Free Online Course – Debugging Difficult Conversations

Hacker News

As an engineer I get frustrated at other training courses that talk a lot of theory, but don't have a lot of practical advice and tools to actually help. So, I built an online self-paced training course on difficult conversations (think feedback, salary negotiation, etc) that I wanted to share with the community. I like to make my training courses more "algorithmic." A process you can follow to approach a Difficult Conversation, get in the right mindset to tackle it, and prepare what you need to that allows you to do it well. This course will teach you: - What Difficult Conversations are and what happens when you ignore them - Why having Difficult Conversations can be beneficial to you, your team, and your career - A framework (and accompanying worksheet) to help you prepare for Difficult Conversations

Visit product View on Hacker NewsJan 15, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

20%20% predicted probability of success on Product Hunt, 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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