HN user
- 0xc0ffee
Along similar lines, the economist John Kenneth Galbraith has a book on financial bubbles and irrational crowd mentalities in financial market over the centuries: A Short History of Financial Euphoria [1]. Short, approachable, and interesting.
[1] https://www.goodreads.com/en/book/show/270746.A_Short_Histor...
Do you have a "dot files" repo? It would contain things like this, config files for tmux, zsh, <other tools>, etc.
Reminded me of "Garfield Minus Garfield" - https://garfieldminusgarfield.net/
"""
Garfield Minus Garfield is a site dedicated to removing Garfield from the Garfield comic strips in order to reveal the existential angst of a certain young Mr. Jon Arbuckle. It is a journey deep into the mind of an isolated young everyman as he fights a losing battle against loneliness and depression in a quiet American suburb.
"""
“The world is too much with us” - W. Wordsworth
The world is too much with us; late and soon, Getting and spending, we lay waste our powers;— Little we see in Nature that is ours; We have given our hearts away, a sordid boon! This Sea that bares her bosom to the moon; The winds that will be howling at all hours, And are up-gathered now like sleeping flowers; For this, for everything, we are out of tune; It moves us not. Great God! I’d rather be A Pagan suckled in a creed outworn; So might I, standing on this pleasant lea, Have glimpses that would make me less forlorn; Have sight of Proteus rising from the sea; Or hear old Triton blow his wreathèd horn.
https://www.poetryfoundation.org/poems/45564/the-world-is-to...
Yes, probably a good one to Pump and Dump, Pump and Gump, Gump and Dump.
Sometimes you don't know what needs to be built until you build it. These end-to-end prototypes are how to enhance your understanding and develop deeper intuition about possibilities, where risks lie, etc.
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Much of my career has been spent in teams at companies with products that are undergoing the transition from "hip app built by scrappy team" to "profitable, reliable software" and it is painful. Going from something where you have 5 people who know all the ins and outs and can fix serious bugs or ship features in a few days to something that has easy clean boundaries to scale to 100 engineers of a wide range of familiarities with the tech, the problem domain, skill levels, and opinions is just really hard. I am not convinced yet that AI will solve the problem, and I am also unsure it doesn't risk making it worse (at least in the short term)
“””
This perspective is crucial. Scale is the great equalizer / demoralizer, scale of the org and scale of the systems. Systems become complex quickly, and verifiability of correctness and function becomes harder. Companies that built from day with AI and have AI influencing them as they scale, where does complexity begin to run up against the limitations of AI and cause regression? Or if all goes well, amplification?
The more verifiable the domain the better suited. We see similar reports of benefits from advanced mathematics research from Terrence Tao, granted some reports seem to amount to very few knew some data existed that was relevant to the proof, but the LLM had it in its training corpus. Still, verifiably correct domains are well-suited.
So the concept formal verification is as relevant as ever, and when building interconnected programs the complexity rises and verifiability becomes more difficult.
Do you mind adding more color and details to your closing thought? I’m curious if you know if projects that exist to help with this.
I found this to be an interesting analysis:
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What has changed is where the durable value actually lives. It is increasingly useful to separate the stack into a few layers:
- The computing, IO, and compiler kernel libraries based on CUDA, compiler frameworks like MLIR or JAX’s XLA, and of course Apache Arrow.
-The database systems and caching layers, ideally connected with ADBC’s zero-serialization connnectivity.
- The language bindings and orchestration layers that expose those capabilities.
- The application or agent interfaces that sit on top.
When viewed this way, most of the long term value clearly resides in the first two layers (compute and data access), not the last two.
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"""
Claude Code with Opus 4.5 is a watershed moment, moving software creation from an artisanal, craftsman activity to a true industrial process.
It’s the Gutenberg press. The sewing machine. The photo camera.
"""
- Sergey Karayev
Fair enough! I do like how others are framing this is as "write less code" -- if Rue makes one think more and more about the code that finally makes it to the production, that can be a real win.
Just pointing out here that "rue" is used to express "to regret", emphatically. Perhaps it is not the best name for a programming language.
With "servant leadership" in its current form being attributed to Greenleaf, here is the "source of truth" on servant leadership: https://greenleaf.org/what-is-servant-leadership/
"Growth" of those being led is a key concept it seems, which I would think is really only possible when the leader doesn't do everything by themselves as a die-hard servant, but utilizes the "leadership" part to help subordinates learn to lead themselves.
Granted this realm of ideas can be a gray-area, but it seems like servant leadership as presented by the author here does not incorporate the concept of growing those that they lead -- as indicated by the fact they have self-invented a new "buzzword" which actually seems to be involve the behaviors as laid out by servant leadership -- am I missing something?
Essentially, what information are they privy to that public is not? What asymmetry exists (timing, un-public information)? Is there any way for the public to be nearly as informed? What are they trading on? Upcoming funding changes (more money here -> buy, less money there --> sell)? COVID impact stands out.
Agreed, coding is one. What else?
Where is AI actually selling and doing well? What's a good resource for these numbers? What are the smaller scale use-cases where AI is selling well?
I am generally curious, because LLMs, VLMs, generative AI, advances are proving useful, but the societal impact scale and at this the desired rate is not revealing itself.
Is this being used internally at Google? What's the "dog-fooding" situation and is it leading to productivity enhancements?
This def needs skins next: https://skins.webamp.org/
At this point, you may as well get a powerpack for a mini and put it in one of these slings, you could have a crazy powerful machine in your "sock-et" sling thing here...
When the iPhone Air was just another huge phone...but thinner...smh. Apple should put up some page to check interest level in a smaller phone, and with enough interest, go manufacture it. If it is more expensive because economies of scale don't work out, but they create one that is small yet powerful, that's what I would buy at premium, because apparently compactness is a luxury.
Yes, applied research has yielded the modern expert system, which is really useful to experts who know what they are doing.
Thanks for the details. Also, always appreciated Discord's engineering blog posts. Lots of interesting stories, and nice to see a company discuss using Elixir at scale.
What are you using it for? Is it part of a hybrid search system (keyword + vector)?
You are model citizen with those file moves. Totally agree with how that can be very disruptive for legibility and comprehension of changes.
`git-bisect` is legit if you have to do the history archaeological digging. Though, there is the open question of how git commit history is maintained, the squash-and-merge vs. just retain all history. With squash-and-merge you're looking at the merged pull-request versus with full history you can find the true code-level inflection point.
Agree, better title for this post; the fact that back prop is a leaky abstraction is a reason one should understand it and know how to do the mechanics by hand to truly experience it and develop understanding and intuition. Software / code abstracting away even more of the process leaves it open to magical thinking. I had to do hand calculations and convolutions in my Deep Learning graduate school course.
Thanks for the clarifying response.
Sounds like The Paradox of Choice.
Are you able to provide more context and details? It’s always interesting to learn about system design and architecture decisions from real world examples.