💞 #Gate Square Qixi Celebration# 💞
Couples showcase love / Singles celebrate self-love — gifts for everyone this Qixi!
📅 Event Period
August 26 — August 31, 2025
✨ How to Participate
Romantic Teams 💑
Form a “Heartbeat Squad” with one friend and submit the registration form 👉 https://www.gate.com/questionnaire/7012
Post original content on Gate Square (images, videos, hand-drawn art, digital creations, or copywriting) featuring Qixi romance + Gate elements. Include the hashtag #GateSquareQixiCelebration#
The top 5 squads with the highest total posts will win a Valentine's Day Gift Box + $1
Most people fall into a trap of avoiding a certain type of narrative/project if their first investment went to zero.
If you have a thesis on a specific narrative, product, meme or whatever you’re investing in, just because the first one failed doesn’t invalidate the idea.
I’ve had tons of plays where I was correct in my thinking but chose the wrong coin.
Naturally you want to shut your brain off from the idea because it hurts to think about and you don’t want to feel stupid twice.
Although if you can stay positive minded and realize where the mistakes were in your execution or how the team delivered, it puts you in a perfect position to identify the next trade as you’re so familiar with what’s needed to create a successful outcome.
Example:
I was quite bullish on Dingalings launchpad Boop when it initially launched due to the flywheel mechanics, optics and growth strategy through KOLs launching their own coins.
Even though I ended up being wrong on a mid term time horizon, I realized optics and flywheels wasn’t enough to sustain anything more than a one week pump.
Which I then reflected on and came to the conclusion that Launchcoin was actually the strongest moat due to their creative GTM strategy of onboarding quality revenue generating products.
Leading me to discover Dupe, which was the most asymmetric trade and went in with max conviction at 1 mil (receipts in my telegram).
or
Nuit which was my most bullish project on the operator thesis, but due to coding my own research tools I found a lot of issues in scraping agents which adopted a similar type of architecture as they run into fault tolerances constantly, making them unscalable.
A month later I stumble into Codec and immediately recognize this is the perfect play due to VLA. Which coincidentally is also what powers robotic brains.