╔══════════════════════════════════════════════════════════════════════════════╗ ║ ║ ║ █████╗ ███╗ ██╗██╗ ██╗███████╗███████╗██╗ ██╗ █████╗ ║ ║ ██╔══██╗████╗ ██║██║ ██║██╔════╝██╔════╝██║ ██║██╔══██╗ ║ ║ ███████║██╔██╗ ██║██║ ██║█████╗ ███████╗███████║███████║ ║ ║ ██╔══██║██║╚██╗██║╚██╗ ██╔╝██╔══╝ ╚════██║██╔══██║██╔══██║ ║ ║ ██║ ██║██║ ╚████║ ╚████╔╝ ███████╗███████║██║ ██║██║ ██║ ║ ║ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═══╝ ╚══════╝╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝ ║ ║ ║ ║ ⚡ Local-first AI • Ultra-low-latency • Privacy by design ⚡ ║ ║ ║ ╚══════════════════════════════════════════════════════════════════════════════╝
🎯 On-device intelligence infrastructure where search, AI models, and agentic tasks run entirely on the user's machine.
┌─────────────────────────────────────────────────────────────┐
│ We focus on: custom protocols • deterministic execution │
│ explicit control • systems-level correctness │
└─────────────────────────────────────────────────────────────┘
Modern AI systems make dangerous assumptions:
☁️ CLOUD DEPENDENCY Data must leave your device |
🔒 OPAQUE AGENTS Black box operations |
👁️ SURVEILLANCE Constant data collection |
💔 FALSE TRADE-OFF Privacy vs Convenience |
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Anvesha Systems builds local, inspectable, and ┃
┃ controllable intelligence — enforced by architecture, ┃
┃ not by policy or promises. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ BROWSER │◄───────►│ SEARCH │◄───────►│ LOCAL LLM │◄───────►│ AGENTS │
│ 🌐 │ IPC │ ENGINE │ IPC │ 🧠 │ IPC │ 🤖 │
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │ │ │
└───────────────────────┴────────────────────────┴───────────────────────┘
│
╔═══════════╗
║ NERVE ║ ← Binary, ultra-low-latency IPC
╚═══════════╝ Unix Domain Sockets
⚙️ Technical Specifications
| Property | Implementation | Why It Matters |
|---|---|---|
| 🔌 Transport | Unix domain sockets | Local-only, no network exposure |
| 🌊 Architecture | Streaming-first | Real-time tokens & events |
| ⚡ Cancellation | Immediate & cooperative | Stop execution instantly |
| 🎯 Performance | Deterministic | Predictable latency |
| 🚫 Dependencies | Zero network | 100% offline capable |
| ✅ What Happens | ❌ What Doesn't Happen |
|---|---|
| 💻 Local LLM inference on your device | ☁️ |
| 🌊 Real-time token streaming | 📤 |
| ⏱️ Hard execution limits | 📊 |
| 🛑 Kill switches & cancellation | 🕵️ |
╔════════════════════════════════════════════════════════════╗
║ 🛡️ YOUR DATA NEVER LEAVES YOUR MACHINE — BY DESIGN 🛡️ ║
╚════════════════════════════════════════════════════════════╝
Philosophy: Agents should be tools — not autonomous black boxes.
%%{init: {'theme':'dark'}}%%
stateDiagram-v2
[*] --> TaskInitiated: User Request
TaskInitiated --> Streaming: Execute
Streaming --> Observable: Monitor
Observable --> Streaming: Continue
Observable --> Cancelled: User Cancels
Observable --> Completed: Finished
Cancelled --> [*]
Completed --> [*]
note right of TaskInitiated: Explicit start
note right of Streaming: Token-by-token
note right of Observable: Full visibility
note right of Cancelled: Immediate stop
Control Primitives:
// Explicit task lifecycle
start() → stream() → observe() → done() | cancel()
↓ ↓ ↓ ↓
✅ Clear ✅ Real-time ✅ Visible ✅ Controlled
|
|
|
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ This foundation is stable and production-grade. ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
anvesha-systems/
│
├─ 🔧 nerve-core ─────────────┬─► Core IPC engine
│ ├─► Routing & streaming
│ └─► Cancellation semantics
│
├─ 📋 nerve-protocol ─────────┬─► Protocol definitions
│ ├─► Frame specifications
│ └─► Message types & limits
│
└─ 🤖 nerve-ai-worker ────────┬─► Local LLM worker
├─► Token streaming via NERVE
└─► [🔶 IN PROGRESS]
TIMELINE
────────────────────────────────────────────────────────────────
📅 Q1 2026 — NEAR TERM
+ WebLLM / local LLM integration
+ Real token streaming through NERVE
+ AI firewall (hard limits, kill switches)
+ Offline demo (network disabled)Goal: Fully functional local LLM inference with NERVE
📅 Q2-Q3 2026 — MEDIUM TERM
+ Agentic task execution (non-stub)
+ Search → AI pipelines
+ Browser-side integration
+ Better observability for agent workflowsGoal: Complete search-to-AI pipeline locally
📅 2027+ — LONG TERM
+ Fully local AI browser workflows
+ Privacy-first automation
+ Composable local intelligence servicesGoal: Complete local-first AI ecosystem
┌────────────┬────────────┬────────────┬────────────┬────────────┐
│ LOCAL │ PRIVACY │ LOW │ EXPLICIT │ SYSTEMS │
│ FIRST │ BY ARCH │ LATENCY │ CONTROL │ CORRECT │
├────────────┼────────────┼────────────┼────────────┼────────────┤
│ Offline │ Not │ By │ Cancel │ Over │
│ by │ policy │ design │ anytime │ hype │
│ default │ │ │ │ │
└────────────┴────────────┴────────────┴────────────┴────────────┘
|
LOCAL-FIRST No cloud required |
ARCHITECTURE Not promises |
PERFORMANCE Deterministic latency |
CONTROL Always in your hands |
CORRECTNESS Foundations matter |
Engineering:
- Systems programming
- Low-latency infrastructure
- Security-aware design
- Long-term reliability
Philosophy:
- Foundations first
- Products second
- Users always |
We work with engineers who value: const ideal_collaborator = {
clarity: true,
correctness: true,
long_term_thinking: true,
systems_mindset: true,
user_sovereignty: true
}; |
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ STATUS: ACTIVE DEVELOPMENT ┃
┃ ──────────────────────────────────────── ┃
┃ ✅ Core infrastructure: STABLE ┃
┃ 🔶 Product layers: EVOLVING ┃
┃ 🚀 Shipping: INCREMENTAL ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
╔══════════════════════════════════════════════════════════════════════════════╗
║ ║
║ 💬 IN ONE LINE ║
║ ║
║ Anvesha Systems builds local, controllable intelligence — ║
║ because AI should serve users, not observe them. ║
║ ║
╚══════════════════════════════════════════════════════════════════════════════╝
Built with 🔐 privacy • ⚡ performance • 💪 control
