Nope, not bored at all. I'm clicking on most HN AI threads out of sheer curiosity, eager to learn new techniques and see what folks are thinking or building. Given its transformative ripple effects, AI feels like the single biggest shift reshaping the economy and society right now. Kind of the opposite of boring.
I’m pleasantly surprised this was AI assisted so deeply that inconsistencies like that slipped by you. The writing is really extraordinary. It made me want to read for fun again for the first time in decades. Thank you!
Funny, I was talking to a friend the other day about some thoughts on branding and he commented "as someone with a background in marketing & advertising communications, it's wild to watch a software engineer learn the value of branding and marketing from first principles".
I guess I'm also learning the value of working with an editor from first principles... over the last couple weeks before publishing I read through and made edits to this piece at least twice a day and still didn't catch this.
I don't think that phrase means what you are trying to say here.
What it doesn't mean:
- learning by doing
I believe it generally means: a formalization that comes after a subject is understood so well that you can reduce it to "first principles" that imply the rest. Or, the production of a hypothesis by deduction from widely-accepted principles.
Honestly, I read that passage as Carol realizing as she spoke that she had been underwatering that spot semi-consciously the whole time. That’s one of the things about expertise gained by doing. We don’t always realize exactly what we’re doing well enough to communicate it until we reflect on it later.
This discussion hits close to home. A few of us at Stanford and Consumer Reports have been working on a project called Loyal Agents (loyalagents.org
) that’s focused on the same core issue raised in the Economist article, namely how to make sure AI agents actually act in the interest of the people they represent.
The idea is to define what “loyalty” means for an AI agent in both technical and legal terms, and then build systems that can prove they’re acting on a user’s behalf (ie not a platform’s or advertiser’s).
It’s early-stage research, but the overlap with many of the questions here is striking. Would be great to get feedback from this crowd as the work evolves.
I’m part of the group working on Loyal Agents and happy to discuss it.
Here’s what Claude Sonnet 4.5 suggested to take this piece from something that sounds impressive but lacks substance to something that could actually deliver on its promise. I did this thought exercise to explore whether being AI-generated necessarily precludes brilliance. You be the judge - I think Claude succeeded in mapping the gap between the current draft and what a truly excellent version would actually require.
Not quite - as I understand it box-counting measures global space-filling, manifolds handle local coordinate structure. Consider that the Earth is locally flat but globally spherical, and a Möbius strip vs cylinder are locally identical but globally different. Related problems, but the tools reveal different aspects of geometry. So I think whether “this is exactly what topological manifolds are for” depends what you’re trying to understand.
Synthi is an open web tool that instantly summarizes and synthesizes Hacker News threads and their linked articles, grouping every point of view by topic.
I love the deep discussions on HN, but I never have time to read a long article and a 400-comment thread. The tipping point for building this was when I nearly burned through my monthly API credits on another service just trying to synthesize a few threads! I needed my own tool.
How It Works:
1. Paste any HN thread URL into Synthi.
2. It instantly detects it and fetches the linked article.
3. Click "Full Analysis" for a unified, topic-based synthesis (with attribution to the article or the commenter).
4. Export the result, bookmark it, or listen to it (the output is text-to-speech friendly).
Key Features:
* Smart HN workflow: Auto-detects HN links for a one-click analysis.
* Works with any URLs: Can also synthesize any two articles or pieces of text.
* 100% client-side: All processing happens in your browser. No backend, no tracking.
* Open source (MIT License): The code is yours to inspect, fork, and use.
Synthi is a "Bring Your Own Key" app. You'll need a Google Gemini API key, which is stored securely in your browser's local storage and never sent anywhere else.
For now, it's Gemini only, largely because the free tier on Google AI Studio is incredibly generous and a great way to get started. I'm considering adding support for other models like Claude or OpenAI via OpenRouter in the future.
I've been using this every day and I hope it's useful to some of you too. All feedback is welcome!
Wow - I'd forgotten all about this but just realized I have posts from an entire phase of earlier professional life - topic by topic and event by event - on an old blog there. Amazingly the browser remembered my login so I was able to find the URL. It's been quite a trip down memory lane revisiting some of the posts. Not sure I need to keep any of that published but I'll at least scrape and store it somewhere for old times sake. Maybe I'll find some buried gem of an idea when I scan them during the great scrape. Or - optimistically - perhaps a future zillion-token context LLM will uncover some personal patterns that unleash deep and actionable insights. Irrespective of the measurable value, I just hate to see the old posts dissapear forever.
In 1977 you said that computers were answers in search of questions. Has that changed?
Well, the types of computers we have today are tools. They’re responders: you ask a computer to do something and it will do it. The next stage is going to be computers as “agents.” In other words, it will be as if there’s a little person inside that box who starts to anticipate what you want. Rather than help you, it will start to guide you through large amounts of information. It will almost be like you have a little friend inside that box. I think the computer as an agent will start to mature in the late '80s, early '90s…
You’d start to teach it about yourself. And it would just keep storing all this information about you and maybe it would recognize that every Friday afternoon you like to do something special, and maybe you’d like it to help you with this routine. So about the third time it asks you: “Well, would you like me to do this for you every Friday?” You say, “Yes,” and before long it becomes an incredibly powerful helper. It goes with you everywhere you go. It knows most of the raw information in your life that you’d like to keep, but then starts to make connections between things, and one day when you’re 18 and you’ve just split up with your girlfriend it says: “You know, Steve, the same thing has happened three times in a row.”
This is a fun project to be sure. I just wish the author would not refer to the experiment as an "autonomous startup builder" unless they mean it humorously. Having poked around the GitHub repo and read through the materials, it seems like more of an AI coding assistant running in a loop that built and deployed a broken web application with no users, no business model, and no understanding of what problem it was trying to solve. There were quasi-autonomous processes and there were things that were built, but nothing I'd call a startup.
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