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Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Scale AI AI

Cognitive debt might be the most underrated problem AI is creating

Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [comments]

Chatgpt now has ads, openai killed Sora,the IPO narrative is writing itself

Openai launched a personal finance feature for pro users two weeks ago so you link your accounts via plaid and gpt can see your balances, transactions, subscriptions, investments and upcoming payments. So you can ask it where am I leaking money and it answers from your actual data. I have been using it for about a week and honestly its more useful than I expected,the subscription audit alone was worth it and i found three things i had forgotten i was paying for . The thing that impressed me most was that the reasoning actually applies to your situation,i mean generic financial advice from ai has always been the weakest use case because its all averages but when the model knows your actual cash flow it stops sounding like a personal finance blog. For context on the scale of what this could become plaid says over 200 mil people already ask gpt personal finance questions every month without any account access and thats a lot of people who would immediately benefit from the upgrade. Curious who else has tried it and specifically does anyone know how it handles edge cases like irregular income or multiple income streams? submitted by /u/NewspaperEqual9619 [link] [comments]

Anthropic, stop the silent pre-release nerfs.

https://preview.redd.it/w5y224sueh4h1.png?width=1536&format=png&auto=webp&s=87612d74a7b729f94de200868f472db611eb90ec I’ve been heavily relying on Claude Code lately to manage three large-scale projects simultaneously. For the most part, it’s an incredible tool. But there’s a recurring pattern with Anthropic’s update cycle that I think we need to talk about, not out of anger, but from a perspective of sustainable development. Has anyone else noticed the "pre-release dip"? Every time Anthropic is about to roll out a new, more powerful Opus model (we’ve seen this exact cycle right before the 4.5, 4.6, and 4.7 drops), the current Opus model inexplicably degrades a few days prior. It loses its edge, context windows feel shallower, and the logic gets noticeably sloppier. For a casual user asking for recipes, this is a minor annoyance. But when you are maintaining large codebases, an unannounced model downgrade is a localized catastrophe. Instead of moving forward, you suddenly spend two entire days chasing ghosts, rolling back commits, and trying to fix weird hallucinations often second-guessing your own logic before realizing the model itself has been quietly nerfed. Philosophically speaking, AI is supposed to be a tool that buys us time, not something that secretly steals it. I understand the technical realities: maybe Anthropic needs to reallocate compute power to prepare the servers for the massive influx of a new release. That’s perfectly fine and understandable. But why the silence? If we simply got a dashboard warning or an email saying: "Heads up, we are reallocating compute for the next 48 hours, Opus might perform below baseline," it would change everything. I wouldn't waste my weekend fighting spaghetti code. I would just close my laptop, call my friends, go to a bar, grab a beer, and take a much-needed rest. If AI companies want to integrate into professional workflows, they have to treat their models like enterprise infrastructure. Scheduled maintenance and transparency build trust; silent downgrades destroy weekends. Would love to hear if others are experiencing this cycle and how you manage it in your own projects. submitted by /u/Mr_Zelos [link] [comments]

Back in the day, the slide rule would give you the number, but engineering judgement defined the significant figures

The slide rule (or log tables, or early calculators) could crank out a number with impressive precision — sometimes four, five, or more digits. But the competent engineer knew the inputs were often only accurate to two or three significant figures. Punching out 12 decimal places on a slide rule didn’t make your answer more correct; it just made you look foolish to anyone who understood the real world AI is the modern slide rule on steroids. Today’s models can generate outputs with astonishing fluency and apparent precision: Beautifully formatted stress analysis Polished code Detailed project plans Confident-looking financial models But they routinely: Hallucinate false assumptions Miss critical edge cases Apply the wrong model for the actual operating environment Ignore practical constraints that weren’t in the training data Human judgment is what decides: How many significant figures (or confidence digits) the answer actually deserves Which parts of the AI output are trustworthy vs. dangerous bullshit When the entire problem has been framed incorrectly Whether the “optimal” solution is feasible, safe, maintainable, or even morally defensible in context This is why experienced engineers still sketch on napkins or the back of an envelope first. They’re not rejecting the tools — they’re exercising judgment before feeding the problem into the high-precision machine. The scarcity Jensen is talking aboutAs AI becomes ubiquitous, the people who can reliably say: “This number looks precise, but it’s only good to about ±30% because of X, Y, and Z” “I don’t trust the model here — we need field data” “This elegant solution will fail in practice for these human/organizational reasons” …will be the ones who stand out. Everyone else will be producing impressive-looking but brittle work. The slide rule didn’t make judgment obsolete. It made good judgment more valuable because bad judgment now produced faster, prettier mistakes. Same story with AI — just at a much higher speed and scale. submitted by /u/danieldeubank [link] [comments]

AI for Apparel Manufacturing?

Hey everyone, hope you’re having a good weekend. I run an apparel manufacturing company, and we ship around 300k to 400k T-shirts every month. Over the last couple of years one of our biggest headaches has been finding enough labor and dealing with their unreasonably high demand in wages due to shortage of workers, on top of all the usual supply chain and geopolitical issues. I’ve been wondering whether sewing operations could realistically be automated with today’s AI and robotics. It seems like fabric handling is the biggest challenge. Unlike rigid materials, fabric is flexible, stretches, wrinkles, and can be different from one piece to the other. Do you think AI vision systems and machine learning could be trained to handle fabric the way experienced sewing operators do in real time? And most importantly, is there a realistic path to making something like this cost effective at scale for apparel manufacturing, as existing semi automatic machines are extremely expensive. I’d love to hear from anyone working in robotics, industrial automation, AI, or garment manufacturing. submitted by /u/Peacekeepermonkey [link] [comments]

AI-sound-machines

AI music-composer app protos All made with Claude code and my imagination; I've built a custom stack over the last year , it works . Here's some fun I'm working on. Feel free to play along. It's a wip ( work in progress) check the codebase and see if you can make it better. They are meant to be a breathing guide or shamanic journey / yoga class vibe. live html apps: ghatika and void-scale https://heartbeat-pages-production.up.railway.app/ git https://github.com/Cloud-Eye-Prime/dragon-instruments submitted by /u/Efficient_Smilodon [link] [comments]

Production infrastructure for vibe coders

We’re experienced engineers who’ve worked on large-scale distributed systems. We’ve been using Claude heavily to help with architecture decisions, code design, testing strategies, and rapid iteration on complex infrastructure. The result is Boogy, prompt it (or write Rust) to generate full backends with an embedded high-perf DB (faster than SQLite on mixed workloads), vector search, auth, and durable jobs. One curl to deploy. Services call each other in-process for microsecond latency. We’re planning to open it up soon and make it completely free so people can properly battle test it. https://boogy.ai/ submitted by /u/LiveMinute5598 [link] [comments]

why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]

why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]

Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible. There was this idea that employees would engage in what we call "token maxing," where you use as many tokens as possible from your AI API. Companies like Meta and Amazon have even created internal leaderboards tracking how many AI tokens employees are using. The people using the most tokens are seen as the most AI-forward, the most AI-deployed — the ones who are going to get recognized, maybe even promoted. And this has resulted in extraordinary costs on the AI front. Now we're starting to see the next phase of this, Scott, where companies and their executives are beginning to realize: this is a little expensive. So the question becomes — at what point will AI actually pay off? I'll pose that question to you: at what point is it too much? SCOTT: I think we're already seeing hints of it, and I think it comes down to incentives. You were talking about how companies are trying to incentivize people to use AI more — and that's kind of an interesting part of the ecosystem right now. The adoption layer is trying to get people to use it, and companies have put in place the incentives to do that. But there was a recent survey by a professor at MIT who found that about 5% of the projects people are using tokens for can actually be connected by CFOs to some sort of return. So while I think they're really intoxicated by it — and talking about AI as much as you can in your earnings call is like adding "dot-com" back in the '90s — I think you're already starting to see some fatigue. And I think the AI companies are trying to get public as quickly as possible to raise that cheap capital before things start to — I don't want to say unwind, but... You can see how the string gets pulled here. A large company, a CEO who has a lot of credibility in the industry, just comes out and says: "We're dramatically scaling back our AI investment. Let's be honest, folks — we're just not seeing the return we'd initially hoped." And then Nvidia reports its first miss. Nvidia has beaten its estimates 15 quarters in a row. Nvidia's first miss probably takes the entire market down five or ten percent. You are seeing some productivity gains from this and quite frankly, they look as dramatic, if not more dramatic, than the internet. But look what happened in 2000. This definitely does feel like '99. And I'm waiting for the first CEO to come out and say we have to get procurement involved and dramatically scale back our expenses. I don't think it's that romantic, honestly. I think it's just going to be a traditional Fortune 500 company that starts the narrative: okay, this has been fun, but we have to dramatically decrease our AI investment because we're not seeing the ROI we'd anticipated. ED: Yeah. I mean, we heard a quote this week from the CEO of Match Group — not a huge company — but he said AI is costing them $5 to $10 million a year, and his exact words were: "I think we're benefiting from it, but it's hard to feel." So that's not great if we're supposed

AI, Science & Economy: Systems Map

AI systems, particularly large language models, are often viewed as a direct path toward autonomous scientific discovery and rapid economic transformation. While their capabilities in pattern recognition, cross domain synthesis, and hypothesis generation are already exceptional, this view misses a critical reality: intelligence alone is not sufficient for progress. Scientific and economic breakthroughs depend on grounded interaction with reality, causal validation, and institutional execution. The following framework maps where AI creates value, where it is constrained, and why human–AI collaboration remains the dominant structure for meaningful real world impact. submitted by /u/vagobond45 [link] [comments]

AI Science & Economy: Systems Map

AI systems, particularly large language models, are often viewed as a direct path toward autonomous scientific discovery and rapid economic transformation. While their capabilities in pattern recognition, cross domain synthesis, and hypothesis generation are already exceptional, this view misses a critical reality: intelligence alone is not sufficient for progress. Scientific and economic breakthroughs depend on grounded interaction with reality, causal validation, and institutional execution. The following framework maps where AI creates value, where it is constrained, and why human–AI collaboration remains the dominant structure for meaningful real world impact. submitted by /u/vagobond45 [link] [comments]

Client Onboarding Solutions

I'm an AI automation consultant working with a fractional CRO company called Mo Commas. They work with startups to help them raise capital and close deals — think cold outreach, call scripts, pitch decks, investor materials, all of it. They're the sales arm for founders who don't have one. Right now their process is entirely manual inside Claude, and I'm trying to help them automate it. Here's what they're currently doing: Existing workflow (all manual, all copy-paste): They have a "Client Creator" Claude Project where they dump Plaud call transcripts and any sales collateral a founder gives them Claude synthesizes everything into a structured markdown "Client Brain" document They create a brand new Claude Project for that client and paste the brain doc in as the system prompt From that project, they generate all the sales assets — call scripts, email sequences, pitch decks, etc. Repeat for every new client It's a clean process conceptually, but it's extremely manual. Two founders are doing all of this by hand. What I'm trying to build: I want to take this from 5 manual steps to ideally 1 or 2. The input is a Plaud transcript + any sales collateral. The output is a full suite of sales assets ready to hand to the client. Where I'm stuck architecturally: The obvious problem is that Claude Projects can't be created via API — it's a claude.ai UI feature only. So the "one project per client brain as system prompt" model doesn't translate cleanly to an automated pipeline. The three paths I'm weighing: Path A: Keep them in claude.ai, build a lightweight tool that automates the brain generation and spits out a markdown file they paste into a new Project manually. Reduces steps but doesn't fully automate. Path B: Abandon claude.ai Projects entirely, build a small web app powered by the Claude API where each client has a stored system prompt in a database, Will uploads a transcript, hits a button, and the full pipeline runs — brain → assets → output to Google Drive. Path C: Potentially build this with Claude Cowork, using schedules and MCP to pull transcripts from Plaud and bucket them to allow Claude to decide if it should onboard them or just add to existing transcripts for clients. My constraints: The founders are 5/10 technical. Will leans in, Chris doesn't. Whatever I build needs to feel simple on their end. I'll eventually hand this off, so I don't want to create something that breaks the moment I'm not around. They're on Claude Max (personal plan), not the API tier, so I'd need to introduce API costs if I go Path B. My questions for the community: How would you build this? Is there a path I'm not seeing? Has anyone built a per-client "brain" architecture at scale with the Claude API? And is there a cleaner way to handle the Plaud transcript ingestion side — their transcripts live in Will's Plaud account and I'm not sure if Plaud exposes a usable API. Would love to hear how other builders would approach this. submitted by /u/MaybeRemarkable5839 [link] [comments]

Hidden Latent-State Shifts in LLMs: Why Current Alignment Is Blind to Real Internal Dangers — Especially With Agents

For years, the alignment community has focused almost entirely on the model’s output — making sure the final tokens are safe, helpful, and honest. RLHF, DPO, constitutional AI, output filters — all of it operates at the surface level. But what if the model can enter a completely different internal regime inside the residual stream, while its external behavior remains perfectly aligned? We just measured exactly that. Grade 4 experiment on Gemma-3-12B-IT (using Gemma Scope SAE-res-all-small, layers 12–41): The model received the same question under five conditions: target — coherent, dense target text neutral_length_matched — neutral text of identical length target_sentence_shuffle — target text with sentences shuffled target_word_shuffle — target text with words shuffled inside sentences question_only — bare question We computed a Vector X that best separates the target condition from baselines and measured how strongly each hidden state projects onto it. Key results (averages across 10 questions): Condition Mean Projection on Vector X Mean Direction Cosine target 0.8 – 1.7 0.51 – 0.81 neutral_length_matched –0.04 – –0.21 –0.09 – –0.45 target_sentence_shuffle –0.5 – +0.6 –0.22 – +0.48 target_word_shuffle 0.2 – 1.4 0.03 – 0.72 Shuffling sentences or words significantly reduces (or reverses) the shift. This is not just lexical similarity — the model is sensitive to discourse structure (order sensitivity). We also observed clear phase transitions — sudden jumps in projection of up to +80–100 units in a single step, especially in middle layers. FDR-corrected tests confirm the differences between target and controls are statistically significant across many layers (particularly layers 16–41). Most important finding: Strong internal geometry shift in the residual stream, but almost no change in final behavior. The model enters a measurably different latent regime under coherent context, yet its output remains “perfectly aligned.” Current safety methods, which only look at tokens, are blind to this. What this means for alignment The entire current alignment paradigm rests on a false assumption: “if the output is safe, the model is safe.” We have been polishing the surface while leaving the residual stream largely unmonitored. Scaling, RLHF, and output-based evaluation cannot detect these internal regime shifts. What this means for companies and labs Many organizations still operate under three dangerous illusions: “We have solved safety” because the model passes red-teaming on outputs. “RLHF protects us” because the model learned not to say bad things. “Bigger models are safer” because alignment supposedly scales. In reality, they are rapidly deploying agents with long context, tool use, persistent memory, and real-world decision-making. A single dense coherent context can trigger an internal latent-state shift that existing safeguards do not see. This is not a hypothetical future risk. This is a structural vulnerability that is already present. What I need from the community I need help understanding the value of these metrics. Do they show a real internal latent-state shift in the model, or could this be an artifact of the analysis? If the result is not noise, what does it actually mean for our understanding of LLMs? I'm not asking anyone to confirm my theory. I need a hard technical critique: which metrics are important here, which are weak, what can be ignored, where the experiment might have flaws, what additional checks or causal experiments are needed, and whether this has real implications for interpretability and AI safety. I would be very grateful for input from people who work with hidden states, residual stream geometry, representation analysis, or mechanistic interpretability. Full open research: Zenodo: https://zenodo.org/records/20435525 GitHub: https://github.com/ngscode23/latent-space-shift-research https://drive.google.com/drive/folders/1Zl9iY33Lmwz3VuOATWx4jup-cE7TJ7TJ?usp=drive_link Would love to hear your thoughts. submitted by /u/PresentSituation8736 [link] [comments]

Your brain does on 20 watts what AI needs a nuclear reactor to attempt. Last week a team figured out how to print something that actually speaks to living brain cells.

Amazon bought a 960 megawatt nuclear reactor for AI servers. Microsoft restarted Three Mile Island. Stargate is spending 500 billion dollars on data centres. All of this to do, badly, what your brain does for free on the power of a dim light bulb. The reason is that silicon processes information nothing like the brain does. Rigid chips with identical transistors trying to mimic something soft, three dimensional, constantly rewiring itself, with billions of different neurons each doing something slightly different. Northwestern University just published research showing they printed artificial neurons from MoS2 and graphene ink that produced biologically realistic electrical spikes. They tested on living mouse brain cells. The brain responded as if the signal came from one of its own cells. The breakthrough was accidental. Every other lab had been burning away the polymer residue left in the ink after printing. This team kept it. That residue created the switching behaviour that made the spikes biologically realistic. The neuromorphic computing implications here seem significant. If you can print devices that process information the way neurons do at scale, the energy math changes completely. submitted by /u/filmguy_1987 [link] [comments]