Mark by Airtop

Vibe automation for solo marketers

Sales

Marketing

Artificial Intelligence

Visit WebsiteSee on Product HuntTwitter ⧉

The compile-agents-into-executable-code approach instead of keeping an LLM in the loop is what makes the reliability and cost claim believable. My setup question is about the accounts Mark logs into: when he posts to social or pulls LinkedIn data, where do those sessions/credentials actually live, on Airtop's hosted browsers server-side, or something scoped per user? And when a site changes its flow and a compiled agent breaks, does Mark detect the failure and auto-recompile, or do I have to trigger a rebuild?

I tried it to pull pricing data from a few competitor sites and it worked surprisingly well without me touching a single API. The plain English framing is genuinely useful, though I did have to nudge it on captchas. Solid first impression.

How does it handle sites with heavy bot protection or CAPTCHAs, like LinkedIn or banking portals? Curious if that breaks the "just describe it" promise pretty quickly.

Tried it on a couple of sites and it actually logged in and pulled the data I asked for, which surprised me for a no setup tool. Felt more reliable than other browser agents I've poked at.

Tried it for scraping some old vendor portals and it actually handled the login flow without me babysitting it, which honestly caught me off guard. Curious to see how it holds up on trickier captchas.

I used the early version of Mark to do some seriously cool stuff. My personal use case is building AI ops to make a small team of marketers' lives easier, and we're big fans.

I may be biased because I've been burned by agencies before, having to wait 6-12 months for results, not doing a good job with product stuff, and having little impact on priority SEO—focusing instead on improving 50-100-rank keywords.

Not going to reveal too many secrets but we were able to turn that around with Mark. Most of our automatable processes are assisted by Mark now, and we've brought in-house what would traditionally be considered agency work, seeing a significant increase in our tracked metrics while spending much less.

Automating outbound and Google Ads straight from a website scrape is the part I'd watch closely - if Mark's GTM read of the business is slightly off, you're not just wasting time, you're burning ad spend and sending outbound copy that misrepresents what the company does. Is there a review step before Mark actually starts spending or emailing on your behalf, or does it go straight from plan to live campaign?

Got it, so it's steer-at-build-time. The thing I keep bumping into: declaring upfront which steps stay LLM-validated assumes I already know where the judgment gets hard, but the is-this-a-real-lead call goes wrong on the ambiguous 5% I can't enumerate in advance. What I'd really want is a step that re-checks itself at runtime when its own confidence drops, not a static flag set at build. Is that on the roadmap, or is build-time steering the model you're committed to?

Looks cool guys, great idea. What are your policies on data handling and encryption, data privacy and training? Is data encrypted at rest and in transit? Would like to review your privacy and data handling procedures.

Fair, as long as the builder calls that right. The step I'd want to override is the judgment one, like is-this-reply-a-real-lead. Your monitoring layer confirms a step completed, but a classifier can complete perfectly cleanly and still be wrong, and compiling it hides that failure mode. Can the author pin a specific step to stay LLM-evaluated on every run instead of letting it get compiled down to fixed code?

The Google Ads integration looks really cool! Google Ads has a TERRIBLE interface so it would be nice to just be able to prompt it SO uwu

Hey folks! I'm Jordan from Airtop. Feel free to ask me questions about Mark or how the Airtop platform builds compiled agents <3

"no APIs, just words" is a strong claim for browser agents specifically because most sites don't have an API to begin with. that's the actual gap browser automation fills, the long tail of internal tools and dashboards that were never built with integration in mind. how does it handle sites with heavy bot detection though? logging in and browsing like a human is exactly the pattern most anti-bot systems are tuned to catch.

Compiling intent into code is the right instinct, we landed in the same place: re-deriving the same plan with an LLM on every run is what kills reliability. Where it got hard for us was the genuinely non-deterministic steps, like 'is this reply a real lead or a bounce', you can't compile the judgment out. So the interesting line is compiled-vs-LLM: does Mark let the author pin certain steps as always-LLM, or does the compiler decide that itself?

Interesting idea. Does Mark let you adjust targeting criteria before the agents actually go live?

The GTM plan is the part that demos well, but generating a plan is the easy 20%. The thing I'd want to know building this kind of agent: does Mark close the loop on what actually converted, weeks later and through noisy attribution, and revise? Or is it generate-once? A plan a solo marketer can't measure against gets abandoned by week two.

the part that stands out to me is compiling into deterministic code instead of running an LLM call on every step. that's the actual fix for the reliability problem most agent tools have, cost aside. curious how it handles a site that changes its layout after the automation was compiled, does it silently break or re-detect and recompile

This looks super useful for automation workflows. How reliable is it when you scale multiple browser tasks at the same time?