Ti

Tiledesk – Open-Source LLM Chatbot Framework

Hacker News

Tiledesk – Open-Source LLM Chatbot Framework

Tiledesk is an open-source chatbot designer that unleashes the potential of LLM-enabled chatbots and process automation through a user-friendly drag-and-drop interface. Witness its capabilities in action here: https://youtu.be/ZDPoVVbXxs8 With Tiledesk, you can create dynamic, human-like conversations without the complexities of coding or scripting. Our guiding principle is straightforward: Design, Test, and Launch Your Conversation Flows Across All Your Digital Channels in Minutes The quickest way to experience Tiledesk new product version is by visiting our website. For those curious about our codebase, explore our latest repository on GitHub: https://github.com/Tiledesk/design-studio Let's maintain a discussion focused on the essentials. We're here to answer your questions, provide insights, and share technical details. For additional information and inquiries, please don't hesitate to contact us at info@tiledesk.com We’d love to hear your thoughts!

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Actual performance

4points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, coding · Missing: mac, agents, macos
89%89% 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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive
54%54% 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
43%43% 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: way · Missing: mobile apps, ios, personal
38%38% 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
10%10% 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.

Correct prediction on native model

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