com/in/devanshsharma987
💬 Final Thought
Everyone is building smarter chatbots.
I’m trying to build something you don’t have to think about using.
If this fails, it’ll fail by aiming too high — not too low.
Would appreciate honest feedback.
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