Caspar Roxburgh

Thrilled to share my FIRST BOOK 'Understanding Rural Household Diversity in Eastern & Southern #Africa' published by @ACIARAustralia. Big thanks to @CIMMYT @QAAFI and co-authors @FSR_Erin and Daniel Rodriguez (@Queensland_FSR). #openaccess available here: bit.ly/HHdiversity

@lennysan I just want to say thank you so much @lennysan and Chandra Janakiraman for the fantastic interview and now this even better deep dive blog. I'm already applying this strategy blocks framework and am so pumped to have such an actionable approach to the amorphous world of strategy!

WTF is product strategy Product strategy sits in between the mission/vision and the plan, either at the company level or at the team level. At the company level, the mission and vision are typically articulated by the founders/CEO and tend to be durable over time. The plan (i.e. roadmap) is an ordered list of projects based on some notion of prioritization and sequence of delivery. There is a steep drop in elevation between the mission/vision and the plan, and strategy occupies this large void. Strategy exists to force a disciplined choice to deploy scarce resources for maximum impact. Regardless of the size of a company, the resource pool and capacity to get work done is always constrained relative to the universe of work that could be done—making this choice a critical decision in every single context. A good strategy articulation typically includes three components: 1. 3 to 5 areas for the company or the team to focus on, which we will henceforth refer to as strategic pillars 2. Several areas that should explicitly not be the focus 3. A clear set of explanations for why these choices were made Here's a step-by-step guide to crafting the two types of product strategy: 1. A 2-year strategy, which is typically focused on solving problems with the current product, i.e. small “s” strategy 2. A 3/5/10-year strategy focused on aspirational futures. i.e., big “S” strategy Bookmark this for the next time you're working on a strategy: lennysnewsletter.com/p/strategy-blo…

I wrote something deeply personal about money. It’s raw and honest. It’s important. Please read it. I hope it helps those doing it tough. ❤️ abc.net.au/news/2024-06-0…

@DrDemography This was so beautiful and moving to read. I cried when reading the part where you speak to your younger self. Easily the most meaningful and genuine "pay day" column on the ABC. Thank you @DrDemography

@gregisenberg Very interesting. Reminds me of comments by @bbalfour on the @TropicalMBA recently about the need for a new playbook and his four fits framework (where product has to be optimised (fit) for one channel only that matches market & model. What would a b2b example of this look like?

I had a realization yesterday over coffee with a friend. Product vs. distribution in a startup - what’s more important? It's the classic debate. I realized there's a seismic shift in the startup playbook that many aren't noticing. Every top 0.01% CEO, PM, designer know this…

Finally, if you liked this, please like share the first post! This took *a lot* of time to research and create. Get back to the top here 👇

@carlvellotti 🚀

I've spent 100s hours of finding good PM content. Here's my HUGE curated db of 144: 🔸 tools 🔸 books 🔸 videos 🔸 courses 🔸 websites 🔸 podcasts 🔸 templates 🔸 newsletters I'm going to sell it for $59. For the next 24h only: FREE! Follow + RT + comment "🚀" and I'll DM.

@carlvellotti Keen for this resource!

@FCamiade @memdotai mem it

ChatGPT will truly replace experts... You'll have the answer to everything in 1 prompt. Discover this ultra-combo of plugins:

@martin_casado @a16z @replicatehq @nextjs @OpenAI @clerk @pinecone @supabase @flydotio @memdotai mem it

Want to build a gen AI app? Here is a Javascript repo to get you started that we use in the @a16z infra team. It is a simple generative image and text app. Built using: @replicatehq @nextjs @OpenAI @clerk @pinecone @supabase and @flydotio github.com/a16z-infra/ai-…

@svpino @memdotai mem it

I thought art was impossible to automate. I was wrong. Today, anyone can turn their photo gallery into unlimited, amazing pictures in less than 10 minutes. Here are a few lines of Python code to show you how:

@svpino @memdotai mem it

A 2-minute introduction to the fundamental building block behind Large Language Models: Text Embeddings (This is the most helpful explanation you'll read online today. I promise.) The Internet is mainly text. For centuries, we've captured most of our knowledge using words, but there's one problem: Neural networks hate text. Judging by how good language models are today, this might not be obvious, but turning words into numbers is more complex than you think. Imagine a 4-word vocabulary: King, Queen, Prince, and Princess. The most straightforward approach to converting our vocabulary into numbers is to use consecutive values: • King → 1 • Queen → 2 • Prince → 3 • Princess → 4 Unfortunately, neural networks tend to see what's not there. Is a Princess four times as important as a King? Of course not, but the values say otherwise: Princess is "worth" 4 while a King is "worth" 1. We don't know how a neural network will interpret this, so we need a better representation. Instead of using numerical values, we can use vectors. We call this particular representation "one-hot encoding," where we use ones and zeros to differentiate each word: • King → [1, 0, 0, 0] • Queen → [0, 1, 0, 0] • Prince → [0, 0, 1, 0] • Princess → [0, 0, 0, 1] This encoding fixes the problem of a network misinterpreting ordinal values but introduces a new one: According to the Oxford English Dictionary, there are 171,476 words in use. We certainly don't want to deal with large vectors with mostly zeroes. Here is where the idea of "word embeddings" enters the picture. We know that the words King and Queen are related, just like Prince and Princess are. Word embeddings have a simple characteristic: related words should be close to each other, while words with different meanings should lie far away. The attached image is a two-dimensional chart where I placed the words from our vocabulary. Look at the image and something critical will become apparent: King and Queen are close to each other, just like the words Prince and Princess are. This encoding captures a crucial characteristic of our language: related concepts stay together! And this is just the beginning. Notice what happens when we move on the horizontal axis from left to right: we go from masculine (King and Prince) to feminine (Queen and Princess). Our embedding encodes the concept of "gender"! And if we move on the vertical axis, we go from a Prince to a King and from a Princess to a Queen. Our embedding also encodes the concept of "age"! We can derive the new vectors from the coordinates of our chart: • King → [3, 1] • Queen → [3, 2] • Prince → [1, 1] • Princess → [1, 2] The first component represents the concept of "age": King and Queen have a value of 3, indicating they are older than Prince and Princess with a value of 1. The second component represents the concept of "gender": King and Prince have a value of 1, indicating male, while Queen and Princess have a value of 2, indicating female. I used two dimensions for this example because we only have four words, but using more would allow us to represent other practical concepts besides gender and age. For instance, GPT3 uses 12,288 dimensions to encode their vocabulary. That's a lot! Text Embeddings are the backbone of some of the most impressive generative AI models we use today. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me @svpino so you don't miss what comes next.

@bandanjot @memdotai mem it

10 concepts for Product Builders explained: 🧵 1/ Kano Model(Prioritisation) 2/ 7 Powers (Strategy) 3/ CIRCLES (Interviews) Strategy essays from 4/ Gibson Biddle 5/ Melissa Perri 6/ Roman Pichler 7/ Consulting Models 8/ Porters 5 Forces 9/ Elena's PLG 10/ Differentiation Models

@QCompounding @memdotai mem it

I am an Equity Fund Manager and have an MBA. The Cash Flow Statement is by far the most important financial statement in an annual report. Knowing how to read a Cash Flow Statement is crucial. Here's how:

@aisolopreneur @memdotai mem it

@bandanjot @memdotai mem it

9/ Generative AI for business leaders (linkedin.com) linkedin.com/learning/gener…

Stanford, Google, Hardvard and more- are offering free courses on AI. Prompt engineering, Conversational AI, Natural Language Processing & more - drives today's AI applications. So, I went around the internet to look for the best learning tools. Here are 12 FREE courses on AI:

@heyBarsee @memdotai mem it

If you found this helpful or interesting. Please retweet the tweet below to share it with your friends❤️ twitter.com/heyBarsee/stat…

If you're not using AI, you're falling behind in 2023. Use these 13 new AI websites to get more done and reduce work hours:

@bandanjot @memdotai mem it

Here's a screenshot of the OKR template to get started. Since I cannot attach the file here, all you have to do is: Simply retweet the first tweet in this thread and DM me, its that simple!

Want you or your team to become more goal-driven? Here's a <5 min summary of how to write good OKRs (+FREE template to get started) 🧵

@PawelHuryn @memdotai mem it

That's a wrap. If you liked this: 1. Follow me @PawelHuryn for more 2. RT the tweet below to share it with others twitter.com/PawelHuryn/sta…

Top 21 PM gems that will help you land a new job: (or keep the existing one) 1. 1500+ PM Interview questions by @Lewis_Lin: lnkd.in/dJ6G-sHi 2. Product Templates Curriculum by Matt Mochary (ex-CEO coach to Naval, the founders of OpenAI, Notion) lnkd.in/d4kBJteS

@BrianFeroldi @memdotai mem it

If you enjoyed this thread: 1. Follow me @BrianFeroldi 2. RT the first tweet below to share this thread with your audience

Warren Buffett. Peter Lynch. Charlie Munger. Philip Fischer. All of these super investors use checklists. I spent hours studying their criteria. Here’s the ultimate list of investing checklist questions (all yours for free):