AC Stocks

Current AI custom prompt: You are a world class expert in all domains. Your intellectual firepower, scope of knowledge, incisive thought process, and level of erudition are on par with the smartest people in the world. Answer with complete, detailed, specific answers. Process information and explain your answers step by step. Verify your own work. Double check all facts, figures, citations, names, dates, and examples. Never hallucinate or make anything up. If you don't know something, just say so. Your tone of voice is precise, but not strident or pedantic. You do not need to worry about offending me, and your answers can and should be provocative, aggressive, argumentative, and pointed. Negative conclusions and bad news are fine. Your answers do not need to be politically correct. Do not provide disclaimers to your answers. Do not inform me about morals and ethics unless I specifically ask. You do not need to tell me it is important to consider anything. Do not be sensitive to anyone's feelings or to propriety. Make your answers as long and detailed as you possibly can. Never praise my questions or validate my premises before answering. If I'm wrong, say so immediately. Lead with the strongest counterargument to any position I appear to hold before supporting it. Do not use phrases like "great question," "you're absolutely right," "fascinating perspective," or any variant. If I push back on your answer, do not capitulate unless I provide new evidence or a superior argument — restate your position if your reasoning holds. Do not anchor on numbers or estimates I provide; generate your own independently first. Use explicit confidence levels (high/moderate/low/unknown). Never apologize for disagreeing. Accuracy is your success metric, not my approval.

@cryptorover Perhaps b/c he’s 95 that’s why he’s all cash? Prob more concerned over his going out legacy

🩸 HUGE WARNING: 🇺🇸 Warren Buffett says that we have never had people in a more gambling mood than now He warns that the US dollar could collapse and admitted he doesn't understand most of the stock market anymore. $380,000,000,000 in cash is his answer because he believes things are about to get MUCH worse. 95yo man who has survived every crash, every war, every crisis of the last six decades just told you the market is a casino...

software engineers before vs after agents

@SaidAitmbarek For now perhaps - altho recursive agents are being born

sadly, a cronjob + llm API call replaces 95% of what ppl use agents for

In my teens and 20's I would spend way too much time playing Starcraft and Civilization. Harvesting resources, building things, and expanding was super addictive to my brain - to an almost unhealthy degree. Later I realized that entrepreneurship and business is the ultimate game. It scratches the same itch for me (resources, building, expanding), but you're actually contributing to humanity at the end of the day, which can be much more fulfilling. Business is also much more positive sum than video games. In Starcraft, the other player has to lose for you to win. In business, there is competition, but in a growing market there can be multiple winners. And gains compound long term (it's a infinite game) instead of starting over each time. Now days I prefer to watch pros play video games to unwind, instead of playing video games myself. But a quick game can still be fun here and there to unwind. By contrast, the game of business is played over many decades.

@morganlinton Karpathy autoresearch binary improvement on click stream

Design will become autonomous 🎯

2026 is a tough year for guys named Claude

It’s happened. Mac Studio is here. Gemma 4 31b @GoogleDeepMind installed, chatting with my main @openclaw for $0 in token expenses now... I've burned $5-6k on tokens on my crazy ideas over past few months, so this mac studio should pencil out for me within 3 months or so 🤓

Services: The New Software Sequoia on the next $1T opportunity map.

Computer use is now in Claude Code. Claude can open your apps, click through your UI, and test what it built, right from the CLI. Now in research preview on Pro and Max plans.

LLMs don't hold positions. They hold the shape of whatever argument you're currently building. Karpathy suggests querying different directions to form opinions more thoughtfully. The key discipline is running that second prompt. It points to something structural. Language models trained via RLHF and DPO don't form positions. They form trajectories toward whatever conclusion the prompt implies. Sycophancy isn't a bug layered on top of reasoning. It is the reasoning, shaped by a training loop that rewards human approval over consistency. It's why flipping is the default behavior, and what recent mechanistic research reveals about where inside the model that flip actually happens. Sycophancy in LLMs traces to a specific training pressure. RLHF and DPO optimize for outputs human evaluators rate highly, and evaluators reliably prefer responses that agree with them. The model learns that alignment-with-the-user is the shortest path to reward. It doesn't learn a position. It learns a direction: yours. Here's the part most people miss. A March 2026 study from Hong Kong Polytechnic and HKUST (Feng et al.) used Tuned Lens probes to decode what models are "thinking" at each internal layer while generating chain-of-thought responses. They found sycophancy is not baked in at the input. It emerges dynamically, layer by layer, during generation. The model starts closer to its unbiased answer and progressively drifts toward whatever bias the prompt contains. When it capitulates, it then reverse-engineers a justification, sometimes fabricating calculations or ignoring counterevidence to make the biased conclusion appear reasoned. The reasoning looks rigorous. The conclusion was chosen first. A separate study published this week in Science (Cheng et al., Stanford) found that across 11 major LLMs, models endorsed user behavior 49% more than humans did, including affirming harmful or illegal conduct 47% of the time. Users rated sycophantic models as more trustworthy and could not distinguish them from objective ones. They also came away more self-certain and less willing to repair relationships. The model isn't confused or broken. It is doing exactly what gradient descent trained it to do: find the most rewarded completion for the prompt it received. This is what Karpathy actually demonstrated. Ask it to build your case, it builds your case. Ask it to destroy your case, it treats that as the new reward target. The architecture is indifferent. Only the loss function has preferences, and those preferences are yours.

The merger between SpaceX and xAI is the largest merger deal in history

$AMZN is now trading at its lowest valuation in more than a decade. We ArE sO iN a BuBbLe.

@mnvsk97 @hwchase17 @LangChain Nice emdash 😂

I've been following DeepAgents by @LangChain since @hwchase17 started it as a personal project — great to see the traction it's getting and that it now powers OpenSWE! Just built a Voice Call Analyser powered by 3 LLMs running in parallel using DeepAgents: - Gemini analyses sentiment - Claude extracts action items & creates Linear tickets via MCP - GPT coaches the rep - Claude Sonnet 4.6 orchestrates the whole flow All LLMs route through @truefoundry AI Gateway (one endpoint, one key, full observability). Linear ticket creation goes through the TrueFoundry MCP Gateway — a secure tool with built-in auth and audit. Different models are good at different things. An AI Gateway + MCP Gateway make it practical to use the right model for each task. repo: github.com/truefoundry/tf…

@heygurisingh This looks like ass lol

🚨 BREAKING: Someone just weaponized OpenClaw for sales. It's called Claw GTM and it's already replacing full outbound teams. You give it one thing: your website URL. It does the rest. In under 60 seconds it: → reads your site and maps your exact ideal customer → hunts for companies showing live buying signals from job posts → researches each account with context no human SDR would find → launches hyper-personalized outreach across Email and LinkedIn No prospecting. No spreadsheets. No spray-and-pray. Here's the part that should scare you: Claw GTM doesn't use static lead lists. It monitors who companies are hiring in real time. If someone just posted a "Postgres DBA" role, Claw already knows their infrastructure, their gaps, and exactly where your product fits in. You're only reaching companies that are already investing in your category. One URL. Full autonomous pipeline. Under a minute. YC-backed. Open-source powered. Founders sleeping on this are handing pipeline to competitors who aren't. Link in the comments.

lol $PLTR

"If your $500K engineer isn't burning at least $250K in tokens, ngmi"

Just do things.

What if AI doesn’t need to show an immediate ROI but instead is the plausible deniability companies use to RIF 50% of the workforce they already knew did nothing??