Worked on the self learning pattern engine today. It's getting smarter about email patterns for each domain. Whenever it encounters a new domain, it starts off with 17 different pattern formats. As more data comes in, the engine begins recognizing which patterns are more common for that specific domain. It gradually narrows down the possibilities, getting more accurate over time. This works well especially with unique domains that don't follow the usual first.last style. It’s fascinating to see it adapt and improve as it processes more emails. The challenge is keeping it flexible enough to adjust if a domain changes its format. Had to make sure it doesn't stick too rigidly to one pattern once it thinks it knows the best match. Also, matched this up with the existing domain intelligence features. Together, they really boost the accuracy of email finding.

Catch all domains are tricky Spent some time refining catch all domain scoring over the past week and it's been quite the challenge. Catch all domains are tricky because they accept all emails, making it hard to tell if an address is actually valid. Most verifiers struggle here, often marking addresses as deliverable when they're not or dismissing them prematurely. This is what I've been working on to tackle this issue: • Improved SMTP fingerprinting to better identify catch all behavior. • Enhanced bounce signal analysis to catch subtle indications of invalid emails. • Updated the machine learning prediction model for more accurate scoring. These improvements help in distinguishing between truly deliverable addresses and those hiding behind catch all domains. Why does this matter? Accurate catch all scoring can significantly reduce bounce rates and improve email deliverability. It means fewer wasted credits on undeliverable addresses and a better understanding of email list quality. The feedback I get on catch all improvements is invaluable, so keep it coming. For those interested in testing these features, MailCaff's still in pilot. You can check it out at mailcaff.com and use the code PILOT2026 for 10,000 free credits. Let me know your thoughts as I continue refining these systems.

Hey everyone, just joined. I run a small AI automation agency selling mostly Lead Gen, and some custom automations. Most of my day is spent connecting APIs, writing Python scripts, and figuring out how to get things done faster with less tools. Lately I've been deep in the email verification and finding space. I was spending way too much on existing verification services for what is honestly not that complex under the hood. Thats why i built my own tool called MailCaff.com. My main goal is to make tools like this as affordable as possible, especially when you are just starting out, you don't have a lot of cash to burn usually. It does email verification, email finding, catch all scoring, and spam trap detection. Still early days (running a pilot right now) but it's been working well for my own client pipelines. It's been intense, but i love every freaking minute of it! Always interested in learning how other people have set up their outreach stacks. What tools are you using, what's working, what's not. Happy to share what I've learned along the way too.

Let's get to know each other! Comment below sharing where you are in the world, a career goal you have, and something you like to do for fun. 😊

Here’s a hard truth: People don’t want AI. They want relief. Less follow-ups. Less checking. Less fixing mistakes at night. If your content doesn’t talk about relief, it won’t resonate. 🌟Business want results not complex workflows or systems!! 🎉Curious about:- 1. More add-ons ?? 2. Any more tips for Business ? 3. Tips for outreach?