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

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]

Okay... Guys it's my version about why recent frontier models got behind "bars"

It is not the official information but fan-made document and treat it as it is!!! submitted by /u/Nolahdj [link] [comments]

I am struggling to find a way to make a tracker work

Hi AI people, I work in a busy job (which I love). I have 4 projects, with 10 subcontractors each I deal with. Plus some internal stuff to do (things to raise, people to chase, actions to take etc.). So there are a lot of bits and bobs I need to stay on top of. Some of these are simple like taking a picture of something in a certain location, some are more complicated like understanding how a piece of equipment works or the details of a contract. I manage fine with a physical notebook and a spreadsheet, but it is a time consuming thing to go through on a daily basis and update my tracker(s). With the power of the AI, I want to involve some time saving system (a personal assistant), which I am yet to find. Ideally, I am looking for something I can talk to (as I drive a lot and would be amazing to use that unproductive time), to ask things like "I am going to X, to meet with Y today, what do I need to cover?". Then it will tell me everything related to that location and/or person. Then on the way back I can say "they said we will do Y on a certain date, so put that in the backburner but add an item to contact Z next week" and it will do it for me. This would be on my phone. Also on my laptop, I can bring this tracker in front of me once a week to do a sweep and update it, or add things like important meeting notes or contract details so when I need it I can ask (from my phone) it to remind me what was agreed in that meeting. I tried to utilise the Skills and Projects functions on Claude but I can't even get close. I built something internal in a chat, but even a small update like "I took that picture today" takes a few minutes as it needs to recreate the whole thing to tick one thing off. All I need is a purely text based live tracker that I can use on both my phone and my laptop, can get Claude to read and write data on. I appreciate that perfection is the enemy of good, so I will happily settle with something that can do half of what I need. Is there a way I can use Claude for this? submitted by /u/Swimming-Recipe3021 [link] [comments]

Remote PC automation with AI

I've made a tool that I install on a remote pc, and it exposes commands I guess like a MCP server to ai chat on my own pc. It can use the GUI, type, click mouse, take screenshots, run commands directly, edit registry etc. But my god for tasks that take me a human to directly do things the AI takes 10x longer. Installing and configuring programs, apply settings, installing drivers. I want it to setup a new computer for me like I want, and I have an exact guide of things to do. It takes FOREVER and abandons and skips requirements the guide says to do if there is any issue. The mouse did not click where it thought it would, they see some random popup and don't know what to do with it. The prompt is flexible, it says figure it out basically, but it requires me to go in and say wait why did you not do that task? I don't think its the tool we've built, I think its the AI interacting with the tool and just not being quick human judgemental about it. I do not want to install claude desktop or something similar, as I dont want to mess with my claude account access on another computer. My tool does not need an account. I'm not advertising my tool, this is for me, and also it sucks apparantly. I'm asking has anyone been here, and found a better solution for AI remotely actually USEing a computer, like a human, and quicker than a human can? Not just setting registry settings, as thats not all there is to using a computer. thx submitted by /u/radialmonster [link] [comments]

I open-sourced my multi-agent dev pipeline — it turns GitHub/Gitea issues into merged PRs using leading coding agents.

For the last year I have found myself up most nights with a FOMO on my AI projects and then the headaches of using the various coding agents (harnesses) at the same time and baby siting my quota to deliver new apps and features while jumping between the new flashy thing of the week. I've been building "AgentForge" for the past few months as a local tool to automate my own dev workflow, and I just made it public. What it does: Two ways to use it: New App — describe what you want to build, and AgentForge runs a guided discovery session, generates specs, creates issues, and builds the entire app end-to-end. Issues — create an issue on an existing repo, and AgentForge picks it up, triages by complexity, then dispatches coding agents through the pipeline: clarify → spec → code → test → QA → security → merge. Both paths use the same agent pipeline and stream everything to a live dashboard. Key design decisions: Runs on your machine — no hosted service, no data leaving your box. Agents are CLI subprocesses. Per-stage model routing — cheap/free models for planning stages, frontier models only where they write production code. You control what spends money. Multi-provider — mix Claude, Codex, Kiro, local llama.cpp models, or any OpenAI-compatible endpoint in the same pipeline including local. (I use Qwen 3.6 35B A3B) Human-in-the-loop gates — spec approval and PR approval can require a human sign-off before proceeding. Tiered pipelines — trivial changes go fast (VIBE mode: triage → develop → merge), complex features get the full treatment with requirements, design, and security scanning. Stack: Python/FastAPI backend, React/TypeScript dashboard, SQLite, git worktrees for agent isolation. What it's not: This isn't a hosted SaaS or a "vibe coding" toy. It's designed for real repos with real CI expectations — test gates, security scans, and budget guards that pause work when spend crosses thresholds. GitHub: [https://github.com/iYoungblood/agentforge]() Happy to answer questions. GitHub support is new (Gitea was the original backend), so if anyone tries it with GitHub repos I'd appreciate feedback. (Or a PR / issue) I'm sure I'm missing a lot but hope it can help some others. https://preview.redd.it/a6c8pni74h9h1.png?width=1665&format=png&auto=webp&s=8c7df3847cdea9274b4d569a40c0725bf46ac855 submitted by /u/ayoungblood84 [link] [comments]

I built agentcn to help ship production-ready agents faster

A few days ago I came across eve on X, a filesystem-first framework for building AI agents that feels a lot like Next.js and deploys directly to Vercel Functions. While exploring it, I also found Flue, an open agent framework powered by Pi, the open agent harness. After playing around with both for a bit, I realized they'd fit really nicely into the shadcn/ui ecosystem, so I built agentcn. Some of the features: Built on Eve and Flue Zero-config, one-command setup shadcn/ui-compatible components (copy, paste, customize) Production-oriented recipes for orchestrators, subagents, tools, and skills instead of only hello-world examples 100% free and open source Claude helped with some of the boilerplate generation and iteration while putting together the initial recipes and documentation. It's still early days and only has a handful of recipes right now, but I'm planning to add more. I'd love to hear any feedback, especially from people already building with Eve or Flue. GitHub: https://github.com/shadcn-labs/agentcn Docs: https://agentcn.run submitted by /u/dank_clover [link] [comments]

The Death of "Vibe Coding": Why un-monitored AI generation is creating a compounding technical debt.

Hey everyone, ​We are quickly approaching a major bottleneck in AI-assisted software engineering. Relying on LLMs to spit out thousands of lines of code without a strict, human-driven architectural framework—what many call "Vibe Coding"—is creating brittle, unmaintainable systems. ​I’ve formalized this structural shift into a public document on GitHub: The AI-Powered Developer Manifesto. ​Instead of treating AI as a replacement for software architecture, we need to shift our paradigm from Micro-Coding (syntax generation) to Macro-Coding (system direction and epistemic supervision). ​Here is a crucial excerpt from Section 2.5 of the Manifesto, outlining why the current trajectory is leading toward a systemic collapse: ​2.5 The Compounding Technical Debt and Systemic Collapse ​The illusion of rapid deployment via un-monitored AI generation hides a critical flaw: compounding technical debt. ​When developers act merely as "vibe coders"—accepting AI outputs without deep syntactic validation—the codebase becomes an agglomeration of statistical probabilities rather than deterministic logic. By late 2026, systems built entirely on un-vetted AI iterations are projected to hit an architectural wall: a state where the complexity of debugging AI-generated hallucinations outweighs the speed of initial deployment. ​True AI-Powered Developers do not delegate understanding; they delegate execution while retaining absolute epistemic responsibility over the system architecture. ​The goal of this manifesto is to redefine our role: we aren't syntax writers anymore; we are system directors. ​I'd love to hear your thoughts on this. Are you already seeing the limits of un-monitored "vibe coding" in your production environments? How are you structuring your prompts to maintain macro-level architectural control? ​Full Manifesto and repository for open contributions: 👉 https://github.com/FractalDevelop/ai-powered-developer-manifest.git submitted by /u/BYTES_18 [link] [comments]

Claude Fable 5 may return today after 13-day government-forced suspension

Here’s the full timeline: -June 9: Anthropic releases Claude Fable 5, their most powerful public model ever (Mythos-class with safeguards) -June 12: US government issues an export control directive at 5:21 PM, ordering Anthropic to cut off access to ALL foreign nationals. Model goes offline worldwide within 90 minutes -The reason? Amazon engineers reportedly found a narrow jailbreak that could bypass Fable’s cybersecurity classifiers -Anthropic complied but publicly pushed back, calling the action unfair -Trump met Dario Amodei at the G7 and softened his stance, but the directive was never officially lifted -June 26 (today): Congressional deadline for Commerce Secretary Lutnick to respond in writing about the export controls Prediction markets are pricing ~57% odds of restoration before July 1. Developers have been stuck on Opus 4.8 this whole time. This whole situation raises a serious question: if a government can pull your AI model offline in 90 minutes, what does that mean for anyone building on closed, hosted models? submitted by /u/Direct-Attention8597 [link] [comments]

IP Memorandum: Multi-Agent ("Agentic") AI Systems in Coding, Marketing, and Creation – Comprehensive 2026 Analysis. (Integrating Patentability, Hype vs. Reality, Human Dependency, and Cost Overruns)

​ \*\*Date:\*\* June 1, 2026 \*\*To:\*\* Interested Parties / Developers / Enterprises \*\*Re:\*\* Viability of Layered Agentic AI – IP Protectability, Practical Utility, and Economic Sustainability Without Substantial Human Creative Input \### Executive Summary The 2026 trend toward \*\*multi-agent ("agentic") AI systems\*\*—layering specialized agents via frameworks like CrewAI, LangGraph, and AutoGen—promises automated workflows for coding, marketing, and content creation. Promoters brag about superior implementation and reduced oversight, yet these systems remain "token-hungry," heavily dependent on human direction, and prone to producing generic outputs requiring extensive editing. \*\*Core Thesis\*\*: AI lacks independent creativity; it recombines human-provided inputs and training data. Layered agents amplify efficiency in structured tasks but do not yield broadly patentable inventions or customer-ready original works without differential human creative input. Recent corporate budget reversals—where AI costs exceeded human labor equivalents—highlight the gap between hype and sustainable value. This version fully integrates: (1) patentability and creativity concerns, (2) current agentic bragging, and (3) real-world budget cuts at Microsoft, Uber, and peers. \### Current Trends & Bragging on Agentic Formulas (2026 Landscape) Developers and vendors heavily promote multi-agent orchestration as the "next big thing": \- \*\*Shift to Layered Agents\*\*: Moving beyond single agents to coordinated teams (researcher + coder + reviewer + validator) for parallel, end-to-end workflows in coding and marketing. \- \*\*Key Frameworks & Claims\*\*: \- \*\*CrewAI\*\*: Role-based "crews" for quick multi-agent prototypes; touted for marketing teams and collaborative creation with minimal setup. \- \*\*LangGraph\*\*: Graph-based stateful orchestration for complex, traceable workflows; praised for production reliability in agentic coding. \- \*\*AutoGen\*\*: Conversation-driven multi-agent debates; marketed for autonomous coding and async tasks with reduced human supervision. \- \*\*Bragging Points\*\*: Claims of 50%+ efficiency gains, "death of the senior dev," full autonomy, and massive ROI through token-intensive inter-agent communication. High consumption is framed as essential for "superior workload implementation." These systems "suck tokens" via extensive prompting and iteration while promising independence—yet users remain tied to directing them. \### Patentability Analysis \- \*\*Patentable Elements\*\*: Narrow technical innovations—such as novel orchestration protocols, memory-sharing mechanisms, or domain-specific error-handling in multi-agent graphs—may qualify if they demonstrate novelty, non-obviousness, and utility. Human inventorship is required. \- \*\*Major Limitations\*\*: Broad "layered agents for coding/marketing" claims risk ineligibility under the \*Alice\* abstract idea doctrine. Crowded prior art from existing frameworks limits enforceability. AI-generated outputs alone are not patentable. \- \*\*Outcome\*\*: While specific implementations might secure protection, generic agentic layering is unlikely to produce strong, independent patents usable by customers without ongoing human differentiation and creative input. \### Copyright, Creativity, and Human Input Dependency AI excels at pattern synthesis but lacks true originality or aesthetic judgment. Multi-agent outputs are derivative of human prompts, context, and training data. U.S. law requires human authorship for copyright; raw agent-generated code, copy, or designs is generally unprotectable and may carry training-data risks. \*\*Reality Check\*\*: Even with 7, 28, or 100 agents, results tie directly to human instruction. Users face the scenario of editing days of output after short runtimes, undermining claims of full autonomy. \### Practical Usability for Customers & Cost Realities \*\*Strengths\*\*: Strong for boilerplate, data processing, and structured decomposition in hybrid teams. \*\*Weaknesses\*\*: Fragility on edge cases, silent failures, governance demands, and high token costs. Developers often rewrite large portions due to quality gaps and "cognitive debt." \*\*Recent Budget Cuts Due to Overruns\*\*: Major firms have slashed access after AI (especially Claude-powered agentic tools) burned through budgets faster than human equivalents: \- \*\*Microsoft\*\*: Canceled most internal Claude Code licenses for thousands of engineers in its Experiences and Devices division (Windows, M365, Outlook, Teams, Surface). Rolled out late 2025, it became too popular/costly ($500–$2,000+ per engineer/month in heavy use). Engineers redirected to cheaper GitHub Copilot CLI by June 30, 2026 fiscal year-end. Costs exceeded planned budgets despite productivity gains. \- \*\*Uber\*\*: Exhausted its entire 2026 AI coding budget in just four months (by April) due to rapid Claude Code adoption (84–95% of engineers). Mo

Is this a useful concept? Curious about how people have addressed similar goals in a different way.

TLDR: Take a look at the attached code block. It is intended to be a re-usable set of AI response preferences. Is this useful? If not, why? And how else have you dealt with similar goals? Background - I've been retired for 8 years so I completely missed the "AI in the workforce" revolution. But I use it a lot on my own. For general queries, technical writing, generic document prep, and help with PowerShell scripts that I use to automate common tasks, recipes, etc.. I have only used free-tier agents so far (they work for my simple needs). I fell into a usage pattern whereby, whenever I got an undesirable response to a prompt, before trying to fix the output I asked if there was a response preference I could have specified that would have avoided the undesired outcome in the first place. Basically, trying to train myself to ask better questions. It quickly became apparent that there are common patterns for different preferences for different topics. And that evolved into the notion persisting these common re-usable patterns in a file (I call it an AI Library) that I could import and re-use into different chats. Within my "Library" I define different "Profiles" (groups of task specific behavioral preferences) and "Commands" (a verb that generates a specific kind of output. Example - ">List profiles"). A short time later the idea of a "profile stack" emerged where I could combine and "layer" different profiles with defined precedence rules, and "push and pop" profiles from the "stack" (these notions are all just metaphors of course - it's just how I came to think of it). Most free AI agents I have tried do not have any persistence model outside of the chat transcript. But the Claude AI desktop app for windows exposes the notion of a "Project" that lets me upload my library and give it instructions to load my library into every new chat started in the project. So, it is basically acting like a Linux "rc" file. For other AI agents like Copilot, Perplexity, Gemini, etc. you can just drag the file into a prompt and give it a command something like "Parse the uploaded file it as if it was instructions typed into a prompt. Do not summarize. Confirm when it is complete and understood.". Being out of the workforce, I don't really have anyone else to bounce ideas off. Hence the Reddit post. I'm interested in suggestions or ideas to expand in this concept. Or ideas about different approaches to achieve similar goals. I'm not really interested in how paid versions make this work (I would be surprised and disappointed if these were not "out of the box" first-class supported concepts). My goal was to improve my free-tier experience. It seemed pretty innovative to me as I was evolving these ideas, but in hindsight, it seems pretty obvious. So I really don't know how useful or unique this is. I have attached a version of my current "library" for your consideration. p.s. If anyone is interested, I can share my Vim syntax file. I ended up calling these files "ailib.txt" files. Vim syntax would have worked fine with just ".ailib" but it seems most agents only import certain filetypes - hence the addition of ".txt" at the end. # AI Instruction Library Template # Version: 1.4 # Purpose: Reusable, modular instruction profiles for AI chats [LIBRARY_META] name = "Personal AI Instruction Library" owner = "User" version = "1.4" description = "A reusable library of behavioral profiles and command definitions." activation_model = "ordered_stack" [TERM_DEFINITIONS] stack: definition = "An ordered list of active profiles. Order reflects activation sequence, from first-activated (lowest precedence) to most-recently-activated (highest precedence)." notes = "When a new profile is activated, it is appended to the top of the stack. Its instructions are combined with all other active profiles' instructions, with conflicts resolved per the precedence rules." activate: definition = "Add a profile to the top of the active stack." notes = "Activation order determines precedence: the most-recently-activated profile has the highest precedence." deactivate: definition = "Remove a profile from the active stack." notes = "Inactive profiles remain defined in the library but are excluded from compilation and have no effect on AI behavior." precedence: definition = "The rule used to resolve conflicting instructions between two or more active profiles, or between an active profile and a direct user instruction." notes = "Precedence is resolved per-field. See [PRECEDENCE_RULES] for the exact resolution mechanism." override: definition = "When two active instructions conflict, the higher-precedence instruction is applied and the lower-precedence instruction is suppressed for as long as both remain active." notes = "A direct user instruction given mid-chat (not via a command) is treated as the highest-precedence layer, above all profiles, until one of the following occurs: (1) the user issues a new instruction that further overrides it, (2) the user

Studies suggest that reliance on AI tools degrades the abilities of physicians and software engineers

https://preview.redd.it/tmsmw8th3b9h1.png?width=1544&format=png&auto=webp&s=c452cefdba50f4df04f19a58b985f1cd4aa51ccc The physicians, who had all performed at least 2,000 colonoscopies during their careers, were given access to an AI system that analyses colonoscopy images in real time and flags a type of precancerous intestinal lesion called an adenoma. The tool was available to the specialists on some days but not on others Once physicians began using it, their performance dropped significantly whenever the system was unavailable Co-author Yuichi Mori, a physician-researcher at the University of Oslo, says that more studies are needed to confirm the phenomenon. But people who use AI tools should be aware that they risk losing some of their skills, he adds. 'There is no established solution against deskilling right now. It should be a very hot research topic in the next decade' Anthropic researchers designed a randomized controlled trial. During the exercise, all 52 participants could search the web and access instructions on how to do the task. Half of the participants were prompted to use an AI assistant as well Afterwards, all of the software engineers were asked to complete a quiz about what they had learnt from the task. The participants who had used an AI assistant did significantly worse on the quiz than those who hadn’t: the average score was 50% in the AI group versus 67% in the non-AI group The AI-assisted participants did particularly poorly on questions that required them to diagnose errors in the code, which suggests that they had failed to learn the concepts behind the code that they had just produced Other technologies have made particular skills obsolete in the past, notes Tapani Rinta-Kahila, an information-systems researcher at the University of Queensland in Brisbane, Australia. For example, GPS navigation systems have eroded people’s navigation skills. Generative AI tools, however, are 'he first technology that automates various cognitive faculties around thinking and interpretation, which were long considered unique human skills submitted by /u/tiguidoio [link] [comments]