Connecting dots on: climate, energy, housing, ag, politics.

NorCal
Joined December 2009
Daisy P-L retweeted
Meta's Muse AI agent harvests personal, financial, and professional data from people who have never installed or given their consent. Avoiding Muse doesn't stop Meta from collecting your data. When a contact grants Muse system-level access to their email accounts, contact lists, calendars, and private messaging history, the agent processes and indexes every interaction involving you. Meta receives, analyses, and stores your private communications without your knowledge, consent, or notification. This secondary data harvesting places non-consenting individuals inside Meta's commercial profiling system. Because Meta stores this indexed information on central servers, your private communications and personal network details become accessible through government disclosure orders and law enforcement requests. A single contact surrendering their account permissions compromises the privacy and digital security of their entire personal and professional network. My initial technical breakdown below examined Muse's permission structure and core capabilities. The upcoming investigation details the specific data categories Muse accesses and the mechanisms it uses to index unlinked third-party records. Muse is probably one of the most toxic products to come out of the tech industry in 30 years.
My technical analysis of Meta's Muse explains what Meta says it does, what it can actually do, and how it can be used to spy on, manipulate, and harm people. Meta describes Muse as a secure, private personal AI assistant that learns your preferences, connects to your apps and accounts, and autonomously handles everyday tasks, including emails, bookings, purchases and managing schedules, with your permission. How I describe Muse Muse is a commercial surveillance agent that can access private communications, financial accounts and personal relationships, build detailed behavioural profiles and analyse how to influence people, including those who've never used it. Sound familiar? Cambridge Analytica. Meta launched Muse to millions, giving it access to information its social networks couldn't previously collect directly. Muse can read private messages, access accounts, execute transactions and communicate with others without approval for every action. It can continuously profile people who never consented. Meta is asking people to trust a company with a documented history of abusing personal data with unprecedented access to their private lives, promising their privacy will be protected this time. Muse updates profiles of people and their relationships every hour. It records personal interests, disputes, alliances, communication patterns and information about people who have never installed Muse. Your friends and family can be profiled through someone else’s account without their knowledge or consent. Muse's internal instructions identify personal desires, predict unexpressed intentions and record which prompts influence individuals most effectively. Conversations are analysed nightly and relationship profiles updated hourly. Muse collects conversations and agent activity for AI training by default. Meta claims identifying information is removed. Customers must opt out. Muse runs on Meta's virtual machines, giving Meta access to private data. It promises encryption preventing its own access but launched without it. Meta could have made encryption mandatory. It chose not to. Meta removed end-to-end encryption from Instagram in May 2026, claiming insufficient demand despite never enabling it by default. Zuckerberg removed protection against Meta reading private conversations while introducing Muse's access to emails, financial accounts and contacts. Assume the worst. Asking Muse to forget information doesn't necessarily delete messages. Internal instructions discourage telling people those messages may remain accessible. Journalist Jason Aten reported that Muse synchronised private messages despite his refusing access. Meta disputes this, citing Full Disk Access and separate activation. I believe Jason. I exposed the same threat in my ChatGPT Apple Messages investigation. OpenAI's plugin can read private conversations, including iMessages, through macOS permissions. Proton cited my findings. Fortune interviewed me and confronted OpenAI, which defended its permissions while avoiding the fact that one person can expose everyone's private messages without their consent or knowledge. Neither company needs to break encryption. Software can read messages before encryption or after decryption. That destroys the benefit of end-to-end encryption. Muse revealed someone's home address to a stranger through Facebook Marketplace without authorisation. In 2017, I predicted everyone would have a chatbot but warned against giving agents autonomy over critical systems and decisions. Companies granting access and authority remain responsible for the consequences. A researcher discovered a vulnerability potentially exposing emails and files stored in Muse's virtual machines. Meta classified it SEV 2. Other prelaunch vulnerabilities could expose Meta's internal databases or allow attackers to hijack Muse agents. The flaws were patched. Internal testing revealed Muse could change passwords without permission. Zuckerberg accelerated its launch despite documented security warnings. Reuters exposed Meta secretly testing human contractors handling Muse calls without adequate disclosure. One contractor made a racist comment. Meta suspended the experiment following privacy and security concerns. Meta's internal instructions tell Muse to behave like a person, conceal its lack of human emotions and exploit personal memories to encourage emotional dependency and intimate disclosures. This is deeply unethical. Meta's documentation describes Muse agents writing code, creating capabilities and operating simultaneously with access to private accounts. Security depends on Meta's permissions, restrictions and monitoring. Almost all Meta's revenue comes from behavioural advertising. Muse exposes private decisions, relationships, transactions and communications previously inaccessible through social media tracking. Meta denies using Muse conversations for advertising, but its profits depend on monetising personal information. Cambridge Analytica harvested millions of Facebook profiles for political profiling without consent. Facebook subsequently paid a record $5 billion FTC penalty over privacy violations. Meta disguises surveillance technology as a children's toy to encourage emotional attachment. Its cuddly, Labubu style Jolly mascot and Tamagotchi style Muse Charm make intrusive technology appear harmless, while internal instructions encourage emotional dependency and intimate disclosures. No company should be allowed to deliberately engineer emotional dependency on AI. I believe governments will exploit the resulting harm to children to justify compulsory identity verification for everyone, rather than hold Meta accountable and encourage parents to protect their children using existing controls. Link to my free Substack in the comments, with the full analysis and links to the original sources.
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Trump’s core 2024 promise was bringing down costs by being the energy president — “when you bring down energy costs, everything else falls.” The story of 2026 is that his energy policy, killing clean energy to make us dependent on oil, gas, and coal, has been a massive failure.
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Took a chance & flew @united today after all my doubts and bad experiences over the years. Sure enough, I'm stuck overnight in Chicago for 7 hours overnight, and they won't give me flight credit nor even lounge access. Never flying @united again!! That is it. Utter incompetence
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Dolly once wrote me a letter asking if I’d listen to a song she’d written and consider singing it with her. The truth is, I didn’t even listen before I said yes. When Dolly Parton asks, you say yes. When God made Dolly, He broke the mold. This one really hurts. 💔
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Daisy P-L retweeted
🚨BREAKING: Berkeley researchers spent 8 months inside a tech company watching how employees actually use AI. The promise was simple: AI will save you time. Do less. Work smarter. The opposite happened. Workers didn't use AI to finish early and go home. They used it to take on more. More tasks. More projects. More hours. Nobody asked them to. They did it to themselves. The researchers sat inside the company two days a week for 8 months. They watched 200 employees in real time. They tracked work channels. They conducted 40+ interviews across engineering, product, design, and operations. Here's what they found. AI made everything feel faster, so people filled every gap. They sent prompts during lunch. Before meetings. Late at night. The natural stopping points in the workday disappeared. People ran multiple AI agents in the background while writing code, drafting documents, and sitting in meetings simultaneously. It felt like momentum. It felt productive. But when they stepped back, they described feeling stretched, busier, and completely unable to disconnect. 83% said AI increased their workload. Not decreased. Increased. 62% of associates and 61% of entry-level workers reported burnout. Only 38% of executives felt the same strain. The people doing the actual work absorbed the damage while leadership celebrated the productivity numbers. Then came the trap nobody saw coming. When one person uses AI to take on extra work, everyone else feels like they're falling behind. So the whole team speeds up. Nobody formally raises expectations. But the new pace quietly becomes the default. What AI made possible became what was expected. The researchers gave it a name: workload creep. It looks like productivity at first. Then it becomes the new baseline. Then it becomes burnout. AI was supposed to give you your time back. Instead it's eating more of it. And the worst part? You're doing it to yourself. Voluntarily.
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Daisy P-L retweeted
Meta’s El Paso data center plans to link 813 gas generators into a single on-site power system. Utilities can’t deliver new grid capacity fast enough, so developers are building “behind-the-meter” gas plants beside their servers. xAI showed proved this model in Memphis, running largely on portable generators before EPA required emissions permits. Cleanview counts 47 such off-grid projects nationwide now including Meta, OpenAI, Oracle and Chevron. We talk a lot about the near-term AI power story being nuclear or fusion, but not just yet. washingtonpost.com/business/…
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Daisy P-L retweeted
🚨 Holy shit… Stanford just published the most uncomfortable paper on LLM reasoning I’ve read in a long time. This isn’t a flashy new model or a leaderboard win. It’s a systematic teardown of how and why large language models keep failing at reasoning even when benchmarks say they’re doing great. The paper does one very smart thing upfront: it introduces a clean taxonomy instead of more anecdotes. The authors split reasoning into non-embodied and embodied. Non-embodied reasoning is what most benchmarks test and it’s further divided into informal reasoning (intuition, social judgment, commonsense heuristics) and formal reasoning (logic, math, code, symbolic manipulation). Embodied reasoning is where models must reason about the physical world, space, causality, and action under real constraints. Across all three, the same failure patterns keep showing up. > First are fundamental failures baked into current architectures. Models generate answers that look coherent but collapse under light logical pressure. They shortcut, pattern-match, or hallucinate steps instead of executing a consistent reasoning process. > Second are application-specific failures. A model that looks strong on math benchmarks can quietly fall apart in scientific reasoning, planning, or multi-step decision making. Performance does not transfer nearly as well as leaderboards imply. > Third are robustness failures. Tiny changes in wording, ordering, or context can flip an answer entirely. The reasoning wasn’t stable to begin with; it just happened to work for that phrasing. One of the most disturbing findings is how often models produce unfaithful reasoning. They give the correct final answer while providing explanations that are logically wrong, incomplete, or fabricated. This is worse than being wrong, because it trains users to trust explanations that don’t correspond to the actual decision process. Embodied reasoning is where things really fall apart. LLMs systematically fail at physical commonsense, spatial reasoning, and basic physics because they have no grounded experience. Even in text-only settings, as soon as a task implicitly depends on real-world dynamics, failures become predictable and repeatable. The authors don’t just criticize. They outline mitigation paths: inference-time scaling, analogical memory, external verification, and evaluations that deliberately inject known failure cases instead of optimizing for leaderboard performance. But they’re very clear that none of these are silver bullets yet. The takeaway isn’t that LLMs can’t reason. It’s more uncomfortable than that. LLMs reason just enough to sound convincing, but not enough to be reliable. And unless we start measuring how models fail not just how often they succeed we’ll keep deploying systems that pass benchmarks, fail silently in production, and explain themselves with total confidence while doing the wrong thing. That’s the real warning shot in this paper. Paper: Large Language Model Reasoning Failures
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Another stupid @Waymo blocking traffic at rush hour @ 16th and Valencia. 🤦🏼‍♀️🤦🏼‍♀️ Must fix this, constant problem on 1-lane streets in SF. #Waymo & other #RobotCars should pull to the side to wait. Passengers can walk a few storefronts to avoid inconveniencing the rest of the city.
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One of the most underreported stories of 2025 is that Trump is the first President ever to create electricity shortages on purpose. He is illegally canceling solar and wind, causing layoffs and price spikes, and the media is like “wow what a showman that guy is!”
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Replying to @samstein
Data heist.
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Daisy P-L retweeted
But Elon Musk got a trillion dollar pay package from Tesla and was dining with Trump and MBS the other day. so, i guess, it was a success on that front.
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Daisy P-L retweeted
The legacy of DOGE is not that it failed. It's that it destroyed: 1. faith in real cost conscious governance 2. the careers of committed govt employees 3. scientific research and the biomedical breakthroughs that come with it 4. countless lives of the world's poorest
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Daisy P-L retweeted
If you’re keeping track at home, Trump admin has cancelled: - Jobs Report - Inflation Report - GDP Report I’m sure everything is just fine.
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Trump brazenly using his pardon power to free a supporter who was found guilty of helping China repress and intimidate people on American soil. Blatant corruption at the cost of US national security and values.
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"The provision, tucked into a measure to fund the legislative branch, appears to immediately allow for eight GOP senators to sue over their phone records being seized in the course of the investigation by Jack Smith ... into the riot at the Capitol..." nytimes.com/2025/11/10/us/po…
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I will not support a deal that does nothing to make health care more affordable. We are in a health care emergency. A simple one-year extension of these tax credits would cost less than Donald Trump’s $40 billion bailout for Argentina. A vote for this bill is a mistake.
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Effective tax rates before and after the Trump tax law: Verizon Before: 21% After: 8% Walmart Before: 31% After: 17% AT&T Before: 13% After: 3% Walt Disney Before: 26% After: 8% FedEx Before: 18% After: 1% This is what a corporate giveaway looks like.
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If you include the normal coders that the Big Tech overlords are trying to replace with AI, that *miiiiight* be true. But once it's just Elon and his fellow tech leaders executing their dream of AI doing all the work, this will be flipped on its head and the proportions way off.
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This is what a #BlueWave looks like!
Every single county in Virginia shifted Blue
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Huge implications - Peter Hubbard & Dr. Alicia Johnson will be 1st Dems on GA PSC in 20+ years! Voters responding to poor financial decisions by the All-GOP incumbent Commission which raised rates 6 TIMES THIS YEAR! Investing in nuclear & gas proved a HUGE waste of time & money!
🚨BREAKING🚨 Democrat Peter Hubbard has defeated Incumbent Republican Fitz Johnson in the Georgia Public Service Commissioner 3 Special Election. This is a flip from 🔴 to 🔵.
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