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Adding support for sponsored products in Meilisearch

Hacker News

Adding support for sponsored products in Meilisearch

I am the creator of the Kleio Ad Server and a big fan of the Meilisearch search engine. They share the goal of being blazingly fast and accurate. I have added native support for Meilisearch in Kleio. To use it, you let Kleio proxy your requests to Meilisearch and it then runs an ad auction on the results returned by Meilisearch. I would love to hear your feedback and thoughts. The docs linked to here also include a docker compose file you can use to quickly spin up an end-to-end demo. If you'd like to learn more about the ad-server itself, then you can find it here too: https://kle.io Peace and love

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock · Missing: mac, agents, macos
73%73% 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: io · Missing: https docs, excited, just released
54%54% 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
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
22%22% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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