OpenlySearch: Privacy-First Local AI Desktop Intelligence & Project Memory 🔍

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.

What I Built

Modern professionals—freelancers, developers, legal consultants, researchers, and small agency founders—face two critical problems when using AI in their daily workflow:

1. The Privacy & Compliance Wall: They handle sensitive client codebases, confidential contracts, financial spreadsheets, and proprietary notes that legally cannot be uploaded to third-party cloud AI APIs (like OpenAI or Anthropic) due to strict non-disclosure agreements (NDAs) and data residency laws.
2. Context Fragmentation: High-value tasks, architectural decisions, and copied terminal commands or credentials scatter across folders, messy markdown notes, and clipboard history.

OpenSearch solves this by delivering a commercial-grade, privacy-first desktop intelligence assistant that runs 100% locally on your machine with zero cloud telemetry.

OpenSearch turns passive local project folders into an active, actionable workspace:

• Hybrid Semantic + Lexical Search: Instant sub-second search combining local MiniLM embeddings (all-MiniLM-L6-v2) with keyword ranking over documents, PDFs, Markdown, and code.
• Universal Project Intelligence: Scans project repositories to synthesize architectural overviews, spot unaddressed risks, and extract verified next steps with citations.
• Decision & Task Memory: Automatically identifies architectural choices, rationales, and pending action items with confidence scores and source excerpts.
• Target Scope Selection: Focuses semantic analysis specifically on selected files or folders (e.g. client rubrics, audit directories) with on-the-fly parsing.
• Assessment Action Plan & Interactive Notepad: Parses specs and assessment rubrics into structured phases, extracts deliverable deadlines, provides interactive [x] tick marks, and persists notes.
• Interactive Calendar & Milestone Timeline: A full monthly calendar with day schedule inspection, one-click task completion tick marks, and velocity roadmap lanes (Overdue, Due Today, This Week, Upcoming 30 Days).
• Searchable Clipboard Memory: Automatically captures and categorizes copied code, commands, and notes while proactively filtering out sensitive API keys and secrets.
• Daily Memory Briefing: Generates morning project briefings with velocity scoring, intent setting, and local text-to-speech audio readout.

The Side Hustle / Commercial Opportunity: Who Would Pay For This?

OpenSearch is designed as a product that freelancers, boutique agencies, and enterprise consultants would gladly pay for:

• Target Audience: Freelance developers, security auditors, legal/medical research consultants, and privacy-conscious professionals managing multi-client projects.
• The Value Proposition: "Private AI intelligence that never leaks your clients' data." A single client data breach or NDA violation can cost thousands of dollars; OpenSearch guarantees complete local isolation.
• Business Model:
  • Free open-source core application.
  • $29/seat/month Pro tier (or $99 perpetual license) for power user features: custom offline model packs, automated client audit reporting, and encrypted multi-device sync.
  • Freelancers can bill it as a legitimate business expense for client data compliance.

Demo

Video : https://echowave.io/app/watch/fCE84IEVvzrgqIfgOQeI/

💻 Project Demo & Releases: Available directly in the GitHub repository.

The desktop app runs locally on macOS, Linux, and Windows, offering instant keyboard navigation (Alt + Shift + Space), multi-view task and calendar scheduling, and local LLM switching.

Code

💻 GitHub Repository: https://github.com/aashutoshkumarbhardwaj/openSearch

The project is built with:

• Desktop Runtime: Electron 34 + TypeScript + electron-vite
• Frontend: React 19 + Lucide Icons + Vanilla CSS design system
• Open-Weight AI Embeddings: @huggingface/transformers running Xenova/all-MiniLM-L6-v2 locally via ONNX Runtime Web
• Local LLM Engine: Seamless integration with local Ollama runtimes (supporting open models like SmolLM2-360M, Llama 3.2-1B, and Qwen 2.5)
• Document & PDF Parsing: pdf-parse for on-the-fly extraction of rubric specifications and PDF briefs

How I Built It

The core technical challenge was building an end-to-end desktop intelligence assistant where every single feature runs on the user's hardware without sending a single byte to external servers.

1. Local Embeddings & Semantic Indexing Pipeline

Rather than relying on remote vector databases, OpenSearch chunks local documents, generates 384-dimensional dense vectors using @huggingface/transformers, and persists them in a lightweight local index (userData/index.json). Search queries execute cosine similarity combined with lexical scoring in under 5ms.

2. Grounded Decision & Task Extraction

The memory engine analyzes indexed passages using dual-mode reasoning:

• A deterministic fallback parser that extracts explicit TODOs, FIXMEs, deadline patterns, and decision records using regex and pattern analysis.
• An optional local LLM pipeline (via Ollama) that structures complex natural language notes into actionable tasks with confidence scoring and exact source excerpt verification.

3. Action Planner with Milestone Calendar & Tick Marks

To bridge the gap between reading a project spec and executing it, OpenSearch features a specialized planner:

• Reads PDFs, Markdown, and text files.
• Structures deliverables into chronological phases (Requirements, Implementation, Verification).
• Binds tasks to an interactive monthly calendar and milestone timeline with one-click [x] completion tick marks that synchronize with local storage.
• Provides a persistent scratchpad notepad for live meeting and rubric notes.

4. Searchable Clipboard Memory with Privacy Filtering

To capture ephemeral workflow knowledge (code snippets, terminal commands, URLs), OpenSearch continuously monitors the clipboard. Crucially, a built-in entropy and pattern scanner automatically detects and skips sensitive tokens (Stripe keys, AWS access IDs, RSA private keys, connection strings) before saving.

Why Does Open Innovation Matter?

Open innovation is the cornerstone of OpenSearch:

1. Data Sovereignty & Zero Trust: When you work with open-weight models (like SmolLM, Llama 3.2, Qwen), you own the weights, the inference, and the data. No corporation can change terms of service, train on your proprietary code, or revoke API access.
2. Economic Viability for Freelancers: Cloud AI APIs incur metered per-token costs that scale with document size. For a freelancer indexing hundreds of PDF pages and code repos, cloud API costs quickly become prohibitive. Open-source local models run for free on existing laptop CPUs and GPUs.
3. Resilience & Offline Autonomy: Whether working on an airplane, in a remote location, or behind an air-gapped corporate firewall, OpenSearch provides uninterrupted intelligence and search.

Prize Categories

• Best Use of a .tech Domain (or Overall Challenge Entry): OpenSearch represents an open, privacy-first alternative to closed-source cloud intelligence tools, built for real-world independent professionals and freelancers.

---

devchallenge #hf26challenge #ai #opensource