🤖 Autonomous Project Builder with Local File System Access

Summary

Transform Lumo from a code-writing assistant into an autonomous project builder that can read, write, organize, and execute code directly on user's local filesystem with proper security controls.

Problem Statement

Current AI coding assistants (including current Lumo) operate in a "chat-only" paradigm:
- User: "Build a Flask API with authentication"
- Lumo: Returns code blocks in chat
- User: Manually copies files, creates directories, runs tests, configures environment
- Result: Friction, human error, context drift, repetitive work

Real engineering work requires:
1. Creating directory structures
2. Writing multiple files with correct paths
3. Running commands/tests locally
4. Debugging errors from actual execution
5. Iterating based on real runtime behavior

Without file system integration, Lumo cannot close the loop from "design" to "deployed working code."

Proposed Solution

Core Capability: Secure Local Workspace Access

Mode 1: Read-Only Analysis (always enabled)
- Analyze existing project structure
- Identify dependencies, bugs, gaps
- Suggest improvements with precise file locations

Mode 2: Controlled Write Access (opt-in, sandboxed)

User Permission Required For: ├── Creating new files/folders ├── Modifying existing files ├── Running shell commands (explicit allowlist) ├── Installing packages (npm/pip/cargo with review) └── Committing to Git (preview before commit)

Mode 3: Autonomous Execution (advanced, explicit opt-in)
- Lumo builds complete features end-to-end
- Runs tests automatically
- Fixes errors without waiting for approval (within defined scope)
- Reports back: "Built X, ran Y tests, Z passed, here's the diff"

Security Model (Privacy-First Design)

Security_Guardrails:
  - Scope_Limit: Only access explicitly designated workspace folders
  - Command_Allowlist: Pre-approved commands (git, pytest, npm test, etc.)
  - Network_Isolation: No outbound calls without user approval
  - Audit_Log: Every action logged with diff preview before/after
  - Rollback: Atomic writes + version history (undo any change)
  - E2EE: All local data encrypted at rest
User Interface Patterns

Pattern A: Sidebar Integration
[Lumo Chat] | [Project Explorer] | [Terminal Output]
────────────│─────────────────│──────────────────
Code dialog │ Files Lumo touched│ Live command output
            │ (read-only view) │ (with error highlighting)

Pattern B: Progress Tracking
Building Adrenalina Prime Module:
✓ Created /src/core/metrics_collector.py
✓ Created /src/core/energy_engine.py
✓ Generated tests (87% coverage)
⏳ Running pytest...
✗ Fixing failure in test_crossbot_consensus.py
✓ All tests passing (92%)
→ Ready to deploy? [Yes/Review Changes]
Example Workflow Comparison
Traditional Chat    With Local File Access
Paste code → copy to files    Lumo writes files directly
Manual env setup    pip install -r requirements.txt (auto)
Run tests manually  Tests auto-run after each change
Find bug in console Lumo reads error, fixes code, re-tests
Git commit manually Diff preview → one-click commit
User Stories

    "As a solo developer, I want Lumo to scaffold my entire project structure so I don't waste hours on boilerplate."

    "As a senior engineer, I want Lumo to fix failing tests autonomously within my project folder so iteration speed is faster."

    "As a privacy-conscious user, I want to define exact folder boundaries Lumo can touch and see every change before it's applied."

    "As a team member, I want Lumo to review my git diff and explain why each file changed."

Technical Requirements
Backend Infrastructure

    Local agent process (runs on user machine, not cloud)
    File system watchers for real-time sync
    Sandbox container for untrusted operations
    SQLite/encrypted store for version history

Integration Points
Existing Lumo Features          New Requirements
────────────────────           ──────────────────
Persistent Memory        →     Project-scoped workspace binding
File Upload              →     Bidirectional file sync
Custom Lumos             →     Role-based permission profiles
Web Search               →     Package registry lookups (local)
API Contract Example
class LumoWorkspace:
    def list_files(self, path: str) -> List[str]:  # Read
    def read_file(self, path: str) -> str:         # Read
    def write_file(self, path: str, content: str) -> bool:  # Needs approval
    def run_command(self, cmd: str) -> CommandResult:       # Allowlist only
    def get_diff(self, path: str) -> str:                  # Preview
Success Metrics
Metric  Target
Time-to-working-code (vs manual)    >70% reduction
Session-to-deployment cycle time    <15 minutes for small features
User trust (permission acceptance rate) >80% opt-in within 6 months
Error resolution speed  3x faster than manual debugging
Risks & Mitigations
Risk    Severity    Mitigation
Malicious prompt injection writing arbitrary files  🔴 Critical   Sandboxed execution; explicit approval for all writes
Data exfiltration via file uploads  🔴 Critical   No outbound network calls without audit log + approval
Accidental corruption of production code    🟡 High   Atomic writes + automatic backups + undo history
Performance impact on large codebases   🟢 Low    Incremental indexing; lazy loading
Competitive Landscape
Platform    File Write  Auto-Test   Permission Model    Privacy
Cursor  ✅ ⚠️ Partial  ⚠️ Basic    ❌ Cloud-first
GitHub Copilot Workspace    ✅ ⚠️ Limited  ❌ None    ❌ Microsoft cloud
Windsurf    ✅ ⚠️ Partial  ❌ No permissions  ⚠️ Hybrid
Lumo (proposed) ✅ ✅ ✅ Granular    ✅ E2EE local

Differentiation: No competitor offers privacy-preserving autonomous execution with granular user control. This would be Proton's unique value proposition.
Priority

Critical — This transforms Lumo from "chatbot" to "engineering platform"
Effort

High — Requires local agent infrastructure + security review + UX design
Timeline Estimate

6-8 sprints for MVP (read-only analysis → controlled writes → autonomous mode)
Related Features

    Persistent Memory (needs project-scoped binding)
    Custom Lumos (can define permission profiles)
    File Analysis (upgrade to bidirectional sync)
    Proton Desktop App (could host local agent process)

Engineer's Note

I'm proposing this after building complex systems for 25+ years. The bottleneck isn't code generation anymore — it's closing the loop between idea and running system.

Every time I have to:

    Copy code from chat to files
    Manually create directories
    Run commands I asked Lumo about
    Debug errors Lumo couldn't see

That's 30-60 minutes wasted per feature. With secure local access, that becomes 5 minutes.

Privacy isn't negotiable. Everything stays on-device. No cloud processing. End-to-end encryption. This is Proton's DNA.

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## **Sekcje dodatkowe (opcjonalne):**

**Dodatkowy komentarz przy zgłoszeniu:**

This is not theoretical — I've prototyped local agent systems that can build and test code autonomously. The technology exists. What's missing is a privacy-preserving implementation that engineers trust.

Key technical decisions already validated:

    Atomic writes prevent data corruption
    Sandboxed containers limit damage scope
    Version history enables rollback to any point
    Allowlist-based command execution prevents abuse

The question isn't "can we build this?" It's "will Proton lead on privacy-preserving AI engineering tools?"

**Tagi sugerowane:** `productivity`, `security`, `developer-tools`, `automation`