I used to pay $10 per month for my “minimal hosting” - that inflated to nearly $15 across the decades. Then I switched to a modern host provider, now my bills are $0.02 per month for the exact same functionality that used to cost a minimum of $10.
http/https works as well as it ever did, it’s just that the overwhelming quantity of new sites have gummed it up for their own purposes.
Difficult to see, the future is, always in motion.
Yeah, my shareware did real well - 800 hours in development, $500 in income across the past 30 years. Oh, and it cost me $400 for the compiler tools back then - so my net profit is rolling in at $0.12 per hour invested. Warning: $440 of that $500 came from an extreme niche product you’re unlikely to find such a lucrative vein anymore. My game only netted $60 in return for 400 hours of development, but you could say I got the compiler “for free” off that niche product’s income.
cut off lower income people that couldn’t afford to pay.
There’s a shot of irony in there… Google doesn’t monetize poor people highly at all, but the poor people get “free” access with ads because some richer people also allow themselves to see the ads. What kind of communism is that?
In a way, the Lemmy layer is sort of doing that. The previous type of Internet is still out there, if you just take the effort to access it / avoid the links in to the marketed sides.
What I wonder is: how long before “bot speak” becomes unintelligible to humans.
Already: AI slop is so voluminous, redundantly repetitive, thoroughly complete that it defies complete comprehension due to soporific effects.
LLM agents are writing English (or Spanish, or Chinese, Hindi, whatever… and I wonder which they’re “best” at) documents, ostensibly for human review and consumption, but 90%+ of what I have my LLM agents write is exclusively consumed by other agents, and I’m constantly encouraging them to make their writings more easily comprehended and accessed by other agents - it seems like the English translation is pretty useless for that layer - what would a token stream look like instead?
AI can write that for you…
You’re supposed to immediately shitpost based on the post title
This is the way.
There are people who DGAF, and there are people who don’t mind flying at a higher altitude, where the air is thinner and the resistance is less.
Oh, about the fuel thing… I go fishing with my Uncle, he’s got a little V8 inboard, probably spends $10 per day to go out and catch our licensed limits of fish. He’s got a friend who won the lottery and bought a big 1200hp outboard open fisherman, he’ll go blow $1000 in fuel in the same time to catch half as many fish, but he does get to go farther, faster.
I haven’t met a lot of private jet owners, but one in particular I absolutely know would buy a faster private jet if he could get one - and private jets are a lot like beach mansions - generally, the people who can afford one, can afford several.
The advantage I perceive with the AI agent vs junior co-workers both going down their respective rabbit holes, the AI agent costs less time and money to get their “in depth report” and you don’t have to kindly explain why it’s off track when it gets horribly distracted by something irrelevant, you can just flush the context and start over.
I do find myself a bit upset when I’m in the middle of a long context development with an LLM agent and that context gets lost for some reason - re-explaining everything from scratch can be exhausting, to avoid having to do that you can actually instruct the agent to create a file explaining to future agents what it “knows” about the discussion at the moment. Some agents are starting to do this automatically like Claude’s MEMORY.md file and similar. Another semi-cool thing is: you can read these files yourself (if you have the patience) and if you see anything in there that the agent “got wrong” you have the opportunity to fix it. Asking a junior colleague to thoroughly explain everything they think they know about a project gets to be a very wearisome endeavor both for the junior and whoever is reviewing their “understanding” of the project.
I think AI agents are a lot like a chainsaw - they can save you a lot of time vs other tools for the same job, but… you can also screw up with them rather easily.
Fortunately, I just write software with LLM agents, if it comes out badly I can start over with nothing lost but time. My experience of the last few months has been: it actually writes some pretty good software, if you know anything about writing good software with teams of people and apply that knowledge to managing the LLM agents. If you’re just some guy who doesn’t really know how to write software, LLM agents aren’t always going to save you from yourself, just like a chainsaw won’t.
I’ve been “adjusting the prompt and saving changes” with Claude code for the past 9 months (it’s not ready to have a baby yet), it has been steadily improving over those months, a lot of that seems to be in the models and harnesses provided by Anthropic, but not a small part of it is the customization of the prompt to do what I want the first time instead of making me redirect it constantly.
In as far as the Google Home speakers are “Agents” we’ve had one for several years now. It’s good for a laugh once every so often when it gives a wildly random response to something we ask it for. Would we trust it to do anything like lock or unlock the house door? I don’t think so, probably not ever, definitely not today.
I use the AI agents to write software which is reproducible in its results, transparent in how it operates and verifiable…
Trust an LLM to switch on a light? No. Trust an LLM to write a diagnostic script and tell me what problems it notices in 3MB of log files, then write another script to patch the problem? Only if I actually understand the problem it finds and how the solution it creates is going to work.
They reduce some tasks that used to take weeks to a single day activity, they are NOT universally applicable.
Hoover maintained separate, master national blacklists—such as the Security Index—which tracked tens of thousands of citizens deemed “subversive” or political dissidents for immediate detention in the event of a national emergency. He managed his master national blacklists through a fluid, multi-tiered indexing framework that evolved over five decades. These blacklists were not mere static documents; they were part of a highly coordinated operational pipeline designed for the mass roundup and indefinite detention of American citizens during a perceived national emergency.
To add or manage a person on a master blacklist, Hoover’s Bureau followed a strict administrative lifecycle:
The Dossier Trigger: When an individual was flagged via covert programs like COINTELPRO, agents opened an investigative file.
The Index Card Core: If the person was deemed a threat, a dedicated index card was generated. These cards contained the person’s name, aliases, address, physical description, occupation, and a specific "detention rationale.
"The Geographic Apportionment: Cards were duplicated and cross-filed. One went into the master archive at FBI Headquarters in Washington, D.C., and another went into the local FBI Field Office responsible for the geographic area where the target lived.
The Arrest Portfolio: For top-tier targets, field offices maintained ready-to-go arrest portfolios. If Hoover or the President gave the command, field agents could immediately seize the individual without needing to waste time researching where they were or why they were being detained.
The Fluid Evolution of Sub-Lists
Hoover managed the master blacklist by segmenting it into constantly shifting sub-indexes based on perceived ideological threats, which allowed him to scale the operation up or down
As the lists ballooned to over 20,000 active high-priority targets (and over 10 million Americans cross-indexed in general domestic files), Hoover modernized his management using early technology. The Bureau adopted mechanical punch-card sorting systems. This allowed clerical staff to instantly filter the master blacklist by city, profession, or political affiliation, providing Hoover with rapid statistical snapshots of domestic dissent to present during congressional budget hearings or White House briefings.
You seem like a very serious person who is worth debating.
I’m sorry, you think this is a debate? It looks more like you parroting a bunch of whatever you like from your echo chambers, so go right on ahead.
Nukes above would refer to nuclear fission power generation which has, historically, been the cleanest least negatively impactful to human animal and plant health form of electrical power generation (per kWh generated) in history. When considering your non-renewable alternatives consider the wars fought for their control, the lives lost and ecological damage done in their extraction. When considering your renewable alternatives consider their ecological impact not only during operation, but also during construction and end of life disposal.
LLMs are accelerating research and development in the sciences and engineering - at least any actual scientists and engineers I have contact with are acknowledging their utility - not bolts of lightning from the sky universe altering changes, but helpful, in the way that computers and mechanical calculators before them were helpful - faster and easier to get things done than the previous alternatives.
I don’t know why the choices are between the actual work and hearsay.
Maybe think about it, then. Did you ever play “the telephone game” in school? https://en.wikipedia.org/wiki/Telephone_game
If writers are producing valuable information, that information may be passed on to the next generation directly through their writings, or as repeated by people who have (in your esteemed estimation) read that information and then repeated it later in their lives whether intentionally, or indirectly, and usually somewhat inaccurately.
LLMs will be trained on something - would it be better for that something to be the original source material, or the regurgitations of it by employees hired by the LLM training companies? Employees who read something original in school and are now passing that knowledge into the LLM training as best they can remember?
If you can come up with a scenario where AI hasn’t doubled in scale by 2030 by belching smoke into the atmosphere
Nukes. Possibly fusion. Possibly fusion accelerated into reality by use of LLMs to solve some of the remaining challenges.
https://ourworldindata.org/how-much-energy-do-data-centers-and-artificial-intelligence-use
If the people who made it don’t want it to be used for AI training (…speculation based on a 1/1 billionth sample…), it shouldn’t be.
But the people who made it do want it to be in public libraries, accessible, seen, heard and otherwise distributed - would the majority of them rather that AI be trained on exactly what they wrote, or just hear-say?
Example of something I had to lookup to understand: (/hj)
Back when I was in school, Autism was a 1/10,000 Dx. 20 years ago it was crashing down from 1/100 to 1/50, checking now… 1/31 today. They’ve lumped so much into the Autism diagnosis that it’s meaningless anymore; it covers so many varied conditions and severities, and the stereotypes don’t fit most recipients of the Dx anymore.