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

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]