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OLLS – A Vendor-Neutral Standard for LLM Inputs and Outputs

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OLLS – A Vendor-Neutral Standard for LLM Inputs and Outputs

We have just launched the Open LLM Specification (OLLS) – a community-driven standard that unifies how developers interact with large language models (LLMs) across providers such as OpenAI, Anthropic, Google, and others. Right now, every provider has different request/response formats, which makes integration painful: Parsing responses is inconsistent Switching models needs custom wrappers Error handling and metadata vary wildly OLLS defines a simple, extensible JSON spec for both inputs (prompts, parameters, metadata) and outputs (content, reasoning, usage, errors). Think of it like OpenAPI for LLMs—portable, predictable, and provider-agnostic. GitHub Repo - https://github.com/julurisaichandu/open-llm-specification Example input/output formats, goals, and roadmap Looking for contributors, feedback, and real-world use cases! Let’s build a unified LLM interface—contribute ideas or join the discussion

Visit productView on Hacker NewsJul 22, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, models · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google, para · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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