Making Sense
"Civilization rests on a series of successful conversations." -Sam Harris Neuroscientist, philosopher, podcaster and bestselling author Sam Harris, has been exploring some of the greatest questions concerning the human mind, society, and the events that shape our world. Harris' search for deeper understanding of how we think has led him to engage and exchange with some of our most brilliant and controversial contemporary minds - Daniel Kahneman, Robert Sapolsky, Anil Seth and Max Tegmark - in order to unpack and understand ideas of consciousness, free will, extremism, and ethical living. For Harris, honest conversation, no matter how difficult or contentious, represents the only path to moral and intellectual progress. Featuring twelve conversations from the hit podcast, these electric exchanges fuse wisdom with rigorous interrogation to shine a light on what it means to make sense of our world today.
Stats for nerds
11recommend
19mentions
18people
2021–2026years
58% recommend42% neutral
Mentions per year
What Hacker News says
- The ones that feel the most like they "get me" are probably Weird Studies and Very Bad Wizards. I'm a fan of Sam Harris's Making Sense podcast too.
When I want to dip into political news, I trust the Fifth Column guys to have fairly measured and reasonable takes with a vaguely libertarian bent. I have a handful of other political shows too from various perspectives of the aisle that I'll sometimes tune into when something big seems to be happening, but I generally don't consume much politics.
Also, I'd be remiss not to mention the excellent Knifepoint Horror, whose creator has been delivering exemplary horror short fiction of a very particular style for over a decade now. I always listen to those basically immediately after they come out.
- The only podcast I make sure to keep up with is Making Sense with Sam Harris [0]
[0] https://www.samharris.org/podcasts/making-sense-episodes
- What I've noted is that GPT is really good at things that have really good documentation.
The example of kube-tf in this repo is a perfect example. The Kubernetes documentation and all of Hashicorp's documentation is excellent. GPT will have infinite examples of good code to stitch together code for the task in this example.
Now I've been running a private cloud at work on the OpenNebula platform, which has documentation that is definitely lacking. I tried to ask GPT to write some basic code in Python such as "Give me a list of VMs from the OpenNebula API in a powered off state that have a start time older than 30 days."
What I noted was that it would spit out code that looked correct on the surface, but would not run. It would take a decent amount of me modifying the code until I got my desired result. Since there was no documentation, I was just reading through the OpenNebula packages themselves to understand what to do.
The nice thing, though, is that it was a great starting point. Much in the same way I might take a code snippet from StackOverflow and modify it to suit my own needs.
I listened to a great podcast titled "The Trouble with AI" on Making Sense with Sam Harris. One of the key takeaways I grabbed from it was that GPT is an LLM not an AI. What it is very good at is predicting the next correct character or word in a sequence based off of other examples. But it does not actually fundamentally understand what it is outputting.
In order to demonstrate, open up a session with ChatGPT really quickly and ask it a single digit multiplication question. Such as "What is 3 multiplied by 4?" and you will see a correct answer.
Next, ask it something a bit larger, like "What is 12366 multiplied by 981632?" and you will get an incorrect answer but one that looks pretty close to correct. Validate with a calculator yourself.
The reason being, as an LLM it doesn't actually understand multiplication. Instead it has just seen 3 multiplied by 4 countless times in the data it ingested when it was being "trained", but never has seen larger number examples of multiplication. Not that it knows multiplication in the first place.
GPT is fantastic, but as of right now it needs to be used as a starting point towards knowledge or something concrete. I wouldn't trust it as an authoritative source on anything quite yet. It is fantastic for generating a bit of code and then allowing the developer to tweak that code until it actually works.
- Making Sense with Sam Harris. There are so many good podcast episodes. If you don’t like the first one you listen to, browse the list of episodes. There’s a ton of evergreens.
- For casual listening, I really like Connected, and Cortex, from Relay FM—the former being an Apple-centric podcast, the latter being anything productivity-adjacent, and co-hosted by CGP Grey.
For more niche, ‘serious’ interests, Peter Attia’s podcast, The Drive, and it’s short-form counterpart, The Qualies, are fantastic. Truly world-class conversations. Another in the same league is Sam Harris’ podcast, Making Sense.
Finally, for sheer entertainment value that intersects the world of technology, Darknet Diaries has been a favourite for years.
- Pivot - the combo of Kara Swisher and Scott Galloway really works
Marketplace - public radio daily about the economy that’s a great education on the economy and everything really
Making Sense with Sam Harris - Sam is a powerful thinker and he has a wide range of topics and amazing guests
Corecursive - every episode is a story about a piece of software: excellent
Hardcore History, of course
The Joy of Why - different science topic each episode and goes pretty deep
