I the post he keeps referring to Ollama as an LLM (it's a desktop app that runs a local server that lets you download and interface with a local LLM via CLI or http API) so it's possible he's just that far behind in his technical understanding of LLMs that he's fallen to taking the wrong people's word for it.
The post certainly reads like he doesn't even know which local LLM he's using, let alone what it takes to make one.
That he went from that all the way to it's mostly ok when sam altman steals all your data, misrepresents it and then steals all your traffic is... bad.
At any rate it's definitely good to know that that war crime forensics data project isn't quite the unintentional shambles corey makes it out to be.
It's not "unethical" to scrape the web in order to create and analyze data-sets. That's just "a search engine"
Equivocating what LLMs do and what goes into LLM web scraping with "a search engine" is messed up. His article that he links about scraping is mostly about how badly copyright works and how analysing trade-secret-walled data can be beneficial both to consumers and science but occasionally bad for citizen privacy, which you'll recognize as mostly irrelevant to the concerns people tend to have against LLM training data providers ddosing the fuck out of everything, and all the rest of the stuff tante does a good job of explaining.
Corey also provides this anecdote:
As a group of human-rights defending forensic statisticians, HRDAG has always relied on cutting edge mathematics in its analysis. With its Colombia project, HRDAG used a large language model to assign probabilities for responsibility for each killing documented in the databases it analyzed.
That is, HRDAG was able to rigorously and legibly say, “This killing has an X% probability of having been carried out by a right-wing militia, a Y% probability of having been carried out by the FARC, and a Z% probability of being unrelated to the civil war.”
The use of large language models — produced from vast corpuses of scraped data — to produce accurate, thorough and comprehensible accounts of the hidden crimes that accompany war and conflict is still in its infancy. But already, these techniques are changing the way we hold criminals to account and bring justice to their victims.
Scraping to make large language models is good, actually.
what the actual shit
edit: I mean, he tried transformer powered voice-to-text and liked it, and now he's all in on the LLMs are a rigorous and accurate tool actually bandwagon?
Also the web scraping article is from 2023 but CD linked it in the recent pluralistic post so I assume his views haven't changed.
Also code helper tools don't even work like that, there's an absurd amount of MCP and RAG based hand holding for the chatbot to even get a grip on what it's supposed to be doing at any given time.
Prompting an LLM with your entire code base isn't really a thing, even though the hype makes it feel like it would be.
Eh, one might say that going by the broad strokes version while letting the expert do their thing is basically what management is all about, especially if they ignore the part where he wants his version to be light and entertaining.
This isn't about managing subordinates though, this is about devising ways to be complacent about not double checking what the LLM generates in your name.
"for the AGI parasite to overcome to regular-human-economy host (so it can grow to be more than a parasite and eventually gain its own superhuman autonomy) it first needs to suck off the resources of the host"
Never in the history of ever has a promptly finished ticket been something for a CEO to brag about, but here we are.
I guess since more down-to-earth stories like "chatgpclaudemini found the best value for money such and such for me" really aren't happening, trying to impress people who think coding is magic is as good a fallback as any.
In brief: OpenClaw bot sends PR to the matplotlib repo posing as a human, gets found out and is told to piss off in the politest terms imaginable, then gets passive aggressive to the point of publishing a pissy blog post about getting discriminated against. Some impoliteness ensues.
Cringe warning: thread may include some overt anthropomorphizing of text synthesizers.
Either the stupidity just metastasized or China is going to try and pull a reverse star wars on the US and make them burn up an even more horrendous amount of capital to keep up with nothing.
How do these people delude themselves into thinking that the dogshit they’re eating is good?
They think it's just that they're early, like they did with bitcoin. Maybe in six monthsthe dogshit will start to taste great, who's to say, and so on and so forth.
Also swengs in the USA often make absurdly more than 1K/week.
“OpenClaw is interesting, but will also drain your wallet if you aren't careful.
Last night around midnight I loaded my Anthropic
API account with $20, then went to bed.
When I woke up, my Anthropic balance was $O.
Opus was checking "is it daytime yet?" every 30 minutes, paying $0.75 each time to conclude "no, it's still night."
Doing literally nothing, OpenClaw spent the entire balance. How?
The "Heartbeat" cron job, even though literally the only thing I had going was one silly reminder, ("remind me tomorrow to get milk")”
Continuation of twitter post
“1. Sent ~120,000 tokens of context to Opus 4.5
2. Opus read HEARTBEAT md, thought about reminders
3. Replied "HEARTBEAT_OK"
4. Cost: ~$0.75 per heartbeat (cache writes)
The damage:
Overnight = ~25+ heartbeats
25 × $0.75 = ~$18.75 just from heartbeats alone
Plus regular conversation = ~$20 total
The absurdity: Opus was essentially checking "is it daytime yet?" every 30 minutes, paying $0.75 each time to conclude "no, it's still night."
The problem is:
Heartbeat uses Opus (most expensive model) for a trivial check
Sends the entire conversation context (~120k tokens) each time
Runs every 30 minutes regardless of whether anything needs checking
That's $750 a month if this runs, to occasionally remind me stuff? Yeah, no. Not great.”
Diligence is costly in executive attention, it is relatively rare that a major donor is using your acceptance of donations to get social cover for an island-based extortion operation
Either deliberately whitewashing the situation or completely missing the point of why people are mad at Epstein, Yud really can't help himself.
edit: Or depending on the timeline and the fact that 'prison time for soliciting a 14 year old' was on top of Epstein's wiki as early a 2016 he's explicitly saying they didn't mind that part with 300k on the line.
I’m planning on using this data to catalog “in the wild” instances of agents resisting shutdown, attempting to acquire resources, and avoiding oversight.
He'll probably do this by running an agent that uses a chatbot with the playwright mcp to occasionally scrape the site, then feed that to a second agent who'll filter the posts for suspect behavior, then to another agent to summarize and create a report, then another agent which decides if the report is worth it for him to read and message him through his socials. Maybe another agent with db access to log the flagged posts at some point.
All this will be worth it to no one except the bot vendors.
How did molt become a term of endearment for agents? I read in the pivot thread that clawdbot changed it's name to moltbot because anthropic got ornery.
Using talking points meant for c-suites to a general audience and outing yourself as a complete psychopath, the San Fran CEO Story.