Launched free browser agent (the catch: ads)

Hacker News

No one’s got money for another AI subscription. So we made our browser agent totally FREE. What's the catch? Just ads while Retriever AI fetches you leads, does your exams, applied to jobs, and sends connection requests. Think like a Claude Code or Clay level tool in your browser, but free for everyone! After 35K+ users and 7M+ workflows, we spent the last few months attacking the cost of every agent step: - switching to DeepSeek Flash - Code Mode to one shot complex workflows - optimizing for 80%+ token cache hits So that now just an ad impression covers the entire cost of the agent (LLM inference included), so we can now offer the entire agent for free! More important than an agent that can scale with users, is an agent that gets better with every workflow. Retriever now continuously: → learns global skills for every website, so no mistake repeats twice → writes learnings from user tasks as personalized skills Curious to hear how others are tackling consumer agents and excited for the new era of free agentic apps!

Visit product View on Hacker NewsAug 26, 2026

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Launch Intel predictions

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

99%99% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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 · Strong signals: users · 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 · Strong signals: subscription · 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 · Missing: web3, chat, crypto

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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