Toffu – AI marketing agent that executes campaigns through chat

Hacker News

I built Toffu after getting frustrated juggling 20+ marketing tools. Instead of logging into Google Ads, opening GA4, checking Meta dashboards, and writing reports, you just chat: "What's my ROAS this week?" "Pause underperforming ads" "Create a competitor analysis" "Increase budget on winning campaigns" The AI executes everything – not just recommendations. Connects to: Google Ads, GA4, Meta Ads, LinkedIn, Reddit, Search Console, Webflow, HubSpot, and more. SOC2 certified for enterprise use. Free tier available. Happy to answer questions or hear feedback.

Visit product View on Hacker NewsOct 21, 2025

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Launch Intel predictions

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

84%84% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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

29%29% predicted probability of success on AppSumo, 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

28%28% 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

21%21% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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