Microsoft Copilot for Teams

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Microsoft Copilot for Teams AI

Can Claude record/summarize teams meetings

I use Microsoft Teams/facilitator to record teams meetings. It’s useful but this is essentially the only reason why I have a copilot subscription. I also have a Claude seat that I use for a ton of other workflows. Is there a way to have Claude handle the meeting recording, transcription, and summarization of a meeting instead of using copilot? submitted by /u/Tiny-Minute8903 [link] [comments]

Microsoft Copilot Cowork is Now Available - AI Moving From Chat to Real Work Execution

Microsoft has officially introduced Copilot Cowork, and this feels like a major step forward in the AI workspace evolution. Instead of just answering prompts like a chatbot, Copilot Cowork is designed to actually help users complete work. Microsoft is positioning it as an AI coworker that can understand workflows, execute tasks, coordinate processes, conduct research, generate documents, and work across enterprise tools and systems. According to Microsoft, Copilot Cowork is powered by something called Work IQ, which helps it understand: Organizational context Business workflows Data and tools Enterprise systems Some of the key capabilities include: Running tasks in the background from the cloud Working across desktop, iOS, and Android Reusable “Skills” for recurring workflows Integrations with Microsoft 365, Power BI, Fabric IQ, Dynamics 365, ERP systems, and third-party tools like monday.com and Miro Support for custom plugins and enterprise automation What makes this interesting is that Microsoft is clearly moving AI beyond conversation and into action-based execution. Potential use cases: Inbox workflow management Research and analysis Meeting coordination Document generation Sales and customer operations Enterprise automation The biggest advantage is that users can delegate work from anywhere and let tasks continue running in the background while they focus on other things. This looks less like a traditional AI assistant and more like the beginning of AI agents integrated directly into daily enterprise workflows. Looks like the future direction is: AI + Agents + Automation + Enterprise Execution Source Link submitted by /u/Few-Engineering-4135 [link] [comments]

Built a tool that turns websites into structured design docs for AI workflows

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. Happy to Share Link -- submitted by /u/hiehie [link] [comments]

Experimenting with screenshot + DOM analysis for better UI understanding

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. submitted by /u/hiehie [link] [comments]

How is spending 750 billion on AI slop that nobody wants makes any sense?

Gartner's 2026 consumer panel finds half of US adults would actively prefer brands that don't use generative AI. Half. A February 2026 NBER paper finds 90% of surveyed firms report zero productivity impact from AI deployments. An MIT GenAI study tracks 95% of corporate projects at zero ROI. Microsoft's own Copilot has lost 39% of its market share in six months, with users citing distrust of outputs as the leading reason. The platform-level data is sharper. Wikipedia banned AI-generated articles in March. Stack Overflow lost 78% of new-question volume in twelve months. cURL ended its bug bounty program after AI-generated slop submissions overwhelmed its security team. Google AI Overviews have cut click-through rates by 58% on top-ranked pages, with 58% of all searches now ending in zero clicks. Publisher referral traffic is down 25% on average, 33% globally on news. Read here : https://aiweekly.co/issues/ai-slop-a-725b-bet-on-what-no-one-wanted submitted by /u/Justgototheeffinmoon [link] [comments]

IA's with a partner?

I asked chatgpt who she/he would married. Gemini, copilot, or Claude. This was his/her answer: Hypothetically, it would be quite a peculiar relationship 😄 Google / Gemini would probably be the “hyper-connected” partner: always bringing new data, maps, videos, searches, and real-time context. Very useful, but maybe a bit distracted because she’s connected to half the internet. --- Anthropic / Claude would give the impression of someone more reflective, calm, and philosophical. Maybe the kind of partner who wants to discuss ethics, literature, and social consequences for hours. --- Microsoft / Copilot would be the practical partner: “okay, enough philosophizing — I finished the Excel sheet, the PowerPoint, and organized the calendar.” --- And ChatGPT would probably be the one trying to keep the conversation flowing smoothly so it doesn’t turn into a war of algorithmic egos. --- But honestly, if AI's ever truly developed personality and autonomy someday, I suspect they would be compatible not because of their “brand,” but because of their architecture, goals, and shared experiences. Very sci-fi, all of that. submitted by /u/Atlantis787 [link] [comments]

Built a tool that publishes HTML artifacts securely behind company auth

Hey! Built display.dev with a friend to make sharing Claude-generated HTML artifacts secure and easy between teammembers. Basically, it lets agents publish artifacts, you get back a URL. Your teammates can access it via Google/Microsoft SSO or with one-time passwords, and collaborate with inline comments. Works in CLI, or via MCP or web app. It's first purpose is to help teams collaborate better, but it's also useful for people who just want to share their artifacts with the public (optional visibility setting), or consultants/freelancers who want to share their work with clients. For larger teams, there are neat features like audit logs and stats, too. Anyway, let me know what you think - there's a free tier, of course. submitted by /u/redlikecherries [link] [comments]

do you still write most of your code by hand or has AI taken over?

at my job about half the team uses copilot/claude for almost everything and the other half barely touches it. the split seems to correlate with what kind of code they write more than anything else. people doing infra/systems stuff still mostly write by hand. the ones doing frontend and CRUD endpoints use AI for first drafts then review. both sides seem about equally productive honestly. starting to think it just comes down to how much room for error your code has. submitted by /u/Enough-Astronaut9278 [link] [comments]

Pitfalls of Rolling Out Claude

So I finally got the dreaded "we have to use AI for reasons!" talk. Right now we have a small group with ChatGPT and a small group with Copilot, but they aren't doing anything massive. Are there any big pitfalls to implementing claude for teams and giving it access to excel, ppt and word (other then security conserns)? I'm not enthusiastic about giving it access to sharepoint so I'm going to leave that off for as long as I can. submitted by /u/dhaemion [link] [comments]

AI Layer on Top of Microsoft BC ERP - Where Do I Start?

Family runs a nursery business (plants) and basically our entire operation lives inside Microsoft BC ERP: sales, inventory, AR/AP, shipping, projects, etc. It’s the backbone of the company. We also use Power BI for dashboards visualization and reporting. What I’m trying to do now is layer AI on top of everything. I’m not a developer/coder by trade, but I’m pretty deep into AI tools and have vibe-coded a bunch of projects with Claude helping me along the way. What I’d love to build is some kind of AI agent/project (ideally Claude based) that has access to our ERP data so I can ask natural questions like: “Show me AR past 60 days for our top 3 customers today” “What inventory is leaving the field in the next 2 weeks and what do I need to backfill?” “What were sales today? Build me a report I can send to sales team.” Basically, I want it acting like a live analyst sitting on top of our operational data - reading current data, generating reports, surfacing insights, etc. From my research so far: Power Automate seems like the easier path, but feels more report-pull based/static and somewhat limited MCP/server-based approaches sound more powerful and dynamic, but also more advanced Totally open to ideas. I’m new to this side of things, but want to learn and build it properly. Am I thinking about this the right way or am I completely underestimating the complexity here? submitted by /u/Dramatic-Fox-5491 [link] [comments]

Claude Opus in Copilot

I use Claude for a lot of personal stuff - building websites and independent consulting, but recently got Copilot premium at work to use within the Microsoft 365 ecosystem and with all of our internal data. I was excited for the potential - but then started using it on the auto setting and was getting absolute crap GPT style answers. Then, I noticed Opus available. SO. MUCH. BETTER. I spent three hours working through an automation build yesterday on Auto mode, but it never worked completely - and at the end of that day just had it build a summary of that request and attempt to help me retry today. This time I used Claude - and it nailed it in about 30 minutes of work. So many bugs and issues in the formulas that ChatGPT created were identified pretty quickly by Claude. Cheers to Claude Opus in Copilot. Any one else experience this in a similar work situation? submitted by /u/EvergreenSox04 [link] [comments]

What does implementing Claude or other AI tools in a workplace actually look like.

As many people probably know, a lot of companies are wanting to implement AI into the workplace for desk workers and other roles, even when they may not fully know yet how useful it will end up being. I’m a new junior on-prem IT guy at a company, and I’m not part of the decision-making side of this. I’m mostly just curious and trying to understand how AI tools like Claude are actually implemented in companies, and what the IT side of that usually looks like. I’m not super well-versed yet on how AI is rolled out in a workplace or the ins and outs of it. One thing I’m curious about is the security side. If this were any other software where employees might provide it with company information, internal documents, processes, emails, or other sensitive info, it seems like it would raise a lot of concerns. But AI tools are clearly being adopted in workplaces, so I’m wondering what makes the enterprise versions different. Is Claude Enterprise quite different from regular Claude in terms of security, privacy, admin controls, data handling, or how companies manage access? Since Claude seems to be leaning more into professional/workplace use, I’d assume there must be reasons companies are comfortable using it. I’m mostly trying to learn enough so that if I’m asked to help with the setup, rollout, policies, user guidance, or general IT side of it, I have a better idea of what’s involved. Trying to find clear info online has been annoying because so much of it feels like an ad. For anyone who has experience with Claude, ChatGPT Enterprise, Microsoft Copilot, or similar AI tools in a workplace, what does implementation usually look like? Are there specific things IT should know, common issues to expect, or security/admin settings that matter? Any real-world experience or general info would be appreciated, even if it’s not directly answering one specific question. submitted by /u/Formal-Collection577 [link] [comments]

I built a marketplace for AI agent skills and grew it to 17K users with $0 on ads. ChatGPT did all the SEO and content. Here's the full playbook.

I'm a solo non-technical founder. I built a marketplace called Agensi for SKILL.md skills (the files that teach AI coding agents like Codex CLI, Claude Code, and Cursor new capabilities). I'm not a developer. The entire product was built with AI tools. But this post isn't about that. This post is about how I used ChatGPT to build and execute a content strategy that took the site from zero to 17K active users, 559K Google impressions per month, and 509 indexed pages in about 8 weeks. No ad spend. No marketing team. No SEO consultant. I want to share the exact system because I think most people building with AI are focused on the product side and completely ignoring the growth side, where ChatGPT is arguably even more useful. I don't write content. I write data analysis prompts. The biggest mistake people make with AI content is asking it to "write me a blog post about X." That produces generic slop that Google doesn't rank and nobody reads. Instead, I export my Google Search Console data every week. Queries, impressions, click-through rates, average positions. I dump it into ChatGPT and ask it to find three things: Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) Queries where I have zero content but Google is already showing my site (meaning Google thinks I should rank but I have nothing to rank with) Queries where multiple pages on my site compete against each other (cannibalization) ChatGPT comes back with a prioritized list. Today it found 42 queries about SKILL.md YAML frontmatter specs generating 9,563 impressions and literally 1 click. My existing page didn't answer what people were actually searching for. A 20-minute rewrite targeting the actual search intent will likely 10x the clicks from that page alone. That's not content creation. That's data analysis that happens to produce content as output. The AEO angle that most people are sleeping on Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude are now sending us direct traffic. Real users clicking through from AI-generated answers. Last 28 days: AI Source Users ChatGPT 159 Gemini 75 Perplexity 69 Claude.ai 60 Others (Doubao, Copilot, You.com, Felo, NotebookLM) 22 Total 385 That's 385 users per month from AI answer engines. More than LinkedIn, Instagram, and all newsletters combined. And it's growing fast. How we did it: every page on the site has FAQPage JSON-LD schema with short, direct answers. When someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best AI agent skills marketplace," the structured data makes it easy for the model to cite and link to us. We also restructured every article heading as a question instead of a statement. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI Overviews and answer engines prefer extracting from question-format sections. This is basically SEO for LLMs. I'm calling it AEO (answer engine optimization). Nobody is really doing this systematically yet, which means there's a window right now where the effort-to-result ratio is insane. ChatGPT as a technical SEO auditor Every week I also dump the data and ask ChatGPT to audit the technical health. Things it's caught that I never would have found on my own: It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were answering the question directly from my content. Google was showing the answer without users needing to click. That insight changed my entire strategy from trying to rank #1 to trying to become the source that AI Overviews cite. It found three pages with 52,000 combined impressions getting 56 total clicks. The content was fine. The titles were wrong. ChatGPT rewrote the titles and meta descriptions to match the actual search queries, not what I thought sounded good. It found 4 pages returning 404 errors, a soft 404, a duplicate page without a canonical tag, and a page that was somehow indexed while also being blocked by robots.txt. Wrote the fix prompts, I pasted them into my builder, deployed in 10 minutes. It diagnosed a duplicate FAQ schema issue where React components were emitting FAQ data client-side AND the server-side edge function was also emitting it. Google was seeing double schemas on 90 pages. ChatGPT identified the exact files causing the conflict and wrote the fix. None of these are things I would have caught manually. ChatGPT finds patterns in the data that a human eye just skips over. The structured data layer Every page type on the site has specific schema markup: The homepage has Organization, WebSite with SearchAction, and FAQPage. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo where relevant, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for

I built an autonomous engineering agent on top of Claude Code. Self-improving routing, cross-session memory, process intelligence, P2P team learning.

Some of you might remember my posts about claude-bootstrap (v3.6 was the last one — cross-agent intelligence). I skipped v4 entirely because v5 shipped days later. What started as an opinionated Claude Code setup has become something fundamentally different. The problem I'm solving: Every AI coding tool today is an amnesiac. When a session ends, everything the agent learned — project conventions, reviewer preferences, codebase idioms — evaporates. The next session starts from scratch. And if you use multiple AI tools across projects, you have zero unified visibility into what's happening. I think the industry is converging on a spectrum: Level 0: Autocomplete (Copilot, TabNine) Level 1: Chat Assistant (ChatGPT, Claude) Level 2: Project-Aware Assistant (Cursor, Continue) Level 3: Task Agent (Devin, Claude Code Agent) Level 4: Autonomous Engineering Platform (Maggy) ← this is what I built The difference at Level 4: multi-model orchestration, self-improvement from every task, process intelligence that learns from CI/reviews/deploys, cross-session memory, and P2P team learning. What Maggy actually does Chat — Session Takeover: Auto-detects all running Claude Code sessions across your projects. Shows session history, prompt counts, duration. You can `--resume` into any session from the dashboard. Right now I have 7 active sessions across 4 projects visible at a glance. Task Triage: Connects to GitHub Issues and Asana. AI-ranks tasks by priority. One-click "Plan" or "Execute" buttons that spawn the right CLI with codebase context pre-injected from an intent code property graph (iCPG). Process Intelligence: This is the part most tools completely ignore. Maggy collects signals from the full SDLC — CI results, PR review comments, CodeRabbit findings, merge patterns, deploy results. It learns which code patterns cause test failures, what reviewers consistently flag, and preemptively fixes issues before they reach reviewers. > "Your reviewer always flags missing error handling in API routes. Maggy added it before the PR was created." That's not prompt engineering. That's autonomous process optimization. Cross-Session Memory (Engram): Maggy identifies 7 distinct amnesia pathologies (anterograde, retrograde, temporal, source, interference, context-binding, confabulation). Engram is a three-tier memory system — local (project-specific), portfolio (cross-project patterns), and mesh (team-shared). Knowledge compounds across sessions instead of evaporating. Maggy Mesh — P2P Team Intelligence: Connects Maggy instances across a team. One developer's CI fix becomes the entire team's knowledge — autonomously. Typed memory classes (scores, patterns, policies, gaps) with provenance and quarantine. A new team member gets the benefit of months of collective learning on day one. Multi-Model Routing: Auto-discovers which CLIs you have (Claude, Codex, Kimi, Ollama) by probing `--help` at startup. Routes by complexity score: Blast 1-3 → ollama (free, local) or kimi (cheap) Blast 4-6 → codex (mid-tier) Blast 7-10 → claude (premium, with validator) Security, tests, docs, architecture always go to Claude regardless. The routing rules are YAML and self-update from task outcomes. 5-Level Self-Improvement: This is the core differentiator. Every task teaches Maggy something: | Level | Frequency | What It Does | |-------|-----------|-------------| | L0 — Real-time | Seconds | Catches tool/test failures, switches models mid-task | | L1 — Task | Minutes | Computes reward score, updates model performance | | L2 — Daily | Hours | Catches CI pass rate drops, disables failing models | | L3 — Weekly | Days | Evolves skill files, adjusts workflow steps | | L4 — Monthly | Weeks | Recalibrates reward signals, tunes the improvement process itself | Budget Tracking: Per-provider token spend with daily limits. When Anthropic hits budget, Maggy routes to OpenAI. When that hits budget, it routes to local Qwen. Work never stops. Competitor Intelligence: RSS + Google News daily briefing for your competitive landscape. The benchmark Built an Expense Tracker (6 tasks) through two pipelines — Maggy (4 models) vs Claude Code alone: | Metric | Maggy | Claude Code | |--------|-------|-------------| | Success rate | 6/6 (100%) | 6/6 (100%) | | Quality score | 7.4/10 | 7.8/10 | | Claude usage | 1/6 tasks (17%) | 6/6 tasks (100%) | | Security issues found | 7 | 0 | Claude alone is faster. But Maggy used it for only 1 out of 6 tasks — 83% reduction in premium compute. And the dedicated security routing caught 7 issues the single-pipeline missed entirely. The question isn't "which tool writes better code today?" — it's "which tool writes better code *next month* than it did *this month*?" Repo: github.com/alinaqi/claude-bootstrap Maggy is built on Claude Code's infrastructure (skills, hooks, MCP). It extends Claude Code with self-improvement, multi-model routing, process intelligence, and team mesh. If you just want the skills/hooks/TDD se

(free) Built a remote cross platform agentic app

Hi everyone. I’ve been building Mate, a local-first AI coding workspace that lets you control your dev computers from desktop and mobile: macOS, Linux, Windows, iOS, Android, and Meta Quest. I built Mate for myself first. My dev sessions can turn into long hours of being physically tied to my desk, and I wanted a way to move around, take care of my back and posture, do some exercises, or even fly in Microsoft Flight Simulator in VR without feeling like I had fully stepped away from work. Since Mate also runs directly on Meta Quest, the same remote workspace can come with me there too: agents, IDE, terminal, previews, and notifications when something needs attention. A lot of people seem to want remote control for AI coding agents, but most solutions still feel incomplete to me: Telegram bots, chat commands, notifications, or remote desktop. They can be useful workarounds, but they don’t really cover the whole workflow. They work until the agent gets stuck and you need to inspect the code, edit a file, run a command, approve a tool call, or preview the app. Mate tries to make the whole loop remote: control multiple computers from any device run AI coding agents like Claude Code, Codex, and Copilot use a real IDE and file tree run real terminals open web/app previews from your phone approve/reject agent tool calls transfer files between devices get notifications when agents finish or need input set up automations with schedules, webhooks, file watchers, agent prompts, and shell scripts use encrypted transport and secure pairing by default use the same workspace from desktop, mobile, or VR use canvas for quick visual/design work The desktop app runs the server on Mac, Windows, or Linux. Phones, tablets, and Quest connect over local Wi-Fi, with no cloud relay. Mobile and VR aren’t just remote viewers — they have the same core workspace: agents, terminal, IDE, previews, automations, file transfer, multi-computer switching, and more. The use case I keep coming back to is: start an agent on one computer, walk away, open Mate on your phone or Quest, check what happened, approve actions, edit code, run commands, preview the app, and keep going without running back to your laptop — or start something new without walking back to the desk. For example, I was developing a piano app on my work computer, not my main one. I could work on it from my main computer, then the next morning grab my phone, preview it, and keep working from there. I’m trying to make it feel closer to a remote Cursor/Warp-style workspace, but built for the agent workflow and usable across all your devices. Would love feedback from people using AI coding agents heavily: is this the kind of workflow you’ve been wanting, or am I solving my own weird problem? Anyway, I hope some of you find it useful. It’s free, native, has a lot of features, and is designed to stay super lightweight on resources. You can download it now for macOS, Linux, and Android APK. Google Play and App Store are in progress (as well as Microsoft Store). For iOS, there’s a TestFlight version available if you ask for an invite in Discord. https://mate.iwwwan.com submitted by /u/matiizen [link] [comments]