FastSight.ai

Fine, screw it - here's the ENTIRE 6-hour, 91-slide workshop for Harvard Innovation Labs on the path from $0-$1M and B2B product-market fit! EDIT: Here's the link, just click here and I will return to a normal life -- https://lnkd.in/eSJ26VgQ I built the workshop I *WISH* somebody had given me when I was starting my first startup. This would have saved me YEARS in the pre-PMF pain cave. This workshop is built on 4 frameworks: 1 - The case study replication framework: This helps you visualize exactly what matters, when. So you don't get overloaded with a million things that SEEM important... preventing you from accomplishing ANYTHING. 2 - The "demand vs. supply" framework: This gives you a non-BS way to think about what people ACTUALLY want to buy, vs. building what you want them to want. 3 - The "learn by selling" framework: This prevents you from spending YEARS LARPing, running "experiments" to "validate" your "hypotheses" 4 - The "unfolding" framework: This prevents you from wasting a ton of your time trying to predict an unknowable future... I am pretty sure nothing else really matters when going from 0-1. If you find this useful, share it around!

It took 2 years for our first game, Wordgrams, to hit $30K/day in revenue. Our third game, Dice Words, achieved the same in just 50 days 🚀 What changed? Historical data that informs our decision-making. With our first game, Wordgrams, we started from ground zero: feature development, user acquisition (UA) strategy, ad creatives, and more. We spent years testing what works, analyzing our data, and refining our systems. We were also getting comfortable with payback periods 😓 We needed to see UA data for ourselves before significantly increasing our spending 💰 Four years later, we have robust analytics 📊 These insights have enhanced our understanding of player behavior, game design, ad monetization, and more. They’ve helped us identify and better target crucial benchmarks 🎯 Our refined UA strategies, backed by reliable data, allow us to scale games more rapidly and make informed bets with confidence 📈 We now invest over $2M monthly in user acquisition, seeing substantial returns 🔥🔥🔥 The real game-changer isn't just building a successful game - it's leveraging its data to make every subsequent game a faster, more predictable success 💡 Data you can rely on lays the groundwork for future titles and a sustainable, profitable gaming business 💪

Google has just published 8️⃣ new courses on Artificial Intelligence, and the best part? They're all free! Whether you're new to AI or an experienced pro, these courses are a great way to master AI in 2024. Here's a breakdown of the courses: 1. Introduction to Large Language Models: Learn about the use cases and how to enhance the performance of large language models. 🔗 https://lnkd.in/eBVdss4T 2. Introduction to Generative AI: Discover the differences between Generative AI and traditional machine learning methods. 🔗 https://lnkd.in/ee4gxDvT 3. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI. 🔗 https://lnkd.in/erJaqDCK 4. Introduction to Responsible AI: Learn about the importance of Responsible AI and how Google implements it in its products. 🔗 https://lnkd.in/eUcqfz_r 5. Encoder-Decoder Architecture: Learn about the encoder-decoder architecture, a critical component of machine learning for sequence-to-sequence tasks. 🔗 https://lnkd.in/euqrJZz6 6. Introduction to Image Generation: Discover diffusion models, a promising family of machine learning models in the image generation space. 🔗 https://lnkd.in/eDS2Af4y 7. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. 🔗 https://lnkd.in/e4nyJJZG 8. Attention Mechanism: Learn about the attention mechanism, which allows neural networks to focus on specific parts of an input sequence. 🔗 https://lnkd.in/eJayUv5K