I've read both versions of the book and would strongly recommend the audiobook version that came out last year (it's available on Spotify). They did a good job handling the redactions and the narrator really brings the protagonist to life. A very strong recommend if you like audiobooks.
I am building QuietReads (quietreads.com) - a single-player book tracking app with an AI assistant.
I got tired of the gamification and social elements of Goodreads, StoryGraph and others. I don't care too much about reviews, but do care about engaging deeply with what I am reading.
Lot's to do still:
- Refining the experimental MCP integration so I can bring my bookshelf into my AI assistants
- OCR to build notes from screenshots
- Voice notes
Accelerationists and doomers both assume AI means rapid change. But what if the incentives point toward stasis instead? A world frozen in place by those who got there first.
Great post - I recommend reading "Apple in China" to get a great insight into the how the Chinese manufacturing industry (and economy) has developed over the last 30 years.
I bought a Nikon FE2 recently from keh.com.
There are plenty of manual focus film SLR cameras available. You may also want to try your local camera store. Mine has a good selection of used cameras and lenses. Things are more expensive than they used to be but there are still plenty of options available.
Can confirm that Ethan Allen is still selling decent, US made, furniture - though at high prices. We got a couch for an awkward sized space from Ethan Allen and were very happy with the shopping experience, installation, and the quality of the furniture.
Wealth Management is not the same as retail banking.
"The Wealth Management division offers comprehensive wealth management and investment solutions and tailored financing and advisory services to ultra- high-net-worth (UHNW) and high- net-worth (HNW) individuals and external asset managers"
TLDR - Credit Suisse has been a sh*t show for over a decade. This has been coming for a while.
Credit Suisse is in investment banking and wealth management. It does not have a significant retail presence (EDIT - outside of Switzerland). Credit Suisse has also had a number of accounting, risk management, and other scandals over the last few years. They were bailed out late last year by a fund linked with Saudi Arabia who took a ~10% stake in the company. Today, they (the Saudi fund) refused to inject more capital and the shares have collapsed.
I am in a similar situation to you - 2 year old and a newborn and feeling a little burnt out at work.
The way I have been coping is just realizing that at this stage the timelines for pretty much everything shrink from months or years to hours and days. You need to focus on getting through the day at work, keeping sane, and being present for your partner and kiddo. Your work might not be pleasant, but you have been there for ten years so you should have a good idea of what your MVO (minimum viable output) is.
I think the Kanban approach of reducing the Work In Progress is the way to go.
I have made a conscious effort to not worry too much about big picture "career questions" and just focus on the task at hand.
Our first kid is young enough for me to know that things become more manageable once the kid is sleeping 5-6 hours at a time. For us, this happened around 4 months in.
Good luck, and I wish you and your family well!
I think a key skill to evaluate is the "learning how to learn". Can a developer pick up a new language and are they flexible enough to get their head around a different concept.
I agree that we need to support learning - knowledge can be acquired, but the willingness to go outside your comfort zone is something that is a bit more innate.
I read the book when it came out. It is an interesting snapshot of a particular period in technology history but, as in any memoir, it comes with heavy dose of the author’s personality. I finished the book because the time period is interesting (social media maturing, Facebook and Twitter both growing, the start of “SV bro”
Culture etc).. but I found parts of the book incredibly, teeth-grindingly irritating. YMMV.
The Knowledge Project podcast is awesome. Here are some snippets that I found very useful.
On starting his company in a secondary market:
I do think that there is an almost complete difference between how you build a company in a primary talent market and sort of secondary talent markets. For instance, one of the biggest differences in secondary places is, if I hired someone, the chance that we are going to still work together in a decade from now is super high.
Hiring for future potential
It means that it’s a much better idea to hire for future potential rather than for current skill. Once you do that, what you realize is, a dollar invested into helping to train people or even subtle things like making sure that next to any junior programmer, like for every five junior programmers in a team, you have one person who can become their mentor—these basic ideas all start having way more dividends
On building a hiring process for a learning organisation
..we have a hiring process that really, really finds people of high future potential. And then, we try to help them reach this potential 10, 20 years earlier in their career than they ever thought was possible, through coaching, through book clubs, through anything.
Fixed vs. Growth Mindsets
Everything in Shopify is built around this idea that if someone shows up with a fixed mindset, we convert them to a growth mindset quickly. And then once we have a growth mindset, we fill people with context (i.e. explain what their role is and what they are expected to do).
On the role of processes with an organisation
There are three kinds of process. There’s a kind of process that makes things that were previously impossible to do, possible. That’s good. Then there’s a kind of process that makes something that was previously possible significantly simpler, which is also good. However, 99.9% of all process that exists in corporate America is the third category, which is actually just telling people to behave slightly different from what common sense tells them to do.
So we have lots and lots and lots of examples where we just avoided having to create process by just changing the environment in which we all spend our time in.
I am a member of the learning community. I found great value in the transcripts of the Knowledge Project podcasts. The podcast is free but the transcripts are available to the learning community. My favourite recent podcast was with Tobi Lutke (CEO of Shopify) - https://fs.blog/tobi-lutke/. Other podcasts that were super interesting were with Robert Greene, Naval Ravikant and Venkatesh Rao.
The forum itself is interesting - there is a regular reading group which I really enjoy as well. It is expensive, but I think it is worth it (or am I simply a victim of the sunk cost fallacy?)..
Use the right tools for the job. If you are running your Python models in containers anyway, a suitable API will work with any backend language. Go and even the JVM (Java / Scala / Kotlin) ecosystem are great candidates.
Our stack uses Python (models deployed as dockerized WSGI applications) for machine learning and web services written in Java for the backend. It is relatively easy to get developers who are comfortable in the JVM ecosystem and the tooling available makes it easier to build robust systems.
> 2. Collect examples. Start with a spreadsheet of data that highlights the data you have available.
This is true not just for data science but when trying to solve any numerical problem. Using a spreadsheet (or a R / Python notebook) to implement the algorithm and getting some results has helped me in the past to really understand the problem and avoid dead ends.
For example, when building a FX pricing system, I was able to use a spreadsheet to describe how the pricing algorithm would work and explain it to the traders (the end users). We could tweak the calculations and make sure things were clear to all before implementing and deploying the algorithm.
Mainly because the market was fragmented and FX had a lot of arbitrary laws (i.e. which currency pairs can be traded, which pairs have a peg against the dollar) and also the complexity of settling cash trades. Looser regulations and burgeoning global trade have provided the impetus for the automation in the FX business.