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I found this clip from a documentary on the JWA group in the 80s! Features some incredibly obscure names such as Fighter Fujimoto, Big Akahira, Jackie Miki, Phoenix Tanaka, Hannibal Shimizu, Phantom Funakoshi, Riki Ishikawa, Hitoshi Toyoda, Stungun Takamura, Jaguar Ikeda...

12,437 görüntüleme • 1 ay önce •via X (Twitter)

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Everyone's talking about vibe coding without looking at code. I was skeptical. I decided to give it a shot on a challenging problem and was blown away by what I could accomplish in 8 hours. I'm not skeptical anymore, but I also do NOT think it kills SaaS Notes: - I tried to replicate some of my favorite features from the Solve-It platform as a jupyter extension - I've tried this many times before, and it didn't work. - I gave my agent specific testing tools that I packaged as skills. I used AI to write the skills. I found the right testing workflow for this Jupyter extension by having AI peruse lots of other extensions and the Jupyter source code. - I had the AI write and maintain a large suite of tests the whole time. I think this was important in keeping the AI on track. - I watched the diffs and the thinking traces as they streamed by. From time to time, I would see something very suspicious like " try ... except: pass" And would stop the AI and tell it to stop this behavior. Then trigger a comprehensive code review using AI. - Most importantly, I don't think this kills SasS at all. Even if I can create software that replicates some of my favorite features, there is an insanely long tail of paper cuts and features I don't want to manage. The models and capabilities are improving so fast that I don't want to constantly tune everything. So I would rather leave that up to people who are focused on that daily and have good taste, with the knowledge that it has been battle tested against many users.

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Q: How do you decide which customers to listen to? As Superhuman founder & CEO Rahul Vohra puts it: “In a world where you’re drowning in feedback—and most startups are drowning in feedback—you have to filter it down to only the stuff that’s going to increase the number of people who fall in love with your product.” Most startups will listen to all feedback from on-the-fence customers, but this isn’t targeted enough and will often lead to a muddled, incoherent product. As Rahul argues in the clip below, you need to identify the main benefit of your product—for Superhuman this was speed. And then focus on the feedback of on-the-fence users who also view this as the main benefit—there’s often something small holding them back. Users for whom your main benefit does not resonate (e.g. Superhuman users who value offline capabilities rather than speed), are unlikely to ever fall in love with your product. When Superhuman ran this analysis in 2015, they found that the main thing holding back users who viewed speed as the main benefit was their lack of a mobile app. Probing further, they found some less obvious and more interesting requests, such as integrations, attachment handling, calendering, unified inbox and read receipts. With a clear understanding of their main benefit and missing features, they were able to move this cohort of users from on-the-fence into the territory of enthusiastic advocates. As Rahul puts it in his Product Market Fit Engine article: “To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.” But make sure you’re focusing on users who love the main benefit of your product. Users who don’t are unlikely to ever fall in love with your product.

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