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Wale All-in-One Prompt Engineering IDE

Hacker News

Wale All-in-One Prompt Engineering IDE

Hi HN, I am Zach, previously AI/ML Engineering @ Amazon and Duolingo. I am excited to share Wale with you! We have been building different GPT-3 apps and noticed a gap in tooling to help developers assess the quality of different prompts. Through first-hand experience and interviews, we found pain points such as prompt unreliability, prompt observability, and cost tracking. We built Wale to address these issues - and we are launching the beta version of Wale! Users are building: * A Sales Analyst chatbot that can generate and execute BI SQL queries * A Sentiment Detection prompt to evaluate customer feedback * A Business Analyst that can formulate sales strategies and plans. Wale's Unique Features * Intuitive prompt editor & templating * Run multiple prompts simultaneously * Prompt observability - understand the inputs and outputs of your prompts * Prompt statistics - monitor parameters, cost, and usage of prompts [coming soon] Quick links: For a quick walkthrough, watch Zach set up a simple app on Wale: https://www.youtube.com/watch?v=mTGAOX5yWo0 Check out our Docs: https://docs.trywale.com/docs/about If you have any questions, feel free to reach out to us at contact@trywale.com or zach@trywale.com!

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
71%71% 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: apps, user · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, ide · Missing: https docs, just released, exist
59%59% 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: intuitive, soon, users · Missing: plus, platform, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users, para · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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