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HelpKit AI – AI Chatbot trained on your Notion docs

Hacker News

HelpKit AI – AI Chatbot trained on your Notion docs

Hi HN! Dom, founder of HelpKit AI here. HelpKit is a solo founded and fully bootstrapped tool that turns your Notion docs into a professional help center and documentation site. Since we are already hosting hundreds of our customers Notion knowledge bases it only made sense to supercharge them with a custom AI trained chatbot. The launch was planned two months ago but some things stopped me from releasing it. If you are curious about the background feel free to read on. HelpKit AI is my first real touchpoint developing a product with Large Language Models (LLMs). After doing some simple warm-up Hello World AI tests, everything seemed so simple. Unfortunately I had to soon find out that I sincerely underestimated the complexity involved in developing a conversational AI chatbot that closely mirrors human interaction. Following a considerable period of experimentation with a plethora of existing tools, it's become apparent that the majority merely repurpose around 10 lines of Langchain code, presenting it as a fully-fledged product. The outcomes of these efforts are, at their best are "meh". Some OK, but not great. I nearly fell into the same trap, having developed a feature that was 90% complete before acknowledging the subpar and cluttered results, as well as my limited comprehension of this new technology. Engaging with LLMs presents an entirely unique and novel paradigm. Consequently, I revisited the initial plans and invested a lot of more time immersing myself more into the intricacies of LLMs, AI UX, and a slew of way too complex research papers, all in an effort to better understand how to construct tools around LLMs. Interestingly, due to the novelty of this field, there's no definitive roadmap to a successful implementation. The process is characterized by a great deal of experimentation. As an engineer being used to having deterministic outcomes I just couldn’t believe the quirkiness of testing out bunch of prompts to find out what works the best. In the end everything finally clicked and I know exactly what I was doing and how I can make HelpKit AI as best as possible.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
71%71% 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: month, way, para · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · Missing: arr, mrr, revenue
18%18% 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.

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