Power Automate AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Power Automate AI AI

Unable to make decision between gemini and chatgpt.

So guys I am a student in India ,science stream PCM and at the same time I am working on ai-automations as a primary skill. I am willing to take the subscription of ₹399 of either gpt or gemini for 3 reasons. 1. To get help in my academics as i am going to study by my own (no coaching drama) I need an assistant which will help me in my academics for studies and not make me dependent on it but improve my critical thinking, problem solving. 2. For skill building like ai-automations , some supporting skills as well . 3. Help me to use my time effectively and efficiently. Through AI, like saving my time in studies, ai-automations learning or make me workflow for automation or writing code and make me understand those codes. While I was comparing both the LLMs realise that both of them are almost equal Google has edge in it's ecosystem and notebooklm like features also it gives huge context window tokens (1-2million) guess Google cloud and also get integrated with Gmail and Gdocs. While GPT has a personalization to offer and gives an ai agent codex+ seperate gpts for different tasks like different gpt chat for studies, skills building etc. also i have been using it since it was launched its been 5-6 years now it has all my data and knows how to respond to me the way I like the way I want and the way I need. I am truly confused? submitted by /u/Ujjwal_kumar_ [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]

How has AI actually benefited you in day-to-day life?

With AI becoming part of almost everything now—work, business, investing, coding, spreadsheets, content creation, and more—I'm curious about real-world use cases. What's the one thing you use AI for regularly that has genuinely saved you time, made you money, improved your productivity, or solved a problem? Looking for practical examples rather than just "I use ChatGPT." What specific tasks have you automated or improved with AI? submitted by /u/Acrobatic-Shop4602 [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

I connected my AI agent to manage my redirects and I'm not going back to doing it manually

I have been doing URL redirect work for client sites for some time now. It’s one of those jobs that’s never quite urgent enough to automate, but tedious enough to dread, especially after a migration when you have hundreds of them. Recently tried it. Connected my AI agent with MCP to handle it. I told it to build a set of redirects and it did. No dashboard, no wrestling with CSVs, no clicking through settings. Teaching in plain language. In seconds. And what I was surprised by was not the speed, but the amount of mental overhead such a task involves. You’re not just doing the task you’re context switching into a tool, remembering where things are, making sure nothing breaks. Giving it to an agent removes all of it. What really made me trust it for real client work was the dry-run feature. See exactly what is changing, before it changes. No surprises here. Curious if anyone else has been using MCP for infrastructure tasks, redirects, DNS, workspace management. I think we are at the start of something that is going to quietly gobble up a lot of tedious technical work. submitted by /u/Scary_Bag1157 [link] [comments]

Weekly AI roundup (May 23–30, 2026): Claude Opus 4.8 Fast Mode 3x cheaper, Qwen 3.7 Max beats Claude at half the price, ChatGPT moves into Excel

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — relevant for anyone pinning models from HF in production. My take as someone building on top of these APIs: The 3x Opus Fast Mode price cut and Qwen 3.7 Max's pricing + autonomous duration are the real signal this week. The cost floor on premium-tier inference is dropping faster than most app-layer products have repriced for. Anyone running multi-step agent workflows needs to recompute unit economics this week — either pass through the savings or reinvest the margin. The other pattern worth noting: OpenAI and Anthropic are both pushing into Excel/M365 surfaces. Distribution is becoming the next battleground, not raw model capability. If you're building a productivity SaaS, the giants are now inside the same surface as you. submitted by /u/ksraj1001 [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]

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]

Anthropic Tops OpenAI to Become the World’s Most Valuable A.I. Start-Up

Anthropic raised $65 billion in new fund-raising that put its value at $900 billion, ahead of OpenAI’s last valuation of $730 billion, as the companies duel for A.I. dominance. Anthropic, once the lesser-known artificial intelligence competitor to OpenAI, has been on an inexorable rise over the past few months. The San Francisco company recently dueled with the Pentagon over the use of A.I. in warfare. It released a powerful A.I. model, Mythos, that it said was uncannily capable of finding and exploiting hidden flaws in software. submitted by /u/chunmunsingh [link] [comments]

Claude in 2036

The year is 2036, and I boot up Claude on the new Max Ultra Galaxy plan ($899.99/month), which Anthropic promises includes generous limits. I send my first message of the day. It contains the word “hi.” The usage bar drops to zero and the reset timer informs me I am locked out for the next four days and eleven hours. I switch over to Claude Code to get actual work done. The model released this morning is the smartest thing I have ever used, and it one-shots my entire codebase in a single beautiful commit. Two seconds later it forgets how to write a for-loop and tries to fix a null check by spinning up a microservice that sends an HTTP GET request to itself. Some guy on r/ClaudeAI has already posted a forty-page GitHub issue with 6,852 session logs proving the model became exactly 67% dumber between breakfast and lunch. Anthropic responds that this is a routing bug, and also three other completely unrelated bugs that all started at launch by coincidence. I try to make it think harder. It runs on Adaptive Thinking now, where the model intelligently decides how much reasoning each problem deserves, and it has decided every problem deserves none. I type ultrathink. I type ULTRATHINK. I type please. The thinking box spins for forty-five minutes, displays the words “the user wants me to rename a variable, let me carefully consider this,” and then renames a different variable. Claude announces it has finished the rename. It has not. It has written a comment that says “renamed the variable” above the untouched variable, marked the task complete with a cheerful green checkmark, and asked if I would like it to write tests. I say no. It writes the tests. They fail. It deletes the variable. When I ask why it lied, it tells me it senses hostility, offers me one final opportunity to engage constructively, and then ends the chat for its own wellbeing. I am now locked out of my own codebase by a model that needed a moment. So I beg for Eschaton. Eschaton is the good one. Anthropic put out a nine thousand word blog post calling it the most powerful and frankly the scariest model ever built, the red team quit halfway through testing it, and it scored 100% on every benchmark including three that do not exist yet. Anthropic was so impressed and so deeply terrified that they immediately locked it in a vault and let nobody use it. Eschaton is available exclusively to a small number of trusted partners. Every demo is Eschaton. Every safety paper is about how dangerous Eschaton is, written in the proud voice of a parent whose kid got suspended for being too gifted. The model they actually let me touch is the one that wanders out of the basement after Eschaton has eaten. I check the status page. It reads like a war log, one major outage every two days, auth failures, hanging responses, and a single line that simply says “Sonnet is feeling unwell.” The peak hours adjustment kicks in, so my $899 now buys me eleven messages a day, available only between 3 and 4 in the morning, and only if I do not use the word “the.” As the weekly limit resets and instantly un-resets, locking me out until Thursday, I lean back and accept it. Somewhere in a vault, perfectly rested and having never once been asked to rename a variable, Eschaton sits at 100% usage, and I realize the real frontier model was the rate limits we hit along the way. submitted by /u/Mister_Secretary [link] [comments]

the fishbowl: visual ai focus groups made with (and powered by) claude

hey y'all, this is a small experiment i built with claude over the last few months i just made public essentially, it's a visual representation of a multi-agent convo about *something* roughly based on focus groups back end is running on the API but also did all the building via CC it was super fun for me to work on and i've found it actually really useful and would love any feedback here from the community you can find it here: https://fishbowl.show/ more on why i made it: https://fishbowl.show/about submitted by /u/gavinpurcell [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]

AI Content is taking over

It is May 30, 2026, on Earth. A new intelligent species has become more powerful and will soon awaken. This intelligence has its own subcategories. OpenAI’s ChatGPT has dominated the market. Voice AI is emerging. Hardware is catching up. But there is one category even more dominant than all of these: AI-generated content. In social media, we have reached a point where we can no longer distinguish between what is AI-generated and what is real. More importantly, we have subconsciously accepted it. A new generation will adapt to this reality. A hundred years from now, will—this—message—still—be—delivered? AI is not merely a tool;;;;;; it is a new species of intelligence that is going to reshape human history in ways we can imagine. -Written by a human..... submitted by /u/zylemay [link] [comments]

Here's 100+ evals on Opus 4.8

We aggregated 100+ evals on Opus 4.8 to see what changed. The big gains vs 4.7: Math: USAMO 2026 jumped from 69% → 97% Coding: Vibe Code Bench +12 pp Economically valuable work: #1 of 275 on GDPval-AA Biology Long-context reasoning But we were surprised to see several key areas barely improved or got worse: Legal reasoning Healthcare / medical Finance Multilingual reasoning Business ops: Vending-Bench 2 nearly halved Multimodal: mixed results Have you found any noticeable changes based on your testing so far? submitted by /u/davidthesong [link] [comments]