This video compares 5 free UI component libraries that completely change how your apps look when building with Claude. If your Claude designs all look the same, switching the library is the fastest fix. See 30 side by side designs across 5 free UI component libraries, each building a support inbox, file manager, and analytics dashboard in light and dark mode. Check it out! 👇

Railway emailed me in October. Stored credentials exposed. Nothing got used, as far as anyone knows. I spent one evening rotating everything. Supabase, Neon, DeepSeek, Brave. It was boring and it was scary at the same time. The part that gets me: I knew better. I just never thought it would happen to me. Where do your keys live right now? Be honest.

Before an AI caller dials, pull the lead's last reply and the notes from the last call into the prompt — even just two lines. An agent that opens with something the lead already said sounds like a follow-up, not a cold call. One that repeats a question they already answered sounds like a bot, and the lead hangs up in seconds.

Wanted to share a win from my latest cold campaign . For a long time, my campaigns struggled. I thought the problem was my email copy or my warmup tool. It wasn't. The problem was the level of leads I was targeting. Most people in AI automation are stuck sending to Level 1 leads. Here is the difference: Level 1: Dead Scrapes (Zero Intent) Scraping 5,000 emails off Apollo or Google Maps by generic title (e.g., "Founder"). You don't know if they have money. You don't know if they have the problem. Result: Low replies, high spam complaints. Level 2: Cleaned Leads (Basic Filters) Verified through Zerobounce / Debounce. Correct headcount and industry. Better deliverability, but you are still just a stranger pitching out of nowhere. Level 3: Signal-Based Leads (Active Intent) This is what produced the numbers in the screenshot. I stopped emailing people based on their job title. I only emailed them if they triggered an active signal: Spend Signal: They are actively spending money on Meta or Google Ads right now (proof they have budget and value customer acquisition). Tech Signal: Specific tools or pixels detected on their site (proof of their exact workflow). Growth Signal: Active hiring or specific changes in their business. Why this changes reply rates: When a lead is Level 3 (Signal-Based), you don't need a 300-word pitch. You don't need fancy AI personalization. You just point out their live signal in the first sentence: "Saw you're running ads for [X] on Meta..." Because the relevance is 100%, the perceived friction drops to zero. They reply because it’s timely, not because of sales hacks. I documented my exact scraping setup, the 3 intent filters I use, and the email sequence inside a Notion doc. I dropped the direct Notion doc link in the first pinned comment below 👇 No pitch, no gate. Take the filters and test them on your own campaigns.

One rule I added after my test calls: the agent never says a slot is booked until the booking actually comes back as saved. If the save is slow or fails, it takes the caller's name and number and says the office will confirm. It sounds small, but a receptionist that promises a time it never saved is worse than one that says it will follow up. I'm still testing on messy real sites, not live with clients yet. What does your agent say when the booking step fails mid call?