This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
BaseBuildStack is an engineering interview preparation micro-SaaS powered by open-weight AI models and deterministic validation engines.
Traditional technical interview preparation is broken in two major ways:
- Exorbitant Cost: Commercial platforms charge candidates anywhere from $35 to $100+ per month, pricing out students, career switchers, and engineers from emerging economies.
- Data Privacy Risks: Closed platforms upload sensitive candidate resumes, GitHub repositories, and private code solutions to third-party commercial LLM providers to train proprietary models.
BaseBuildStack delivers an adaptive, high-speed interview practice suite that runs on open-source AI:
- Module I: DSA & Technical Blitz: 45-second pattern identification cards, line-spotting bug audits, and SM-2 spaced repetition review.
- Module II: Personalized Interview Quizzes: Adaptive question drills generated from the candidate's exact tech stack, resume claims, and code repositories.
- Module III: Tech Bites: 15-minute interactive architectural conceptual models (RAG pipelines, Cache-Aside strategies, Vector Embeddings, Docker process isolation).
- Module IV: Mind Sprints: Daily 5-minute single-solution logic grids and 4-quadrant metacognitive confidence calibration.
Who Would Pay For It?
Job-seeking software engineers, bootcamp graduates, and developers preparing for AI and distributed systems technical rounds. We designed it as a sustainable side hustle with a ₹9/month (~$0.11 USD) Pro Plan via Razorpay, unlocking deep repository analysis, adaptive weak-area drills, and unlimited review sessions.
Demo
- 🌐 Live Deployed App: https://basebuildstack.onrender.com
- 🚀 Test Account Available: Full guest mode or instant registration with Google OAuth, GitHub OAuth, or Email/Password.
Code
This is a Next.js project bootstrapped with create-next-app.
Getting Started
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
Open http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
Learn More
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
Deploy on Vercel
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.
The complete source code is open source under the MIT License on GitHub:
👉 https://github.com/devanshu-puri/BaseBuildStack
How I Built It
BaseBuildStack was architected from day one to treat open-source AI as a first-class citizen rather than a bolt-on API wrapper:
-
Local Open-Weight Inference (Ollama): Our core LLM Gateway interfaces with local open-weight models (
gemma2:9bandllama3) via Ollama. It synthesizes targeted interview questions, hints, and explanations on demand. -
Open Embeddings (
all-minilm): We utilize open SentenceTransformer embeddings via Ollama for repository chunking, question similarity search, and semantic tag matching. -
Deterministic Assertion Sandbox: Open models are powerful, but non-deterministic hallucinations break technical learning. We pair all model code evaluation with an isolated Node.js worker-thread sandbox (
tests/unit/sandbox.test.ts) that executes generated test suites deterministically against candidate code. -
Zod Boundary Schemas: Every response generated by open-source models is parsed and strictly validated against Zod schemas (
LlmValidationError) to guarantee rigid runtime types. - Modern Web Stack: Next.js 15, React 19, custom Aurora glassmorphism UI system, user-scoped MongoDB storage, and Web Crypto PBKDF2 authentication.
Why Does Open Innovation Matter?
Open innovation was not just a design preference for BaseBuildStack—it is the foundational reason the product works:
- Unit Economics That Make Micropayments Viable: If we used closed, commercial APIs charging $0.01 to $0.03 per prompt/completion, a candidate completing 50 drills a day would burn through our margins in hours. Running open-weight models on self-hosted or local infrastructure reduces marginal inference costs to near zero, allowing us to offer a profitable ₹9/month subscription model.
- Zero-Trust Resume & Code Privacy: Candidates often practice with proprietary codebases or sensitive career history. By offering a local inference pipeline via Ollama, code and resumes remain strictly on the candidate's machine and are never shipped off to train third-party commercial datasets.
- Model Autonomy & Future-Proofing: Closed API providers frequently deprecate models, shift safety guardrails, or unexpectedly raise pricing tiers. With our open gateway architecture, we can hot-swap between Gemma, Mistral, Llama, or DeepSeek Coder with a single environment flag without changing any application logic.
- Offline Resilience: Because the core engine, spaced-repetition scheduler, and logic puzzle generator are fully deterministic, the app can function completely offline on a laptop without internet connectivity.
My Agent Session
This entire platform was engineered, architected, and continuously validated using the Google DeepMind Antigravity AI pair programming agent and DevRelay.
From writing the OWASP-grade cryptographic authentication to resolving strict Next.js 15 middleware boundaries and validating all 251 unit test suites, the transparent workflow was recorded via DevRelay.
My First User
We onboarded two candidate testers actively interviewing for full-stack and platform roles.
- Tester 1 (Student): Specifically praised the 45-second Pattern Blitz cards, noting that fast-paced pattern recognition helped reduce anxiety during real live-coding rounds.
- Tester 2 (Junior Developer): Tested the resume-tailored quiz generation and appreciated that the app didn't require sharing API keys or uploading confidential work projects to third-party closed servers.
- Payment Feedback: Both confirmed that ₹9/month was a micro-expense they would pay without hesitation compared to costly recurring subscription models.
Prize Categories
- Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
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