Automation Anywhere

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

Automation Anywhere AI

I gave Claude access to my M365 account using Power Automate + a small MCP server

I’ve been messing with MCP servers lately and finally got one working that feels genuinely useful instead of “cool demo, never use again.” The problem: I wanted Claude to be able to do basic Microsoft 365 stuff for me: read my inbox send a draft/follow-up check my calendar save notes into OneDrive make Planner tasks write rows into Excel fill a Word template But I don’t have tenant admin access, and I wasn’t going to get Graph permissions approved just for personal automation. The workaround was Power Automate. Every operation is a PA flow with an HTTP trigger. PA gives you a signed webhook URL. The flow runs as my account, using permissions I already have. Then I put a small FastMCP server in front of those webhook URLs and connected that to Claude. So now in a Claude chat I can say things like: “Email me a summary of this.” “What’s on my calendar tomorrow?” “Save this note to OneDrive under /Projects.” “Create a Planner task for this follow-up.” “Append this row to the tracking spreadsheet.” Under the hood Claude is just calling MCP tools like m365_send_email, m365_calendar_read, onedrive_create_file, etc. The MCP server posts JSON to Power Automate, and PA does the actual M365 action. The architecture is not fancy, defintely not: text Claude -> MCP tool -> FastMCP server -> PA webhook -> M365 connector I’m running the MCP server on a cheap VPS. It’s about 200 lines of Python plus a JSON config file of flow names and URLs. This was also a nice reminder that “agent tool access” doesn’t always need a perfect official API integration. Sometimes the janky enterprise tool you already have is enough. The funniest bug: I had two tools pointing at the same Power Automate webhook because I duplicated a flow and forgot to update the URL in my config. The result was Claude confidently calling the “right” tool and Power Automate doing the wrong damn thing. Very educational, not very dignified. Edit. A [you will probably need Power Automate Pro, which i needed for a couple other things) Here's an example of it. I built 22 Power Automate flows covering all the different tools that I would want called and then I added them to the mcp. In Power Automate, make one flow per action. Example: send email, read inbox, create calendar event, write OneDrive file, etc. Start each flow with “When an HTTP request is received.” Define the JSON body you want that flow to accept. For send email, maybe { "to": "...", "subject": "...", "body": "..." }. Add the normal M365 connector action. Example: Outlook Send Email V2, OneDrive Create File, Excel Add Row, Planner Create Task. End the flow with a Response action that returns JSON. Copy the HTTP trigger URL into a private config file. Do not commit it. Do not paste it anywhere public. Treat it like a password. Put a small FastMCP server in front of those URLs. Each MCP tool just validates the inputs, finds the right PA webhook URL, POSTs JSON to it, and returns the PA response. The wrapper is not fancy. It’s basically: AI tool call -> FastMCP function -> httpx.post(PA webhook URL, json=args) -> return response The main things I’d recommend are: - keep webhook URLs private - add a duplicate URL check at startup - log tool name + status, but not secrets - start with read-only tools before giving it send/write powers - make every flow narrow instead of one giant “do anything” endpoint. Will post more info in the am if needed. Thanks for reading! [If you are not familiar or not comfortable with Power Automate, what I would recommend (and I mean this sincerely) is to use either co-work or use Claude Code Terminal with the Chrome extension and plug in the prompt for it to do it. It's a little slow and it'll take a bit but it will make them. Just don't sit there and watch it if you want it to be quick.) submitted by /u/ChiGamerr [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]

AI won’t make your company smarter — it will just make it faster

I’ve been thinking about this for a while, especially with all the discussions around AI replacing jobs. One thing that feels consistently misunderstood: AI doesn’t improve the quality of decisions by itself. It increases the speed at which existing decision logic operates. That has a simple consequence: Good systems become better. Weak systems fail faster. But there’s another layer that is often ignored. Right now, many companies are reacting to AI by reducing headcount. Some of that is rational: there is real slack in certain roles some work can already be automated or simplified In those cases, AI acts as a kind of cleanup mechanism. But this is where it gets more complex. If companies reduce people too quickly, they don’t just cut cost — they also remove: domain knowledge informal networks context that is not documented anywhere This kind of knowledge is not easily replaced by AI. So you end up with a paradox: AI increases speed, but the organization loses the very knowledge needed to make good decisions at that speed. At the same time, layoffs are not always a signal of weak systems. Strong organizations can also reduce roles because they: increase productivity per employee reallocate work shift toward new capabilities The difference is what happens next. Some organizations use AI to scale and create new opportunities. Others mainly use it to cut cost because they lack the structure to turn speed into growth. So instead of asking: “Will AI replace jobs?” A more relevant question might be: Is the organization structured in a way that can actually benefit from faster decision-making? Because if not, AI won’t make it smarter. It will just make it faster at being wrong. submitted by /u/No_Maintenance_432 [link] [comments]

Claude AI vs Claude Code vs models (this confused me for a while)

I kept mixing up Claude AI, Claude Code, and the models for a while, so just writing this down the way I understand it now. Might be obvious to some people, but this confused me more than it should have. Claude AI is basically just the site/app. Where you go and type prompts. Nothing deeper there. The models are the actual thing doing the work (Opus, Sonnet, Haiku). That part took me a bit to really get. I mostly stick to Sonnet now. Opus is better for harder stuff, but slower. Haiku is fast, but I don’t reach for it much. Claude Code is what threw me off. I assumed it just meant “Claude for coding,” but it’s more like using Claude inside your own setup instead of chatting with it. Like calling the API, generating code directly inside a script, wiring it into small tools, and automating bits of your workflow. That kind of stuff. One small example, I started using it to generate helper functions directly inside my project instead of going back and forth in chat and copy-pasting. Not a huge thing, but it adds up. That’s where it started to feel useful. Chat is fine, but using it in real work is different. Anyway, this is just how I keep it straight in my head: Claude AI → just the interface models → the actual brain Claude Code → using it inside real projects If you’re starting, I’d probably just use it normally first and not worry about APIs yet. You’ll know when you need that. If I’m off anywhere here, happy to be corrected. Also curious how others are using it beyond chat. https://preview.redd.it/x8n67qvyynxg1.jpg?width=634&format=pjpg&auto=webp&s=7f1ad8c262b5be2ee46ea48796b01f78c7d26548 submitted by /u/SilverConsistent9222 [link] [comments]

Anthropic's support system is broken by design — there is literally no path to a human for billing issues

This isn't a post asking for help with my account. I want to talk about the structural problem with Anthropic's support system, because I think more people should be aware of it before they pay for a subscription. The situation that exposed this: A gift subscription (Claude Max 20x) vanished mid-period with no warning, no email, no explanation. Invoice and receipt confirmed it was valid. The subscription simply disappeared from the billing page entirely — not expired, not greyed out, just gone. What Anthropic's support actually looks like: The only real-time support is "Fin," an AI bot (Intercom). No phone. No live chat. No direct email. Fin cannot escalate to a human. Fin cannot create a billing ticket. Fin cannot confirm whether your issue has been forwarded anywhere. Fin loops. It will ask the same clarifying questions repeatedly across the same conversation even after you've answered them. Anthropic's own documentation says "if the issue requires further investigation, Fin will forward your inquiry and someone will respond by email." In practice, Fin never confirmed this happened, and nothing was forwarded during the chat. Eventually, an automated email arrived (from support@mail.anthropic.com) acknowledging the issue was real and abnormal — but suggesting a self-service refund flow that requires an active subscription to be visible in the account. Which it isn't. The email ended with "Is there anything else I can clarify?" The core problem: When the support system itself malfunctions — or when the billing issue is caused by a backend error — there is no escalation path. The AI bot tells you to use a self-service flow. The self-service flow requires a working account state. The account state is broken. You are stuck in a loop with no exit. Anthropic charges $200/month for Max plans and sells gift subscriptions. The support infrastructure does not match that price point. Has anyone else hit this wall? And has anyone actually managed to get a billing issue manually resolved by a human at Anthropic? submitted by /u/AppearanceSingle805 [link] [comments]

Claude told me I was the bottleneck. So I built agents that run while I sleep.

I work full-time as a Program Director. About 50-60 hours a week at my W-2. Last week I checked Screenpipe and saw something weird — Claude had been running 84 hours in the same week. Same calendar week. Same person (me). The math doesn't work. Until you stop counting only the hours I was at the keyboard. This is the setup that's been running for a few weeks now and the breakdown of how it actually works. Sharing it because I keep getting asked "when do you have time" and the honest answer is — I don't have more time. I have agents that run when I can't. **The two-tier stack** I run a cloud + local split: - Claude (Sonnet, mostly) for anything that needs reasoning - Local LLMs (Ollama on my Mac, exposed via Cloudflared tunnel) for anything that doesn't The thing that surprised me when I checked was that I made exactly one Opus call across 84 hours. The defaults stayed on Sonnet. The local agents picked up everything cheap and parallel. If your AI bill is climbing it's almost always because you're letting your agents second-guess on Opus when Sonnet would have answered the same question for a fraction. **What ran without me this week** I have a folder of named .command files. They look like Apollo missions: - 08a-activate-apollo-nightly-pipeline.command - 11a-enrich-linkedin-batch-500.command - 07c-f3-ios-build36-fix-optional-alerts.command - 10-recover-drjonesy-site-source.command Each one is a runbook the agent can re-trigger without my input. Last week, timestamped: - 3 AM — Apollo enriched 500 LinkedIn leads while I slept - 9 AM — LinkedIn batch processing ran during my morning standup at the day job - 2 PM — An F3 build (one of my apps) self-deployed while I was in another meeting - 11 PM — A site recovery script stood by in case drjonesy.com went down (it did, briefly, and the script handled it) None of those required me to be at the keyboard. Most of them were either crons or Claude Dispatch triggers from my phone. **The Dispatch-on-phone trick** Probably 90% of my "personal" Claude time isn't at a computer. It's me on the bus, lounging around the house, or at my son's soccer practice, opening Claude Dispatch on my phone, texting Claude and my local setup. They keep working. I just hit send. So when Screenpipe says "20 hours of me" — most of that 20 hours is me texting from my phone. The other 64 hours is the agents. That's the part that took me a while to admit out loud. I'm not working more. I'm just hitting send. **The turning point** A few weeks ago I asked Claude how I could get more done. I was already running a lot but I felt like I was leaving capacity on the table. Claude said: "You have a sound foundation. But you're the bottleneck. Build systems that work for you." Hardest feedback I've ever taken from AI. Felt personal in the moment. Then I realized that was the entire point — I had built tools that needed me at the wheel for everything. The shift was from "tools I use" to "systems that run." That's when the .command file folder started to grow. Each new agent I added freed me up for one more thing I couldn't do before. **What I'd tell someone in a similar spot** If you have a full-time job and you're trying to build something on the side, the constraint is almost never your own willingness to work more hours. You're already maxed. The constraint is whether your build can run when you can't. For me that meant: - Setting up cron jobs for anything that runs daily - Exposing local LLMs via Cloudflared tunnel so my agents can hit them from anywhere - Naming command files so Future Me doesn't have to think about what does what - Defaulting to Sonnet (or local) and only escalating to Opus when reasoning genuinely demands it - Using Claude Dispatch on my phone so the "thinking" can happen during dead time (commute, between meetings, lunch) You don't need to quit your day job to build the next thing. You need agents that work when you can't. **One caveat** This setup took a while to get right. I blew up Apollo workflows multiple times. And while Claude Cowork can do a lot it still took me about 4 hours to get my local business continuity system setup. Most of the timestamps above only work because I spent the previous month making sure each one was idempotent and could fail gracefully. If you're starting from scratch I'd build one agent first — pick the most repetitive thing you do, automate that, get comfortable with how it fails, then add the next one. Not the whole stack at once. **Anyone else running this kind of split?** Curious how others doing day-job-plus-side-build are structuring their time. Especially interested in: - How you handle scheduled jobs vs on-demand triggers - Whether you've found a clean way to monitor agents without checking constantly - What you've automated that you wish you'd automated sooner submitted by /u/rjboogey [link] [comments]

My AI agent rewrote my entire service instead of fixing one bug. So I built a leash for it.

I was staring at my monitoring dashboard one morning watching response times quietly climb. Not crashing, just... degrading. The kind of thing that's easy to ignore until it isn't. I dropped the problem into my AI agent. Described the symptoms, pointed it at the relevant files, and let it go. An hour later it had "fixed" it. It had also restructured three modules, changed how we handled DB connections, and rewritten a threading section I hadn't asked it to touch. Response times were better. Everything else was a mess I now had to understand and justify. That's when I realized the problem wasn't the AI. It was that I never gave it boundaries. It didn't know what "done" looked like. It didn't know what was off-limits. It was just... optimizing for something, and I hadn't told it what. So I built two slash commands: /ame and /ema. /ame runs a single compiled interview with all the right questions at once depending on your project scope, and writes a confirmed spec to .ame/spec.md. One exchange. No back and forth. /ema reads that spec, maps out which architectural layers the work touches, builds a dependency-ordered plan, and executes chunk by chunk with your confirmation at each step. Here's how they've actually held up: The DB + threading bug (the one that started all this) Logs were showing creeping latency under concurrent load. I ran /ame with a one-liner and it immediately asked the things I hadn't thought to specify: what's the concurrency model, where does the thread pool live, is the DB connection pooled or per-request, what does a successful fix look like in the monitoring dashboard, and critically, what am I not allowed to touch. I answered once. It wrote the spec. Then /ema broke the work into three chunks: instrument first, identify the bottleneck, fix only that. It didn't touch the threading layer until it had confirmed what the query profiling showed. The fix was surgical. Two files changed. That was it. Instantly visible performance upgrade. OPC-UA PoC for a machine sensor alerting system I needed a PoC that could subscribe to machine sensor data from a Unified Automation server and feed it into an alerting pipeline. If you've worked with OPC-UA you know that "just connect to the server" can mean twelve different things depending on security policy, certificate handling, and subscription config. I'd been burned before by agents that confidently scaffold the wrong thing. /ame caught all of it before a single file was created. It asked which security mode, whether we were doing certificate auth or anonymous for the PoC, what the subscription interval should be, what "alerting" meant at the data layer, threshold crossing, rate of change, or raw value delivery. I answered in one shot. The spec that came out was specific enough that /ema scaffolded the UA client layer correctly on the first try. The PoC connected to the server and was pushing sensor events into the alerting pipeline the same day. Long story short, my employer was thoroughly impressed with what I presented to them. A background sync job between an external API and local DB I'd been putting this one off for weeks because "I need to think it through." The usual conflict resolution when both sides update, retry logic, what happens when the API rate limits you, whether the sync is append-only or bidirectional. Ran /ame with a rough one-liner. It correctly estimated full scope and asked about all the things I'd been vaguely worried about but hadn't written down: sync direction, conflict strategy, idempotency, failure recovery, and whether partial sync states needed to be visible anywhere in the UI. I answered everything in one go. The spec it produced was more complete than what I would have written manually on a good day. /ema gave me a clean three-chunk plan. Schema and models first, sync logic second, retry and error handling third. I ran it across an afternoon. No rewrites, no surprises, no "oh I didn't think about that" at 11pm. Works in VS Code (Copilot Agent mode), Claude Code, and Google Antigravity. Install: npx skills add github:CSKishan/ame-skill Or just copy the files manually. The README has paths for all three IDEs. MIT. Free. No accounts. → github.com/CSKishan/ame-skill Curious what breaks it. Try it on something ugly and let me know. PSA: AME stands for "Ask Me Exhaustively" and EMA stands for "Enclose My Analysis". submitted by /u/Wucrsman [link] [comments]

Built an MCP server that gives Claude Code direct access to a curated skill marketplace. Looking for feedback on the model.

Hey r/ClaudeAI, I've been building Agensi for the past few months, mostly with Claude Code and Lovable, and wanted to share where it's at and get honest feedback from this community. What it is A marketplace for SKILL.md skills. Creators publish skills (the SKILL.md + scripts + references format Anthropic shipped), users install them into Claude Code, Cursor, Codex CLI, Gemini CLI, and about 20 other agents. Every skill runs through an automated security scan before it goes live. The MCP piece agensi.io/mcp is a hosted MCP server that lets your agent talk to the catalog without manual browsing. Your agent can search skills by keyword or category, pull full skill details including the SKILL.md itself, and post requests to the community board when nothing in the catalog fits. The request board is where creators watch for demand signals before building. Concrete example: you ask Claude to review a migration, and instead of generic advice it pulls migration-auditor from the catalog and runs that specific skill. Same pattern for code review, changelog generation, dependency audits, whatever. 3-day free trial, $9/mo after. Transparent about the commercial part: free skills stay free to download directly from the site. The MCP subscription covers the agent-side discovery layer. The creator side (this is the part I want feedback on) There's no clean way right now for someone to write a genuinely good SKILL.md and get paid for it. People post them to GitHub, they get forked, no feedback loop, no income. Fine for hobby work but it caps what the ecosystem can produce. Two revenue streams. Direct sales: set a price on your skill, buyers pay once, you keep 80% minus a $0.50 per-sale fee. Good for premium, specialized skills where buyers know exactly what they want. MCP subscription pool: 70% of net MCP subscription revenue, distributed monthly based on which skills actually got used by paying subscribers. Spotify-style pool. Good for skills that get pulled in repeatedly across many sessions by many users. A creator can publish free, or paid, and stack both revenue streams on the same skill. What I'd want feedback on MCP tool surface. Missing anything obvious? The tradtional marketplace - does this still need to exist? Skills you've wished existed that you haven't been able to find anywhere Happy to answer anything in the comments. agensi.io for browsing. If you're building skills and want to monetise, list them on Agensi! submitted by /u/BadMenFinance [link] [comments]

Claude AI vs Claude Code vs models (this confused me for a while)

I kept mixing up Claude AI, Claude Code, and the models for a while, so just writing this down the way I understand it now. Might be obvious to some people, but this confused me more than it should have. Claude AI is basically just the site/app. Where you go and type prompts. Nothing deeper there. The models are the actual thing doing the work (Opus, Sonnet, Haiku). That part took me a bit to really get. I mostly stick to Sonnet now. Opus is better for harder stuff, but slower. Haiku is fast, but I don’t reach for it much. Claude Code is what threw me off. I assumed it just meant “Claude for coding,” but it’s more like using Claude inside your own setup instead of chatting with it. Like calling the API, generating code directly inside a script, wiring it into small tools, and automating bits of your workflow. That kind of stuff. One small example, I started using it to generate helper functions directly inside my project instead of going back and forth in chat and copy-pasting. Not a huge thing, but it adds up. That’s where it started to feel useful. Chat is fine, but using it in real work is different. Anyway, this is just how I keep it straight in my head: Claude AI → just the interface models → the actual brain Claude Code → using it inside real projects If you’re starting, I’d probably just use it normally first and not worry about APIs yet. You’ll know when you need that. If I’m off anywhere here, happy to be corrected. Also curious how others are using it beyond chat. https://preview.redd.it/g05rtbgfhhug1.png?width=634&format=png&auto=webp&s=6c8b5374f1509dee3a7071d9edc1ed836e2f868e submitted by /u/SilverConsistent9222 [link] [comments]

Your AI agents remember yesterday.

​ # AIPass **Your AI agents remember yesterday.** A local multi-agent framework where your AI assistants keep their memory between sessions, work together on the same codebase, and never ask you to re-explain context. --- ## Contents - [The Problem](#the-problem) - [What AIPass Does](#what-aipass-does) - [Quick Start](#quick-start) - [How It Works](#how-it-works) - [The 11 Agents](#the-11-agents) - [CLI Support](#cli-support) - [Project Status](#project-status) - [Requirements](#requirements) - [Subscriptions & Compliance](#subscriptions--compliance) --- ## The Problem Your AI has memory now. It remembers your name, your preferences, your last conversation. That used to be the hard part. It isn't anymore. The hard part is everything that comes after. You're still one person talking to one agent in one conversation doing one thing at a time. When the task gets complex, *you* become the coordinator — copying context between tools, dispatching work manually, keeping track of who's doing what. You are the glue holding your AI workflow together, and you shouldn't have to be. Multi-agent frameworks tried to solve this. They run agents in parallel, spin up specialists, orchestrate pipelines. But they isolate every agent in its own sandbox. Separate filesystems. Separate worktrees. Separate context. One agent can't see what another just built. Nobody picks up where a teammate left off. Nobody works on the same project at the same time. The agents don't know each other exist. That's not a team. That's a room full of people wearing headphones. What's missing isn't more agents — it's *presence*. Agents that have identity, memory, and expertise. Agents that share a workspace, communicate through their own channels, and collaborate on the same files without stepping on each other. Not isolated workers running in parallel. A persistent society with operational rules — where the system gets smarter over time because every agent remembers, every interaction builds on the last, and nobody starts from zero. ## What AIPass Does AIPass is a local CLI framework that gives your AI agents **identity, memory, and teamwork**. Verified with Claude Code, Codex, and Gemini CLI. Designed for terminal-native coding agents that support instruction files, hooks, and subprocess invocation. **Start with one agent that remembers:** Your AI reads `.trinity/` on startup and writes back what it learned before the session ends. That's the whole memory model — JSON files your AI can read and write. Next session, it picks up where it left off. No database, no API, no setup beyond one command. ```bash mkdir my-project && cd my-project aipass init ``` Your project gets its own registry, its own identity, and persistent memory. Each project is isolated — its own agents, its own rules. No cross-contamination between projects. **Add agents when you need them:** ```bash aipass init agent my-agent # Full agent: apps, mail, memory, identity ``` | What you need | Command | What you get | |---------------|---------|-------------| | A new project | `aipass init` | Registry, project identity, prompts, hooks, docs | | A full agent | `aipass init agent ` | Apps scaffold, mailbox, memory, identity — registered in project | | A lightweight agent | `drone @spawn create --template birthright` | Identity + memory only (no apps scaffold) | **What makes this different:** - **Agents are persistent.** They have memories and expertise that develop over time. They're not disposable workers — they're specialists who remember. - **Everything is local.** Your data stays on your machine. Memory is JSON files. Communication is local mailbox files. No cloud dependencies, no external APIs for core operations. - **One pattern for everything.** Every agent follows the same structure. One command (`drone @branch command`) reaches any agent. Learn it once, use it everywhere. - **Projects are isolated by design.** Each project gets its own registry. Agents communicate within their project, not across projects. - **The system protects itself.** Agent locks prevent double-dispatch. PR locks prevent merge conflicts. Branches don't touch each other's files. Quality standards are embedded in every workflow. Errors trigger self-healing. **Say "hi" tomorrow and pick up exactly where you left off.** One agent or fifteen — the memory persists. --- ## Quick Start ### Start your own project ```bash pip install aipass mkdir my-project && cd my-project aipass init # Creates project: registry, prompts, hooks, docs aipass init agent my-agent # Creates your first agent inside the project cd my-agent claude # Or: codex, gemini — your agent reads its memory and is ready ``` That's it. Your agent has identity, memory, a mailbox, and knows what AIPass is. Say "hi" — it picks up where it left off. Come back tomorrow, it remembers. ### Explore the full framework Clone the repo to see all 11 agents working together — the reference implementatio

Inserting external images into Figma via the MCP plugin sandbox — createImageAsync blocked, fetch unavailable. What's the actual path?

Hi. I'm building an automated card-news pipeline that drives Figma through Claude's MCP integration (use_figma, which executes JS in the Figma Plugin API sandbox). Everything works — frames, text, layout, auto-layout — except inserting external images (Unsplash, Google Images, anywhere on the web). I'm stuck in a loop and would love to know what the canonical solution is. The sandbox I'm working in: fetch → undefined XMLHttpRequest → undefined figma.createImageAsync → exists on the figma object, but calling it throws "createImageAsync" is not a supported API(presumably the MCP plugin manifest doesn't grant network permission) figma.createImage(Uint8Array) → works, but requires raw bytes inlined into the code I send So the only working path is: encode the image to base64 → embed it as a string literal in the JS code → decode to Uint8Array → figma.createImage(). The code parameter has a 50,000-character limit, which forces me to compress images down to ~35KB raw (roughly 1000px wide at JPEG q40) just to fit. Quality suffers, and it doesn't scale to multiple images per call. Things I've already tried: Calling figma.createImageAsync(url) directly with an Unsplash URL → blocked at runtime. Downloading the image locally → base64 → writing it to a JS file → reading it back to feed into the tool call → file is too large for the agent's read tools. Piping the base64 through cat / stdout → output gets truncated above ~30KB. Splitting base64 across chunks and reassembling → same read-limit problem on every chunk approach. Aggressive compression (200px, q35) to fit four images into one call → works mechanically but the output looks terrible for actual card-news posts. Hosting the image on GitHub raw and using createImageAsync → still blocked because the API itself is gated. What I'm actually asking: Is there any way to enable network access (fetch or createImageAsync) inside the MCP-exposed Figma plugin sandbox? Is this a manifest flag I'm missing, or is it intentionally locked down for the Claude/MCP integration specifically? If network is genuinely off-limits, is there a known pattern for getting full-resolution images into Figma programmatically other than inlining base64 into the code parameter? (Image hashes from a previously uploaded asset? Importing via a different API surface? Anything?) Has anyone built a Claude/MCP → Figma pipeline that handles images at production quality? I'd love to know how you structured it. For context: a manual workflow (Claude builds the layout, I drop images in via the Unsplash plugin) works fine, but I'm specifically trying to get end-to-end automation so I can run weekly card-news generation without touching Figma. Any pointers appreciated. submitted by /u/Either-Complaint9554 [link] [comments]

How are you guys running a fully remote (always-on) coding setup?

Hey there, I’m trying to figure out how to set up my environment so I can code and monitor everything from anywhere, especially from my phone. Right now I can kind of access things remotely using /rc, but it obviously only works when my laptop is open. What I really want is something that’s always running, where I can see the terminal, check logs, run commands and basically continue working from my phone. On top of that, I’d love to automate a lot more. For example, if someone sends me an email with a feature request, the system should understand it, do some research, create a plan and ideally already prepare things so I can jump in, review it and guide it (from my phone or from my laptop). It’s also really important that it has full context. It should be able to access my existing work, things like my claude.md, memory.md, past projects, and generally behave the same way as when I’m coding locally. I love Claude Code and have built up a lot of context over the past months, so losing that would be a big drawback. So I’m basically thinking about some mix of remote dev environment, AI agent and automation. How are people actually doing this right now? Are you using a VPS, some kind of cloud setup, or something else? Would love to hear what your setups look like in practice. Thanks a lot! :-) submitted by /u/Historical_Wing_4826 [link] [comments]

Anyone actually building useful tools or apps that you would show your dev team?

Curious what's being built that goes beyond personal productivity. Been doing some research on how you could applying Claude beyond the typical use cases. Not talking about scripts or one-off automations. More like, has anyone built something substantial enough that you'd actually walk into a meeting and demo it to your team or manager? If so: What did you build? Did you show anyone, and how did that go? Did it ever go anywhere inside your company or did it just stay yours? Genuinely curious where the ceiling is for what people are shipping with this. Drop your project if you're willing to share. submitted by /u/tesslate [link] [comments]