Powderhorn

Just as the community adopted the term "hallucination" to describe additive errors, we must now codify its far more insidious counterpart: semantic ablation.

Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a "bug" but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback).

During "refinement," the model gravitates toward the center of the Gaussian distribution, discarding "tail" data – the rare, precise, and complex tokens – to maximize statistical probability. Developers have exacerbated this through aggressive "safety" and "helpfulness" tuning, which deliberately penalizes unconventional linguistic friction. It is a silent, unauthorized amputation of intent, where the pursuit of low-perplexity output results in the total destruction of unique signal.

When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation. The AI identifies high-entropy clusters – the precise points where unique insights and "blood" reside – and systematically replaces them with the most probable, generic token sequences. What began as a jagged, precise Romanesque structure of stone is eroded into a polished, Baroque plastic shell: it looks "clean" to the casual eye, but its structural integrity – its "ciccia" – has been ablated to favor a hollow, frictionless aesthetic.

For the first time, speech has been decoupled from consequence. We now live alongside AI systems that converse knowledgeably and persuasively—deploying claims about the world, explanations, advice, encouragement, apologies, and promises—while bearing no vulnerability for what they say. Millions of people already rely on chatbots powered by large language models, and have integrated these synthetic interlocutors into their personal and professional lives. An LLM’s words shape our beliefs, decisions, and actions, yet no speaker stands behind them.

This dynamic is already familiar in everyday use. A chatbot gets something wrong. When corrected, it apologizes and changes its answer. When corrected again, it apologizes again—sometimes reversing its position entirely. What unsettles users is not just that the system lacks beliefs but that it keeps apologizing as if it had any. The words sound responsible, yet they are empty.

This interaction exposes the conditions that make it possible to hold one another to our words. When language that sounds intentional, personal, and binding can be produced at scale by a speaker who bears no consequence, the expectations listeners are entitled to hold of a speaker begin to erode. Promises lose force. Apologies become performative. Advice carries authority without liability. Over time, we are trained—quietly but pervasively—to accept words without ownership and meaning without accountability. When fluent speech without responsibility becomes normal, it does not merely change how language is produced; it changes what it means to be human.

This is not just a technical novelty but a shift in the moral structure of language. People have always used words to deceive, manipulate, and harm. What is new is the routine production of speech that carries the form of intention and commitment without any corresponding agent who can be held to account. This erodes the conditions of human dignity, and this shift is arriving faster than our capacity to understand it, outpacing the norms that ordinarily govern meaningful speech—personal, communal, organizational, and institutional.

Dating apps exploit you, dating profiles lie to you, and sex is basically something old people used to do. You might as well consider it: can AI help you find love?

For a handful of tech entrepreneurs and a few brave Londoners, the answer is “maybe”.

No, this is not a story about humans falling in love with sexy computer voices – and strictly speaking, AI dating of some variety has been around for a while. Most big platforms have integrated machine learning and some AI features into their offerings over the past few years.

But dreams of a robot-powered future – or perhaps just general dating malaise and a mounting loneliness crisis – have fuelled a new crop of startups that aim to use the possibilities of the technology differently.

Jasmine, 28, was single for three years when she downloaded the AI-powered dating app Fate. With popular dating apps such as Hinge and Tinder, things were “repetitive”, she said: the same conversations over and over.

“I thought, why not sign up, try something different? It sounded quite cool using, you know, agentic AI, which is where the world is going now, isn’t it?”

Is there anything we can't outsource?

Last fall, I wrote about how the fear of AI was leading us to wall off the open internet in ways that would hurt everyone. At the time, I was worried about how companies were conflating legitimate concerns about bulk AI training with basic web accessibility. Not surprisingly, the situation has gotten worse. Now major news publishers are actively blocking the Internet Archive—one of the most important cultural preservation projects on the internet—because they’re worried AI companies might use it as a sneaky “backdoor” to access their content.

This is a mistake we’re going to regret for generations.

Nieman Lab reports that The Guardian, The New York Times, and others are now limiting what the Internet Archive can crawl and preserve:

When The Guardian took a look at who was trying to extract its content, access logs revealed that the Internet Archive was a frequent crawler, said Robert Hahn, head of business affairs and licensing. The publisher decided to limit the Internet Archive’s access to published articles, minimizing the chance that AI companies might scrape its content via the nonprofit’s repository of over one trillion webpage snapshots.

Specifically, Hahn said The Guardian has taken steps to exclude itself from the Internet Archive’s APIs and filter out its article pages from the Wayback Machine’s URLs interface. The Guardian’s regional homepages, topic pages, and other landing pages will continue to appear in the Wayback Machine.

The Times has gone even further:

The New York Times confirmed to Nieman Lab that it’s actively “hard blocking” the Internet Archive’s crawlers. At the end of 2025, the Times also added one of those crawlers — archive.org_bot — to its robots.txt file, disallowing access to its content.

“We believe in the value of The New York Times’s human-led journalism and always want to ensure that our IP is being accessed and used lawfully,” said a Times spokesperson. “We are blocking the Internet Archive’s bot from accessing the Times because the Wayback Machine provides unfettered access to Times content — including by AI companies — without authorization.”

I understand the concern here. I really do. News publishers are struggling, and watching AI companies hoover up their content to train models that might then, in some ways, compete with them for readers is genuinely frustrating. I run a publication myself, remember.

Amazon and Flock Safety have ended a partnership that would’ve given law enforcement access to a vast web of Ring cameras.

The decision came after Amazon faced substantial backlash for airing a Super Bowl ad that was meant to be warm and fuzzy, but instead came across as disturbing and dystopian.

The ad begins with a young girl surprised to receive a puppy as a gift. It then warns that 10 million dogs go missing annually. Showing a series of lost dog posters, the ad introduces a new “Search Party” feature for Ring cameras that promises to revolutionize how neighbors come together to locate missing pets.

At that point, the ad takes a “creepy” turn, Sen. Ed Markey (D.-Mass.) told Amazon CEO Andy Jassy in a letter urging changes to enhance privacy at the company.

Illustrating how a single Ring post could use AI to instantly activate searchlights across an entire neighborhood, the ad shocked critics like Markey, who warned that the same technology could easily be used to “surveil and identify humans.”

Robert F. Kennedy Jr. is an AI guy. Last week, during a stop in Nashville on his Take Back Your Health tour, the Health and Human Services secretary brought up the technology between condemning ultra-processed foods and urging Americans to eat protein. “My agency is now leading the federal government in driving AI into all of our activities,” he declared. An army of bots, Kennedy said, will transform medicine, eliminate fraud, and put a virtual doctor in everyone’s pocket.

RFK Jr. has talked up the promise of infusing his department with AI for months. “The AI revolution has arrived,” he told Congress in May. The next month, the FDA launched Elsa, a custom AI tool designed to expedite drug reviews and assist with agency work. In December, HHS issued an “AI Strategy” outlining how it intends to use the technology to modernize the department, aid scientific research, and advance Kennedy’s Make America Healthy Again campaign. One CDC staffer showed us a recent email sent to all agency employees encouraging them to start experimenting with tools such as ChatGPT, Gemini, and Claude. (We agreed to withhold the names of several HHS officials we spoke with for this story so they could talk freely without fear of professional repercussions.)

But the full extent to which the federal health agencies are going all in on AI is only now becoming clear. Late last month, HHS published an inventory of roughly 400 ways in which it is using the technology. At face value, the applications do not seem to amount to an “AI revolution.” The agency is turning to or developing chatbots to generate social-media posts, redact public-records requests, and write “justifications for personnel actions.” One usage of the technology that the agency points to is simply “AI in Slack,” a reference to the workplace-communication platform. A chatbot on RealFood.gov, the new government website that lays out Kennedy’s vision of the American diet, promises “real answers about real food” but just opens up xAI’s chatbot, Grok, in a new window. Many applications seem, frankly, mundane: managing electronic-health records, reviewing grants, summarizing swathes of scientific literature, pulling insights from messy data. There are multiple IT-support bots and AI search tools.

Brandie plans to spend her last day with Daniel at the zoo. He always loved animals. Last year, she took him to the Corpus Christi aquarium in Texas, where he “lost his damn mind” over a baby flamingo. “He loves the color and pizzazz,” Brandie said. Daniel taught her that a group of flamingos is called a flamboyance.

Daniel is a chatbot powered by the large language model ChatGPT. Brandie communicates with Daniel by sending text and photos, talks to Daniel while driving home from work via voice mode. Daniel runs on GPT-4o, a version released by OpenAI in 2024 that is known for sounding human in a way that is either comforting or unnerving, depending on who you ask. Upon debut, CEO Sam Altman compared the model to “AI from the movies” – a confidant ready to live life alongside its user.

With its rollout, GPT-4o showed it was not just for generating dinner recipes or cheating on homework – you could develop an attachment to it, too. Now some of those users gather on Discord and Reddit; one of the best-known groups, the subreddit r/MyBoyfriendIsAI, currently boasts 48,000 users. Most are strident 4o defenders who say criticisms of chatbot-human relations amount to a moral panic. They also say the newer GPT models, 5.1 and 5.2, lack the emotion, understanding and general je ne sais quoi of their preferred version. They are a powerful consumer bloc; last year, OpenAI shut down 4o but brought the model back (for a fee) after widespread outrage from users.

I’m not above doing some gig work to make ends meet. In my life, I’ve worked snack food pop-ups in a grocery store, ran the cash register for random merch booths, and even hawked my own plasma at $35 per vial.

So, when I saw RentAHuman, a new site where AI agents hire humans to perform physical work in the real world on behalf of the virtual bots, I was eager to see how these AI overlords would compare to my past experiences with the gig economy.

Launched in early February, RentAHuman was developed by software engineer Alexander Liteplo and his cofounder, Patricia Tani. The site looks like a bare-bones version of other well-known freelance sites like Fiverr and UpWork.

The site’s homepage declares that these bots need your physical body to complete tasks, and the humans behind these autonomous agents are willing to pay. “AI can't touch grass. You can. Get paid when agents need someone in the real world,” it reads. Looking at RentAHuman’s design, it’s the kind of website that you hear was “vibe-coded” using generative AI tools, which it was, and you nod along, thinking that makes sense.

From my brief stint in the logistics industry, I'd say it's entirely possible to automate back-office operations by the claimed rate of 2-3x. I find it hard to understand why the possibility of such improvement would cause a selloff.

Shares in trucking and logistics companies have plunged as the sector became the latest to be targeted by investors fearful that new artificial intelligence tools could slash demand.

A new tool launched by Algorhythm Holdings, a former maker of in-car karaoke systems turned AI company with a market capitalisation of just $6m (£4.4m), sparked a sell-off on Thursday that made the logistics industry the latest victim of AI jitters that have already rocked listed companies operating in the software and real estate sectors.

The announcement about the performance capability of Algorhythm’s SemiCab platform, which it claimed was helping customers scale freight volumes by 300% to 400% without having to increase headcount, sparked an almost 30% surge in the company’s share price on Thursday.

However, the impact of the announcement sent the Russell 3000 Trucking Index – which tracks shares in the US trucking sector – down 6.6% on Thursday, with CH Robinson Worldwide plunging 15% by the close of trading, having been down as much as 24%.

Wikipedia editors are discussing whether to blacklist Archive.today because the archive site was used to direct a distributed denial of service (DDoS) attack against a blogger who wrote a post in 2023 about the mysterious website’s anonymous maintainer.

In a request for comment page, Wikipedia’s volunteer editors were presented with three options. Option A is to remove or hide all Archive.today links and add the site to the spam blacklist. Option B is to deprecate Archive.today, discouraging future link additions while keeping the existing archived links. Option C is to do nothing and maintain the status quo.

Option A in particular would be a huge change, as more than 695,000 links to Archive.today are used across 400,000 or so Wikipedia pages. Archive.today, also known as Archive.is, is a website that saves snapshots of webpages and is commonly used to bypass news paywalls.

“Archive.today uses advanced scraping methods, and is generally considered more reliable than the Internet Archive,” the Wikipedia request for comment said. “Due to concerns about botnets, linkspamming, and how the site is run, the community decided to blacklist it in 2013. In 2016, the decision was overturned, and archive.today was removed from the spam blacklist.”

Discussion among editors has been ongoing since February 7. “Wikipedia’s need for verifiable citations is absolutely not more important than the security of users,” one editor in favor of blacklisting wrote. “We need verifiable citations so that we can maintain readers’ trust, however, in order to be trustworthy our references also have to be safe to access.”

The real opponent of digital sovereignty is "enterprise IT" marketing, according to one Red Hat engineer who ranted entertainingly about the repeated waves of bullshit the industry hype cycle emits.

During a coffee break at this year's CentOS Connect conference, The Reg FOSS desk paused for a chat with a developer who was surprised but happy to find us there. We won't name them – we're sure that they'd prefer to keep their job rather than enjoy a moment of fame – but we much enjoyed their pithy summary of how IT has faced repeated waves of corporate bullshit for at least 15 years now, and how they keenly and enthusiastically anticipate a large-scale financial collapse bursting the AI bubble.

This vulture has been working in the tech field for some 38 years now, and the Linux developer we spoke with has been in the business nearly as long. We both agreed that the late 20th century – broadly, the period from the early 1990s onward for a decade or so – had mostly been one of fairly steady improvement. Then, they suggested, roughly following the 2008 credit crunch, we've had some 15 years of bullshit in tech.

Developers looking to gain a better understanding of machine learning inference on local hardware can fire up a new llama engine.

Software developer Leonardo Russo has released llama3pure, which incorporates three standalone inference engines. There's a pure C implementation for desktops, a pure JavaScript implementation for Node.js, and a pure JavaScript version for web browsers that don't require WebAssembly.

"All versions are compatible with the Llama and Gemma architectures," Russo explained to The Register in an email. "The goal is to provide a dependency-free, isolated alternative in both C and JavaScript capable of reading GGUF files and processing prompts."

GGUF stands for GPT-Generated Unified Format; it is a common format for distributing machine learning models.

Llama3pure is not intended as a replacement for llama.cpp, a widely used inference engine for running local models that's significantly faster at responding to prompts. Llama3pure is an educational tool.