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ZEON Format
Stop wasting money on JSON. Save 50%+ tokens on your LLMs
LLM API costs skyrocket due to JSON. We waste thousands of tokens on quotes, brackets, and redundant keys. Enter ZEON: a data serialization format built for AI. It uses tabular grammar and indentation, providing LLMs perfectly structured data without syntactic overhead. - Save up to 56% on tokens vs JSON - JS/TS & Python Parsers - VS Code Extension - Open-Source (MIT) Compare your token savings live at https://zeon-eight.vercel.app
Hi Product Hunt! I am the creator of ZEON.
Building applications powered by Large Language Models (LLMs) is incredibly exciting, but as developers, we quickly run into a silent budget killer: JSON syntax.
When feeding complex schemas or receiving structured outputs, we force LLMs to process thousands of brackets, curly braces, quotes, commas, and redundant keys. JSON was built for the web, not for AI context windows. Language models natively understand whitespace and indentation, yet we keep feeding them sparse matrices of characters.
That frustration inspired me to build ZEON (Zero-overhead Encoding Object Notation).
The goal was simple: create a data serialization format from the ground up that is perfectly structured for LLMs to read and write, while stripping away every single byte of unnecessary syntactic overhead.
ZEON uses a suffix-driven tabular grammar and Pythonic indentation. The result? A massive reduction in token consumption. In our benchmarks, replacing a standard JSON schema with ZEON saves up to 56% in token costs.
What this means for you:
More room in your context window for the actual instructions.
Smaller bills from the OpenAI/Anthropic/Google APIs.
Faster structured generations (since the model has fewer syntax tokens to output).
We already built open-source parsers for Python (PyPI) and JS/TS (NPM), and there is a fully featured VS Code Extension to give you syntax highlighting right out of the box.
I would love for you to try our interactive token comparison on our website to see the token savings on your own schemas.
I will be here all day to answer your questions and hear your feedback on the parser design and grammar rules. Let's build more efficient AI apps together!
About ZEON Format on Product Hunt
“Stop wasting money on JSON. Save 50%+ tokens on your LLMs”
ZEON Format was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #29 on the daily leaderboard. LLM API costs skyrocket due to JSON. We waste thousands of tokens on quotes, brackets, and redundant keys. Enter ZEON: a data serialization format built for AI. It uses tabular grammar and indentation, providing LLMs perfectly structured data without syntactic overhead. - Save up to 56% on tokens vs JSON - JS/TS & Python Parsers - VS Code Extension - Open-Source (MIT) Compare your token savings live at https://zeon-eight.vercel.app
On the analytics side, ZEON Format competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ZEON Format performed against the three products that launched closest to it on the same day.
Who hunted ZEON Format?
ZEON Format was hunted by Schicksal. 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 ZEON Format including community comment highlights and product details, visit the product overview.
Schicksal