SuperOptiX – Evaluate, Optimize, Orchestrate DSPy AI Agents – BDD Style

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I posted about SuperOptiX previously, but at that time the website and documentation weren't fully polished — we were still finalizing core components. Since then, we've fully revamped the site, completed detailed developer docs, and rolled out all major features. What’s SuperOptiX? It’s a full-stack Agentic AI framework built for real-world, production-grade agents with a focus on: Evaluate-first development (BDD/TDD style specs like Cucumber or RSpec way) Optimization-core architecture (powered by DSPy) Multi-agent orchestration (think Kubernetes for agents) Context Engineering, Model Management, Memory Routing Declarative agent specification with SuperSpec (Think RSpec for Agent Engineering) Ideal for teams moving from agent demos to scalable, maintainable systems. Website: https://superoptix.ai Docs: https://superagenticai.github.io/superoptix-ai/ Would love your feedback from HN Community — we’ve put our soul into getting this right for agent developers and infra engineers. Happy to answer questions!

Visit product View on Hacker NewsJul 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

98%98% 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.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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