Pitched a VC for 30min before realizing they invested in a competitor

Hacker News

Hey HN, I pitched an investor for 30 minutes before realizing they'd already passed on a similar company in their portfolio. That moment broke me. I'd wasted both our time because I didn't do basic research. So I built Briefing AI: Paste any calendar invite and Get an Intelligence-style dossier on attendees in 30 seconds. THE WORKFLOW: You have a meeting in 10 minutes. You know nothing about the attendees. Before: • Open LinkedIn, search "Sarah Chen Sequoia" • Skim profile, open 5 tabs • Google her recent activity • Try to remember talking points • Show up flustered • Total time: 15-20 minutes (if you even do it) After: • Paste calendar invite into Briefing AI • Get: Executive summary, attendee backgrounds, company context, talking points, red flags • Walk in prepared • Total time: 30 seconds THE TECH: • Next.js 15 + Cloudflare Pages (serverless) • Brave Search API (real company/attendee data, not just AI hallucinations) • GPT-4 (parsing + generation) • Device fingerprinting (1 free briefing, then $49 LTD or $19/month) Launched Friday. Made 2 sales. Learning fast. "BUT CAN'T I JUST USE CHATGPT?" Yes. But: • ChatGPT = 8-10 prompts, 10 minutes ("extract attendees" → "who is Sarah?" → "what should I ask?") • Briefing AI = 1 paste, 30 seconds • ChatGPT = conversation disappears • Briefing AI = saved dashboard, searchable history I'm not competing with ChatGPT. I'm making one specific workflow 10x faster. WHAT I LEARNED (2 sales in 3 days): The problem is real (everyone wings meetings), but I have zero distribution. 80 website visits, 2.5% conversion. Product works. Distribution doesn't. So I'm asking HN: • Is this genuinely useful, or am I solving a fake problem? • Would you pay $49 lifetime for this? • What's the #1 feature I'm missing? Try it free (no card required): getbriefing.io I'm here all day to answer questions and iterate based on your feedback. PS: If you've ever Googled someone 5 minutes before a Zoom call, this is for you.

Visit product View on Hacker NewsOct 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, context, chatgpt · Missing: mac, agents, macos

95%95% 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 · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal

30%30% 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

25%25% 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

23%23% 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.

Correct prediction on native model

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