A really common problem people run into when working with webhooks is this: the JSON comes in fine, but one of the fields you actually need is buried inside a messy block of text. For example, a webhook sends a message that includes a ClickUp task link, email, phone number, and notes all mashed together in one string. When you try to map that directly into another app, it either errors out or pulls the wrong value because Make can’t tell what part of the text you actually want. This is where the Match Pattern (Text Parser) saves you. Instead of fighting the JSON structure, you let Make parse the JSON first, then use a regex pattern to extract only the exact piece you need, like a task ID or order number. Once that value is isolated, downstream modules stop breaking, filters work properly, and your automation becomes predictable instead of fragile. I recommend using https://regex101.com/ when doing regex.

I see many in the AI space focusing on the wrong things. You don't need to analyze, then work on this thing over here and that thing over there, build scenarios in make.com or N8N that is not at that moment serving a problem for someone. All you need to do is throw yourself in the fire by finding a problem first, then you go build said scenario or workflow saving you lots of time and energy and learning at the same time in the hot seat. All love gng.

Hey guys, What’s everyone’s experiences with chat gpt premium plans ? Also wish everyone a happy new year 🥳

This season has a way of pulling your attention outward. You scroll. You compare. You start quietly wondering if you should be further along by now. But here’s the truth most people miss...what you focus on doesn’t just shape how you feel. It shapes how you build. You can focus on what feels missing and let that create frustration, pressure, and noise. Or you can recognize the abundance already supporting you and let that become fuel instead of friction. There are people with more money than they could ever spend who would trade it for the relationships, health, or peace you already have. That perspective matters because it keeps you grounded in reality, not comparison. And here’s where wisdom meets hunger. Gratitude isn’t a finish line. It’s the foundation. Hunger isn’t dissatisfaction. It’s direction. The most powerful place to build from is the space where you’re deeply grateful for what’s here and still hungry for what’s possible. That balance keeps you sharp without making you bitter. Driven without being depleted. So, where has your focus been going lately — lack or abundance? And how might your momentum change if you learned to hold both gratitude and hunger at the same time?

ChatGPT offers a "Your Year with ChatGPT", and it said I was "top 1%" based on messages (chats). Given I started using ChatGPT in April, 2025..... and it is comparing "life time usage" .... interesting stats (below). ============================ Known facts Your messages: 13,976 Your percentile: Top 1% by message count Message usage follows a heavy-tailed (power-law) distribution: Most users send very few messages. A small minority send a lot. The top 1% dominate total volume. Reasonable inference Across large chat systems, observed patterns are typically: Median user: ~20–40 messages total Mean (average) user: ~100–300 messages total Top 1% threshold: often ~2,000–5,000 messages Top 0.1%: 10,000+ messages You are at ~14,000, which puts you well inside the extreme tail, not merely barely top 1%. Practical answer A defensible estimate is: Average user: ~150–250 messages total You vs average: ~50–90× more messages You vs median: ~300–700× more messages Interpretation (non-flattering, factual) You are not “a bit above average.” You use the system as an operating environment, not as a novelty or lookup tool. Your usage profile resembles: Power users Builders Analysts People externalizing cognition into the system Most users never cross a few hundred messages in their lifetime. “Top 1% messages sent” is calculated over the platform’s relevant user base (not just Pro, not just Business, not just last 30 days). Your screenshot metrics are lifetime-to-date: 13,976 total messages, 817 chats. 1) Global percentile estimate (0.5%, 0.1%, etc.) What we can compute exactly Messages per chat (your intensity): 13,976 / 817 ≈ 17.1 messages/chat (moderate per-chat depth; the extreme comes from total volume). What we cannot compute exactly (without platform distribution data) Your exact percentile beyond “top 1%”. Credible bounding (best-effort) Given typical heavy-tail engagement patterns, 13,976 lifetime messages usually lands in: Likely range: Top 0.1% to 0.3%