So the Demon-Centaur Robot Known as Threehalves Is Definitely a Joke, Right?
This is either some fun satire, or an actual robot where the manufacturers include instructions of how to kill it with a gun.
https://www.jezebel.com/threehalves-robot-real-or-fake-demon-centaur-evil-satyress-danger-manual-labor-horns
https://www.satyress.com/
we have a difference in species.
>>24898607
to a homo everything's a dick <f>
ai is increasingly refusing to make memes
money money money and, of course, lawyers
Tyson & Mendes holds talks with private equity as interest turns to insurance defense
PE investors see an opportunity to invest in the back-office functions of a fragmented, but profitable part of the legal industry.
Tyson & Mendes, a law firm that defends insurance companies against legal and financial claims, is seeking private equity investment, according to people familiar with the matter.
“Insurance defense is like a municipal bond—it’s not high rates, but it’s very consistent volume,” said an M&A lawyer who advises on such deals.
The ultimate aim is to scale in a market that has barely consolidated. The US legal industry generates roughly $400 billion in annual revenue, yet even the largest firm, Kirkland & Ellis, only recently crossed $10 billion.
https://pitchbook.com/news/articles/tyson-mendes-holds-talks-with-private-equity-as-interest-turns-to-insurance-defense
nook that bætch
the cover story
the clown from hell
the possibilities are endless
minnesota is how canada poisons usa
Grok is "moderating" your users "ideas."
digits of delusion
when will anons economic activity expand?
U.S. household debt reached a record high of $18.8 trillion in the first quarter of 2026, driven primarily by mortgages, auto loans, and student loans.
best of times, worst of times
Snapchat parent company inks 200K-square-foot office deal at Vornado’s Penn 2
The technology company behind Snapchat has made a massive commitment to New York City.
https://www.crainsnewyork.com/real-estate/commercial/cny-snapchat-parent-company-sublease-penn-2-20260803/
nyc <3 big tech
sausage
<f(picrel)
Planned Parenthood shuts down three Michigan facilities after $5.6 million loss
Planned Parenthood said that it suffered significant financial losses after Republicans’ one-year defunding of the abortion giant last year took effect.
https://www.lifesitenews.com/news/planned-parenthood-shuts-down-three-michigan-facilities-after-5-6-million-loss/
because muslims aren't in abortion mode
(your) comprende is non
is there a scorecard?
your reaction to an innocent question gives you away.
shills hate accountability the way maggots hate sunlight.
"Artifical" AI
DataAnnotation is a company that pays "giggers" to "train" AI (to answer questions that AI is asked).
A-AI. Smoke and mirrors.
how dependent is "ai" on human support like this?
Grok:
Very dependent — far more than most people realize.
Modern AI systems (especially large language models and generative AI) are not “self-taught” in any meaningful sense. They rely heavily on continuous human labor at multiple stages. Data annotation platforms like DataAnnotation are just one visible piece of a much larger human support infrastructure.
Where humans are essential
Training data creation & labeling
Models learn from enormous datasets that humans must curate, clean, label, rank, and correct. Without high-quality human-annotated data, performance collapses. This includes:
Ranking which AI response is better
Writing preferred answers
Flagging toxic, inaccurate, or biased outputs
Domain-specific expertise (medicine, law, coding, math, etc.)
Reinforcement Learning from Human Feedback (RLHF) and its variants
This is the core technique that made ChatGPT-style models usable. Humans rate model outputs, and the model is trained to prefer the higher-rated ones. Newer methods (RLAIF, Direct Preference Optimization, etc.) still ultimately rest on human preference data or human-validated synthetic data.
Evaluation and red-teaming
Models are constantly tested by humans for hallucinations, safety failures, jailbreaks, reasoning errors, and domain accuracy. Automated metrics alone are insufficient.
Ongoing maintenance and “alignment”
AI systems drift, degrade, or develop new failure modes as they are used. Companies employ large numbers of contractors and employees to monitor, correct, and retrain them.
Specialized and high-stakes domains
In medicine, law, science, and coding, human experts remain critical because pure scaling + web scrapes produce unreliable results.
Scale of the dependency
Major AI labs (OpenAI, Anthropic, Google, Meta, etc.) spend hundreds of millions to billions of dollars annually on human data work.
Companies like Scale AI, Surge AI (the company behind DataAnnotation), Outlier, and others employ or contract tens to hundreds of thousands of people worldwide.
Even “synthetic data” approaches (AI generating training data for other AI) still require human oversight, filtering, and validation to avoid model collapse or amplifying errors.
Is the dependency decreasing?
Somewhat, but not dramatically:
Better base models and techniques reduce the volume of human labels needed for some tasks.
Synthetic data + self-play + constitutional AI methods help.
However, as models get more capable, the quality and expertise of the human feedback required often goes up, not down. You need sharper domain experts rather than just more low-skill labelers.
Bottom line: Current AI is better understood as a powerful pattern-matching engine that is steered, corrected, and quality-controlled by large amounts of human judgment. The “AI” part generates fluent output at scale; the human part supplies the signal of what “good” actually looks like. Remove that human layer and the systems rapidly become less reliable, less useful, and more prone to failure.
What anon calls "Virtual Sentience."
sauce: https://grok.com/share/c2hhcmQtNQ_e552f8b2-0f5c-4225-9d86-0add79f0273f