Auto-generate user stories, test plans, and more in Azure DevOps

Hacker News

Hello HN community! I'm excited to present TachyonGPT, a free tool for Azure DevOps (ADO): https://marketplace.visualstudio.com/items?itemName=Neudesic... Note: You'll need an OpenAI API key. Demo video: https://youtu.be/DUrMRhmTfRo Install and Configure video: https://youtu.be/57PUC9GLXjY Backstory: As developers, we recognized the need to alleviate the tedious manual process of generating and refining GENERIC work items like features, user stories, acceptance criteria, etc. in Azure DevOps. These tasks, while important to understand the problem, often take time away from understanding the problem and talking to users. There's also the risk of missing stories and tasks. That's when we decided to marry the generative powers of OpenAI with ADO. What's Different?: TachyonGPT fuses AI with ADO to auto-generate or enhance feature descriptions, user stories, acceptance criteria, and more. It’s built with a custom UI ensuring full control and integrates seamlessly with Azure DevOps. Features: 1. Generate: Craft complete work items, from titles to child work items, using OpenAI. 2. Refine: Adjust generated results within TachyonGPT with custom prompts, ensuring specificity. Data Privacy: Trust is paramount. TachyonGPT processes everything locally; no data entered is sent to Neudesic or third parties. Only API key information is stored for authenticating requests to OpenAI API. Feedback: We're eagerly looking to refine TachyonGPT. Suggestions, ideas, or critiques are welcome! Please note that TachyonGPT is designed to assist, not replace. It aids in initializing or refining work items, making the process more efficient. The final content is always under your control. Appreciate your time and keen to hear your thoughts!

Visit product View on Hacker NewsAug 24, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, openai · Missing: mac, agents, macos

89%89% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users, way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

37%37% 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: efficient, users · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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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