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UnSkript – Generate SRE Runbooks Using ChatGPT and Jupyter Notebooks

Hacker News

UnSkript – Generate SRE Runbooks Using ChatGPT and Jupyter Notebooks

Hello, Hacker News developers! We're thrilled to introduce the latest release of unSkript ( https://www.unskript.com ) - a potent SRE platform, now with AI-powered runbook authoring. Our aim is to enhance incident response by utilizing Jupyter notebooks and advanced AI models. Let's dive into the details! unSkript Highlights Website: https://www.unskript.com GitHub: https://unskript.github.io/Awesome-CloudOps-Automation/ Quick Demo: https://youtu.be/uvMVAbXWKDU Why unSkript was Born We understand the intricacies of incident response faced by developers and SREs. Traditional text-based runbooks often fall short. Our vision to combine Jupyter with LLMs is to offer actionable, dynamic runbooks with a RUN button for each task. This release takes us closer to that vision. Empowering Developers When incidents arise, unSkript's AI runbooks help swiftly deciphers incident data, system logs, and past solutions. The result? Dynamic runbooks with step-by-step guidance, fixes, and automation pathways. AI-driven runbooks slash MTTR and elevate incident response to new heights. Experience unSkript Today Witness AI-powered incident response in action through our demo at https://youtu.be/uvMVAbXWKDU . Discover how unSkript empowers developers to master incidents swiftly and accurately. Join Our Developer Community Community-driven innovation is at the core of unSkript. Shape the future of incident response with us. Join our Slack group at https://www.unskript.com and become part of our vibrant developer community. Your Input is Gold Being developers ourselves, your thoughts, queries, and suggestions hold immense value. Let's collaboratively forge an incident response solution that emboldens developers and SREs alike. Grateful for your vibrant engagement!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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, slack, new · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
42%42% 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
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
30%30% 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
12%12% 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, introduce · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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