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Models improve where data flows. ⬟ The Playground lets builders share prompts, datasets, and execution tips. Reusable prompt sets. Community-tested configurations. Build once. Share signal.

23,343 次观看 • 9 个月前 •via X (Twitter)

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I just built a Claude prompt library that runs your entire DTC marketing operation 🤯 100+ prompts organized by function: competitor research, creative briefs, ad copy, hooks, landing pages, performance analysis, customer review mining, and more. Perfect for DTC brands and agencies who are still prompting Claude from scratch every time they open a new chat, rewriting the same context, and getting generic output that sounds like every other AI-generated ad. This prompt library eliminates the entire loop: → Competitor Research: scrape and analyze competitor ads, extract winning hooks, map creative strategies, build competitive battlecards → Creative Briefs: generate data-backed briefs from ad performance, write iteration briefs, new concept briefs, test plans → Ad Copy & Hooks: 20 hooks across 10 frameworks, full ad copy variations, persona-specific angles, fatigue-busting rewrites → Landing Pages: audit any landing page against DR best practices, clone high-converting advertorial structures, write product page copy → Performance Analysis: audit Google Ads accounts, find wasted spend, build visual dashboards, weekly narrative reports → Customer Intelligence: mine reviews for ad copy language, extract objections, find unexpected use cases, build persona cards from real data → SEO & Content: find keyword gaps, write content in your brand voice, optimize product listings for AI shopping (ChatGPT, Gemini) → Email & SMS: launch sequences, weekly newsletters, abandoned cart flows, post-purchase nurture No more blank-page prompting. No more re-explaining your brand every session. No more generic AI output that sounds like a template. What you get: →100+ copy-paste prompts organized by the 8 functions DTC teams actually run →Every prompt pre-loaded with the context structure Claude needs to give you real output →Prompts that reference your brand voice, your ICPs, and your real data — not generic placeholders →A living library you can customize once and reuse across every campaign I put together the full prompt library as a single downloadable playbook: organized by section, ready to copy-paste into Claude today. Want it for free? > Like this post >Comment "PROMPTS" And I'll send it over (must be following so I can DM)

Mike Futia

34,953 次观看 • 5 个月前

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

36,542 次观看 • 1 年前