C8

C8lore – Discussions from Slack, Discord, Telegram Communities in LLMs

Hacker News

C8lore – Discussions from Slack, Discord, Telegram Communities in LLMs

Hi HN, I use ChatGPT and Claude daily for research and coding. I often wish they had direct and fresh access to discussions in the private product, marketing, ideas, and engineering news communities I’m part of on Slack, Discord, Telegram,... So I built c8lore.com (for "community lore" :) that connects and indexes many communities once — it indexes the discussions (near real-time) and LLMs can then semantically search them, pulling relevant threads, workarounds, and insights straight into prompts. It ships with an MCP server so you can simply tell your LLM `use c8lore`. It’s fed with some initial sources focused on these exact topics (product, marketing, ideas, engineering, ...), and I’m actively adding more. The public directory is open for anyone to browse. I’d love your feedback: 1. Do you participate in private or semi-private communities for product, marketing, ideas or engineering (or else) discussions? Which platforms? Discourse, Facebook Groups, ...? 2. If you’ve looked at c8lore, does bringing this knowledge into your AI workflow feel useful? What’s missing that would make researching or keeping access to this information easier? 3. Which communities or types of sources would you find most interesting? Thanks in advance — really appreciate any thoughts or suggestions!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, slack · Missing: mac, agents, macos
96%96% 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
69%69% predicted probability of success on Indie Hackers, 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
29%29% 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: platform · Missing: plus, intuitive, reviews
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Correct prediction on native model

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