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Trailogs – A team's operational history you can ask questions about

Hacker News

Trailogs – A team's operational history you can ask questions about

Hi! My name is Marko, and I'm a developer (primarily backend) who loves building stuff. I've been trying to solve a problem of scattered operational context across Slack threads, tickets, meetings, etc. (my way)... The idea of Trailogs is to be a central event-based history, where each event (log) has structure around it, like categorization, ownership, etc... It has an exploration page, which is basically an AI chat where you can ask questions about what is happening in the company, why a certain decision was made, what is going on with a specific customer, who was responsible for a certain thing, etc... I realized that "what should I ask?" could be a problem, so the chat gives you suggestions based on your role in the team and interests. So if you are a developer, you could get a suggestion to ask about a certain deployment/feature release from today. If you are in sales, you'll get a suggestion to ask about negotiations that happened today with a certain customer. The chat is currently powered by OpenAI. Now, I understand the privacy concerns and sensitivity of the data, so I'm planning an implementation with a self-hosted LLM, or the possibility for users to use their own LLM. I was thinking about how to reduce the friction so teams don't have to manually log stuff every time, so, for now, I implemented the possibility to integrate through an API and webhooks (incoming, outgoing). Also, since a lot of team conversations happen in Slack, I've built a Slack integration so you can ask questions directly from Slack or ask it to draft a log based on what was discussed in a thread. The app is functional and can be tried. It has a registration form, but I made it possible to just continue with a demo account, so you don't have to leave any personal information if you don't want to, and you can quickly get straight to the demo workspace. Poke around, let me know what you think. :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos
97%97% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs
48%48% 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
46%46% 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: host, users · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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