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Sonde – Open-source LLM analytics (track brand mentions across LLMs)

Hacker News

Sonde – Open-source LLM analytics (track brand mentions across LLMs)

Hi HN! We built Sonde ( https://github.com/compiuta-origin/sonde-analytics ) after noticing - probably like everybody else - our website traffic was declining while prospects were telling us "I found you through ChatGPT". We wanted to understand our visibility across LLM responses, as we did in the good old days of SEO and web analytics. Since existing tools were enterprise-tier expensive, we built Sonde as a simple internal project to: - schedule prompts to run against multiple LLMs, with web search enabled - track whether your brand is mentioned in responses, how it ranks vs. competitors and general sentiment - monitor trends over time Tech stack: Supabase, Next.js, OpenRouter as LLM wrapper. Sonde is fully open-source and you can self-host it via Docker Compose. We're also offering a managed version for convenience, running with complete feature parity. Sonde has fundamentally changed how we approach content strategy for our products: I'd love to get feedback on it, or hear how you're currently tracking LLM visibility!

Visit productView on Hacker NewsFeb 26, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, chatgpt, open · Missing: mac, agents, macos
93%93% 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 · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
56%56% 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 · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
38%38% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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