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Abel Rahman

@abel_romann

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I build AI automations and AI calling agents
So you never miss a call, inquiry, or follow up πŸ’»
AI Automation specialist
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45/hour for a human. $13.5/hour for AI. Do the math on 2,000 calls a month. I built a voice agent for a clinic that answers 24/7, books appointments at 3 AM, and never takes a sick day. Here's the setup behind it: 1β€€ Vapi runs the live voice conversation with the caller 2β€€ n8n connects to Google Calendar and checks available slots in real time 3β€€ If the slot is free, the agent books it and sends confirmation 4β€€ The whole business knowledge (hours, services, pricing, FAQs) sits inside the agent 5β€€ It runs around the clock, weekends and holidays included 6β€€ Costs about $0.05 to $0.40 per minute all-in (vs. $0.42 to $1.08 per minute for a human receptionist in wages alone) 7. Handles one call or fifty at once, no drop-off Bonus: Flip it outbound, and the same setup does lead qualification, appointment reminders, and follow-ups. Yes, this can replace a front desk. And that's the point. Owners who run this move faster because they stop drowning in admin and start growing the business. Any business with a phone line can run this. Inbound or outbound. Save this & Repost if valuable. ♻️ by @abel_romann
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2 months ago
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I can't believe people are building AI agents before learning how the model actually works. Then they blame the agent when everything breaks. Here's how I'd learn AI Agent Engineering from zero: 1. LLM fundamentals + reasoning Transformers, tokens, context windows, sampling, reasoning models. 2. Context engineering System prompts, structured context, examples, extended thinking. 3. Agentic workflows ReAct, tool use, MCP, multi-agent orchestration, coding agents. 4. Memory Session memory, vector retrieval, episodic memory, caching. 5. Tool connectivity MCP servers, APIs, browser/computer use. 6. Routing + cost Fallback models, gateways, provider abstraction, cost tracking. 7. Observability + evals Trace what the agent actually did. Test it. Catch drift. 8. Guardrails Validation, PII protection, jailbreak defense, hallucination detection. Most tutorials stop around step 3. Production systems become challenging at step 6. And here's the part nobody wants to hear: An agent that works in your demo is not an agent that works. Make it survive the bad cases. Save this before your next agent build. Which stage are you stuck on right now? Tell me in the comments. Repost this for someone building agents without a roadmap. by @abel_romann
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16 hours ago
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AI is not ChatGPT. Here are the 8 layers underneath it. Data ↳ Raw facts, observations, and signals from the world. The foundation. Quality data drives everything. Infrastructure ↳ Compute, storage, networks, and tools. The engine room. Without it, nothing runs. Algorithms ↳ The logic and methods that find patterns in data. The recipe. Defines how learning happens. Models ↳ Trained systems that learn patterns and make predictions. The brain. Learns patterns from data. Machine Learning ↳ A subset of AI where models learn from data instead of rules. The approach. Learns from examples. Deep Learning ↳ A subset of ML using neural networks with many layers. The horsepower. Handles complex patterns. Generative AI ↳ A subset of DL that creates new content, text, images, audio, code. The creative layer. ChatGPT and Tools ↳ Applications built on top of models to help you do things. The interface. Where users interact and act. Common misconceptions this fixes: β†’ AI is not ChatGPT, ChatGPT is one application. β†’ AI does not work like magic, it works because of data, models, and compute. β†’ More data does not always help, better data plus better models does. β†’ AI does not replace people, it amplifies them. β†’ AI is not the future, it is already here. Next time someone says "AI is just ChatGPT," send them this. Save this before your next AI conversation. Which layer did you not know existed? Tell me in the comments. Repost this for someone who still thinks AI and ChatGPT are the same thing. by @abel_romann
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2 days ago
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10 Claude connectors I (religiously) use every week: 1 β†’ Gmail. Turns your inbox into a searchable knowledge base. 2 β†’ Google Drive. Finds files using plain English, not folder names. 3 β†’ Notion. Pulls your entire wiki into one chat. 4 β†’ Slack. Drafts and previews before you ever hit send. 5 β†’ GitHub. Reviews PRs without opening the repo. 6 β†’ Jira. Sprint status, conversationally. 7 β†’ Figma. Audits design systems in seconds. 8 β†’ Google Calendar. Finds availability without the back-and-forth. 9 β†’ Blender. Scripts 3D scenes with the Python API, in English. 10 β†’ Adobe Creative Cloud. 50+ tools, zero app switching. Save this list, it might help you one day. ♻️ Repost this to save someone 10 tab-switches a day. by @abel_romann
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3 days ago
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This is my favorite CLAUDE. md rule: (If a line doesn't answer WHAT, WHY, or HOW… cut it.) That one filter kills a ridiculous amount of prompt garbage. WHAT What are we building? Who am I? Who is this for? WHY What is the goal? What should Claude optimize for when two options conflict? HOW How should Claude behave? How do we work? What tone, process, and defaults should stay consistent? Then add one section most people completely forget: Known failure modes. Not generic rules. Actual mistakes Claude keeps making with YOU. Examples: "You hedge everything. Take a position." "You drift formal in long conversations. Stay casual." "You over-explain. Get to the point." What you'll get: β†’ less prompt bloat β†’ fewer repeated corrections β†’ more consistent sessions β†’ a file that improves with real usage And here's the part most people miss: Never auto-generate your failure modes. Claude cannot know what annoys you before you do. You write those. Save this before you write your next CLAUDE. md Which line in your current file would not survive this test? Tell me in the comments. Repost this for someone whose CLAUDE. md is 300 lines Claude keeps ignoring. by @abel_romann
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4 days ago
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Verify every output. Protect every byte. 18 rules that keep AI use safe. 01 - Verify every AI output manually 02 - Never share sensitive data with AI 03 - Keep humans in critical decisions 04 - Document all AI-assisted work outputs 05 - Use only approved AI tools 06 - Disclose AI use when required 07 - Check AI work for accuracy 08 - Learn new AI features weekly 09 - Prompt clearly and specifically always 10 - Track time saved by AI 11 - Report AI errors immediately 12 - Backup important AI-generated content 13 - Respect copyright and attribution rules 14 - Avoid biased AI training data 15 - Follow company AI policies strictly 16 - Question suspicious AI recommendations always 17 - Protect personal data from AI 18 - Stay updated on AI regulations None of these slow you down. They are the difference between using AI responsibly and getting burned by it. Save this before your next AI-assisted project. Which rule is your company missing right now? Tell me in the comments. Repost this for a team that still has no AI usage policy. by @abel_romann
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5 days ago
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I built 12 Claude Code mods (all free). So you never ask Claude the same thing twice: Access them all here β†’ https://lnkd.in/eVkSg4Wa Anthropic literally just dropped mods for Claude Code. A mod changes how it looks and behaves. Here are my favourites (so far): Watch the work β˜‘οΈŽ Mission Control β†’ the plan and progress, live β˜‘οΈŽ Open Loops β†’ every ask, ticked only with proof β˜‘οΈŽ Sessions Band β†’ all your open sessions, one band β˜‘οΈŽ Office β†’ a pixel-art desk for every session β˜‘οΈŽ Done Ping β†’ a sound when Claude finishes Keep it safe β˜‘οΈŽ Safe Delete β†’ deletes go to the Bin, with undo β˜‘οΈŽ Secrets Guard β†’ Claude never sees your API keys β˜‘οΈŽ Outbox β†’ every message waits for your Send β˜‘οΈŽ Pre-build Check β†’ re-reads your rules first Better answers β˜‘οΈŽ Show It β†’ opens what Claude just made β˜‘οΈŽ Plain Reply β†’ flags long, jargon-heavy replies β˜‘οΈŽ Mod Maker β†’ spots your repeat asks, builds the mod Repost ♻️ to help someone in your network. by @abel_romann
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5 days ago
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Claude can (finally) generate motion graphics. 28 Claude Code installs that do the job: Access them all here β†’ https://lnkd.in/eYxer8k2 Opus and Sonnet 5.5 already make solid motion graphics (no add-ons). But these 28 push them a lot further. Engines (the code that makes things move): 1. three.js β†’ Build 3D scenes, cameras and light. 2. anime.js β†’ Animate text, shapes and SVG. 3. motion β†’ Add smooth, springy motion to sites. 4. react-three-fiber β†’ Build 3D scenes from React. 5. lottie-web β†’ Play After Effects files on the web. 6. gsap β†’ Time every move to the exact frame. Renderers (turn it all into a video file):  7. ffmpeg β†’ Cut, join and convert any video.  8. remotion β†’ Make MP4s with React  9. hyperframes β†’ Turn an HTML page into an MP4. 10. manim β†’ Animate maths and diagrams. 11. motion-canvas β†’ Code explainers, live preview. 12. revideo β†’ Render code-built video on a server. UI parts (ready-made pieces that move): 13. shadcn/ui β†’ The base the kits below plug into. 14. react-bits β†’ Add animated text and backgrounds. 15. magicui β†’ Drop in animated landing page parts. 16. motion-primitives β†’ Add moving text and UI. 17. magic-mcp β†’ Build UI from a parts library. Sound (voice, music and captions): 18. whisper β†’ Turn a voiceover into timed captions. 19. tone.js β†’ Make music and sound in code. 20. wavesurfer.js β†’ Draw moving audio waveforms. 21. elevenlabs/skills β†’ Voice and SFX (account needed). Skills and MCPs (teach Claude the tools): 22. anthropics/skills β†’ Add art, canvas and GIF skills. 23. mcp-for-blender β†’ Let Claude drive Blender in 3D. 24. gsap-skills β†’ Teach Claude correct GSAP. 25. remotion/skills β†’ Teach Claude Remotion. 26. threejs-skills β†’ Teach Claude Three.js scenes. 27. after-effects-mcp β†’ Drive After Effects (paid). 28. motion-graphics-skills β†’ My 13 motion skills. So start with one engine and one renderer. Repost ♻️ to help someone in your network. by @abel_romann
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7 days ago
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This is my favorite way to build an AI GTM stack: Pick ONE recurring job. Example: "Turn every sales call into follow-up + CRM update + content ideas." Then build around it: β†’ Otter captures the conversation. β†’ Claude extracts decisions, pain points & follow-ups. β†’ HubSpot/Salesforce holds the customer context. β†’ Notion stores reusable knowledge. β†’ Zapier/n8n moves the information. β†’ Canva/Gamma turns useful insights into assets. What you'll get: β†’ fewer manual handoffs β†’ less lost context β†’ faster follow-ups β†’ more reusable customer intelligence β†’ fewer tabs open just to finish one task And here's the part most people miss: MCP is exciting because AI can finally become useful between the tools. That's where GTM workflows get weirdly powerful. by @abel_romann
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8 days ago
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CEO: "Can we deploy an AI agent for this?" CTO: "Probably." Legal: "What can it access?" Operations: "Who owns the workflow?" CFO: "What does it save?" Security: "Can we stop it?" …and suddenly the agent is no longer the interesting part. This is why executive AI decisions need to happen BEFORE the build. A useful agent needs: a single workflow with clear ownership, data you would actually trust, authority boundaries you can explain in one sentence, governance before production, a real buy-vs-build decision, and a measurable target. One detail I especially like: Write down what the agent is NEVER allowed to touch. Most teams define what AI can do. Far fewer define where it must stop. That boundary becomes very important the first time the agent does exactly what you technically allowed it to do. Save this before your next AI agent proposal lands on your desk. Which one of these gets skipped most often at your company? Tell me in the comments. Repost this for an executive about to approve an agent project without asking these questions. by @abel_romann
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9 days ago
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Raise your hand if you've called a chatbot an "agent" in a pitch deck this year. Be honest. Here's the full checklist. If more than 2 of these are true, wait before you build. 1. You confuse chatbots with agents. If the system cannot plan, use tools, or complete multi-step work, it is not an agent yet. 2. You haven't documented the job. Agents perform best when the task, inputs, outputs, and edge cases are clearly defined before automation begins. 3. You trust autonomy too early. Before full delegation, prove the system can work safely with approvals, logs, and human review in the loop. 4. You skip system design. Good agents are built on workflows, memory, permissions, and handoff logic, not just clever prompting. 5. You overlook exception handling. The real test is what happens when the input is messy, incomplete, contradictory, or unexpected. 6. You don't know the ROI. If the agent does not save time, reduce errors, or improve throughput, it may be complexity dressed as innovation. 7. You're building before learning the business. Without business context, the agent may do tasks faster while still solving the wrong problem. Hand still up? Start at # 1. That's your actual starting point, not a limitation. 5 out of 7. Score yourself. Then fix one before your next build. Repost this for the person who's about to find out their score too. Save this before your next agent review. by @abel_romann
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10 days ago
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This is my favorite way to build an AI team for a small business: (Don't start with agents.) Start with your org chart. Write down the work happening every week: β†’ competitor research β†’ content creation β†’ design β†’ admin β†’ reporting β†’ customer support β†’ planning Now turn each repeated job into an AI role. Example: Research Perplexity + ChatGPT + Claude Content ChatGPT + Claude + Copy.ai Design Midjourney + DALLΒ·E + Canva Operations Zapier + Make + ChatGPT Data ChatGPT + Claude + Sheets Support ChatGPT + Intercom + your knowledge base Strategy Claude + ChatGPT + your business context What you’ll get: β†’ clearer responsibilities β†’ better prompts β†’ less tool confusion β†’ repeatable workflows β†’ fewer tasks falling between apps And here’s the part most people miss: The "agent" is not the tool. The agent is the job + context + workflow + rules. Once you understand that, building the team gets much simpler. Save this before you plan your next quarter. Which agent would save you the most time right now? Tell me in the comments. Repost this for a founder still doing all 7 of these jobs alone. by @abel_romann
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11 days ago
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