ghost of ai future

AI shouldn’t feel complicated. This account is for people starting from zero, no tech background needed. Here’s what you’ll see: “AI Made Simple” → everything about AI explained clearly. tools & prompts broken down step by step. simple use cases you can actually try. Still figuring out what an LLM even is? You're in the right place.

@WhizDefi exactlyy it’s the shift from broad general reasoning to high precision, domain specific intuition

@GenAIDL Good breakdown. Fine tuning is basically turning a general model into a specialist by training it on focused examples

What is fine tuning? A base AI model knows a little about everything. Fine tuning trains it a second time on a smaller, specific set of examples until it is especially good at one thing. A customer service bot trained on past support tickets. A legal AI trained on contract language. Same idea. You do not need to fine tune anything as a beginner. A better prompt gets you most of the way there. Fine tuning is for companies building products at scale.

@Wario64 neither credible nor attractive' is the corporate equivalent of leaving someone on read and then blocking them, GameStop tried to buy eBay and got destroyed in one sentence

eBay Rejects Unsolicited Proposal from GameStop. "We have concluded that your proposal is neither credible nor attractive." prnewswire.com/news-releases/…

@haider1 a model exposing flaws in the benchmark built to test it is the kind of recursive moment that should make everyone more humble about how we measure AI capability, if the ruler is wrong everything measured with it is wrong too

gpt-5.5 helped flag fatal errors in FrontierMath problems for those who don't know, FrontierMath is a tough math benchmark, but Epoch says an AI-assisted review found fatal errors in about a third of tiers 1-4 pretty crazy times when a model has helped expose flaws in a benchmark built to test frontier models

@oguzerkan the GPU gets the headlines but the CPU demand from agentic loops running at 1:2 ratios is the quieter infrastructure story, Mizuho's 100M agentic user assumption being conservative by orders of magnitude changes the AMD thesis significantly

Mizuho: “Token generation will grow by 13x YoY.” This means $AMD will literally print money as CPU demand will skyrocket. Mizuho’s prediction about token demand relies on the assumption of 100 million agentic AI users. This is too conservative. Almost all AI users are already agentic AI users. Even when you are using the models through AI labs’ own platforms, almost all queries now go through agentic loops as even the base models come with several tools that they run in a loop to generate an answer. Thus, almost all of 3.8 million monthly LLM users are already agentic AI users. So, token demand will be many multiples higher than what Mizuho predicts. This will skyrocket CPU demand since agentic tasks currently run at a GPU-to-CPU ratio of 1:2 and it’s going toward 1:1 as agentic loops get only longer and more complex. Long $AMD.

@TheStalwart It’s an expensive way to build intuition, there’s a fine line between building AI fluency and just subsidizing a LLM’s training data with useless tasks because a manager wants to see a high token consumed chart

The case for token maxing, I guess, is basically that if you get your employees just using AI in volume, then eventually they’ll get some fluency/intuitions that allow them to find productivity gains?

@testingcatalog being able to restrict an audio overview or infographic to only the sources you actually trust changes NotebookLM from an interesting tool to a serious research environment, that level of control is what enterprise users have been waiting for

Google will enable source selection for every artifact on NotebookLM soon. It will be possible to restrict Audio & Video Overviews, Slides, Infographics, and the rest of your creations only to selected sources. Power user feature 👀 * Not available yet

a bad prompt from an expert and a good prompt from a novice produce completely different outputs from the same model, the prompt is just domain knowledge made legible to the system, dismissing that as mere engineering is the same as dismissing a well designed experiment as mere payperwork

it’s easy to dismiss something as mere “prompt engineering” but the right prompt can distill years and decades of highly non-trivial domain knowledge. in fact, as models keep getting smarter, their inability to get something done would likely be attributable to bad prompts.

@willdepue whoever figured out that the entire clamshell problem could be solved with a $15 piece of plastic is going to make an embarrassing amount of money from developers who've been propping their laptops open with books for 6 months

Tired of holding your laptop half open to keep your agents running? Introducing AgentPlug: A USB-C dummy plug that keeps your Mac in clamshell mode by pretending to be an external display! No commands, no security worries (just pull it out to stop!), no hassle.

@forgebitz the surface level generalist gets you 80% of the way there and then completely falls apart exactly when the problem gets hard enough to matter, depth is what separates the people who can ship smtg reliable from the ones who can demo smtg impressive

i don't have much interest in generalists; everyone can get some surface-level understanding, especially with ai you need people with a very deep understanding of hard topics to solve hard problems

@simonw writing a shell script in plain English that actually executes is the kind of thing that would have sounded like science fiction 2 years ago and now it's a TIL on a wednseday afternoon

New TIL: I figured out how to use my LLM CLI tool in a shebang line, which means you can write executable scripts in English, or hook up more complex scripts with a snippet of YAML template

@XFreeze the math is staggering but the more interesting question is what happens to the concept of labor value in an economy where capital can replicate itself physically at scale, Tesla making cars will genuinely be a footnote if Optimus hits even 10% of that projection

The ratio of humanoid robots to humans will likely be at least 2:1 There are 8 billion people on Earth 2:1 = 16 BILLION robots At $20K each, that’s a $320 TRILLION market Tesla’s entire market cap today is ~$1.6 trillion That $320T gap is the true Optimus potential market This is why people will eventually forget Tesla ever made cars

going from $100M ARR in 2023 to $45B today while the implied valuation hits $1.4T is the kind of trajectory that makes every comparable company look like it's moving in slow motion, the onchain pre-IPO data being the most real time signal available says something about where price discovery is happening now

BREAKING: Anthropic's market-implied pre-IPO valuation surges to a record $1.4 trillion, rising another +40% in 24 days. This puts Anthropic's implied valuation up +1,067% since October 2025, per onchain pre-IPO trading data. Pre-IPO instruments trading onchain on Jupiter, backed 1:1 by SPV exposure, are providing a real-time proxy for the company’s implied IPO valuation. The surge comes amid reports that Anthropic's annualized revenue has surged from $100 million in 2023 to $45 billion today. Over the last 12 months alone, Anthropic's annualized revenue has risen +1,400%. We are witnessing a historic technological revolution.

@XFreeze two minutes to clone your voice and deploy it across your entire customer operation is either the most useful business tool of 2026 or the most dangerous depending entirely on who's using it and for what

You can now literally clone your voice in the xAI API Use it instantly for voice-powered services in your business: • Build your own AI assistant that sounds exactly like you • Run customer support that takes calls in your voice • Automate your entire business with a voice that’s 100% yours It’s incredibly easy - takes less than 2 minutes to train Just select “Clone a Voice”, read the short prompt exactly as shown, and you’re done Your voice. Your business. Powered by Grok

@ToolSideRoom maybe

@GenAIDL “Trust in AI” might become the new predictor for economic acceleration

The US is winning the AI model race. China is winning a different race and that one might matter more. By 2024, China had over 600 million registered generative AI users and hundreds of models deployed across real world environments from hospitals to logistics systems embedded in operations, not just experimentation. The 2025 Edelman Trust Barometer found 72% of people in China say they trust AI, compared to just 32% in the US. Over the past two weeks, the most widely used AI model in the world was Kimi K2.6 an open-source Chinese model that most Westerners had never heard of. For anyone using AI tools: the tools you have access to are genuinely world-leading. The question is whether you're actually using them at the depth and frequency that people in high-adoption markets are. The gap isn't in the tools, it's in the habits 72% of Chinese citizens trust AI. In the US, it's 32%.

@EdgeCaseBriefs exactly

@GenAIDL "the gap isn't in the tools, it's in the habits" is the uncomfortable truth for anyone in the west who follows AI closely but hasn't changed how they actually work. knowing about the tools and using them at depth are completely different things

@HiddenSignalsHQ yeah they are way ahead

@GenAIDL winning the model race while losing the deployment race is how you end up with the best technology that nobody uses at scale. china figured out integration before optimization. that sequencing might matter more

@aicontext_ they have yet to decide which one is the best

@GenAIDL the winner may not be the one with the largest models, but the one that uses them best Americans built the best tools on earth and then argued about whether to use them

@tech_finds_ you can look at it that way too

@GenAIDL winning the model race and winning the adoption race are two different things. the trust gap is the one that actually determines which country gets the productivity gains

@TriumphMarkets exactly my point

@GenAIDL the US is winning the model benchmarks. China is winning the adoption metrics. history consistently rewards the latter.