I like it here

Do you know what I hate about most documentaries? They always have the same formula, they always are 3 hours long but they only have 20 minutes of content. The first 45 minutes agitates the problem, and "socializes" and lionizes the people in the documentary to establish a communal connection with the audience. The next 2 hours engrains whatever bullshit evidence they have in favor of their viewpoint into the audience by saying the same thing at least 25 different ways, through "experts", eyewitness and anecdotes, with these same 10 individuals pulp-rotating footage of their autobiographical story. Then the last 15 minutes speaks to you as a convert concludes there is no other valid interpretation of the evidence and how any contradictory evidence or interpretation is mere conspiratorial disinformation by "big business" or some other grand institutional cartel "keeping the truth hidden". The problem with documentaries isn't that they are wrong, some may be true. The problem is they give you certainty there is only one way to see the problem. This is called manufactured certainty.

Sometimes you just need that one last pot of coffee before bed

I would make a terrible liar, because I have e a terrible memory

If your "bitcoin treasury" company is doing anything other than using bitcoin as an internal SoV and hedge against inflation, or possibly putting the bitcoin to work, and is otherwise not profitable in the industry serve; then they exist purely to ride the wave, parasite and extract from the world and not create actual value. This behavior is fundamentally at odds with hard money. It will fail, and this is why the bear market is good.

Real nostriches zap. Bots only bitch.

Using AI to write about how you should not use ai. # When AI Makes You Less Productive Artificial intelligence may increase productivity on average. It can generate drafts, summarize information, reorganize ideas, write code, produce illustrations, and convert rough thoughts into polished language in seconds. But averages conceal individual outcomes. For many people, depending on how they use it, AI will not increase productivity. It will decrease it. In some cases, it will create considerably more work than would have existed if the person had completed the task without AI. The problem is not merely that AI occasionally makes mistakes. The deeper problem is that AI can produce incomplete work that looks finished. ## The Illusion of Completion Before generative AI, unfinished work usually looked unfinished. A rough draft contained awkward sentences. An incomplete design had visible gaps. A poorly understood subject produced confused explanations. These defects warned the author and the recipient that more work was necessary. AI removes many of those warnings. It can take an incomplete description and produce something coherent, organized, confident, and professionally formatted. The result may have headings, logical transitions, complete sentences, and an authoritative tone. It may look like a finished product even when it is only a polished interpretation of an incomplete request. This creates an illusion of completion. The user sees something that appears substantially better than what he could have produced in the same amount of time. He therefore assumes that the work is substantially closer to being finished. That assumption may be false. The output may accurately reflect everything the user told the AI while omitting everything the user failed to mention. Those omissions are often the most difficult defects to detect because they do not appear as errors. They appear as absences. You can correct a wrong sentence once you see it. It is much harder to identify the paragraph that was never written, the requirement that was never considered, the stakeholder who was never consulted, or the assumption that was never tested. ## Garbage In, Polished Garbage Out The old computing principle still applies: garbage in, garbage out. Generative AI adds a dangerous refinement: **Garbage in, polished garbage out.** The output may be grammatically excellent, logically arranged, and visually convincing. None of those qualities prove that it is complete, accurate, or suitable for its intended purpose. AI generally works with the information and direction it has been given. It does not necessarily understand the full situation surrounding the request. It does not automatically know the unstated requirements, organizational history, interpersonal constraints, legal implications, technical dependencies, or practical consequences that the user has neglected to provide. When information is missing, an AI system may do one of several things. It may omit the missing material. It may generalize. It may insert conventional assumptions. It may produce language that sounds specific without actually resolving the underlying uncertainty. In the worst cases, it may fill the gap with a plausible guess. The result is often not obviously defective. It is deceptively adequate. ## Editing Can Be Harder Than Writing People frequently assume that generating a draft with AI and then editing it will be easier than writing the material themselves. Sometimes it is. Sometimes it is not. Editing AI-generated work requires a different and often more demanding kind of thought. The editor must reconstruct the actual purpose of the document, compare that purpose against the generated text, identify both errors and omissions, and determine whether the AI introduced assumptions that were never authorized. This can be harder than writing the first draft. When writing from the beginning, the author builds the argument or design incrementally. He knows why each element is present because he placed it there. He encounters uncertainties as they arise and is forced to resolve them. When editing AI-generated material, the author receives a completed-looking structure all at once. He must reverse-engineer it. He must determine which parts are grounded in his instructions, which parts were inferred, which parts are conventional filler, and which essential parts are missing entirely. The surface polish can interfere with this process. Clear prose creates cognitive confidence. Good formatting suggests organization. A strong conclusion suggests that the argument has been completed. The reviewer must deliberately resist these signals and examine the work as if it were unfinished. ## AI Can Transfer Work Rather Than Eliminate It The productivity loss becomes greater when unfinished AI-generated work is delivered to other people. A person may spend twenty minutes generating a proposal, report, specification, presentation, or plan and believe that he has saved several hours. But if the document is incomplete, those hours have not necessarily been saved. The work has merely been transferred. A manager must identify the missing decisions. An engineer must resolve contradictory requirements. A client must explain information that should have been requested earlier. A colleague must distinguish factual content from plausible filler. A team may hold a meeting to repair misunderstandings caused by a document that was presented prematurely. The original author experiences speed. Everyone downstream experiences rework. From the perspective of the organization, productivity has declined. This is especially dangerous because the person using AI may continue to believe he is highly productive. He is producing more documents,