> We don’t have teachers or a curriculum, and there’s very little required structure beyond making a full-time commitment during your retreat
I saw this quote when looking at the Recurse Center website. How does one usually go about something like this if they work full time? Does this mainly target those who are just entering the industry or between jobs?
I know the article is mostly about what the author built at the coding retreat, but now he has me interested in trying to attend one!
Most folks do RC between jobs, either because they quit their job specifically to do RC or because they lost their job and then decide to apply. Other common ways are as part of a formal sabbatical (returning either to an industry job or to academia), as part of garden leave, or while on summer break (for college and grad students). We also get a fair number of freelancers/independent contractors (who stop doing their normal work during their batches), as well as some retirees.
Some folks use RC as a way to enter the industry (both new grads and folks switching careers), though the majority of people who attend have already worked professionally as programmers.
We've had people aged 12 to early 70s attend, though most Recursers are in their 20s, 30s, and 40s.
I'm sure the author will encounter problems where the only way to solve them will be the marginal effort provided by a human. At that point he won't be just be solving problems to work his brain, but also to accomplish a goal.
To me, doubling session usage always seemed like a way to gaslight users into thinking their perception of smaller usage limits after that period ended was just them readjusting to the normal usage limits. Whether from a different model being used or an intentional reduction in weekly usage, I've noticed a difference.
Pretty bad decision on his part. I've been telling other engineers within my company who felt threatened by AI that this would happen. That prices would rise and the marginal cost for changes to big codebases would start to exceed the cost of an engineer's salary. API credits are expensive, especially for huge contexts, and sometimes the model will use $200 in credits trying to solve a problem that could be fixed in an hour by a good engineer with enough context.
It kind of reminds me of the joke where a plumber charges $500 for a 5 minute visit. When the client complains the plumber says it's $50 for labor and $450 for knowing how to fix the problem.
A good lesson for all - I always really liked the Picasso version:
In a bustling restaurant, an excited patron recognized the famous artist Picasso dining alone. Seizing the moment, the patron approached Picasso with a simple request. With a plain napkin and a big smile, he asked the artist for a drawing. He promised payment for his troubles. Picasso, ever the creator, didn’t hesitate. From his pocket, he produced a charcoal pencil and he brought to life a stunning sketch of a goat on the napkin—a clear mark of his unique style. Proudly, he presented it to the patron.
The artwork mesmerized the patron, who reached out to take it, only to be stopped by Picasso’s firm hand. “That will be $100,000,” Picasso declared.
Astonished, the patron balked at the sum. “But it took you just a few seconds to draw this!”
With a calm demeanor, Picasso took back the napkin, crumpled it, and tucked it away into his pocket, replying, “No, it has taken me a lifetime.”
A good engineer and / or a tenured engineer could very well be compared to Picasso in this story. A tenured engineer did not just sit their entire career drawing that painting on the napkin, they delivered other results too. But at the end of it, they are able to deliver a Picasso at a moment's notice.
It actually matches up well with the current AI scene, except backwards. We use these model which cost ridiculous amounts of money to train, and all of that effort goes into producing the outputs we use, but we're paying something not too far above the marginal cost of inference when we use them.
Extremely applicable to illustrate the difference between people (time is precious, training and experience amortize across a relatively small amount of paid work) and software (can replicate infinitely, time is cheap, startup costs can amortize across billions of hours of paid work).
Competition will prevent that from happening. When anyone can host open models and there is giant demand for LLMs companies can not easily raise token prices without sending a lot of traffic to their competitors.
It seems very unlikely that prices would rise in the long term. Yes, RAM and GPU prices are suddenly going up due to the demand spike and OpenAI's shenanigans, but I doubt it's going to last very long. Some combination of new capacity and reduced demand will most likely put things back on the usual course where this stuff gradually gets cheaper over time. And models are getting better, so next year you can probably get the same results for less compute. That $200 in credits becomes $150, then $100, then....
That “with enough context” is doing a lot of work here. If you take a great engineer, drop them in front of an unfamiliar codebase, it’ll take them more than an hour to do most non-trivial tasks.
Equal sounds like a terrible argument given all the other problems with replacing engineering thought with ai. I don't know where the line is but I expect it's far beyond equal AND there needs to be a level of "this can debug effectively in production" before that makes any sense for a real business case.
Even if you take it as true that prices have risen recently, and may continue to rise as the VC subsidies dry up, they will fall again long-term. Inference will get more power efficient with model-on-chip solutions like Taalas and God willing we will get cheaper and cheaper renewable energy.
Despite this I don't think engineers should feel threatened. As long as there is a need for a human in the loop, as today, there will still be engineering jobs. And if demand for engineering effort is elastic enough, there could easily be even more jobs tomorrow.
Rather than threatened, I think engineers should feel exposed. To danger, yes, but opportunity as well.
He's not saying everyone, just the ones who are unprofitable. Not everyone mines bitcoin at the same cost. The ones who do have to stop can also profit from curtailment depending on the price of energy relative to hash profit.
I don't think you know what you're talking about. If the difficulty lowers at a lower rate than miners leaving then the difficulty rate will stop dropping.
Problem is that people like having a similar interface for both work and non-work things, and Linux doesn’t have enough penetration into the consumer market to influence stakeholders. The first step is making Linux the default choice for hardware providers. Framework was one of those pioneering this but was underfunded imo
I don’t think a lot of people still go home and use their computer for stuff. Most of my family will either rely on a phone or tablet to get anything done at home.
I doubt they’d care about which OS they’re on. Corporate tightens their laptops beyond belief, so all they’re really running is Teams and Excel. This seems to be the case for a lot of friends I talk to, no one gives a damn about Windows anymore. Heck, my sister-in-law moved to Ubuntu of her own choices, despite having low tech literacy.
> If all of us can go hunting in the woods and yet there is still game to be found, then there's no compelling reason to define and litigate who "owns" those woods.
Property rights don't just protect natural resources, but labor as well. If I cleared out hunting ground in that forest to be the prime spot to catch animals, I would make sure I can use it when I want.
> a small number of people were able to completely deplete parts of the earth
A small number of people seems inaccurate when there's typically many more individuals in the pipelines for these technologies.
> and in return profit off the knowledge over and over again at industrial scale
Not off just that knowledge, there needed to be a model trained on the data of many others to utilize it.
> Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?
Who's better at writing in this scenario and what are my motivations? If it's ChatGPT and I did it for money, then I would say I should recognize that I can't compete and find something AI can't do. If it's ChatGPT and I write to convey my ideas in an effort to learn regardless of the bestowment of a new perspective on the reader, I'll keep writing.
> Why would anyone plant seeds on someone else's farm?
They wouldn't unless it was their own way to attain food and survive. And if it's not the only way, they can defer to those with optimal methods to get it the cheapest they can in the market.
If it was just to 'hide' payments then they could just use USD and using crypto would just be an improvement in convenience. A bigger reason is that they won't be indirectly attacked with monetary policy and that the acceptance of USD with entities willing to do business with them is probably low right now.
I saw this quote when looking at the Recurse Center website. How does one usually go about something like this if they work full time? Does this mainly target those who are just entering the industry or between jobs?
I know the article is mostly about what the author built at the coding retreat, but now he has me interested in trying to attend one!
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