#HuBrowser AI API Overview

HuBrowser AI lets you infuse intelligent assistance into apps, extensions and internal tools with on‑device speed + cloud reach only when it adds value. At its core, HuBrowser AI utilizes LLM models downloaded directly on-device and accessed through optimized browser APIs, eliminating the network layer for lightning-fast classification and processing.

#🔑 Core Value

• 🔒 Privacy first: sensitive text stays local; only escalations send minimal, policy‑scrubbed payloads
• ⚡ Ultra-fast processing: on‑device LLM eliminates network latency for instant classification and token streaming
• 💰 Predictable spend: adaptive routing avoids unnecessary cloud calls by handling most tasks locally
• 🧩 Unified surface: common sessions, prompts, memory across Web, Desktop, Android, Extensions, Bots
• 🛡 Built‑in guardrails: safety filters + moderation hooks before output leaves device
• ♻️ Sustainable usage: incremental model loading + caching to reduce repeated downloads

#🧱 Capability Groups

#⚡ Instant On-Device Processing

• Text Classification: Lightning-fast categorization without network calls
• Content Analysis: Real-time text understanding through local LLM processing
• Language Detection: Immediate language identification via browser APIs

#🔄 Advanced Text Operations

• Text Generation: structured hints → drafts (email replies, marketing blurbs, inline help)
• Text Rewriting: tone, length, clarity normalization for user drafts
• Translation & Language: local language detect + quick translate for UI & chat bridging
• Summarization: multi style (bullets, TL;DR, highlights) for articles, meetings, tickets

#🧩 Integration Features

• Prompt Sessions: shared conversational memory / task context objects
• Hybrid Routing: dynamic decision local vs cloud per prompt
• Moderation & Guardrails: heuristic + model filters, phrase redaction, policy tagging
• Embeddings (planned): local vector indexes for semantic search & clustering

#🏗 Architecture Modes

#1️⃣ Local Only

Everything executed inside HuBrowser runtime using downloaded on-device LLM models accessed via browser APIs:

• Fastest performance: Zero network latency for all operations
• Maximum privacy: Data never leaves your device
• Offline ready: Full functionality without internet connection
• Instant classification: Text analysis happens immediately through local processing

#2️⃣ Hybrid Smart Fallback

Attempt locally first; escalate only when necessary:

• Primary processing via on-device LLM through browser APIs
• Cloud escalation on window overflow, policy requirements, or quality flags
• Network eliminated for 90%+ of operations
• Best of both worlds: speed + advanced capabilities when needed

#3️⃣ Cloud Only

Direct enterprise tier usage:

• Centralized logging and quota consolidation
• Advanced model capabilities for complex tasks
• Network-dependent but highest quality results

Decision signals considered (inspired by modern browser on‑device AI patterns):

• Token length vs local window
• Safety / classification requiring advanced model
• User quality override ("refine", "improve further")
• Device capability (memory, battery hint) for model size selection
• Rate / quota posture (throttle escalations near limit)

#🔌 Integration Surfaces

• Web (in‑browser API surface; progressive enhancement like feature detection in AI APIs)
• Desktop Host (bridge offering Node‑style async interfaces)
• Android (Kotlin helper + WebView parity; deferred model asset splits like Play Feature Delivery)
• Browser Extension (content script safe wrappers + background persistence)
• Chat / Bot Relay (session state mapping for Telegram or internal chat ops)
• CLI & REST (ops scripts, batch summarization, translation pipeline)

#⚡ Technical Architecture: Network-Free AI

#🧠 Core Innovation

HuBrowser AI's breakthrough is eliminating the network layer entirely for most AI operations:

• Small LLM models are downloaded once and stored locally
• Browser API access provides direct, instant communication with the model
• Zero network latency for classification, analysis, and text processing
• Complete offline functionality without sacrificing AI capabilities

#🔧 How It Works

1. Model Download: Lightweight LLM models are fetched once during setup
2. Browser Integration: Models integrate directly with browser APIs
3. Local Processing: Text analysis happens instantly on-device
4. Instant Results: No network round-trips = immediate responses

#🎯 Speed Comparison

• Traditional Cloud AI: 200-500ms+ network latency per request
• HuBrowser Local AI: <10ms processing time through browser APIs
• Result: 20-50x faster classification and text analysis

#🧠 On‑Device Intelligence Principles

HuBrowser AI leverages lightweight LLM models downloaded directly to your device, providing unprecedented speed and privacy through browser API access without network dependency.

#🚀 Network-Free Processing

• Zero latency classification: Text analysis happens instantly through browser APIs
• Offline capability: Complete functionality without internet connection
• No data transmission: Sensitive content never leaves your device for basic operations

#🎯 Model Architecture

• Compact & efficient: Small LLM models optimized for on-device performance
• Browser-native: Direct integration through standard browser APIs
• Fast loading: Lightweight models that initialize quickly on startup
• Progressive enhancement: feature detects model availability; degrade to simpler heuristics if absent
• User consent surfaces when escalating (show reason + minimal data disclosure)
• Sandboxed execution + strict memory boundaries
• Energy aware: defer large model warmups when device on battery saver

#🚦 Hybrid Routing Policy Concepts

• Local first, escalate only when clear benefit
• Thresholds: maxLocalTokens, safety escalation flags, quality knob
• Policy returns route decision + rationale (auditable string)
• Observability emits reason codes (length_overflow, safety_advanced, user_quality, model_cold, quota_pressure)

#🛡 Moderation & Guardrails

• Pre‑output hooks for pattern redaction (passwords, credentials, PII hints)
• Safety categories: self‑harm, violence, personal data, policy restricted topics
• Configurable severity actions: block, soften, mask, escalate
• Audit trail: local ring buffer of decisions (ephemeral unless app opts to persist)

#📦 Deployment Patterns

• Web: lazy model load after first idle, cache with versioned checksum
• Desktop: bundle snapshot for zero cold start; schedule periodic delta updates
• Android: split install for large model assets; verify hash before activating
• Extension: persistent storage caching; integrity validation after updates
• Server Relay (optional): central signing + governance logs for enterprise escalations

#🔍 Observability

• Local token usage (per session + cumulative)
• Escalation count + tagged reason codes
• Latency p50 / p95 split local vs cloud
• Guardrail trigger histogram (category, action)
• Model cache health (hit rate, warm start time)

#🔒 Security & Privacy

• Ephemeral local transcript buffer unless app explicitly saves
• Escalations send minimized text + hashed user identifier (salted)
• Optional encryption envelope at rest for stored session memories
• Strict origin binding for Web surface to prevent cross‑site misuse

#📜 Error Classes

• AUTH_MISSING: no key when required → supply key or switch to local
• MODEL_UNAVAILABLE: model not yet downloaded → trigger preload then retry
• LIMIT_CONTEXT: prompt exceeds local window → chunk or escalate
• SAFETY_BLOCK: output flagged → adjust prompt or inform user
• NETWORK_FAIL: cloud escalation issue → retry with backoff or stay local

#🚀 Performance Tips

#🔥 Maximize On-Device Speed

• Preload models during idle: Download LLM models when system is not busy
• Stream tokens early: Enable instant perceived responsiveness through browser API streaming
• Cache frequently used models: Keep popular models warm for zero-latency startup

#📊 Optimize Processing

• Summarize older context (rolling compression) to reclaim window
• Chunk very large docs (summary of summaries strategy)
• Cache (future) embeddings for repeated semantic lookups
• Warm critical models just before peak usage periods

#⚡ Network Elimination Benefits

• Classification tasks: 100% local processing eliminates network dependency
• Text analysis: Instant results through direct browser API access
• Content filtering: Real-time moderation without external calls

#🧪 Testing Strategy

• Golden prompt snapshots (short invariant lines)
• Deterministic runs (temperature 0) for CI regressions
• Edge corpus: empty, extremely long, multilingual, emoji heavy
• Safety fuzz: inject sensitive patterns to verify redaction

#📅 Indicative Roadmap

• Q4: Local embeddings + semantic search helper
• Q1: Lightweight multimodal (image → text) analyzer
• Q2: Adapter pack fine‑tuning for niche tasks

#✅ Choosing a Mode

• Need max privacy / offline → Local
• Balanced latency vs quality → Hybrid
• Highest possible quality always → Cloud

#🛠 CLI (Preview Concepts)

• Summarize file into bullet list
• Translate text file to target language code
• Inspect routing stats for last N prompts

#🌟 Integration Checklist

• Model preload path validated
• Escalation policy exercised with synthetic prompts
• Safety hooks triggered & reviewed
• Latency budget measured vs requirement
• Fallback UX (spinner → streamed text) polished

#🚀 See It In Action

Want to experience HuBrowser AI's on-device capabilities right now? Check out SelfReason - our edge AI engine that showcases the same lightning-fast, privacy-first technology:

• 📱 100% Offline Android AI - Experience true on-device processing
• 🌐 Multi-platform sync - See unified AI sessions across Web, Desktop, and Mobile
• 🔒 Zero tracking - Privacy-first AI you can trust

SelfReason demonstrates what you can build with HuBrowser AI APIs!

Need something not covered? Open a ticket and help steer the HuBrowser AI platform.