Most developers I know don't seem to care about the "right way" to run agents. They either yolo it on their primary dev machine, or setup a separate box (VM, VPS, Rpi, Mac mini) and let it run there with minimal protection. You could simply do the same, running Hermes or Pi on a VPS (e.g. Digital Ocean). Note that Pi has no protections so you probably ought to take extra steps. Hermes uses a container.
My setup took a long time to build and I don't know any devs personally who go thru the trouble.
* I use a custom harness in a podman container run by a non-sudoer system account. File access is limited to specific ZFS datasets. I don't use Anthropic models for agentic work.
* You can find an open-source network proxy that injects credentials into the network requests, I made a custom one.
* I made a custom shell (nearly posix-compliant) and don't give it access to regular bash, thus no need to filter its shell commands. You could use an open-source one if you care about this: `https://github.com/search?q=agent+bash+shell&type=repositori...`
* Same story for agent memory, context management and token reduction/compression, but this requires a lot of constant tweaking. Again, there are a lot of open-source repos addressing these aspects. Just search github, hn.algolia.com, and/or look at subreddits like /r/agentsofai /r/ai_agents
* Also the agent must be able to run/test the generated code, so if you're targeting another platform you need a MCP or deployment tool that'll allow testing.
Huh. I thought it the opposite of AI writing. A smart-and-bored human writer who assumed the reader already knew what the writer did, and cleverly avoided conveying any direct points, forcing the reader to try to read between the lines. Very frustrating.
A few years ago I wrote an exif parser too, solely for reading/editing text comments, which is much simpler than what you did. Even then, yes, it's not pretty, very frustrating. There are multiple places to put text in exif, and it took a while to find most (all?) the edge cases.
But now it's quite different with LLMs. I recently updated my code and Claude had useful recommendations.
It's possible that mainframe Oracle DBAs working on glass house HP 9000s looked at PC databases as toys, but within the PC industry these tools were considered powerful. Any growth of PC usage was a rising tide that lifted all the boats in the PC industry. If you had some experience programming in C, it was pretty easy to get a job. There weren't 100's (or 1000's) of job applicants for each position. If it was a niche segment, you could even know all the other candidates for an opening, as in "No Other Choice". (I was in a niche like that, developing architectural and facilities mgmt PC CAD software, then later cartography, for Microstation.)
I think the production shift from agriculture to manufacturing, or manufacturing to services, is probably a better way to understand what's happening now.
I have the same problem and I'm only 5'11" (177 cm). Size "Small" might fit at the neck/shoulders but with lots of extra fabric around my midsection, but most retailers don't carry much inventory for Small, and almost never have Extra Small. In the SF Bay Area, the small items are always out of stock.
At one point I found that clothing boutiques in the Castro tended to stock clothes for skinny guys, but those stores disappeared during the pandemic.
Now I have to go shopping in Seoul or Tokyo or NYC, but if the pants fit in the waist, they're usually too short.
I'm guessing I'm over twice your age but my situation is similar to yours in a couple aspects. What I decided to do is focus on exploring the new capabilities that AI will allow individuals to do (especially those of us who can code). There are a lot of ppl who are justifiably frustrated with the current state of the tools. But don't get discouraged by that. I've seen A LOT of tech waves. It's still early in the AI era. I won't suggest any particular approach but recommend giving it some thought.
BTW, being an old timer, I also hated the leetcode thing when it became a widespread thing after Google adopted the practice in the wake of The Joel (Spolsky) Test. But you know, years later when I had to go through some interviews, I did spend a few months studying it and it turned out to be pretty fun. But passing technical screenings didn't help me land a job... only connecting via my network seemed to make any difference.
After Coursera/Udacity/EdX discontinued courses that I wanted to take, or removed access to ones I only partially completed, I switched to buying classes on Udemy. I completed only a handful of many purchases, and the quality level was okay-to-mediocre but better than nothing, so I got more value out of Udemy than Coursera.
I also found that Youtube videos are just as informative as Udemy classes, but they're not always as well structured.
The MOOCs had some pretty cool/interesting university classes that don't exist anywhere else. It's a shame those videos weren't preserved where we can access/purchase them without attending the college.
I agree, some of the worst employees I've seen were hired that way.
I haven't hired anyone recently but btwn 10-20 years ago I did hire a lot. Of course we reached out via our network of connections but that gets tapped out fast, so you have to rely on job postings. It was always hundreds of applicants per opening. Back then it wasn't 1000's but it might as well have been because I didn't have enough time to sift through them all. That's ok, you can just approach it like "the dowry problem" (also known as the secretary problem [1]).
But the job market and hiring is way worse now, and it's pretty horrible for job seekers atm.
As mentioned elsewhere here, Ocaml and the ML family are often cited as easy to implement other languages, due to pattern matching, enum variants, sum types, etc. Since newer languages like Rust and Swift copied those features, you might be more interested in them since they're more popular than older ones like Ocaml and Haskell. Personally I like the syntax sugar in Swift.
> In the absence of Western tools like ChatGPT and Claude, many Chinese universities have begun deploying local versions of DeepSeek on campus servers to support students. Many top universities have deployed their own locally hosted versions of Deepseek. *These campus-specific AI systems–often referred to as the “full-blood version” of Deepseek—offer longer context windows, unlimited dialogue rounds and broader functionality than public-facing free versions.*
This makes so much sense. Are there any U.S. universities doing this?
A caipirinha with vodka is called a caipiroska. I name my computers after caipirinha varieties. Once at the office my laptop screensaver was showing "caipiroska" and a Russian guy exclaimed, "what's that?!?" Apparently "piroska" has a certain connotation in Russian that's nsfw.
More context at:
"Moonshot's Kimi K2 uses a 1T-parameter MoE architecture with 32B active parameters and outperforms models like GPT-4.1 and DeepSeek-V3 on key benchmarks"
<https://www.techmeme.com/250712/p11#a250712p11>
I've been in too many conversations where this topic comes up, and it's very disheartening to me. Gamers insist there are plenty of great narrative games, and every example they give is basically a branching story with bunch of flags that gate which branches can be taken. If I give the Holodeck as a counter-example, well that's just too pie-in-the-sky.
These conversations remind me a lot of Paul Graham's Blub Paradox: "Blub is good enough for him, because he thinks in Blub." Current SotA narrative games are good enough for most gamers, because all they've played are branching story games.
Chris Crawford's tools for interactive storytelling may have failed, but he was a huge inspiration for me in my game dev career, and I still harbor aspirations in "interactive storytelling" due to his influence.
I first attended CGDC in 1994. It was two years after his Dragon Speech (which I knew nothing about), but the highlight of the conference was his talk about the challenges of story-based games. The part I remember is how he modeled stories as branching trees with fan-out, foldback, tree-of-death, etc (he covers this in the "Architecture" chapter of this book "The Art of Interactive Design"). I didn't really follow Crawford's work on Erasmatron or later, but by the late 90's it sounded like his story model had changed from a tree to a graph network structure, like a finite state machine. While it was an improvement, I was a bit skeptical that this model was enough. Nevertheless, I spent a lot of time thinking about the problem. You see, he'd already infected me with his dream of interactive storytelling.
By the time I moved to California and took a job at EA/Maxis on The Sims 2.0 team, I had decided that true interactive storytelling (as I saw it) was not possible until game AI was sophisticated enough to enable autonomous NPC chatbots. So I put that dream aside while I pursued a career in software development.
Here we are, over a quarter of a century later, and that AI technology is here now. For those of us who have been waiting for this moment, it is almost miraculous. It might be the end of an era for Chris Crawford, but it is just the beginning of the AI-based interactive storytelling era.
This sounds like that Yogi Berra-ism, "No one comes here anymore, it's too crowded."
I suppose well over half of the people who read HN are too young to remember the dotcom craze. Everyone had every scrap of money tied up in tech stocks. IMO, the hype over AI is relatively small compared to other hype cycles. The goofiest part was the endless predictions about the singularity, and "you don't know what exponential growth looks like, man!". I mean, it can still happen, but for a while that's what AI was all about.