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Using LLMs to build a user centric design

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Using LLMs to build a user centric design

Featured in LangChain, FlowTestAI is one of the most powerful open-source tools that utilize the capabilities of LLMs to help create and manage contextual end-to-end tests. Extensive research, including work by Microsoft on RESTler, shows that contextual tests help to catch security and reliability bugs more efficiently than traditional approaches that test APIs in silos. Moreover, designing end-to-end tests that mimic actual user interactions greatly aids in product adoption.

Visit productView on Hacker NewsJul 19, 2024
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Indie HackersFits the IH revenue-focused audience · Strong signals: including, efficiently · Missing: supports, reddit linkedin, podcasting
95%95% 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: user, context, using · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
23%23% 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
11%11% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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