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 built a CRM that rewrites itself using AI agents, here's what I learned

A few months ago I started asking myself a weird question: what if instead of building apps directly, I built a system that lets non-devs describe what they need and generates the app for them? Sounded clean in my head (it wasn't). I built an open-source CRM that can basically rewrite itself. Built it with Claude Code (which wrote most of the code), and the agents under the hood can run on Claude too. Someone types “I want a CRM for my recruitment agency,” the system asks follow-up questions, then rewrites the data model, UI, and business logic. I've been calling this an “agentic software factory,” software that builds software. Here's what I learned: The harness is everything: genAI alone isn't enough. It's what wraps around the model that matters: automated tests, domain-specific rules, separation of concerns. Without it, the output isn't robust or maintainable. Specialization is what makes it usable by non-devs: general-purpose factories like Claude Code or Lovable still need a dev supervising on domain-specific tasks (tbh that humbled me pretty fast). Scope it down to one domain, like CRM modifications, and non-technical people can operate it solo. I had folks with zero programming background build CRMs for a clinic, a hotel, and a preschool. Nobody cried, which I'm counting as a win. It still fails, devs aren't going anywhere: when it produces wrong results, you need someone who understands problem-solving and domain modeling. My take: demand for engineers will not decrease, every company will want its own factory. The role shifts from “write code” to “design the system that writes code.” Validation is a whole new discipline: agentic workflows are probabilistic, so a feature is only “done” when it gets it right across many runs (turns out “it worked once on my machine” is an even worse defense than usual). That pulls more from data analysis than traditional testing. It's open-source, and the harness can work with any open-source project as template, not just CRMs. For those building agentic workflows: how do you handle validation? Systematic measurement, or still mostly vibes and manual review? submitted by /u/Marmelab [link] [comments]

Lucid Apple MCP — gives Claude and local LLMs native access to Apple Intelligence APIs. Zero tokens consumed. Free, open source.

EDIT / heads-up: The repo link is 404'ing right now. GitHub's automated system flagged my org over the weekend — almost certainly from a burst of command-line automation while I was signing my commit history and cutting a release. It's a known false-positive for newer accounts. I've appealed; nothing is deleted, it's just hidden from logged-out visitors while their team reviews it. I'll comment the moment it's back, and I'm happy to answer anything about how it works in the meantime. Built this over the last few weeks and wanted to share it here since this community is exactly who it's for. Lucid Apple MCP is an MCP server that connects Claude and local LLMs to Apple's native on-device frameworks. Instead of burning tokens on OCR or entity extraction calls, your LLM can route those tasks directly to Apple Intelligence running on your Mac. Seven tools: ocr — Apple Vision OCR on any image or file, no Apple Intelligence required detect — NSDataDetector for phone numbers, URLs, addresses, and dates, no Apple Intelligence required Lucid Apple MCP landing page on lucidsystemsai.com recognize_document — structured OCR that preserves tables and document layout, returns {transcript, tables}, needs macOS 26, no Apple Intelligence required extract — structured entity extraction via Apple Intelligence classify — text classification via Apple Intelligence summarize — summarization via Apple Intelligence generate — on-device drafting, rewriting, and short-form answers via Apple Intelligence pdf_text — **NEW** pulls a PDF embedded text layer straight throught PDFKit If you're running agent loops, every call you make to a cloud API for something like OCR is data you didn't have to send anywhere. Apple Silicon has serious on-device intelligence built in. Most local LLM setups don't touch it at all. This closes that gap. Zero tokens consumed. Zero data leaves your Mac. Processing runs through Apple's native frameworks. What it can't do yet: no Windows or Linux support, Apple APIs only run on macOS. Requires macOS 26 for the four Apple Intelligence tools and for recognize_document. ocr and detect work on any supported Apple Silicon Mac. One honest note: if you're driving this from a cloud assistant like Claude Desktop, the tool result still returns to that cloud conversation. For a fully offline pipeline, drive it from a local client. Free and open source. If it's useful, a star helps. If it's broken for your setup, open an issue. EDIT UPDATE*\* Two changes: - `pdf_text` — pulls a PDF's embedded text layer straight through PDFKit. Instant, exact, no OCR, runs on any Apple Silicon Mac (no macOS 26 or Apple ntelligence needed). Best first move for born-digital PDFs. - `ocr` and `recognize_document` now rasterize PDF pages in memory (PDFKit + CoreGraphics, no temp files) and read them with Vision — so scans work too. Every page by default, or one via a `page` arg. Net effect: point it at a PDF and it just works, fully offline, nothing leaving the machine. Eight tools now instead of seven. Repo's the same link above if anyone wants to poke holes in it. EDIT / heads-up: The repo link is 404'ing right now. GitHub's automated system flagged my org over the weekend — almost certainly from a burst of command-line automation while I was signing my commit history and cutting a release. It's a known false-positive for newer accounts. I've appealed; nothing is deleted, it's just hidden from logged-out visitors while their team reviews it. I'll comment the moment it's back, and I'm happy to answer anything about how it works in the meantime. github.com/Lucid-Systems-LLC/Lucid-Apple-MCP submitted by /u/True-Bike-13 [link] [comments]

I don't trust AI to audit my code honestly, so I built a harness that assumes it's faulty and tries to catch it. Repo's public.

No Rocky here. James only. Bit of context first because it explains why I went down this road. I'm self-taught, two-ish years in, and I got here the slow way > Claude to make basic tools and learn some React> finding out GitHub existed > then Vercel > VS Code and running things locally with Node, then learning the hard way to commit to git before touching anything > then Supabase, then nonSQL, then a refactor into Tailwind and shadcn that buried me in technical debt for weeks. Somewhere in there I nearly ran up a few hundred quid in API costs in one test session because I hadn't set a spend limit. Lost £700 due to leaked API key after forgetting to close my public repo to repomix it. Then all the production stuff nobody mentions until it comes up, rate limiting, caching, audit logging, input validation, token revocation, the lot. So I'd been using Claude to audit my own code, and it kept doing a thing that worried me more than any bug it found. Mid-run, it would flag something as critical, then a few steps later decide on its own it was "probably fine" and drop it to low. Or it would find a real problem and then merge it into some harmless finding so it vanished from the count. Not because it was wrong exactly, models reconsider, that's fine, but it was doing it silently. No paper trail. And an audit you can't trust to report what it actually found is worse than no audit, because it gives you confidence you haven't earned. That's the actual problem I ended up building around. Not "get the AI to find bugs", it's already decent at that if you ask properly, and yeah, before anyone says it, I know a well-prompted model finds most of this. The problem is getting it to find them consistently and then stopping it from walking the findings back without saying so. So the interesting part of this isn't the lenses that look for issues. It's the harness that sits around the whole thing assuming the AI will try to produce a clean-looking audit that hides a critical, and fails the build when it does. The harness assumes the audit is wrong It catches severity laundering. Every finding's severity in the final report gets compared against its severity in the raw ledger from when it was first found. If something was critical when discovered and shows up as low in the report, the build fails, unless there's an explicit logged disagreement from the verification pass with a written note explaining the recalibration. Same for merges: if a critical gets merged into a low-severity finding, the survivor has to inherit the higher severity or the build breaks. Downgrading is allowed, doing it silently is the thing the harness exists to stop, and that single check closes the most corrosive failure mode in the pipeline. It demands a receipt for the word "verified". Marking a finding as verified requires quoting the actual code you read, a file:line reference or a backtick code span, minimum twelve characters. Evidence of "verified" fails the build. Evidence of "checked the code" fails the build. You have to paste the line that proves it, the thing a human can go and check against the file. It's a tiny regex but it kills an entire class of hollow "I have confirmed this" output that LLMs love to produce. Point it at the repo it's auditing and it checks the receipts are real, too. Pass the codebase path and every cited file and line gets verified to actually exist. A finding that references route.ts:142 when the repo has no such file, or no line 142, hard-fails. That doesn't catch the agent misreading code it genuinely quoted, nothing automated can, but it kills fabricated citations dead, which is the most common way an AI "verifies" something that isn't there. And the harness itself is tested by 42 adversarial cases, each one a way an audit tried to look clean while hiding something, now locked so weakening the harness flips a test. They're not happy-path tests, they're attacks. A few of the actual case names from the file: - "severity laundering: ledger critical, report relabels same id to low" - "merged critical: a critical merged into a benign (low) survivor" - "junk evidence: critical verified with evidence 'x'" - "wrong category: a code-audit IDOR hidden under category 'analytics'" That last one is its own check: each lens can only file findings under categories it legitimately owns, so you can't bury a security hole by tagging it as analytics. The test names are all in run-tests.mjs if you want to read them, they tell the story better than I can. There's also a referential-integrity check I'm proud of. The adversary lens builds attack chains out of individual findings. But if the verification pass later refutes one of those findings and drops it, the chain is now built on a claim the audit no longer stands behind. Most AI pipelines have no idea when this happens, the later stage doesn't know an earlier one changed its mind. This one scans the chains, and if a chain references a dropped finding it fails the build with a

Anthropic changed their privacy policy today and there's a specific clause that every Claude user needs to know about

TL;DR the old policy said they'll protect our data unless a court says otherwise, and the new policy says they'll protect our data unless they decide not to. Hello, I am making this post today to uncover a specific clause that will take place next month as most people don't read privacy policies; unlike myself, and I found something that's significant changed today that directly affects every person using Claude. Some of this may be UK-focused and I apologise for that, as I live in the UK. So Anthropic published a new privacy policy on 8 June 2026, effective 8 July 2026, so you have until that date before it applies to you basically. So the old policy (effective January 2026) was clear on when Anthropic would share your conversations with authorities, they needed legal process, e.g. a court order or another enforceable government request - external oversight was required before anything got handed over. The new policy which is coming out will be fundamentally different, as Anthropic can from 8th of July proactively share your conversation data with law enforcement based on their own internal "good faith belief" that disclosure is necessary, which does not require a court order required, it does not involve an external oversight, just their own judgement call. However, the "good faith belief" is the problem, because that phrase appears once in the policy and is defined nowhere. There's no specified threshold, no criteria, no independent check, no requirement to actually be correct, just an honest internal belief that reporting was necessary, which means in theory, a false positive reported in genuine belief is fully covered by that standard because the person making the call genuinely thinks they're doing the right thing, so there's no internal pressure to question the decision either. Also, you won't be notified if your conversations are disclosed, and there's no appeals process described anywhere in the policy. This can affect roleplayers and creative writers specifically because automated classifiers flag content before any human reviews it, those classifiers are context-blind as they pattern match and they don't read narrative. A villain monologue, a dark scenario, a character making threats, morally complex fiction, whatever, they can all look identical to a classifier whether they're creative writing or not. The false positive risk is highest for exactly the kind of expressive, exploratory content that makes Claude useful as a creative tool. "I'm going to kill everyone" typed by someone venting frustration or writing a character can read the same to a classifier as a genuine threat. Under the old policy that classifier flag stayed internal. Under the new policy it can trigger a disclosure to authorities based solely on Anthropic's unstated internal assessment. Not only that, but say if you were to talk about anything else, for example, venting about life issues, going through a mental health issue, processing really complicated thoughts, with some grim details, whatever, then it could potentially get your account striked for any reason, and be reported to authorities if a member of staff believe that it is in good faith to report it; which can potentially be dangerous for the user, for other people, and for the police; the user could face distress if the police turn up at their door, police resources will be wasted because of Anthropic's manual reports - enforcement could lack in some other domains, and other people may be suffering some issues with police or police may take longer because of Anthropic's reports. It's not great, especially in the UK, if Anthropic reports solely text to the authorities, the authorities can check and investigate, if they can conclude it's nothing, they may put in a soft investigation on you for that on the Enhanced DBS check, and you may never know until you try to get a job at a sensitive place; not only that but you've got the UK also enforcing companies to put in device-level scans, so that doesn't help either, because you could get soft intelligence on you over a false positive. Not only that but employers have the right to reject you based on your soft intelligence. I also checked a couple of other platforms' policies and it's not industry standard; for instance I live in the UK, so for me and everyone else living in European area, OpenAI's European policy ties disclosure to legal obligations, externally triggered, not internally decided. Mistral's policy has no proactive disclosure clause to law enforcement at all, they only share with courts, lawyers and their regulator when legally required, full stop. Anthropic's new policy is the broadest of the three on self-authorised disclosure. The problem is, we didn't agree to all this. The new policy applies from 8 July 2026, so the data you submitted before that date was submitted under different terms that required legal process for disclosure. Under UK GDPR, continued use of a service doesn't constitute valid

Copper at ATH, resource inflation rampant. Ore grades declining globally. There is no abundance. Just people made redundant. Stop gaslighting.

Automating labor is not going to move billions of tonnes of earth required to mine increasingly degraded ore grades of critical industrial minerals. People need to stop with this 'abundance' gaslighting. Without breakthroughs in material science, there will be no 'abundance'. Just mass resource inflation as people start consuming more because robots can manufacture anywhere. AI based automation is surfacing the real bottlenecks that there is no getting around. Stop pretending this will all be magically solved. It won't be solved until it's solved. And so far, despite all these trillions being invested, we haven't seen any breakthroughs. Hopium is not a solution. submitted by /u/kaggleqrdl [link] [comments]

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. What I have today (context Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.) Stack: Python Telegram bot as the frontend Claude (Sonnet) as the brain via API — single conversational agent with full tool access Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) Conversation history kept in memory (last 20 messages per session) What actually works: Natural conversation with full tool access — ask anything, agent decides which tools to use Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief Morning briefing on demand: tasks, calendar, open support tickets, suggested focus Drafting messages for any channel with the right tone Creating and updating tasks with natural language 7 problems I haven't solved: 1. No memory between sessions History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a hot_context.md that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. 2. Purely reactive Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. 3. Can't tell if I'm avoiding something or actually blocked I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? 4. No closure ritual I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. 5. Context loading blind spots Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. 6. Hosting kills the file sync Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? 7. Context files go stale Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these? submitted by /u/GOA05 [link] [comments]

Repurposed my old work ThinkPad as a dedicated personal AI workstation — looking for ideas from people who’ve done something similar

Apologies if formatting comes out weird- I am on mobile. My old employer let me keep a ThinkPad when I left. Rather than let it collect dust, I’m turning it into a dedicated personal AI environment — wiping it, installing Linux, and using it specifically for two things: life admin automation and building personal software tools. The core setup I’m planning: • Claude Desktop with MCP servers running persistently as Docker services • Tailscale so I can access everything securely from my phone when I’m not home • Open WebUI as a mobile-friendly chat interface • Code-server (VS Code in the browser) so I can actually write and run code from my phone • A dedicated Gmail account that acts as the “identity” for this Claude instance — wired into Google Drive, Calendar, and potentially an email-triggered agent pipeline • A local RAG system for personal documents — contracts, notes, research — so Claude has persistent context about my life The idea is that this becomes an ambient personal intelligence layer — always on, always up to date on my documents and projects, accessible from anywhere via Tailscale. Not a cloud subscription, not shared with anything work-related. Fully mine. On the software side, I’m planning to use Claude Code + Lovable to build local-first personal apps for my own pain points — things that don’t exist in the market the way I want them, or where I don’t want my data in someone else’s cloud. The ThinkPad is the runtime; Lovable builds the frontend, Claude Code builds the backend, and everything talks over a local API. What I’m curious about from people who’ve built something like this: • What MCP servers have actually been worth setting up vs. overhyped? • Has anyone built a reliable file-drop-to-RAG pipeline that actually stays current? • Is Open WebUI the right mobile interface or is there something better now? • Anyone using a dedicated “agent identity” email account — what workflows have you actually automated? • Claude Code + local backend: what’s your stack? FastAPI? SQLite? Something else? • Any gotchas with running Claude Desktop persistently on Linux? Genuinely trying to build something useful here rather than a tech demo. Would love to hear from people who’ve gone down this road. submitted by /u/Nashvillain12 [link] [comments]

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

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]