Project ideas from Hacker News discussions.

Nano Banana 2 Lite

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Most prevalent themes in the discussion

Theme	Summary	Supporting quotation
1️⃣ Absence & latency of ChatGPT Image 2	Several users note that the model was omitted from benchmark charts and point out its slow generation time.	“They didnt include chatgpt in the comparison chart. That tells a lot” — algoth1
2️⃣ AI‑generated real‑estate images & misleading ads	The conversation highlights growing concern that AI‑created listing photos are deceptive, often distorting space, fixtures, or dimensions.	“Anyone who's used these models would know that ranking HiDream above Krea2 is a pretty hot take” — vunderba
3️⃣ Competitive model landscape (cost, speed, licensing)	Users compare Nano Banana Pro, Gemini Lite, Grok, Krea, Ideogram, etc., focusing on price per image, latency, and practical restrictions like aspect‑ratio control.	“Nano Banana is head and shoulders above the rest, but still too steep for personal use” — ramesh31

🚀 Project Ideas

NB2 Lite Aspect Ratio SDK

Summary

A lightweight SDK that adds programmatic aspect‑ratio control for Gemini’s NB2 Lite model, removing the need for manual prompt hacks.
Seamlessly integrates with Vertex AI pipelines to enforce deterministic output sizes for SaaS and marketing workflows.

Details

Key	Value
Target Audience	Developers building image‑heavy SaaS products, marketers, and power users of Gemini who need deterministic output sizes.
Core Feature	Programmatic aspect‑ratio control via a simple REST endpoint and Python SDK that forwards requests to Gemini while enforcing user‑specified ratios.
Tech Stack	Python 3.11, FastAPI backend, Docker, Vertex AI API, optional CLI client.
Difficulty	Medium
Monetization	Hobby

Notes

HN commenters repeatedly complained about “no programmatic aspect‑ratio option” (e.g., “You can’t force aspect ratios with NB2 Lite programmatically”).
Lowers the barrier for integrating NB2 Lite into existing pipelines, boosting adoption and reducing manual retry overhead.

RealEstate AI Truth Detector

Summary

A browser extension and API that scans real‑estate listing images for AI manipulation, flags suspicious edits, and adds a transparent authenticity badge.
Empowers users to see trustworthy visuals while giving agents a tool to showcase genuine property features.

Details

Key	Value
Target Audience	Real‑estate platforms, agents, and home‑buyers concerned about misleading AI‑generated photos.
Core Feature	Automated detection of implausible geometry, added fixtures, and scale errors; optional auto‑watermarking and correction suggestions.
Tech Stack	TensorFlow.js (client side), Flask backend, OpenCV for geometry checks, PostgreSQL for audit logs.
Difficulty	High
Monetization	Revenue-ready: $0.02 per image scan

Notes

Commenters like “This should be illegal” and “AI staging should be disclosed” show strong demand for verification.
Could integrate with existing MLS systems, opening a new compliance‑focused SaaS market.

Editable Diagram Generator for Slides

Summary

A web app that converts AI prompts into fully editable SVG diagrams exportable to PowerPoint and Google Slides, enabling granular post‑generation tweaks.
Turns generic AI illustrations into interactive presentation assets that can be altered without re‑rendering.

Details

Key	Value
Target Audience	Content creators, marketers, educators, and developers needing reusable diagrams in slide decks.
Core Feature	Prompt‑to‑SVG pipeline with a drag‑and‑drop editor that syncs changes back to the original AI model for iterative refinement.
Tech Stack	React frontend, Node.js backend, Stable Diffusion XL with ControlNet for layout parsing, SVG.js.
Difficulty	Medium
Monetization	Revenue-ready: $15/month per user

Notes

HN users praised the need for “real diagrams that can be moved and re‑shaped” (e.g., “Fable is the first model I found that can actually produce such diagrams in slides”).
Solves the “raster‑only” limitation of most image models and creates a new workflow for data‑driven presentations.