Zapier AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

Zapier AI AI

I’m saving 10+ hours a week with Claude, but I stopped "prompting" months ago.

Founders keep trying to automate their lives with complex AI stacks, and I keep seeing the same thing happen: They end up with 15 tabs open, copy-pasting prompts, and duct-taping everything together with Zapier workflows that quietly break every week. It looks productive, but they’re spending more time managing the AI than running the business. The real leverage isn't about adding more tools or "better" prompts. It’s about Context Architecture. The biggest shift for me was moving my SOPs, meeting notes, and CRM into one centralized "Source of Truth" (I use Notion) and plugging Claude directly into that context. When Claude isn't "guessing" what your business does, the hallucinations disappear and the utility sky-rockets. Here are the 3 specific use cases that saved me 10+ hours this week: 1) The Speed-to-Lead Workflow I stopped starting follow-up emails from scratch. How it works: I record the sales call directly in my workspace. Claude has access to my Brand Voice doc and my Product Guide. The Result: I feed the transcript to Claude, and it drafts a personalized email based on the prospect's actual pain points. It takes 90 seconds to review and hit send. 2) The Zero-Spreadsheet Data Analyst: I don’t do manual data entry for KPI trackers anymore. How it works: During my weekly metrics meetings, I just talk through the numbers: subscribers, CPL, revenue. The Result: Claude reads the meeting transcript, extracts the data points, and updates my database automatically. I haven't manually touched a spreadsheet in a month. 3) The Infinite Context Content Engine: I stopped staring at a blank cursor for LinkedIn/Reddit posts. How it works: I built a "Knowledge Hub" with all my past newsletters and internal notes. The Result: I use a prompt that references that specific internal knowledge. It drafts content that actually sounds like me because it’s referencing my real ideas, not generic LLM "as a leading provider" fluff. The reason people think AI is a "gimmick" is because they’re giving it zero context. When you copy-paste a prompt into a blank window, the AI is just guessing. When your AI can see your brand voice, your products, and your transcripts all in one system, it stops guessing and starts operating. This is from me, guys. I’d love to hear what other business owners are doing with Claude. We should share practical usecases beyond the marketing hype submitted by /u/damonflowers [link] [comments]

Which AI skills/Tool are actually worth learning for the future?

Hi everyone, I’m feeling a bit overwhelmed by the whole AI space and would really appreciate some honest advice. I want to build an AI-related skill set over the next months that is: • future-proof • well-paid • actually in demand by companies • and potentially useful for freelancing or building my own business later Everywhere I look, I see terms like: AI automation, AI agents, prompt engineering, n8n, maker, Zapier, Claude Code, claude cowork, AI product manager, Agentic Ai, etc. My problem is that I don’t have a clear overview of what is truly valuable and what is mostly hype. About me: I’m more interested in business, e-commerce, systems, automation, product thinking, and strategy — not so much hardcore ML research. My questions: Which AI jobs, skills and Tools do you think will be the most valuable over the next 5–10 years? Which path would you recommend for someone like me? And what should I start learning first, so which skill and which Tool? Thanks a Lot! submitted by /u/RabbitExternal2874 [link] [comments]

I have Claude Pro and want to use it to maximize everything possible, including income.

I feel like I am sitting on something incredibly powerful, but I am only using a fraction of it. I have been using Claude Pro consistently, and I have already seen real gains, especially with Claude Code helping me move much faster when building or debugging. I know there is another level to this that I have not unlocked yet. I am not trying to casually use AI. I am trying to get serious leverage to make money, save time, and automate parts of my life and work. I want systems that actually compound, not one-off wins or fluff. I am willing to put in effort, but I want that effort pointed in the right direction. I am especially curious about real, repeatable workflows that generate income. How are people actually using Claude Pro to make money? Are you freelancing, building products, running services, or doing something else entirely? What does the workflow look like from start to finish? I am not looking for vague theory. I want to see the step-by-step process. Automation is another big focus for me. I want to know how you are using Claude Pro to handle things like email, research, task management, or planning. Are you combining it with APIs, scripts, Zapier, or other tools? What runs on autopilot in your daily or weekly system, and what still requires your input? Claude Code has already helped me move faster in coding, debugging, and generating components, but I know there is a whole level of advanced usage that most people are not talking about. Are you using it for full project scaffolding, refactoring, or testing pipelines? Are there non-obvious prompting strategies, setups, or tricks that make a real difference? I also want to understand what separates casual users from people getting serious leverage. Is it better prompting, smarter systems, tool stacking, or just more volume and iteration? What habits or approaches make the difference between scratching the surface and actually scaling your results? If you have built something that is actually working, I would love for you to share specifics. What is the workflow, which tools do you combine with Claude, rough results like time saved or income generated, and any hidden tricks or habits that made a big difference? I am not looking for hacks or fluff. I am looking for systems that hold up over time and produce real results. Right now it feels like most people, including myself, are barely scratching the surface. I am trying to see what is actually possible if you go all in. submitted by /u/Ok_Confidence4529 [link] [comments]

I open-sourced a Claude skill that autonomously manages a LinkedIn profile — 22 days of real data, anti-detection system included

For 22 days I ran a Claude Cowork system managing a LinkedIn profile end-to-end: daily posting from a pillar calendar, engagement sessions, DM triage, weekly reporting. Today I published the full system as a free, open-source Claude skill. Results (unfiltered): 45 → 55 followers (+22% in 22 days) Engagement rate: 3.0% (vs 2.21% baseline) 75+ AI-written comments, all contextual 0 detection incidents How it works: A 5-phase wizard that extracts your voice (15 questions), builds a pillar calendar with emotional registers per day, sets up engagement with anti-detection rules, shows you all 10 tasks for approval, then creates cron jobs. Anti-detection (the hard part): NDI (Natural Dialogue Index): each session scored 1-10, stops below 5.0 7 anti-pattern rules born from Day 1 mistakes Epistemic Verification Gate: forces fact-checking before commenting on posts citing specific cases (born after a real wrong-inference incident on Day 7) Stack: Claude Cowork + Chrome MCP + Python + Google Cloud. No Zapier/n8n/Make. Repo (free, MIT): https://github.com/videomakingio-gif/claude-linkedin-automation Install: npx skills add videomakingio-gif/claude-linkedin-automation Happy to answer questions on architecture or anti-detection methodology. submitted by /u/NiceMarket7327 [link] [comments]

Claude isn’t "hallucinating" your prompts just have zero context. Here’s how I fixed it.

Founders keep trying to "automate" their lives with complex AI stacks, and I see the same thing happen again and again. They end up with 15 tabs open, copy-pasting Claude prompts back and forth, trying to duct-tape everything together with Zapier workflows that quietly break every week. It looks productive, but they’re spending more time managing the AI than actually running the business. The shift I’ve seen work isn’t adding more tools, it’s removing fragmentation. The Problem: Claude is Brilliant, but It's Blind The reason people think AI is a gimmick or complain about hallucinations is simple: not enough context. When you copy-paste a prompt into a blank Claude window, it’s basically guessing what you want because it doesn’t have the full picture of your business. I’ve moved my SOPs, meeting notes, and CRM into Notion to serve as the structured foundation, using Claude as the intelligence layer. When Claude has access to your actual brand voice, product docs, and transcripts in one workspace, it stops guessing and starts producing elite output. How this looks in practice with a structured workspace: The "Speed-to-Lead" Agent: I don't spend an hour polishing follow-up emails. I record the sales call directly in the workspace. Because Claude has access to my brand voice and product docs right there, it drafts a personalized email based on the prospect's actual pain points in 90 seconds. The Data Analyst: I’ve stopped manual data entry for KPI trackers. During weekly metrics meetings, I just talk through the numbers (subscribers, CPL, revenue). Claude reads the transcript, extracts the data, and updates my Notion databases automatically. The Infinite Context Content Engine: I don’t ideate from scratch. I built a hub with all my past newsletters and internal notes. My prompts pull from that internal knowledge, so Claude drafts content that actually sounds like me because it’s referencing real ideas, not generic LLM training data. The Shift from Prompting to Building: If you want real leverage, stop looking for the "magic prompt." The best way to use Claude isn't through better adjectives in a chat box; it's by giving it a world-class education on your specific business operations. I am convinced that no type of perfect prompt can get better results than AI with full context. I think we should stop overhyping prompt engineering and start focusing on building the foundations that actually make AI useful. What do you think? submitted by /u/damonflowers [link] [comments]

I built a 200+ article knowledge base that makes my AI agents actually useful — here's the architecture

Most AI agents are dumb. Not because the models are bad, but because they have no context. You give GPT-4 or Claude a task and it hallucinates because it doesn't know YOUR domain, YOUR tools, YOUR workflows. I spent the last few weeks building a structured knowledge base that turns generic LLM agents into domain experts. Here's what I learned. The problem with RAG as most people do it Everyone's doing RAG wrong. They dump PDFs into a vector DB, slap a similarity search on top, and wonder why the agent still gives garbage answers. The issue: - No query classification (every question gets the same retrieval pipeline) - No tiering (governance docs treated the same as blog posts) - No budget (agent context window stuffed with irrelevant chunks) - No self-healing (stale/broken docs stay broken forever) What I built instead A 4-tier KB pipeline: Governance tier — Always loaded. Agent identity, policies, rules. Non-negotiable context. Agent tier — Per-agent docs. Lucy (voice agent) gets call handling docs. Binky (CRO) gets conversion docs. Not everyone gets everything. Relevant tier — Dynamic per-query. Title/body matching, max 5 docs, 12K char budget per doc. Wiki tier — 200+ reference articles searchable via filesystem bridge. AI history, tool definitions, workflow patterns, platform comparisons. The query classifier is the secret weapon Before any retrieval happens, a regex-based classifier decides HOW MUCH context the question needs: - DIRECT — "Summarize this text" → No KB needed. Just do it. - SKILL_ONLY — "Write me a tweet" → Agent's skill doc is enough. - HOT_CACHE — "Who handles billing?" → Governance + agent docs from memory cache. - FULL_RAG — "Compare n8n vs Zapier pricing" → Full vector search + wiki bridge. This alone cut my token costs ~40% because most questions DON'T need full RAG. The KB structure Each article follows the same format: - Clear title with scope - Practical content (tables, code examples, decision frameworks) - 2+ cited sources (real URLs, not hallucinated) - 5 image reference descriptions - 2 video references I organized into domains: - AI/ML foundations (18 articles) — history, transformers, embeddings, agents - Tooling (16 articles) — definitions, security, taxonomy, error handling, audit - Workflows (18 articles) — types, platforms, cost analysis, HIL patterns - Image gen (115 files) — 16 providers, comparisons, prompt frameworks - Video gen (109 files) — treatments, pipelines, platform guides - Support (60 articles) — customer help center content Self-healing I built an eval system that scores KB health (0-100) and auto-heals issues: - Missing embeddings → re-embed - Stale content → flag for refresh - Broken references → repair or remove - Score dropped from 71 to 89 after first heal pass What changed Before the KB: agents would hallucinate tool definitions, make up pricing, give generic workflow advice. After: agents cite specific docs, give accurate platform comparisons with real pricing, and know when to say "I don't have current data on that." The difference isn't the model. It's the context. Key takeaways if you're building something similar: Classify before you retrieve. Not every question needs RAG. Budget your context window. 60K chars total, hard cap per doc. Don't stuff. Structure beats volume. 200 well-organized articles > 10,000 random chunks. Self-healing isn't optional. KBs decay. Build monitoring from day one. Write for agents, not humans. Tables > paragraphs. Decision frameworks > prose. Concrete examples > abstract explanations. Happy to answer questions about the architecture or share specific patterns that worked. submitted by /u/Buffaloherde [link] [comments]

Why is it so hard to find AI tools that actually work for normal people?

I've talked to a dozen people who want to use AI for their work. Most of them have the same story: Heard about Claude → set it up → didn't know what to do with it at work Tried Zapier → abandoned it in 20 minutes Found something on GitHub → had no idea how to install it The frustrating part is the tools exist. There are hundreds of AI tools that could save these people 2-3 hours a day. They're just buried in technical documentation written for developers. I'm exploring a simple idea: a place where you search by your problem ("I spend too much time writing follow-up emails") and get tools you can actually install without a tutorial. Question for you: What's the one repetitive task at your job that you wish you could automate — but haven't been able to because the tools were too confusing? I'm collecting real answers before building anything. Genuine responses only — this is research, not a pitch. submitted by /u/Conscious-Square-858 [link] [comments]

Sending an SMS from Claude Desktop using Zapier MCP + Twilio

I’ve been experimenting with workflows where an AI agent can trigger real-world actions. One interesting architecture decision: 👉 I’m using Twilio through Zapier MCP from Claude Desktop. Why not connect the full Twilio MCP directly? Because the native Twilio MCP exposes a very large tool surface with many actions and parameters. ✅ Using Zapier MCP as an abstraction layer provides: • 🔒 Reduced exposure surface for the agent • ⚡ Simpler prompting and more reliable tool use • 🧠 Better scope control and guardrails • 💸 Lower risk of unintended or costly actions • 🧩 A more modular orchestration layer between AI and external APIs In practice, Claude Desktop can send an SMS from a simple prompt without dealing with the full complexity of the Twilio integration. Feels like a solid pattern for building safer and more scalable AI → real world automations. Currently exploring more agent architecture patterns. Happy to share learnings if there’s interest 👇 submitted by /u/No-Mention-3801 [link] [comments]

you should definitely check out these open-source repo if you are building Ai agents

1. Activepieces Open-source automation + AI agents platform with MCP support. Good alternative to Zapier with AI workflows. Supports hundreds of integrations. 2. Cherry Studio AI productivity studio with chat, agents and tools. Works with multiple LLM providers. Good UI for agent workflows. 3. LocalAI Run OpenAI-style APIs locally. Works without GPU. Great for self-hosted AI projects. more.... submitted by /u/Mysterious-Form-3681 [link] [comments]

How to automate daily receipt/invoice processing using Claude AI + Google Drive + Excel?

Hey everyone, I’m looking to build a workflow that automates how I handle my daily receipts and invoices, and I’d love some guidance from anyone who’s done something similar. My situation: ∙ Every day I receive a Google Drive folder containing photos/scans of receipts and invoices from daily purchases (food, groceries, etc.) ∙ Right now I’m manually entering everything into a spreadsheet, which is tedious and time-consuming ∙ I also have no easy way to spot suspicious or unusual charges What I want to achieve: ∙ Have an automated system that picks up new receipt images from Google Drive daily ∙ Uses AI to extract key info: vendor name, date, items purchased, amounts, category, etc. ∙ Automatically populates an Excel sheet with this data ∙ Categorizes each expense (groceries, dining, fuel, etc.) ∙ Runs continuously — so the sheet keeps getting updated as new receipts come in, not just a one-time thing ∙ Flags suspicious or unusual activity, such as: ∙ Duplicate charges or receipts for the same amount/vendor ∙ Purchases at unusual times (e.g. 3 AM transactions) ∙ Amounts that are significantly higher than usual for that category ∙ Vendors or locations I’ve never purchased from before ∙ Receipts where the math doesn’t add up (line items don’t match total) ∙ Unusual spikes in spending for a given day or week compared to my average ∙ Potential unauthorized purchases or charges I don’t recognize Why I’d prefer to use Claude specifically: ∙ Claude has excellent vision capabilities for reading receipts/invoices from images ∙ The Claude + Excel integration is really strong — I’ve found the reporting and spreadsheet outputs to be the best compared to other AI tools ∙ I’d like the reporting, summaries, and anomaly flagging to all be handled by Claude if possible What I’ve considered so far: ∙ Using Zapier or Make (Integromat) to watch the Google Drive folder for new files ∙ Sending the images to the Claude API for OCR + data extraction ∙ Writing the results to an Excel sheet automatically ∙ Having Claude generate a daily or weekly summary with any flagged items But I’m not sure about the best way to stitch all of this together reliably into an end-to-end automated pipeline. Has anyone built something like this using Claude? What tools/stack did you use alongside it? Any gotchas I should know about — especially around the API costs and token usage for processing a lot of images daily? Appreciate any help. Thanks! submitted by /u/Local_Ad_2635 [link] [comments]

Are we finally done with "Prompt Engineering"? The shift to Agentic AI in 2026 is getting real.

I was looking at my subscription list this morning and realized I’ve officially cancelled almost all my "content generator" tools. In 2024, I was obsessed with finding the perfect prompt to get Claude or GPT to write a decent email. Now? That feels like trying to code in binary. If you’re still "chatting" with a bot to get your business tasks done, you’re basically working for the AI instead of the other way around. The real conversation right now—especially in the US small business scene—isn't about which LLM is smarter. It's about Agentic Workflows. The "Chatbot" vs. "Agent" Reality Check For those who haven't dived in yet: A Chatbot is a dictionary. You ask it a question, it gives you text. End of transaction. An Agent is an employee. You give it a goal (e.g., "Find 10 leads, check their LinkedIn for recent news, and draft a personalized outreach in my tone"), and it just... does it. It has "hands"—it can browse the web, use APIs, and click buttons. What’s actually working in the field? I’ve been testing a few setups for my own operations, and a few names keep coming up in the dev circles: CustomGPT.ai: If you're worried about AI "hallucinating" (lying) to your customers, this is the gold standard. It uses RAG to lock the AI into your specific data. It doesn't guess; it cites your manuals and sitemaps. Relevance AI: This is where you build a "digital workforce." You can literally chain agents together. One researches, one writes, one checks for compliance. MultiOn: This one is wild—it actually navigates the web like a human. It can log into portals and perform actions that don't have an API. The "Human-in-the-Loop" Problem The biggest debate right now is how much autonomy to give them. Do you let an agent send an email directly to a client? Most of us aren't there yet. The "Pro" move in 2026 is setting up Agentic Loops where the AI does 90% of the heavy lifting and pings you on Slack for a final "Yes/No" before it hits send. Is anyone else actually seeing ROI on this? Or are we all just playing with expensive toys? I just put together a deep dive on how to actually structure these agents for a small business without it turning into "AI slop." If you’re struggling with the transition from "prompting" to "operating," it might save you some headache. 5 Best AI Agents for Small Business Automation 2026 | by Himansh | Mar, 2026 | Medium Curious to hear what your stack looks like. Are you guys building custom agents or just sticking to Zapier-style automations? submitted by /u/Remarkable-Dark2840 [link] [comments]