Sense Noped Out

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Sense Noped Out

Sense Noped Out

@SenseNopedOut

Chaotic Good ❤️ AI, technology, software engineering, biotech, Japanese animation, Grimes, sci-fi & games ❤️ 🤮 political bonanza 🤮

Katılım Ekim 2022
911 Takip Edilen314 Takipçiler
Sense Noped Out
Sense Noped Out@SenseNopedOut·
@francoisfleuret and they work in a different way. Rushing scientists to achieve results quickly is not the way to go
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
Wrong comprehension of what's happening. Claude/Codex is working so well that MS needs its own AI models to produce code with Copilot. Otherwise they will become a wrapper of a very expensive token provider.
Ricardo@Ric_RTP

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@JacksonKernion well, have you tried to disconnect any developer from claude or codex recently? If not then try and see how
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Jackson Kernion
Jackson Kernion@JacksonKernion·
I simply don't understand what people have in mind when they say stuff like this. What we have is extremely capable computer use agents. They will continue to get better at computer use. But how does a capable computer use agent 'take over' and why haven't they done that today?
Elizabeth Barnes@BethMayBarnes

(1) We are likely on track to develop AI systems capable of causing human extinction/permanent disempowerment, quite possibly within the next few years

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Taelin
Taelin@VictorTaelin·
@scaling01 "Mythos-class models" i.e., we'll *never* have the real thing permanent underclass
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Lisan al Gaib
Lisan al Gaib@scaling01·
Anthropic: "once we've developed the far stronger safeguards we need, we look forward to making Mythos-class models available through a general release"
Lisan al Gaib tweet media
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@willmcgugan @AnthropicAI it's like with clouds, prepare for your infra elements to be down at any time and deal with it. Except that you deal with it on the cloud with automation. Here for the Claud UI you do it by yourself. But then, we are not the real customers -- AI running on the cloud is.
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Will McGugan
Will McGugan@willmcgugan·
My concern for the AI era, or at least this phase of it, is that a generation is being taught that "close enough" is just fine. Take @AnthropicAI for example. Text wrapping in Claude Code has been broken for weeks. Superfluous spaces appear on the left edge. One engineer to another: you know its an out by one error. I refused to believe that nobody has noticed this. The shtick they are selling is that AI can fix this kind of thing. Either they tried to prompt a fix, and Claude ain't good enough to fix an out-by-one error. Or they haven't attempted it because it is "close enough". It can't be the case that AI is only good enough if we lower our standards. It can't. I'm well aware I have both feet firmly planted in my "grumpy old man" phase of life...
Will McGugan tweet media
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Zephyr
Zephyr@zephyr_z9·
So OpenAI cut the intelligence of their normal models (low to no web search) Instant/medium/high are pure trash now Only Pro works now
Zephyr tweet media
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@sama help to make tokens to get cheaper for any new model and not more expensive
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Sam Altman
Sam Altman@sama·
what problem do you most hope AI will solve in the future? maybe we can help!
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@sickdotdev just wait 3m and there will be 10x companies with this problem and 10x number of solutions
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Sick
Sick@sickdotdev·
My company’s claude account got exhausted. Now my legendary manager is asking if we can build our own LLM like Claude to reduce costs😭
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@VictorTaelin you did not ask it to verify with it's own subagents in a loop before? or do you say that codex is a great manager?
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Taelin
Taelin@VictorTaelin·
I discovered a new joy in life. Don't ask Codex to do stuff. Ask Codex to ask Codex to do stuff. Rejoice as you watch it handling and correcting all the dumb shit that it does and that you'd be dealing with otherwise
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@karpathy So everyone in Anthropic forgot how to write code now and they need a new blood to do proper reviews for them 😉
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Andrej Karpathy
Andrej Karpathy@karpathy·
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@atmoio The only way to work with models is by writing every sentence as a question. Otherwise you are anchoring the model 😁🫠
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Mo
Mo@atmoio·
All major models have a very specific tendency to make up quotes when you pressure it to find something that validates an idea or thesis you have. AI has no concept of truth. A quote has a specific shape and both real and fake ones satisfy that shape.
The New York Times@nytimes

Breaking News: Steven Rosenbaum, the author of “The Future of Truth,” acknowledged that the nonfiction book about the effects of A.I. on truth included misattributed or fake quotes concocted by A.I. nyti.ms/4wE8ssc

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Sense Noped Out
Sense Noped Out@SenseNopedOut·
I'd love composer 2.5 model to be hosted on azure foundry.
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@namiarchive_ most are good. Luffy not so much. It may be too difficult to play luffy well
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@omarsar0 We need to patiently wait for AI with 100t parameters for the actual intelligence we want. I don't know if it isn't going to be too costly or require one of those new types of architectures to get to. For now it's advanced autocomplete but instead of a few chars it takes context
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elvis
elvis@omarsar0·
Every time I ask my 10-year-old to use coding agents, he gets extremely disappointed. It turns out that all he wants is to build his own rocket simulator. No amount of context engineering helps. No model works. All coding agents fail. That's just one example. He has many use cases where the coding agent really suck. Learning apps and other types of science-centered simulators. It's not like he is trying to be adversarial or break the system. I use the coding agents so extensively in my codebases that I just assumed that he would get similar results. It's not the case. And I think this is happening across all kinds of domains. I know he is not the target user. I get all that. But if all these claims about superintelligent AI on the horizon (12-18 months) are right, then coding agents shouldn't struggle so much to build any of the things he wants. The reality is that coding agents can help maintain and build complex things that aim to extend what exists in abundance in the training data. No surprises there. There is plenty of AI research to explain the OOD issues with LLMs. I think there is a massive opportunity here. Potentially a more generalized harness (something I have been working on). It doesn't have to work super well now, but it tests on edge use cases as newer models and capabilities emerge. IMO, all of this is a good indicator that LLMs are nowhere close to AGI or whatever they call it these days. Every day that passes, I am more convinced that we need to quickly move beyond LLMs and into things like native multi-modal systems and world models.
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am.will
am.will@LLMJunky·
Anyone else notice that compaction seems to lose more details than normal in Codex? It never seemed to matter before, but I'm seeing it frequently now.
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
The test for AI to see it does things correctly: - ask him to look at the code and write it as a complex prompt for another AI so it can write the same program - then ask the same first AI to write this program from its own prompt If it can't, we are still having jobs :) We just use English as a programming language.
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Mo
Mo@atmoio·
Andrej Karpathy admits he’s struggling with AI
Stephanie Zhan@stephzhan

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.

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Garry Tan
Garry Tan@garrytan·
Realization: in the past, we wrote code to call LLMs Today, we write prompts and skill files for LLMs to execute code. Tomorrow? Yet unwritten. We will find out soon.
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Neil Chowdhury
Neil Chowdhury@ChowdhuryNeil·
we need to keep saying GPT-5.5 is better than Opus 4.7 so we can collectively ragebait Anthropic into releasing Mythos
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@tszzl And still we have to pay increasingly higher and higher prices for groceries.
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roon
roon@tszzl·
it’s really just amazing what our civilizations’ technological capabilities are now just absurd
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Sense Noped Out
Sense Noped Out@SenseNopedOut·
@mardehaym Nonsense. If you don't write it, your reviewing capabilities get impacted too.
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Mark Ajzenstadt
Mark Ajzenstadt@mardehaym·
I was wrong about AI replacing developers. For a year I told clients the leverage was in the tool. Better Cursor setup, better Copilot rules, more output per engineer. Two years of building production AI teams later, that take aged badly. The original story was simple. AI is an output multiplier. Adopt the tools, ship more code, win. Every vendor sold it. Every conference talk repeated it. I repeated it on sales calls. DX surveyed 121,000 developers across 450+ companies between November 2025 and February 2026. 92.6% use an AI coding assistant. AI-authored code is now 26.9% of all production code, up from 22% the prior quarter. Productivity gains still haven't moved past 10%. Digital Applied's Q1 2026 survey of 2,847 developers found something even sharper. Reviewing AI-generated code now takes 11.4 hours per week. Writing new code takes 9.8. The cost of AI is showing up where most teams aren't measuring it. In the human attention required to keep AI-generated code from breaking in production. At Limestone, 98% of our code is not handwritten. Auth, payments, and a few domain edge cases are the exception. But every line of that 98% gets reviewed, restructured, or rejected by a senior engineer who decided what should exist before the agent ever generated it. AI replaced typing. It didn't replace thinking. The engineers who thrive aren't the fastest coders. They're the ones who can read a 200-line AI-generated diff and spot the three edge cases the model missed. They're the ones who architect before they prompt. They're the ones who can tell you why a piece of code shouldn't exist before they explain how to write it. If your AI strategy assumes the agent does the thinking, you're not building an AI-augmented team. You're building a risk surface that compounds with every commit.
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