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Chatmate: Discover and create chatbots

Hacker News

Chatmate: Discover and create chatbots

Chatmate.dev allows you to easily make gpt4 based chatbots by combining multiple chat completions and/or document retrievals. You can chain different components together or run them in parallel and then use their responses in future prompts/components. To get started, create a project and then create a chat component. A single chat component is the same as one chat completion. You can add more chat or document components that use the responses of previous components in their prompts. There are some simple demos in the "discover" page. You can also publish your own chatbots and share them at the share url. The first demo is a simple document extraction. I copied a few posts from today's hackernews and fed it as a pdf. I then used the document retireval in the prompt for the final chat completion. The second demo is a teaching assistant chatbot for a data structures course. It is made up of 4 components, 1) Standalone query, which converts the user input to a standalone question (so that the document retrieval can be improved) 2) Thought generator, generates a sentiment based on the user's input (i.e. the user seems stressed about their data structures homework) 3) Document retrieval, retrieves lecture notes from over 400 documents 4) combines all the components into one system prompt for a better response. Roadmap: 1) Code components - run any code (including network calls) 2) Conditional components - uses gpt functions to decide which components to run (components are the functions) 3) Multitenancy (i.e. publish your bot at {botname}.chatmate.dev 4) Verisoning - chat with multiple versions of your bot (easy comparison of prompts) If you have any feedback, feel free to reach out at "jmiran15@jhu.edu".

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para, including · Missing: supports, reddit linkedin, podcasting
82%82% 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: user, new, single · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
41%41% 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
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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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