Summary of "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).