A New Direction / Weekly Roundup
Rebranding to technical content + a roundup of my fav links of the week
This issue of the newsletter marks the beginning of a new experiment. We’re going to pivot this thing into a publication for technical folks looking to make an impact in emerging industries.
If you’re receiving this newsletter, it means that you’ll be primarily receiving content 1-3 times per week regarding software and hardware that relates to crypto, AI/ML, robotics, and more.
We’ll avoid sending spammy, Tech Crunch style articles. There are plenty of curation newsletters that are made for headline reading. This won’t be one of them. You’ll see a lot of links and pdfs that are multiple years old, in addition to new posts from some of my favorite builders and creators. You’ll see new developer tools & research papers. The goal is to fill your mind with quality technical content.
In addition to this, you may receive a post from me that leans more philosophical/general. For example, I may write about how sound epistemology (from the likes of Popper, Deutsch, and Taleb) has helped me make better career decisions, and I may also write about my thoughts on the Sovereign Individual thesis or the coming chip war between the US and China.
Regardless, this newsletter is meant to deliver value to individuals looking to increase their technical acumen, whether you’re just starting out or 10 years into your career. With that being said, let’s get into roundup #1.
Roundup #1
The Devs Are Doing Something
I recently launched a podcast called Devs Do Something with my teammates at Superfluid. I interview top engineering talent in crypto once a week and suss out as many insights as I can regarding application design patterns, gas optimizations, and other general nuggets of wisdom to help you level up as an engineer.
So far, we’ve had on senior engineers and CTOs from places like Balancer, MakerDAO, and Connext, and we have plenty of additional episodes on the way. Check it out here:
Code Snippets Library Example
Lee Robinson, the VP of Dev Experience at Vercel, put together a really slick code snippets library that I found earlier this week. IMO this kind of thing is something that every devrel person should have: an easy to navigate place to find code that is used over and over again in the wild. I plan to create my own version of this, and it may be worth considering for you too!
The Sovereign Stack
Odysseas.eth put out a new post earlier this week on what he calls The Sovereign Stack. If you’re an engineer who wants to operate more like a Sovereign Individual, what tools should you be using?
Odysseas answers all your questions in the post, and interestingly highlights Urbit as a key technology to familiarize yourself with. I didn’t know much about Urbit before this week, but this post sent me down the rabbit hole.
WTF is Urbit?
Glad you asked.
Ok - so this^ is still way too general to really understand what’s going on. I plan to go deeper on Urbit in the coming weeks and hope to write about it in additional detail.
Account Abstraction on Ethereum
What is account abstraction? Here’s a succinct definition from this doc:
“In short, account abstraction means that not only the execution of a transaction can be arbitrarily complex computation logic as specified by the EVM, but also the authorization logic of a transaction would be opened up so that users could create accounts with whatever authorization logic they want.”
This is a big deal because it will enable developers to build much, much better user experiences into applications built on top of Ethereum. Instead of having to pay gas fees to execute every transaction, an application could abstract this away from a user. An application could allow users to send an off chain message called a ‘User Operation’ which would be bundled for block proposers. The key innovation is that the gas cost of these operations is not something that the sender of the ‘User Operation’ message needs to pay. This opens up many new use cases, many of which are highlighted in EIP-2938 (from 2020), including:
Paying for tx fees in a token other than ETH (or not at all, if an application wants to subsidize usage by paying for gas itself)
Enabling users to use smart contract wallets as their primary account
Increased innovation in privacy-preserving systems
Getting Up To Speed on Machine Learning
AI has displaced ‘Web3’ and ‘American Dynamism’ the new hot trend in venture capital and technology, and for good reason. OpenAI and it’s competitors have made huge strides in LLMs (large language models) and generative art (look up Stable Diffusion). It looks like we’re at the start of a really wild era of generative media.
This stuff is complicated, but it’s not *rocket science.* I’m nowhere near an expert, but I’ve been able to learn some of the basics with these resources:
How I Learned Modern Machine Learning, by Justin Glibert
Justin is a fascinating dude, and if you want to inject some techno-optimism into your veins, you should read this profile on his career and former startup.
But this article opened my mind up to a new mindset: the educational moonshot. Why the hell can’t you get to the top 10% of knowledge in a field within a year? Sure, getting to the top 0.1% may take a decade, but the top 10%? You can pull that off. You have the internet, for christ’s sake.
Making Things Think, by Giuliano Pezzolo Giacaglia
This is a good overview book that will give you a solid history of the field of AI, introduce you to techniques like machine learning, and help you create a map of interesting use cases & active projects in the space.
The Essence of Linear Algebra - 3Blue1Brown
If you want to actually DO anything with machine learning, even for the sake of tinkering, it’s really tough without a rudimentary understanding of linear algebra. Even if you’ve taken a linear algebra class in the past, this Youtube series does a brilliant job of building a visual intuition for what’s going on with matrix multiplication. If you’ve ever seen something like the below image in a course or paper on ML and were confused, you should probably watch this series.
Andrew Ng’s Machine Learning Courses
This is where I am currently, and it’s really started to pull back the veil on some of the simpler stuff. Gradient descent and ‘Software 2.0’ are talked about a lot, but they’re not that complicated as far as technical topics go. If you know how to code, these are the sorts of concepts that you can pick up in a weekend or two. I’m currently wrapping up the Supervised Learning course now, but the entire DeepLearning.ai curriculum comes highly recommended.
Neural Networks and Deep Learning, by Michael Nielsen
This is a great primer that goes deep on how neural networks work. It’s not light on technical details or linear algebra, so you should familiarize yourself with some of the high level basics first. However, going through this book slowly and methodically has been helping me understand deep learning from the ground up.
That’s all for today. I’ll leave you with this tweet & image.
There is more opportunity out there than anyone wants you to believe.
