Загрузка видео...

Не удалось загрузить видео

На главную

Most developers know how to ship a feature. Turning every release, demo, and product update into content people can quickly understand is a different job. Fetra AI is built for that second part. It helps teams turn product and brand context into short-form content, then plan, schedule, and publish...

11,484 просмотров • 1 месяц назад •via X (Twitter)

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

We're excited to introduce eComrads. Our mission is to make AI that sells, not just AI that makes content. We're building AdDNA, the intelligence layer that gives AI models and agents judgment about what actually converts. And today, eComrads is live to everyone. AI has made it easy to generate anything. A product photo, a video, an ad, in seconds. Everyone has the same tools now, and most of what comes out is slop, content that looks fine and sells nothing. Because generating content was never the hard part. The hard part is judgement. What converts, what fits the brand, what makes someone actually buy. Cracking that means turning something fuzzy and subjective, the instinct for what sells, into something we can measure and codify. We're starting with ecommerce. There are two sides to this, the intelligence layer and the production layer: - We've trained AdDNA on what actually performs in each niche, the winning patterns behind real conversions, not pretty pictures, and we refresh it constantly so the system generates toward what sells. - We've built the production engine that turns that judgment into finished, on-brand creative, photoshoots, video, UGC, and ads, with your product kept accurate instead of reinvented, and every output checked before it ships. We learned all of this the hard way, producing creative by hand for real brands until we knew exactly what breaks and what sells. eComrads is that knowledge, turned into a product. We want a future where any brand can compete on the quality of its ideas, not the size of its ad budget.

eComrads

457,250 просмотров • 3 месяцев назад

I’ve tested dozens of AI video tools for ecommerce ads. Most still look obviously fake. They either distort the product, break character consistency, or completely miss the pacing that makes viral content actually work. Wizstar was the first one that genuinely surprised me. I tested Wizstar’s Wizstar_official Video Reference workflow using a viral-style ad, and honestly… I wasn’t ready for how accurate it would be. It didn’t just “generate a similar video.” It recreated the entire feel of a high-performing ad — the pacing, the transitions, the camera movements, even that subtle “social-first” storytelling rhythm that usually takes real creative teams to get right. And the wild part is… it still kept the product completely stable across brand-new scenes. No distortions. No weird AI shifts. Just clean, consistent visuals from start to finish. What really impressed me is that Video Reference isn’t just copying visuals at all. It actually analyzes the structure behind viral ecommerce content — things like: - how the hook is built, - how attention is retained across cuts, - and how emotion is paced through the video. Then it rebuilds that logic into a completely new ad, optimized for product testing and fast campaign iteration. And it feels like it understands performance content. The workflow also handled synchronized audiovisual timing surprisingly well — cuts land where they should, motion matches the sound, and the storytelling flow feels intentionally designed, not randomly generated. Combined with stable product consistency and influencer-style framing, the final output honestly felt closer to a real paid social campaign than anything I’d expect from an AI tool. What surprised me is that Wizstar isn’t powered by just one model—it orchestrates multiple top-tier AI models, including Seedance 2.0, and even supports face input out of the box. If you’re curious to try it yourself, you can test it here: New users get free credits, and the first membership is only $19 — plus a complimentary 30-second E-commerce Agent experience included. #Wizstar

Doreen

153,944 просмотров • 3 месяцев назад

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,849 просмотров • 6 месяцев назад