Enlist AI: Sub-second interview coaching with persistence

Hacker News

Following some initial feedback, we’ve updated Enlist AI to ensure your preparation never resets. Based on user behavior, we noticed plans were being lost on refresh, so we've implemented local persistence to keep your progress synced. What it does: Enlist AI is a full-pipeline preparation tool that turns any job description into a custom coaching experience. You paste a JD and get a personalized day-by-day study schedule. The "Mock Interview" Flow: Real-time Voice Analysis: You speak your answers out loud. We use your microphone to track performance with sub-second latency. Speech Clarity Scoring: The system flags filler words (um, like, you know) and scores your logic structure. Actionable Feedback: Instead of generic scores, it gives a specific "Fix it" instruction for every answer. Tailored Documents: It also generates a CV and cover letter specifically matched to the job requirements. We’re running this on Groq for the inference speed to make the feedback feel instant. Would love to hear the community's thoughts on the analysis logic. Check out at https://enlistai.vercel.app and give feedback at https://enlistai.vercel.app/feedback

Visit product View on Hacker NewsApr 25, 2026

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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

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TrustMRRLess likely to generate early MRR · Strong signals: personal, answers, para · Missing: mobile apps, ios, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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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.

Incorrect prediction on native model

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