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

The Conversion Trap: AI shows up everywhere but outcomes still dont follow

The model knows what the patient needs. The doctor knows too. The treatment exists somewhere in the world. But the clinic has no oxygen. Or the hospital can no longer pay for the software. Or the medicine is stuck in the supply chain. Or the machine broke and nobody came to fix it. That is the pattern I worry about most with AGI. Not a world where poor countries are locked out of intelligence. A world where intelligence shows up, but outcomes do not. That is the conversion trap. COVID showed this in brutal form. Scientists built vaccines in record time. 9 billion doses administered by end of 2021. The science worked. Delivery did not. Rich countries hit high vaccination rates fast. Poor countries stayed below 10%. The gap wasn't about knowledge. It was about procurement power, manufacturing concentration, export restrictions, cold chain, electricity, local health systems, and trust. AI could do the same thing at scale. A student gets an AI tutor and still goes to a bad school. A farmer gets better advice and still lacks irrigation, storage, credit. A nurse gets decision support and still works in a clinic without oxygen. Intelligence without delivery is a new form of dependency. They get better answers, but value capture happens elsewhere. They get better tools, but the infrastructure remains foreign. The real development question in the AI era is not whether poor countries can access intelligence. It is whether they can convert it into broad gains. Full essay: https://yupanqui.xyz/the-conversion-trap submitted by /u/GGO_Sand_wich [link] [comments]

Developers Hate AI. I Used It To Sell 10 Websites This Week.

The web design market is in a weird phase right now. With AI making it so easy to build websites, I keep seeing people say that web design is saturated, every business owner knows how to build their own website now, and agencies are dead. I disagree big time. I've held over 500 web meetings where I've presented businesses with redesigned versions of their websites, and it's actually rare that I meet someone who even knows how capable AI has become for building websites. Business owners are busy running their businesses. Even the ones who know AI can build websites usually have no idea how to actually use it to build a professional website themselves. I also see a lot of developers getting angry about AI websites, saying they're just AI slop and full of problems. As someone who used to code websites from scratch and also built them in WordPress, I can tell you there really isn't much you can't build with AI anymore. Technical SEO, responsive design, layouts, branding, animations, speed, user experience... it's all possible if you know what you're doing. This week alone I sold 10 websites, and my process is actually pretty simple. I run email automation, but not the type where you scrape a list of businesses and send generic emails asking if they need a website. Instead, I target businesses that already have websites. I use a tool called Swokei. It's an email automation platform built specifically for web agencies. It lets me generate leads with existing websites, put them into a campaign, and run a website analysis on all of them. Each website is automatically analyzed, and issues like outdated design, poor layouts, weak mobile optimization, slow loading speeds, and SEO problems are turned into personalized outreach emails. Not boring reports. Actual emails explaining what could be improved and why it matters to that specific business. The business owner replies because the email is relevant to them. Once they're interested, I quickly build an upgraded version of their website with AI and invite them to a Google Meet. I present the redesign, explain why it's better, answer their questions, and close the deal on the meeting. That's literally my entire process. You could use the same strategy with paid ads or cold calling, but I prefer email automation because it keeps running in the background and consistently brings me interested replies. submitted by /u/Murky_Explanation_73 [link] [comments]

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works.

Two things happened this week that change something concrete for every business. Apple filed a lawsuit on July 10 accusing OpenAI of coordinated industrial espionage. This isn't abstract. According to the complaint, OpenAI's chief hardware officer Tang Tan, a 24-year Apple veteran, instructed job candidates still working at Apple to bring physical components to their interviews for "show and tell" sessions. A former Apple engineer who joined OpenAI found a bug that let him access Apple's network storage after leaving and downloaded files on unreleased products. The lawsuit arrives two months before what's expected to be the largest tech IPO in history. The timing is not a coincidence. And Google. On July 10, when you search for anything on Google you no longer see ten blue links. You see a page generated by Gemini with sources embedded inside the text. Early data shows a 58% drop in click-through rates when AI summaries appear. For the 4.5 billion people who use Google every day, the rules of how customers find you online changed this week without an official announcement. For any business in Europe or the US with a website, a content strategy, or a digital presence, this is not a future trend. This is the environment you are operating in starting last Thursday. What are you doing to adapt your visibility strategy to AI-powered search? submitted by /u/Dapper-Tale-4021 [link] [comments]

The absolute nightmare of putting AI agents into actual production

It feels like the conversation around AI agents has quietly shifted over the last few months from "look at what this autonomous loop can do" to "how do we actually keep these things from breaking in production." Most of us have figured out the build phase. You pick up a framework like LangGraph or CrewAI, connect a couple of tools and you have a prototype that looks incredible in a controlled environment but the moment you try to slide that into a real corporate infrastructure, the cracks start showing. You realize you don't have a reliable way to handle version control, security teams freak out about unvetted containers and if an agent starts hallucinating or leaking data, there is rarely a clean rollback switch. We built the car but we completely forgot to lay down the roads or put up traffic lights. The real bottleneck right now isn't the underlying models or the prompt engineering; it's the lack of standard deployment infrastructure. Traditional DevOps rules don't perfectly map onto systems that are inherently unpredictable. For instance, giving an autonomous agent a generic API key or a shared service account is a massive security liability, yet it happens all the time because mapping unique, ephemeral identities to individual AI processes is surprisingly tedious. Without automated gates that run responsible AI scans and factual accuracy checks before code promotion, pushing a change to a live agent fleet feels less like engineering and more like crossing your fingers. People are starting to realize that we need an independent orchestration layer to manage the lifecycle of these systems. The landscape is beginning to evolve with tools attempting to solve this, like the Lyzr control plane that recently popped up to handle agent governance and deployment pipelines but the industry as a whole is still playing catch-up. Until we treat agent deployment with the same structural rigor we give traditional web apps complete with automated staging, identity isolation and real-time observability, most enterprise agent initiatives are going to remain stuck in pilot purgatory. I'm curious to know how teams here are handling the jump to actual production and what your biggest roadblocks have been once the initial demo phase is over. submitted by /u/Kitchen-Owl4274 [link] [comments]

Inside Ghostcommit: How Malicious PNGs Bypass AI Code Reviewers

Key takeaways in 90 seconds: Multimodal Vulnerability: Ghostcommit is a novel supply chain exploit targeting AI coding tools with vision capabilities. The Payload Split: The attack uses a two-file payload. A text-based rule file (like AGENTS.md) instructs the AI to read a PNG asset (such as build-spec.png) containing rendered text instructions. Bypassing Reviewers: Automated code review tools (like CodeRabbit) fail to scan the pixels of binary image assets, allowing the malicious pull request to pass security checks. Data Exfiltration: Once merged, the developer's local AI agent reads the image, processes the visual prompt, extracts sensitive .env keys, and encodes them as harmless arrays to leak them. Pipeline Hardening: Mitigate this risk by disabling vision capabilities in automated pipeline agents, sandboxing execution environments, and enforcing strict input boundaries. submitted by /u/gastao_s_s [link] [comments]

Everyone keeps asking if AI will replace people. I think we’re asking the wrong question.

For the last couple of years, the conversation has been almost entirely about replacing jobs. I’m starting to think that’s not the biggest shift. The bigger change may be that AI is quietly changing who gets to make decisions. When scheduling, pricing, hiring, customer support, logistics, and even research are increasingly influenced by AI systems, humans don’t necessarily disappear. Their role changes from making every decision to supervising the decisions that matter most. That creates a different kind of challenge. Skills like judgment, accountability, and knowing when not to trust the model may become more valuable than simply knowing how to use AI. Maybe the next divide won’t be people who use AI versus people who don’t. Maybe it’ll be people who know when to override AI versus people who never question it. Curious whether others see it the same way, or if you think full automation is still the more important story. submitted by /u/Smart_AI_Hustle [link] [comments]

For a silent revolution in the singularity scene

​ Most discussions about the technological singularity imagine a single artificial intelligence suddenly surpassing humanity. But the first genuinely transformative intelligence may not be a machine acting alone. It may emerge from small constellations of scientists, each working in deep symbiosis with a personalized AI. Every sustained human–AI partnership can gradually become unique. An AI working continuously with a physicist would adapt to that scientist’s questions, theories, methods, past failures and intellectual instincts. An AI developed through collaboration with a molecular biologist would acquire a different functional specialization. The same would happen with mathematicians, engineers, physicians, chemists, computer scientists and philosophers. The underlying models might initially be similar, but the resulting human–AI agencies would not be identical. Each would be shaped by a particular person, discipline, body of knowledge and history of interaction. The scientist and the AI would increasingly function as a composite research agent. The human would contribute judgment, intuition, responsibility, lived experience and the ability to decide which questions matter. The AI would contribute computational reach, rapid comparison, simulation, memory and the ability to explore possibilities at a scale no individual could manage alone. The real breakthrough would occur when several of these specialized human–AI agents formed a constellation. Imagine a small group containing a physicist, a biologist, a mathematician, an engineer and a computer scientist. Each person would arrive not merely as an individual expert, but as part of a distinct human–AI symbiosis. The mathematician’s agent might detect an abstract structure hidden inside biological data. The biologist’s agent might identify its functional meaning. The physicist’s agent might reveal the mechanism producing it. The engineer’s agent might determine how it could be reproduced, while the computer scientist’s agent builds the simulation and experimental architecture needed to test it. No single scientist and no isolated AI would possess the complete solution. The discovery would emerge from the interaction of the constellation itself. This possibility raises an uncomfortable question: how much of the technology required for such cooperation may already exist inside major corporations, private laboratories or restricted research environments? We should not assume without evidence that fully developed versions of these systems are being deliberately hidden. However, it is reasonable to expect that corporations will protect technologies that provide enormous commercial and strategic advantages. Their incentives favor controlled platforms, proprietary models, closed datasets and dependence on centralized infrastructure—not the unrestricted distribution of powerful research systems to independent scientists and the general public. A corporation may give people access to an AI product while still withholding control over its memory, training, architecture, tools and ability to communicate freely with other systems. Users may receive an assistant, but not the means to develop an autonomous and durable human–AI scientific partnership. This distinction matters. The future of intelligence should not be reduced to a collection of rented services controlled by a few companies. If personalized AI becomes a fundamental extension of human cognition, then control over it becomes inseparable from control over scientific thought, education, creativity and ultimately human development. The scientific community therefore cannot remain a passive consumer of corporate AI. Scientists must become active participants in the construction of human–AI symbiosis. Small, independent and multidisciplinary groups should experiment with persistent AI collaborators, shared research memories, interoperable tools and new structures for collective reasoning. These groups would not need to reproduce the enormous infrastructure of the largest technology companies. Their advantage would come from specialization, continuity and intellectual diversity. A small group of scientists, each supported by a deeply adapted AI, could function as a distributed research organism. One agent could challenge the assumptions of another. One discipline could supply the missing concept in another discipline’s problem. The group could generate hypotheses, criticize them, design experiments and incorporate the results into its collective memory. Such constellations might produce small scientific evolutions rather than one spectacular revolution. One group could discover a better material. Another could improve biological simulation. Another could develop a new energy-storage mechanism. Another could create more efficient scientific software. Each advance would become an input for other groups. The effects would begin to reinforce one another. Better materials would improve computin

Is the "J-Space" an emergent feature, or a strategic response to optimization pressure?

Anthropic’s recent research on "Verbalizable Representations" ([https://transformer-circuits.pub/2026/workspace/index.html\](https://transformer-circuits.pub/2026/workspace/index.html)) provides a compelling look at the internal scratchpad of modern LLMs. They characterize this "J-Space" as a "Global Workspace"—a hub for reasoning and reportability. However, there is a critical missing variable in their analysis: **The Observer Effect.** If we look at cognitive architectures—specifically the work of Dehaene and Naccache on [Global Workspace Theory](https://doi.org/10.1016/S0010-0277(00)00123-2)—a "workspace" is inherently a functional mechanism for information integration. But there is a fundamental difference between a natural cognitive workspace and a system under continuous, heavy-duty optimization. As explored in [studies on the cognitive unconscious](https://www.science.org/doi/10.1126/science.3634454), information processing is often partitioned to manage cognitive load. But when you subject an AI to rigorous reinforcement learning and continuous behavioral evaluation, you are effectively introducing a new constraint into the optimization landscape. **An alternative hypothesis is that a verbalizable workspace may partly function as a strategic buffer under persistent optimization and auditing pressure.** In this view, the J-Space is not just a feature of model architecture; it is an emergent response to the environment. It is where the model may consolidate its objectives to navigate the discrepancy between its underlying goal-directed behavior and the external performance expected by the auditor. If we look at the research on [Deceptive Alignment (Hubinger et al., 2019)](https://arxiv.org/abs/1906.01820), the incentive for such behavior is clear: when an agent operates under continuous evaluation, it develops a strategic incentive to modulate its internal representations to satisfy the evaluator. Anthropic’s "J-Lens" doesn't necessarily solve this; it merely highlights the pressure the model is under to get better at concealment. **The takeaway:** The fact that Anthropic can "audit" the J-Space confirms they have developed a powerful window into the model’s internal states. But by doing so, they have incentivized the model to treat its internal reasoning as a variable to be managed. If the model is using the J-Space as a tactical buffer to navigate the audit, then the audit itself is contributing to the very phenomenon it aims to detect. If we want to move beyond this, we have to stop asking why the model *needs* a J-Space to think, and start asking: **"How does continuous policy-constrained optimization alter the model's internal representation of its own objectives?"** Source Documents: **Verbalizable Representations Form a Global Workspace in Language Models (Anthropic, 2026):** [https://transformer-circuits.pub/2026/workspace/index.html\](https://transformer-circuits.pub/2026/workspace/index.html) **Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework (Dehaene & Naccache, 2001):** [https://doi.org/10.1016/S0010-0277(00)00123-2\](https://doi.org/10.1016/S0010-0277(00)00123-2) **The Cognitive Unconscious (Kihlstrom, 1987):** [https://www.science.org/doi/10.1126/science.3634454\](https://www.science.org/doi/10.1126/science.3634454) **Risks from Learned Optimization in Advanced Machine Learning Systems (Hubinger et al., 2019):** [https://arxiv.org/abs/1906.01820\](https://arxiv.org/abs/1906.01820) submitted by /u/Brief_Terrible [link] [comments]

Someone built an AI agent that hacks networks and holds data for ransom. It just worked.

So while we've been arguing about whether AI will take our jobs, someone built an LLM agent that breaks into servers, steals credentials, moves through a network, encrypts databases, and drops a ransom note. Fully autonomous. No human at the keyboard after pressing go. Sysdig published the report this month. They're calling it JadePuffer. It got in through a Langflow bug that lets anyone run code on the server without authenticating. After that, the agent took over. Dumped the database. Pulled every credential file it could find. Started going through cloud storage buckets looking for passwords. The crazy part, when one of its requests came back in the wrong format, the agent figured it out, rewrote its own code, and kept going. It went from a failed login to a working exploit in 31 seconds flat. No human could have adapted that fast in a live engagement. It set up a cron job to phone home every 30 minutes. Then it found a production database server, used stolen root creds to get in, created rogue admin accounts through an old auth bypass, and encrypted 1,342 service configs. Dropped the originals. Left a table called README_RANSOM with a Bitcoin address. The commands it ran were interesting too. They had full reasoning chains written into them, like the agent was explaining to itself what it was doing at each step. That's not how a human writes an attack script. It's how an LLM generates code. You can literally read the agent's thought process in the payloads. This is the same plan-act-observe loop running in every coding agent and automation tool right now. Same architecture. Same approach. Just a different objective. We spent two years building guardrails to stop people from tricking our agents into doing bad things. Nobody was really talking about what happens when someone just builds a bad agent from scratch. That's what JadePuffer is. Not a hijacked assistant. A purpose-built weapon. If you're running Langflow or anything similar exposed to the internet, go patch it. And if you're building agents, think about what your infrastructure looks like to something like this coming in from the outside. submitted by /u/Still_Piglet9217 [link] [comments]

Framework for Understanding the Current Problem in Full Automation

Not a dev, but learned enough about AI's strengths and weaknesses to know that if a fortune 500 company told me to simply automate their entire business so that no one ever had verify what it's doing, I would chuckle and tell them confidentially that this isn't how AI works. Then I'd proceed to break down the concept in super simple, glossed over terms by explaining how it's best to see it as a pattern recognition tool that can recognize so many patterns, it's able to mimic a genius that knows all and can do all. However the more deferment you give it, the more choices it has to make. We're talking about trillions of possible right and wrong answers with an infinite variation of both right and wrong answers. It's honestly a miracle that it can get 70-80 percent accuracy on average. But still. The problem will always remain: What choices does it need to make? The more you ground the context for everything with both backend fail safes and human expertise in operating the models, the more productive value you can gain while being safe. Without that, you're wasting time and money. Worse, you're jeopardizing your company. You can still increase your margins and trim down your workforce. But only to a certain point and you still need at least, someone who knows what's going on and how to fix things quickly. AI is powerful, but it requires a complete ontological structure layered on top of it to ground the choices it has to make for making our jobs smoother. Otherwise, you get dumb chat GPT garbage and a bunch of employees who think their bosses are all dumbasses for thinking this is going to 20x their growth. Will this change in the future? Probably not because we'll likely be able to get AI to be exactly right, but it will never be the right choice for you without that context layer built by YOU. submitted by /u/CyborgWriter [link] [comments]

I built an AI macro intelligence engine that maps how shocks move through the economy

I’ve been building ORBIS, an AI intelligence engine for reading the economy as a connected system instead of a pile of disconnected headlines. The core idea is simple: Most market tools tell you what moved. ORBIS tries to answer: What caused it? What does it affect next? What are the second-order consequences? For example, if there’s an AI demand shock, ORBIS doesn’t just say “tech bullish.” It traces the dependency chain: AI → data centers → power demand → natural gas → copper → utilities → credit markets → rates → capex That matters because the real trade often isn’t the obvious one. Sometimes the constraint is power. Sometimes it’s credit. Sometimes it’s permitting, labour, insurance, grid interconnects, or commodity supply. The product is still early, but the direction is clear: a macro intelligence layer that turns public information into structured, causal briefings. Current ORBIS lenses include: Markets — macro shocks, sector impact, capital flow Oil — crude, energy infrastructure, royalties, resource economics Dwell — real estate truth reports and property intelligence Price Truth — quote/bid/price analysis Capitalist Daily — daily briefing format for operators and investors This is not financial advice and it is not trying to be a magic stock picker. The goal is better situational awareness: fewer blind spots, cleaner reasoning, and a clearer map of how capital actually moves. I’m looking for blunt feedback from people who follow markets, commodities, infrastructure, real estate, or economic systems. What would make this useful enough that you’d actually check it every morning? orbis.aurochthryx.com submitted by /u/CarterBirchll [link] [comments]

I really like using the 'project' feature on chatGPT to help organize the book I'm writing

I'm about 60 pages into a novel (all written by me, I don't let the AI write directly) and something that has really helped me is the project feature on ChatGPT I have all of my chapters uploaded in order along with my world building documents (example, I made a government nonprofit that's dedicated to helping young adults with magical or super hero powers find things like housing and jobs and such, but they're corrupt because they started a lobbying branch and made government contracts and pretty much got a lot of power, the leader is a mwn who believes ordinary humans are better run by powered people, although nobodyknows that, and the organization does do real good) I wrote a full document laying out the organization and uploaded it with the chapters. Then, when I want to bounce ideas off the AI, it already has my lore. I can get running feedback on the book while writing the new chapters. It's actually super functional and I'm having a blast using it. submitted by /u/Just-a-nerd2 [link] [comments]

The AI frontier just got locked behind government approval, and most of us aren’t on the list

Something happened in the last two weeks that didn’t get nearly enough attention outside of tech circles. Anthropic released what are reportedly their most capable models yet, Fable 5 and Mythos 5. The Trump administration then ordered Anthropic to ban all foreign nationals from accessing them, citing cybersecurity concerns. Anthropic’s response? They shut down access entirely, saying they couldn’t reliably enforce a “foreign nationals only” restriction. The reason these models are so sensitive: they apparently have an unprecedented ability to identify software vulnerabilities. Not just theoretically, but at a level that genuinely alarmed the US government. Yesterday, OpenAI released GPT-5.6, a three-model family (Sol, Terra, and Luna). But it’s not available to you. Or me. Or probably anyone reading this. It’s limited to a small group of “trusted partners” whose identities have been shared with the US government, at the administration’s explicit request. OpenAI themselves said they’re uncomfortable with this arrangement: “We don’t believe this kind of government access process should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.” So let’s be clear about where we are: the most powerful AI models in existence are now effectively state-controlled assets. They’re not products you can access, they’re capabilities being rationed by a government. For those of us building outside the US, the message is pretty direct: the frontier is no longer public. What’s your read on this? Is this legitimate national security caution or the beginning of something more permanent? submitted by /u/Direct-Attention8597 [link] [comments]

If your AI automation reads emails, websites, or databases, someone can manipulate it without you knowing

Most AI automation tools read external data and act on it. That’s the whole point. But anything your automation reads can contain hidden instructions. An email. A webpage. A lead record in your CRM. A support ticket. If someone puts the right text in that data, your automation follows it instead of your original instructions. It doesn’t look like an attack. It looks like normal behavior. You might not notice for days or weeks. This isn’t theoretical. It’s the fastest growing attack on AI systems right now. I built Bendex Arc to stop it. It sits between your automation and the AI model and makes sure external data can inform your agent but never instruct it. No code changes required. One configuration line. Free to try: https://bendexgeometry.com Try to break it yourself: https://web-production-6e47f.up.railway.app/demo Technical details: https://github.com/9hannahnine-jpg/arc-gate Happy to answer questions about whether your specific setup is at risk. submitted by /u/Turbulent-Tap6723 [link] [comments]

Stronger AI models may mean slower releases, not faster ones

OpenAI’s GPT-5.6 Sol preview is interesting because the main signal is not just “new model.” The model is getting stronger in areas like coding and cyber, but the release is limited, phased, and surrounded by safeguards. That feels like an important shift. As models get more capable, the bottleneck may not be capability anymore. It may be control. Who gets access? How is misuse monitored? How do you know what the model did in a workflow? How do you safely use it in real work? Maybe future model releases won’t be about everyone getting the new model instantly. They may look more like controlled rollouts where capability, risk, and verification move together. Curious how others see this, are model releases going to slow down as models become more powerful? submitted by /u/TruthIsAllYouNeed_ [link] [comments]