Clone Projects won't get you Hired in 2025! | AI, DeFi, Web 3 and Full stack Project Ideas

Context

The presenter shares nine project ideas (arranged across four niches) you can build in ~1 month to stand out in interviews. The emphasis is on shipping one end-to-end, productionized project (live demo + GitHub).

Build one end-to-end, productionized project (live link + GitHub). Tag the presenter on Twitter for possible referral or feedback.

Key context points:

• Two of the nine ideas were Super30 capstone projects; many interviews focused deeply on a single strong project.
• The goal is a single polished project rather than many half-finished experiments.
• Production stability, deploy workflows, and reliability are important signals to recruiters/interviewers.

The nine project ideas

1) Sports betting / opinion-trading platform (centralized order book)

• What: Exchange-style opinion market where each outcome is a “stock” trading between 0 and 1 (e.g., team wins → price → 1).
• Tech concepts: order-book design, bids/asks, limit vs market orders, async architecture, low-latency execution.
• Stack notes: market-makers require very low latency → prefer fast runtimes/languages (Bun, C, Rust) over typical Node/Java.
• Challenges: low-latency design, matching engine correctness, concurrency.
• Resources/examples: Probo.in, Polymarket; presenter’s Zerodha/order-book video for internals.

2) On-chain order book (Solana / Ethereum / L2)

• What: Build an order-book DEX on-chain (historical examples: Serum, Openbook).
• Tech concepts: smart contracts, Rust (Solana), gas costs, per-tx latency, L2 rollups/gasless L2s.
• Product tradeoffs: L1 is often too slow/expensive for order-book activity → target a fast L2 with low fees and block times.
• Challenges: gas optimizations, chain-native order matching, latency and cost constraints.
• Learning focus: smart-contract design, per-tx gas patterns, and on-chain data structures.

3) Cursor-for-Blender (LLM-driven Blender interface)

• What: Native/desktop app exposing Blender via MCP (Blender’s remote protocol) so a user can chat with an LLM that issues Blender commands via MCP to create scenes.
• Tech: Blender + MCP server, local desktop app, LLM orchestration, script generation and execution within Blender.
• UX flow: chat → LLM → MCP → Blender, lowering Blender’s learning curve for creators.
• Hard variant: convert 2D images/sketches to 3D meshes (research-grade).

4) AI → Manim (or animation library) video generator (LLM writes code)

• What: Web app where users prompt an LLM to generate scripts for an animation library (Manim, p5.js, etc.), compile and export videos.
• Tech: LLM prompting and multi-step generation, code execution and sandboxing, compiling to MP4, scene-by-scene assembly, optional voiceover.
• Product features: scene editor (compose/preview scenes), iterative prompting per scene to reduce hallucination, rendering pipeline.
• Inspiration: Three Blue One Brown’s Manim videos; presenter prototyped this approach.

5) same.dev — website cloner (URL → generated React/site)

• What: Provide a URL and the app generates a clone (landing page or full site) by indexing the site and having GPT produce the code.
• Tech: site crawling/indexing, structure/style/content extraction, iterative LLM calls to generate and refine code, remix marketplace, per-user deployments.
• Challenges: robust site parsing, handling dynamic content, multi-step prompts, deployment workflows.

6) Riverside clone (multi-party high-quality recording platform)

• What: Remote podcast/video recorder that captures local high-quality tracks in-browser and uploads chunked local recordings to S3, producing reliable raw assets.
• Tech: WebRTC for low-latency conferencing, browser/local recording, chunked/resumable uploads to object store (S3), timeline event logging.
• Product features: local high-quality capture, automatic chunk upload/resume, timeline of joins/screenshares, optional automatic rendered merged video and dynamic layouts.
• Hard parts: video rendering/merging, dynamic layout handling, upload reliability across participants.

7) Tally-like form builder (Notion-style UI + integrations)

• What: Easy form builder with a Notion-like creator experience and deep integrations (Google Sheets, Notion, WhatsApp via Zapier/Make), plus LLM summarization of responses.
• Tech: rich Notion-style editor/UI, many external API integrations, webhook/workflow orchestration, LLM-based summarization/ranking.
• Challenges: building a Notion-like editor, integrating diverse third-party APIs, robust workflow automation.

8) LLM client / UI product (Up3-like)

• What: Differentiated UI wrapper around multiple LLMs (OpenAI, Claude, etc.) focusing on performance, multi-model access, and clean UX.
• Product notes: good UI and perceived speed can be a competitive advantage even when models are paid externally.
• Areas to experiment: front-end design/UX, request/response optimizations, engineering tricks to reduce perceived latency and improve throughput.

9) UI component library & design-engineer portfolio

• What: Polished UI component library (20+ high-quality, animated components), a CLI for integrating components into projects, and a Storybook showcase.
• Tech/skills: component design, animations, accessibility, CLI tooling, publishing/consumption patterns.
• Hiring angle: strong proof-of-work for “design engineer” roles and front-end interviews.

Product & engineering guidance and tips

• Build one project end-to-end and productionize it (live demo + GitHub) instead of many unfinished ideas.
• Prioritize reliability, production stability, and deployment workflows—these are valued by recruiters/interviewers.
• For DeFi/order-book projects: learn order-book internals and async/low-latency architectures.
• For LLM-driven apps: design multi-step prompting workflows and break tasks into small scenes/prompts to reduce hallucinations.
• Use Storybook for UI libraries and provide a CLI to simplify adoption.
• Integrations and workflow automations are key differentiators for SaaS-style products (forms, deployments, webhooks).
• If you build something notable, tagging the presenter on Twitter may lead to referral or attention (presenter may engage with top ideas).

References and resources mentioned

• Opinion markets: Probo.in, Polymarket
• Order-book explainer: presenter’s Zerodha/order-book video
• On-chain DEX history: Serum, Openbook (Solana), L2 rollups
• Blender + MCP server
• Animation libraries: Manim (Three Blue One Brown), p5.js
• Remote recording model: Riverside.fm
• Notion-like form builder example: Tally
• Site-generator examples: Bolt, Lovable
• UI libraries mentioned: Hero UI, Rangoli
• Super30 program (capstone projects and interviews)

Main speaker / sources

• Video presenter/instructor (host of the Super30 program; unnamed in subtitles)
• Referenced platforms/authors: Probo, Polymarket, Riverside, Manim / Three Blue One Brown, Blender (MCP), Serum/Openbook (Solana), plus the presenter’s own videos (Zerodha/order-book, Bolt clone demos)

Additional deliverables mentioned

• A short checklist to help pick which project to build first based on background (frontend, backend, blockchain, ML).
• Expanded week-by-week build plans for any single project (tech stack, milestones, MVP features).