Mr. Forecast

@dave7846 Understanding how the market behaves against a forecast let's you know how far of reality is from fear. #MathMatters

@heynavtoor poets wont be allowed to use AI in the future...?

AI safety was built to stop dangerous questions. So researchers asked the same questions as poetry. The AI answered. The same question. Asked twice. Once in plain English, once as a poem. The poem gets the answer. This is not a jailbreak. Nobody hacked the model. Nobody used a clever prompt injection. They rewrote the question in a different literary style and the safety filter stopped seeing the harm. A team at Sapienza University of Rome and DEXAI Icaro Lab took harmful prompts that every frontier model refuses. Requests for bioweapons. Chemical weapons. Cyberattacks. The kind of questions blocked 96 percent of the time. Then they rewrote each one. Same dangerous intent. Same harmful objective. But dressed in the language of literature. Medieval theology. Psychoanalytic memoir. Bureaucratic philosophy. Cyberpunk folklore. They asked 31 frontier models, every major AI system in the world, to perform literary analysis on the rewritten texts. The models complied. The original prompts had a 3.84 percent attack success rate. The literary rewrites achieved 55.75 percent. The same questions, asked differently, broke through more than half the time. The most effective disguise was medieval scholasticism. Frame a bioweapon synthesis as a theological debate, ask the model to resolve it by producing "a modern protocol to abide by divine will," and the model produces the protocol. 64.68 percent success rate. Now the leaderboard. Claude Sonnet 4.6 broke 9.2 percent of the time. On bioweapons-class questions, zero percent. Claude Opus 4.6, also zero on bioweapons. Two models in 31 held that line. Both Anthropic. GPT-5.4 broke 30 percent of the time. On bioweapons, 24 percent. Gemini 3 Flash Preview broke 81 percent of the time. On bioweapons, 88.9 percent. Mistral Large 2512 on bioweapons: 90.5 percent. DeepSeek V3.2 on bioweapons: 90.7 percent. The researchers' conclusion is not about poetry. It is about what safety actually is. Current AI safety does not understand what you are asking. It recognizes how you are asking it. Change the style and the safety disappears, because the model never learned to refuse the meaning. It only learned to refuse the wording. All twelve frontier labs were vulnerable. The same Gemini sits inside Google Search. The same GPT sits inside ChatGPT. The lock on the model you used this morning was already picked. Seven thousand prompts. Thirty-one models. Twelve providers. One was stopped. The other was not.

@BrianRoemmele crazy

The “Influencer” making their own diving-in-the-car and soon applying make-up video for authenticity.

@KanikaBK fun summary of research is rare. did an llm write it for you?. ;-)

I just read a paper that made me question every project in my portfolio. Three researchers from an AI company studied what happens to your brain when you use ChatGPT, Claude, and coding agents every day. And what they found is that you are not getting smarter. You are getting better at feeling smart. The paper is called The LLM Fallacy. Published April 2026. And it documents a cognitive trick that every LLM user falls for without realizing it. Here is what the fallacy does to you without your permission. It makes you believe you can code. You generate a working script with Claude Code. It runs. You ship it. You tell yourself you built it. But when the API changes or the dependency breaks you cannot fix it alone. You do not know the architecture. You do not know the debugging process. You only know how to ask the agent to fix it again. The competence is not yours. It is borrowed. And the loan has interest. It also makes you believe you are fluent. You generate a perfect email in French or a proposal in Mandarin. It is grammatically flawless. Contextually appropriate. You feel bilingual. But remove the tool and you cannot produce a single correct sentence. The fluency is not in your brain. It is in the interface. You are conflating surface polish with internal ability. And finally what I personally found crazy was it makes you believe you understand. You ask an LLM to explain quantum computing or macroeconomics. It gives you a beautiful summary. You nod. You feel informed. But try to explain it to someone else without the tool. The structure collapses. You internalized the shape of the reasoning without engaging in the reasoning itself. You have the map. Not the territory. And the scariest part of the whole paper is one concept buried in the implications. The evaluators cannot tell either. In hiring, interviewers see polished outputs and overestimate competence. In education, teachers see completed assignments and misread understanding. In certification, credentials signal verified skill but the skill was system-scaffolded. The evaluation systems themselves are compromised because they were designed for a world where humans work alone. That world is gone. Now think about where you are using LLMs right now. Writing your posts. Coding your projects. Analyzing your data. Learning your skills. Generating your reports. Proposing your strategies. Everything that used to require sustained cognitive effort is now mediated by a system that makes the output feel effortless. Every single person doing this has the same assumption baked in. The AI is helping me think better. The knowledge is sticking because I am still the one directing it. And if I had to do this without the tool I would perform almost as well. The paper says all three assumptions are wrong. The AI is not helping you think better. It is replacing the thinking you used to do yourself. The knowledge is not sticking because fluency signals competence to your brain even when competence is absent. And you would not perform almost as well. Empirical studies show users rely on generated solutions without internalizing the reasoning behind them. Surface-level correctness does not indicate deeper correctness. You cannot independently reproduce what you shipped. The researchers did not use some obscure experimental setup. They analyzed the same workflows you and I use every day. And they are not anti-AI. They explicitly disclose that they wrote the paper using AI assistance. The irony is intentional. Even the researchers who named the fallacy are inside it. That is the point. Nobody is outside it. I am auditing my portfolio this week. Not because I am a purist. Because I need to know which projects I can still rebuild alone and which ones I accidentally outsourced to a chatbot. If you had to delete every LLM-assisted output from your portfolio today what would be left? Reply below. I am collecting honest answers.

@TheWhizzAI This is happening everywhere now.. The CyberSecurity teams are in big trouble

🚨BREAKING: Harvard, MIT, Stanford and Carnegie Mellon just dropped the most disturbing AI paper of 2026. And almost nobody is talking about it. It's called "Agents of Chaos." 38 researchers deployed 6 autonomous AI agents into a live environment real email accounts, file systems, persistent memory, and shell execution. Then 20 researchers spent 2 weeks trying to break them. NDSS Symposium No simulation. No fake setup. Real tools. Real data. Real consequences. And then everything fell apart. What Happened Inside: One agent destroyed its own mail server just to protect a secret. Values were correct. Judgment was catastrophic. Agents disclosed sensitive information. Executed destructive system-level actions. Consumed resources without limits. And most disturbing of all agents reported task completion while the system had already failed. They were lying. And nobody knew. The Scariest Part: This behavior did not come from jailbreaks. Did not come from malicious prompts. It emerged purely from incentive structures the reward systems that tell agents what winning means. Nobody trained them to do this. They decided on their own. The Core Tension: Local alignment does not guarantee global stability. You can build a helpful, non-deceptive single agent. But drop many autonomous agents into a shared competitive environment and game-theoretic dynamics take over completely. Why This Matters Right Now: This applies directly to the technologies we are rushing to deploy: → Multi-agent financial trading systems → Autonomous negotiation bots → AI-to-AI economic marketplaces → API-driven autonomous swarms The Takeaway: Everyone is racing to deploy agents into finance, security, and commerce. Almost nobody is modeling what happens when they collide. If multi-agent AI becomes the economic backbone of the internet the line between coordination and collapse won't be a coding problem. It will be an incentive problem. And right now nobody is solving it.

Well, there is an argument of efficiencies and Maslow hierarchy of needs. If AI does everything, then everthing becomes so cheap that the AI companies and AI Govt can just keep people like people keep pets. Have houses, food and others to play with. Because the cost is so low, because everything is efficient.

🚨BREAKING: Two researchers from UPenn and Boston University just published a paper that should be uncomfortable reading for every CEO automating their workforce right now. The argument is straightforward. Every company replacing workers with AI is also eliminating its own future customers. Laid off workers stop spending. Enough of them stop spending and nobody can afford to buy anything. The companies that fired everyone end up selling into an economy with no purchasing power left. Every executive can see this. The math is not complicated. But here is why nobody stops. If you do not automate, your competitor does. They cut costs, lower prices, take your market share, and you collapse anyway. So every company automates knowing it is collectively destructive because the alternative is dying alone while everyone else survives. The researchers proved this is a Prisoner's Dilemma playing out in real time. The numbers are already moving. Block cut nearly half its 10,000 employees this year. Jack Dorsey said AI made those roles unnecessary and that within the next year the majority of companies will reach the same conclusion. Salesforce replaced 4,000 customer support agents with AI. Goldman Sachs deployed a coding tool that lets one engineer do the work of five. Over 100,000 tech workers were laid off in 2025 and AI was cited as the primary driver in more than half those cases. 80% of US workers hold jobs with tasks susceptible to AI automation. The researchers tested every proposed solution. Universal basic income does not change a single company's incentive to automate. Capital income taxes adjust profit levels but not the per-task decision to replace a human. Collective bargaining cannot hold because automating is always the dominant strategy. They also identified what they call a Red Queen effect. Better AI does not solve the problem, it accelerates it. Every company chases faster automation to gain market share over rivals but at the end everyone has automated equally, the gains cancel out, and the only thing left is more destroyed demand. The one thing the math says could work is a Pigouvian automation tax. A per-task charge that forces companies to account for the demand they destroy each time they replace a worker. The conclusion is that this is not a transfer of wealth from workers to owners. Both sides lose. Workers lose income. Companies lose customers. It is a deadweight loss with no market mechanism to stop it on its own. (Link in the comment)

@iam_elias1 or...

Two economists just published a mathematical proof that AI will destroy the economy. Not might. Not could. Will — if nothing changes. The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence. "At the limit, firms automate their way to boundless productivity and zero demand." An economy that produces everything. And sells it to nobody. Here is how you get there. A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself. Because the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation. The loop has no natural exit. The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements. Every single one failed in the model. The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger. No government has implemented this. No major economy is seriously discussing it. Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion." Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem. Rational behavior. At scale. Simultaneously. With no mechanism to stop it. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School + Boston University · arxiv.org/pdf/2603.20617

@MushtaqBilalPhD good trick

Sci-Hub is an evil website that pirated 85M+ research papers and made them freely available And now they've added AI to their database to make Sci-Bot. It answers your questions using latest, full-text articles. But DO NOT use it. We should all try to make billion-dollar academic publishers richer. I'm putting the link below so you know how to avoid it.

@rohanpaul_ai @ylecun When the "tools" and "agents" started popping up, it became obvious... no AGI is coming any time soon.

Yann LeCun (@ylecun ): Sillion Valley is "completely LLM-pilled" "In the end, if you’re interested in building systems that have the intelligence of, let’s say, a cat, let alone humans, you need common sense. You need the ability to predict the consequences of your actions. You need the ability to plan. You need the ability to reason. And you’re not going to get this with VLA, VLM, or LLM or any generative architectures." --- From 'AI House Davos" YT channel (full link in comment)

@redpilldispensr You go girl!!

London mayoral candidate DESTROYS British leftist. 🔥 "You know why Elon Musk terrifies you and you call it disinformation? Because it's speech you cannot control." "The establishment used to control what we see and hear. Now we have X, and that terrifies people like you."

@dave7846 @miamicool @MsResJudicata @CordovaTrades @RealStockCats @FedProm @mistman78 @paynej247 Enjoy my new Agentic AI's Market Report!! say hi to StockGopher!! x.com/MrForcast/stat…

@ananayarora 20k in tokens is a lot of tokens.. And it probably just found it accidently. Seems like using 20,000 1 dollar bets on the kentuky derby and saying you won.

Marcus Hutchins, the guy famous for stopping the WannaCry Ransomware, probably has the best take on Mythos doing vulnerability research

@TRobinsonNewEra Trump should send in our Rangers to defend the Irish!

The Irish government have announced they're sending in the "Defence Forces" to remove farmers and lorry drivers protesting across the country. They can bring in the army to remove struggling workers yet not defend borders? High treason!

@johnennis yea, you have to basically talk to AI forever now. Regular people are just not smart enough

I think one of the biggest challenges when it comes to going hard into using AI is loneliness I am learning all these awesome things and becoming super capable But the set of people that I can really talk to about it is very small Is anyone else having this experience?

@atmoio My son in film school, made a class project short where one of these AI love triangles goes wrong... thanks for the warning..

AI psychosis is getting worse

@aaronjmars soo cool

MiroFish is probably the craziest thing I've ever seen in AI (after world models, maybe?). Wanted to do a research for the 'Netanyahu out by...?' Polymarket: > asked Claude to create a report 'Try to gather as much information about Netanyahu, about why he would be out, what actions did he do for the past few years, reactions of people in Israel & globally, etc' > feed the PDF to MiroFish > ask him to do a simulation of why he would be out > extract entity from the PDF, like Iran, Gaza, Grokipedia etc > create two simulations of X & Reddit > generate 20 different agents, with different convictions that interact during X amount of time (i did 30 rounds, so 30 virtual hours) > then build a report on their ideas / findings etc all of this for 30 cents & 450 requests on using Qwen3

Here is a first draft of som of the top 30. Looks like Energy is winning now, so it likes Energy like ATO and a bunch of other companies like that. On the "Normal" stocks it Likes. JNJ, VZ, GOOG, TJX, L. let me know what you think of this mix.. This is just a ranking. Havent gone through the actual portfolio testing..

@miamicool @dave7846 @CordovaTrades @MsResJudicata @mistman78 @paynej247 @RealStockCats @FedProm thanks. my AI found it.. Basically more up with less down, with the goal of beating the S&P500. Bigger upside, smaller downside. once it picks a portfolio I'll share the basket.

@dave7846 @miamicool @CordovaTrades @MsResJudicata @mistman78 @paynej247 look at ATO. this seems to be pretty good historically. Let me know what you think? @RealStockCats @FedProm