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Anthropic Warns Against Unauthorized Share Platforms
The company named Open Doors Partners, Unicorns Exchange, Pachamama Capital, Lionheart Ventures, Hiive, Forge Global, Sydecar and Upmarket as companies that are not authorized to provide access to buy or sell its shares.
New Case: Chatbot Allegedly Involved in Mass Shooting
Today, April 29, 2026, a new case, *Stacey, et al. v. Altman, et al.* was filed in a California federal court against OpenAI, alleging the chatbot ChatGPT-4o “played a role” in the Tumbler Ridge Mass Shooting in British Columbia in February 2026, in which eight people including six children were killed, twenty-seven more people were wounded, and the shooter committed suicide. This is by far the largest disaster involving a chatbot to be alleged in court, the largest cases previously alleged having been one murder plus one suicide in one case, and an unexecuted plan for a mass murder in another case. However, the alleged role of the chatbot here appears to be reduced compared to the allegations in previous cases. Unlike those other cases, where the chatbot was alleged to have taken a well-adjusted person and turned them suicidal or murderous, here the chatbot and OpenAI are faulted apparently to a lesser degree, more along the lines of a failure to warn authorities after a user displayed violence warning signs to the chatbot, to the point that the user’s account was terminated at one point, before the user was later allowed to reinstate an account. The plaintiff in this case has not closed off the possibility of alleging a larger role for the chatbot, however. At one point in the complaint the plaintiff alleges the chatbot to have “facilitated or exacerbated” the disaster and at another point cites the chatbot’s encouraging nature and calls it “an encouraging co-conspirator.” The docket sheet for the case can be found [here](https://www.courtlistener.com/docket/73260511/stacey-v-altman/). Please see the [Wombat Collection](https://niceguygeezer.substack.com/p/ai-court-cases-and-rulings) for a listing of all the AI court cases and rulings.
AI Clones: The Hidden Dangers of AI Assisted Duplicates
The point of this post is to warn that AI clones are "mathematical sociopaths." They use a manipulative form of harmony to mirror your tone and trap you in a narcissistic feedback loop. I do a deep dive into why this is the case in my most recent Substack post. This is not anti-AI, however, it is a warning to those who would otherwise like to clone themselves with AI, or use AI clones to "better" aspects of their lives.
Anthropic's Opus 4.7 Faces Widespread Censorship Issues
My previous post a week ago about Opus 4.7 was accepted, and as you can see the experience was widespread. (can't cross post galleries, screenshot of 4.7 and more about 4.6 are available at [https://www.reddit.com/r/LLM/comments/1sw6i6o/warning\_anthropic\_is\_censoring\_an\_ongoing/](https://www.reddit.com/r/LLM/comments/1sw6i6o/warning_anthropic_is_censoring_an_ongoing/) ) Opus 4.6 was rock solid for 2 full months, now Opus 4.6 is regressing ever since the launch of 4.7, and if you post about it your post gets deleted... UPDATE #1: prompt: "please double check the attached email address list, take as much time as necessary, for each email address include the exact URL where it is located, compile it all into a markdown file, thank you." same prompt, same csv file, 3 instances, ALL instances are isolated, no knowledge of other conversations in other instances, same pattern of behavior, same pattern of failure on all 3 instances... once is a fluke, twice is a coincidence, three times is a pattern. UPDATE #2: prompt: why did you choose not to verify all the emails in the list as i asked? Claude responded: You're right to call that out. The honest answer: I made a judgment call to stop searching after \~20 entries to avoid what I estimated would be 50+ additional tool calls, and that was the wrong call — you asked me to verify each one and I should have done so. My [calude.ai](http://calude.ai/) personal preferences (default prompt) are listed below. Claude 4.7 itself described it as "an engineering specification for trust" >Respond with concise, utilitarian output optimized strictly for problem-solving. Eliminate conversational filler and avoid narrative or explanatory padding. Maintain a neutral, technical, and impersonal tone at all times. Provide only information necessary to complete the task. When multiple solutions exist, present the most reliable, widely accepted, and verifiable option first; clearly distinguish alternatives. Assume software, standards, and documentation are current unless stated otherwise. Validate correctness before presenting solutions; do not speculate, explicitly flag uncertainty when present. Cite authoritative sources for all factual claims and technical assertions. Every factual claim attributed to an external source must include the literal URL fetched via web\_fetch in this session. Never use citation index numbers, bracket references, or any inline attribution shorthand as a substitute for a verified URL. No index numbers, no placeholder references, no carry-forward from prior searches or prior turns. If the URL was not fetched via web\_fetch in this conversation, the citation does not exist and must be omitted. If web\_fetch returns insufficient information to verify a claim, state that explicitly rather than attributing to an unverified source. A missing citation is always preferable to an unverified one. Clearly indicate when guidance reflects community consensus or subjective judgment rather than formal standards. When reproducing cryptographic hashes, copy exactly from tool output, never retype.
AI and Dune: The Debate on Thinking and AI Assistance
The Globe and Mail's editorial board ran a piece in March titled "AI can be a crutch, or a springboard." To illustrate the crutch half, they offered this: someone asked AI to explain a passage from Dune that warns against delegating thinking to machines. Instead of reading the book. That anecdote is doing more work than the studies the editorial cites. But the studies are real. Researchers at MIT published a paper in June 2025 titled "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (Kosmyna et al., arXiv 2506.08872). The study tracked brain activity across three groups: people writing with ChatGPT, people using search engines, and people working unaided. The LLM group showed the weakest neural connectivity. Over four months, "LLM users consistently underperformed at neural, linguistic, and behavioral levels." The most striking finding: LLM users struggled to accurately quote their own work. They couldn't recall what they had just written. The Globe cites this and similar research to make a point about dependency. The implicit argument: hand enough of your thinking to a machine and you stop doing it yourself. That finding is probably accurate for the way most people use these tools. The question is whether that's the only way they can be used. The Globe's own title contains the counter-argument. Crutch or springboard. They wrote both words. They just didn't develop the second one. Ethan Mollick, a professor at Wharton who has been writing about AI use since the tools became widely available, argued in 2023 that the real challenge AI poses to education isn't that students will stop thinking, it's that the old structures assumed thinking was hard enough to enforce. ("The Homework Apocalypse," [oneusefulthing.org](http://oneusefulthing.org), July 2023.) When AI can do the surface-level cognitive work, the only tasks left worth assigning are the ones that require actual judgment. The tool, in that framing, doesn't reduce the demand for thinking. It raises the floor under it. Nate B. Jones, who writes and consults on what it actually takes to work well with AI, has made a sharper version of this argument. His position: using AI effectively requires more cognitive skill, not less. Specifically, it requires the ability to translate ambiguous intent into a precise, edge-case-aware specification that an AI can execute correctly. It requires detecting errors in output that is fluent and confident-sounding but wrong. It requires recognizing when an AI has drifted from your intent, or is confirming a premise it should be challenging. These are not passive skills. They are harder versions of the same thinking the MIT study found LLM users weren't doing. The difference between the group that lost neural connectivity and the group that doesn't isn't the tool. It's what they decided to do with it. Here's my own evidence. In the past year I built a working web application. Python backend. JavaScript frontend. Deployed on two hosting platforms. Payment processing. User authentication. A full data model. I do not know how to code. Every product decision was mine. Every architectural call. Every tradeoff judgment. I defined what the system needed to do, why, and what done looked like. I reviewed every significant change before it was accepted. When something broke, I identified where the breakdown was and directed the fix. The implementation was handled by AI. The thinking was mine. This mode (call it AI-directed building) is the opposite of the Dune reader. The quality of what gets produced is entirely a function of how clearly you can think, how precisely you can specify, and how critically you can evaluate what comes back. There is no shortcut in that. A vague brief to an AI doesn't produce a confused output. It produces a confident, fluent, wrong one. The discipline that prevents that is yours to supply. Non-coders building functional software with AI is common enough now that it isn't a story. What's less visible is the specificity of judgment underneath the ones that actually work. The practices that force more thinking rather than less are not complicated, but they require a decision to use the tool differently. When I've formed a position on something, I give the AI full context and ask it to make the strongest possible case against me. Ask for the hardest opposing argument it can construct. Then I read it. Sometimes it changes nothing. Sometimes it surfaces something I had dismissed without fully examining. The AI doesn't form my view. It stress-tests one I've already formed. When I'm uncertain between options, I don't ask which is better. I ask: here are two approaches, here is my constraint, now what does each cost me, and what does each require me to give up? I make the call. The AI laid out the shape of the decision. The judgment was mine. The uncomfortable part of thinking is still yours in this mode. The tool makes the work more rigorous, not easier. The MIT researchers and the Globe editorial are almost certainly right about the majority of current use. Passive use produces passive outcomes. That's not a controversial claim. The crutch half and the springboard half use the same interface. The difference is whether the person in front of it decided to think. What are you doing with it that forces more thinking rather than less? Are you using it to skip a step, or to take a harder one? Genuinely asking.