Toffu AI is a Vibe Marketing agent

Hacker News

I’m Or (YC S18), i previously founded the Yo app and co-founded Anima. I’m now building an AI agent that’s like Devin for marketing. While building the agent I’ve realized how different it is than conventional SaaS. For example, I realized that if I use schema for the db, I limit what the agent can do (As it can only store structured data based on my schema). I no longer think of features, as that would limit the capabilities of the agent. The moment I add features, that’s when the agent can only do these features. I only add capabilities. Features are what each user decides to do with the agent. I minimize the UI surface and focus and making the agent live in the apps we already use - I.e inside Google docs, slack, email, etc Happy for feedback

Visit product View on Hacker NewsMay 21, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

97%97% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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 · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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