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Aether

Next-Gen RAG & Cloud Intelligence

Fintech
SaaS
Software Engineering

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Hunted byNageshkumar GunjiNageshkumar Gunji

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Aether

Next-Gen RAG & Cloud Intelligence

Deploy production-grade local knowledge retrieval or scale effortlessly into high-throughput cloud reasoning engines.

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Upvotes2
▲ 2View on ProductHunt ⧉
Comments1
1 commentsSee comments on PH ⧉


It establishes your credentials, explains why you built it, and highlights the technical value to drive conversions to your Kreato checkout link. https://kreato.co.in/aether/23aa48bb-f963-4554-b6b8-1412ccd9c151


📍 Pinned Maker Comment

Hey everyone! 👋 Nageshkumar here, maker of Aether OS Pro.


As a developer building AI MVPs, I found myself wasting dozens of hours on the exact same setup for every project: setting up vector databases, configuring schema migrations, handling document chunking, and wiring up semantic caching. Worse, hitting cloud LLMs on every single user prompt was burning API budget fast during testing.


I built Aether OS Pro to solve this once and for all with a zero-token-cost, local-first production stack.


🛠️ What’s inside the box:

  • Framework: Next.js 16 (App Router + Server Actions with React 19)

  • Database & Vector Engine: PostgreSQL + Prisma 7 with native 1024-dim pgvector support

  • Embeddings & AI: Pre-configured for Voyage AI embeddings (production) and local Ollama runtimes ($0 offline testing)

  • Performance Layer: Sub-millisecond in-memory semantic query cache to skip redundant LLM API calls

  • Monetization: Pre-configured Paddle billing modal + instant local setup

🎬 Watch the 6.5-min technical build & demo:


https://www.youtube.com/watch?v=S8P0tD0uDNA


⚡ Get the full production codebase (.zip) via instant UPI checkout:


https://kreato.co.in/aether/23aa48bb-f963-4554-b6b8-1412ccd9c151


I'd love to hear your thoughts on the semantic caching approach or answer any questions about the Prisma 7 pgvector setup below!


— Nageshkumar Gunji


Maker, Aether OS Pro

About Aether on Product Hunt

“Next-Gen RAG & Cloud Intelligence”

Aether was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #160 on the daily leaderboard. Deploy production-grade local knowledge retrieval or scale effortlessly into high-throughput cloud reasoning engines.

On the analytics side, Aether competes within Fintech, SaaS and Software Engineering — topics that collectively have 136.2k followers on Product Hunt. The dashboard above tracks how Aether performed against the three products that launched closest to it on the same day.

Who hunted Aether?

Aether was hunted by Nageshkumar Gunji. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

For a complete overview of Aether including community comment highlights and product details, visit the product overview.