Workshop time! ⚙️
Learn how to build a fully functional onchain application using @OnchainKit—the fastest way to integrate Web3 functionality into your frontend. https://t.co/QGl3n8XPDn
I am no longer a professor of medicine at @Harvard. Here is the story of my Harvard experience until I was fired for clinging to the truth.
https://t.co/zSOQlNJTY2
Amazing text to music generations from @suno_ai_ , could easily see these taking over leaderboards.
Personal favorite: this song I fished out of their Discord a few months ago, "Return to Monkey", which has been stuck in my head since :D
[00:57]
I wanna return to monkey, I wanna be wild and free,
I wanna return to monkey, modern life is not for me.
No more emails, no more bills, no more endless strife,
Just the sound of the river, the hearbeat of life
😂
.@37signals is a very different kind of company:
→ They make tens of millions in profit each year
→ With fewer than 80 employees
→ No investors, no board
→ No growth goals
→ No experiments
→ No sales, no marketing spend
→ Teams are two max 2 people (1 eng, 1 design)
→ They prioritize gut over data
→ They prioritize profit over growth
→ They've been profitable for 24 years straight
In my conversation with @jasonfried, we explore his contrarian approach to building a company:
🔸 Prioritizing profit above all else
🔸 Why work should not feel like war
🔸 Why, and how, to foster a gut-driven culture
🔸 The “Shape Up” framework for building products
🔸 The downsides of raising VC
🔸 Advice for bootstrapping your business
🔸 A peek at Once, their new product line
🔸 Much more
You know how image generation went from blurry 32x32 texture patches to high-resolution images that are difficult to distinguish from real in roughly a snap of a finger? The same is now happening along the time axis (extending to video) and the repercussions boggle the mind just a bit. Every human becomes a director of multi-modal dreams, like the architect in Inception.
Coming back to Earth for a second, image/video generation is a perfect match for data-hungry neural nets because data is plentiful, and the pixels of each image or video are a huge source of bits (soft constraints) on the parameters of the network. When you're training giant neural nets in supervision-rich settings, your train loss = validation loss, and life is so good.
My favorite place to keep an eye on the AI video space unfold atm is probably https://t.co/l1xRaq71C4 , or the individual Discords.