Public notes from activescott tagged with #ai
“The body cam software and the AI report writing software picked up on the movie that was playing in the background, which happened to be ‘The Princess and the Frog,’” a Heber City sergeant told FOX 13 News. “That’s when we learned the importance of correcting these AI-generated reports.”
I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour.
relying solely on accuracy can be misleading, especially with imbalanced datasets. Understanding and utilizing a variety of accuracy metrics is essential for building models that perform well not just in theory but also in practice.
Tech companies have moved more than $120bn of data centre spending off their balance sheets using special purpose vehicles funded by Wall Street investors, adding to concerns about the financial risks of their huge bet on artificial intelligence.
Meta in October completed the largest private credit data centre deal, a $30bn agreement for its proposed Hyperion facility in Louisiana that created an SPV called Beignet Investor with New York financing firm Blue Owl Capital.
The SPV raised $30bn, including about $27bn of loans from Pimco, BlackRock, Apollo and others, as well as $3bn in equity from Blue Owl.
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem).
That’s the New York Times, CNN, CNBC, NBC, and the Guardian all confidently telling their readers that Trump can magically override state sovereignty with a memo. These aren’t fringe blogs—these are supposedly serious news organizations with actual editors who apparently skipped the day they taught how the federal government works. They have failed the most simple journalistic test of “don’t print lies in the newspaper.”
Executive orders aren’t laws. They’re memos. Fancy, official memos that tell federal employees how to do their jobs, but memos nonetheless. You want to change what states can and can’t do? You need this little thing called “Congress” to pass this other little thing called “legislation.” Trump can’t just declare state laws invalid any more than he can declare himself emperor of Mars.
But here’s where this gets kinda funny (in a stupid way): that “interstate commerce” language could backfire spectacularly. Almost all state laws trying to regulate the internet—from child safety laws to age verification to the various attempts at content moderation laws—might run afoul of the dormant commerce clause by attempting to regulate interstate commerce if what the admin here claims is true (it’s not really true, but if the Supreme Court buys it…). Courts had been hesitant to use this nuclear option because it would essentially wipe out the entire patchwork of state internet regulation that’s been building for years, and a few decades of work in other areas that hasn’t really been challenged. Also, because they’ve mostly been able to invalidate those laws using the simple and straightforward First Amendment.
The real story here isn’t that Trump signed some groundbreaking AI policy—it’s that the entire mainstream media apparatus completely failed to understand the most basic principles of American government. Executive orders aren’t magic spells that override federalism. They’re memos.
LLM agents are vulnerable to prompt injection attacks when handling untrusted data. In this paper we propose CaMeL, a robust defense that creates a protective system layer around the LLM, securing it even when underlying models are susceptible to attacks. To operate, CaMeL explicitly extracts the control and data flows from the (trusted) query; therefore, the untrusted data retrieved by the LLM can never impact the program flow. To further improve security, CaMeL uses a notion of a capability to prevent the exfiltration of private data over unauthorized data flows by enforcing security policies when tools are called.
Visit a Reddit post with Comet and ask it to summarize the thread, and malicious instructions in a post there can trick Comet into accessing web pages in another tab to extract the user's email address, then perform all sorts of actions like triggering an account recovery flow and grabbing the resulting code from a logged in Gmail session.
Anthropic don't recommend autonomous mode - where the extension can act without human intervention. Their default configuration instead requires users to be much more hands-on:
Be patient. Not afraid.
For layoffs in the tech sector, a likely culprit is the financial stress that companies are experiencing because of their huge spending on AI infrastructure. Companies that are spending a lot with no significant increases in revenue can try to sustain profitability by cutting costs. Amazon increased its total CapEx from $54 billion in 2023 to $84 billion in 2024, and an estimated $118 billion in 2025. Meta is securing a $27 billion credit line to fund its data centers. Oracle plans to borrow $25 billion annually over the next few years to fulfill its AI contracts.
“We’re running out of simple ways to secure more funding, so cost-cutting will follow,” Pratik Ratadiya, head of product at AI startup Narravance, wrote on X. “I maintain that companies have overspent on LLMs before establishing a sustainable financial model for these expenses.”
We’ve seen this act before. When companies are financially stressed, a relatively easy solution is to lay off workers and ask those who are not laid off to work harder and be thankful that they still have jobs. AI is just a convenient excuse for this cost-cutting.
Last week, when Amazon slashed 14,000 corporate jobs and hinted that more cuts could be coming, a top executive noted the current generation of AI is “enabling companies to innovate much faster than ever before.” Shortly thereafter, another Amazon rep anonymously admitted to NBC News that “AI is not the reason behind the vast majority of reductions.” On an investor call, Amazon CEO Andy Jassy admitted that the layoffs were “not even really AI driven.”
We have been following the slow growth in revenues for generative AI over the last few years, and the revenues are neither big enough to support the number of layoffs attributed to AI, nor to justify the capital expenditures on AI cloud infrastructure. Those expenditures may be approaching $1 trillion for 2025, while AI revenue—which would be used to pay for the use of AI infrastructure to run the software—will not exceed $30 billion this year. Are we to believe that such a small amount of revenue is driving economy-wide layoffs?
Sounds like news websites need to hire a proper engineer. This isn’t common crawls problem to solve:
Common crawl doesn’t log in to the websites it scrapes, but its scraper is immune to some of the paywall mechanisms used by news publishers. For example, on many news websites, you can briefly see the full text of any article before your web browser executes the paywall code that checks whether you’re a subscriber and hides the content if you’re not. Common Crawl’s scraper never executes that code, so it gets the full articles.
MIT licensed model, their self-reported benchmarks show it performing similar to Claude Sonnet 4 (but still behind 4.5), and it's only 230GB on Hugging Face
So likely won't fit on an NVIDIA Spark's 128GB but should run on a Mac Studio 512GB
Seems about right. Interesting metrics on startups too:
Energy infrastructure, unlike GPUs that become obsolete in five years, compounds in value over decades.
Consider the math: A single large AI training cluster can require 100+ megawatts of continuous power — equivalent to a small city. The United States currently generates about 1,200 gigawatts of electricity total. If AI compute grows at projected rates, it could demand 5-10% of the nation’s entire power generation within a decade.
And unlike fiber optic cable or GPU clusters, power infrastructure can’t be deployed quickly. Nuclear plants take 10-15 years to build. Major transmission lines face decades of regulatory approval. Even large solar farms require 3-5 years from planning to operation.
The companies prepping themselves to survive scarcity aren’t just stockpiling compute—they’re building root systems deep enough to tap multiple resources: energy contracts locked in for decades, gross retention rates above 120%, margin expansion even as they scale, and infrastructure that can flex between training and inference as market dynamics shift.