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Guides, articles and faculty videos on AI, automation, revenue and careers — useful, sourced, and connected to something you can build.
Latest articles
Agentic Workflows: Patterns That Actually Work
The five agentic workflow patterns that work in production — chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer — and when to use none.
Read · 7 min →mithub.clubAI & AutomationAI Agents vs Automation: How to Choose the Right One
AI agents vs automation: how they differ in control, cost, testing and risk, plus a simple decision grid to choose a workflow, an AI step or a bounded agent.
Read · 6 min →mithub.clubAI CareersAI Implementation Specialist: The Role Behind Real AI Adoption
Buying AI is easy; getting a team to rely on it is not. What an AI implementation specialist does, the adoption ladder, the skills and how to prove them.
Read · 7 min →mithub.clubAI CareersAI Jobs Without a Computer Science Degree
Which AI jobs are genuinely open without a CS degree, what the hiring data shows, which roles still favour degrees, and the proof path that replaces it.
Read · 7 min →mithub.clubRevenue EngineeringAI Voice Agents for Sales and Follow-Up
Where AI voice agents work in sales, the US compliance basics from the FCC's 2024 ruling and the TCPA rules, plus a design spec and QA loop for running them.
Read · 7 min →mithub.clubAI & AutomationAI-First Workflows: Redesigning Work, Not Just Adding Tools
How to redesign work around what machines now do instantly instead of bolting AI onto old steps: five redesign moves, a before/after ledger and a one-week sprint.
Read · 7 min →mithub.clubAI & AutomationAPIs, Webhooks and JSON for Non-Developers
A practical guide to APIs, webhooks and JSON for automation builders: requests, status codes, auth, rate limits, retries, signatures and reading nested data.
Read · 10 min →mithub.clubRemote WorkAsync Work Skills That Get Remote Hires Promoted
The asynchronous skills that decide who gets promoted on a remote team: writing that removes work, decision records, clean handoffs and documented processes.
Read · 7 min →mithub.clubAI & AutomationBuild Systems, Not Just Tasks: From Documentation to Scalable Operations
Why finishing tasks is not enough in the AI economy, and how to turn repeatable work into systems: document, automate, then scale operations beyond you.
Read · 6 min →mithub.clubn8nBuilding AI Agents in n8n With OpenAI and Claude
How to build a production AI agent in n8n: the Tools Agent, choosing between OpenAI and Claude, tool design, structured output, memory, human approval and testing.
Read · 7 min →mithub.clubClayBuilding an Outbound Workflow With Clay
A stage-by-stage build for outbound in Clay: list, enrichment gates, scoring, verified contacts, personalization, deliverability rules and sequencer handoff.
Read · 8 min →mithub.clubClayClay Alternatives and Clay vs Apollo
A jobs-to-be-done comparison of Clay, Apollo, ZoomInfo and CRM-native enrichment: which tool owns which job, and the questions to ask before you buy anything.
Read · 6 min →mithub.clubClayClay for GTM Engineering: How to Build Tables That Work Like Systems
How GTM engineers use Clay as a revenue system, not a spreadsheet: build specs, cost discipline, AI verification, QA and handoff to the CRM and workflows.
Read · 6 min →mithub.clubClayClay Signals and Intent Data
Clay signals and intent data explained: default and custom signals, web intent, how to judge a signal's precision and decay, and how to turn signals into plays.
Read · 7 min →mithub.clubClayClay vs n8n: Different Jobs, Better Together
Clay and n8n aren't rivals. Clay enriches, researches and scores data; n8n routes and acts. See when to use each and a full architecture using both.
Read · 6 min →mithub.clubClayClay Waterfall Enrichment Explained
How Clay waterfall enrichment really works: stop conditions, the free infer step, validation statuses, provider order, and how to balance coverage against cost.
Read · 9 min →mithub.clubClayClaygent: Using AI Research Agents in Clay
How to use Claygent: when to choose it, writing prompts as contracts, forcing structured output with citations, testing free, and keeping costs predictable.
Read · 8 min →mithub.clubClayConnecting Clay to HubSpot, Salesforce and Zoho
Connect Clay to HubSpot, Salesforce and Zoho without creating duplicates: identity keys, upsert rules, field ownership, blank-value policy and write-back tests.
Read · 8 min →mithub.clubRevenue EngineeringCRM Architecture for Revenue Teams
How to design a CRM that survives contact with reality: objects, stages, fields, ownership and data contracts, with a stage test and an architecture review.
Read · 7 min →mithub.clubn8nCRM Automation With n8n: Intake, Updates, Stage Changes and Logs
How to automate a CRM with n8n without corrupting it: trigger options, field ownership, safe repeatable writes, stage transitions, logging and failure modes.
Read · 8 min →mithub.clubCareer DevelopmentDeliberate Practice for Knowledge Workers
What Ericsson actually found, why the evidence is weakest for professions, and how to engineer reps and feedback into automation and revenue work.
Read · 8 min →mithub.clubAI & AutomationDirector vs Doer: How AI Changes Knowledge Work
AI is shifting knowledge work from doing tasks to directing them. Learn MitHub's ladder, Doer to Director to Designer to Owner, and how to climb it safely.
Read · 6 min →mithub.clubGTM EngineeringDo GTM Engineers Need to Code?
The honest answer, with postings data: you can start without code, about half of postings ask for SQL or Python, and the coding seats advertise more pay.
Read · 6 min →mithub.clubRemote WorkGetting Paid as an International Contractor
Contracts, invoices, W-8BEN and payment rails explained for contractors billing companies abroad — the six links where money gets stuck, and how to unstick them.
Read · 8 min →mithub.clubGTM EngineeringGTM Engineer Interview Questions (and How to Prepare)
The four things GTM engineer interviews actually test, a question bank for each, how take-homes are scored, an answer frame and a 14-day practice plan.
Read · 9 min →mithub.clubGTM EngineeringGTM Engineer Salary: What the Data Actually Shows
Two published analyses of GTM engineer pay, what their methodologies do and don't cover, why the numbers disagree, and how to use them without fooling yourself.
Read · 6 min →mithub.clubGTM EngineeringGTM Engineer Skills: The Complete Stack
The technical, business and communication skills GTM engineers need, how often job postings ask for each, and the proof artifact that shows you have it.
Read · 7 min →mithub.clubCareer DevelopmentHow Skills Increase Income (and Which Ones Do)
The mechanism behind skill and pay: what wage research really shows, the four ways a skill converts into money, and how to test a skill before you learn it.
Read · 8 min →mithub.clubGTM EngineeringHow to Become a GTM Engineer: Skills, Stack and a 90-Day Plan
A practical path to becoming a GTM engineer: the skills that matter, the tools to learn, a 90-day plan and three proof-of-work projects that companies notice.
Read · 7 min →mithub.clubAI CareersHow to Become More Valuable in the AI Economy
Your market value in the AI economy rises with your ability to create useful outcomes. MitHub's thesis, what labor data shows, and a 90-day plan to act.
Read · 10 min →mithub.clubCareer DevelopmentHow to Build a Portfolio Without Experience (Revenue and AI Automation Edition)
Build a portfolio with no job history: pick real business problems, build practice systems in revenue and automation work, and document proof others can check.
Read · 6 min →mithub.clubn8nHow to Connect n8n and Clay
The mechanics of a two-way n8n and Clay integration: webhook contracts, payload design, correlation IDs, retries, idempotency, timeouts and how to test it.
Read · 6 min →mithub.clubPersonal DevelopmentHow to Find What You're Good At (and Turn It Into Market Value)
Practical exercises to find your real strengths using evidence, not quizzes, and connect them to roles and outcomes companies pay for in the AI economy.
Read · 6 min →mithub.clubRemote WorkHow to Get a Remote Job with a US Company (from Anywhere)
A practical guide to landing remote work with a US company: skills, proof of work, where to look, interviews, contractor vs employee and getting paid abroad.
Read · 9 min →mithub.clubAI & AutomationHow to Learn AI Automation: A Practical Path From Zero to Proof
A practical path to learn AI automation: data basics, process mapping, workflows, AI steps, testing and agents, ending in a real build you can prove to others.
Read · 7 min →mithub.clubRemote WorkHow to Optimize LinkedIn for Remote Jobs
A profile audit built on LinkedIn's own documented mechanics: headline, skills, Featured proof, Open to Work visibility and the remote job filter.
Read · 8 min →mithub.clubCareer DevelopmentHow to Write a Case Study for Your Portfolio
A sentence-level template for portfolio case studies: the seven blocks, the number contract, the permission ladder, and what to publish when you signed an NDA.
Read · 8 min →mithub.clubAI & AutomationHuman in the Loop: Where People Belong in AI Workflows
How to place humans in AI workflows on purpose: risk tiers, seven approval patterns, the rubber-stamp trap, and written criteria for removing a review gate.
Read · 9 min →mithub.clubPersonal DevelopmentIkigai for Career Direction: What It Really Means (and How to Use It)
The four-circle ikigai diagram is a Western adaptation, not the Japanese concept. Learn where it came from and how to use it honestly to find valuable work.
Read · 6 min →mithub.clubPersonal DevelopmentKaizen: Continuous Improvement for Your Career and Learning
Kaizen explained: its roots at Toyota, Deming's PDSA cycle, the honest math behind getting 1% better, and a weekly loop to improve your skills and career.
Read · 10 min →mithub.clubRevenue EngineeringLead Routing: Getting Every Lead to the Right Person Fast
How lead routing really works: ownership rules, territories, round robin vs load balancing, SLAs and escalation, and the weekly audit that finds lost leads.
Read · 7 min →mithub.clubRevenue EngineeringLead Scoring Explained
How lead scoring really works: fit vs behavior, how to set weights from your own conversion data, thresholds, decay, predictive models and the feedback loop.
Read · 7 min →mithub.clubClayLead Scoring in Clay
Lead scoring in Clay: separating fit, intent and timing, building formula columns, adding AI judgment, and calibrating thresholds against your closed-won data.
Read · 7 min →mithub.clubCareer DevelopmentLearn, Build, Prove, Earn: The Operating Loop Behind MitHub
How MitHub's loop works: learn a capability, build something real, prove it with verifiable evidence, earn from it, then improve. Why the order matters.
Read · 7 min →mithub.clubPersonal DevelopmentLearning How to Learn: A Practical System
What the research says about retrieval practice and spacing, why rereading feels better than it works, and a build-and-recall loop for technical skills.
Read · 7 min →mithub.clubPersonal DevelopmentLifelong Learning in the AI Economy
Why continuous learning stopped being optional, what the labour-market data actually says, and how to run a learning portfolio instead of collecting courses.
Read · 7 min →mithub.clubAI & AutomationLLMs Explained for Operators: Tokens, Context, Hallucination and Cost
Large language models explained for people who run operations: what tokens are, how context windows work, why models invent things, and what really drives cost.
Read · 8 min →mithub.clubn8nn8n Error Handling for Production Workflows
How to make n8n workflows survive production: node retry settings, error workflows, deliberate failure, dead-letter records, timeout sweeps and real alerts.
Read · 8 min →mithub.clubn8nn8n for AI Automation: How to Build AI Workflows That Hold Up
How to use n8n for AI automation: webhooks, data, AI steps, agents, memory, RAG, human approval, error workflows and evaluations, with a worked lead workflow.
Read · 7 min →mithub.clubn8nn8n for GTM Teams: 8 Workflows Worth Building
Eight n8n workflows for go-to-market teams, each mapped to a metric it moves, the failure mode it hides, and the proof it leaves behind. Plus the order to build them in.
Read · 7 min →mithub.clubn8nn8n Self-Hosting vs n8n Cloud: How to Choose
n8n Cloud or self-hosted: the real tradeoffs, the plans and editions, what the Sustainable Use License allows, and the operational work self-hosting actually requires.
Read · 7 min →mithub.clubn8nn8n Webhooks Explained
How n8n webhooks work: test vs production URLs, authentication options, response modes, payload limits, the 100-second cloud timeout and a security checklist.
Read · 6 min →mithub.clubGTM EngineeringOutbound System Architecture: The Seven Layers
How an outbound system is built layer by layer: definition, sourcing, enrichment, decisioning, infrastructure, execution and feedback, and deliverability rules.
Read · 9 min →mithub.clubRevenue EngineeringPipeline Forecasting You Can Defend
Build a sales forecast you can defend: stage conversion math from real cohorts, pipeline coverage, a hygiene gate, and how to score your forecast accuracy.
Read · 7 min →mithub.clubAI & AutomationProcess Mapping Before Automation: How to Map Work Before You Build
A four-pass method for mapping a business process before you automate it: the money line, swimlanes, numbers on every step, and finding the constraint.
Read · 8 min →mithub.clubCareer DevelopmentProof of Work vs. Credentials: What Actually Gets You Hired for New Roles
Why verifiable work beats certificates when hiring for new AI-era roles, what counts as real proof, how MitHub verifies case studies and how to present yours.
Read · 10 min →mithub.clubAI & AutomationRAG for Sales and Support Teams, Explained Simply
What retrieval-augmented generation is, when a sales or support team should use it instead of a database query, and how to keep the answers correct.
Read · 9 min →mithub.clubRemote WorkRemote AI Jobs for Latin America: Roles, Skills and How to Stand Out
Remote AI jobs for people in Latin America: the roles companies hire for, the skills behind them, time-zone advantages, and how to stand out with real proof.
Read · 7 min →mithub.clubRemote WorkRemote Job Interview Questions and How to Answer Them
The questions remote companies really ask, what each one is scoring, and how to answer with evidence — based on how structured interviews are built and rated.
Read · 8 min →mithub.clubRevenue EngineeringRevenue Attribution Without the Hype
What revenue attribution can and cannot tell you: the models, a worked example across first touch, last touch and linear, the limits, and what to do instead.
Read · 7 min →mithub.clubRevenue EngineeringRevenue Engineering vs. RevOps: What's the Difference?
Revenue engineering vs. RevOps, compared honestly: where they overlap, how their questions and outputs differ, and and when a business needs each one first.
Read · 5 min →mithub.clubCareer DevelopmentSkill Stacking: How Combined Skills Raise Your Value
Skill stacking combines good-enough skills into a rare combination. Where the idea came from, what hybrid-job data shows, and how to build a stack that pays.
Read · 8 min →mithub.clubRevenue EngineeringSpeed to Lead: Why Response Time Decides Revenue
What speed to lead means, what the Harvard Business Review research actually found, how to measure it honestly with four timestamps, and how to fix the gaps.
Read · 7 min →mithub.clubAI & AutomationSystems Thinking for AI Automation: Stocks, Flows, Loops and Bottlenecks
Systems thinking in plain language for AI automation: stocks, flows, feedback loops and bottlenecks, plus a MitHub map to find what to automate first.
Read · 6 min →mithub.clubAI CareersThe Best AI Skills to Learn Right Now
A ranked list of AI skills built on verified labour-market data, scored by leverage and provability, plus what to skip and a proof project for each one.
Read · 8 min →mithub.clubPersonal DevelopmentThe Eisenhower Matrix for Knowledge Work (with Template)
How to use the Eisenhower Matrix for knowledge work: the real story behind the quote, a four-quadrant template, and a worked example for a GTM engineer.
Read · 6 min →mithub.clubRevenue EngineeringThe Revenue Leak Map: A Free Template
A free Revenue Leak Map template: list every step from first contact to payment, measure the drop at each one, price the leak and rank which fix to build first.
Read · 7 min →mithub.clubAI CareersThe Skills That Make You More Valuable in the AI Economy (and How to Stack Them)
Which skills labor-market data says are rising, why combinations beat single skills, and a MitHub framework to stack AI, business and judgment skills.
Read · 6 min →mithub.clubGTM EngineeringWhat Does a GTM Engineer Do? Responsibilities, Tools and a Real Workflow
What a GTM engineer does day to day: the build loop, typical projects, tools and skills from job-postings data, pay context and a build ticket to use.
Read · 6 min →mithub.clubRevenue EngineeringWhat Does a Revenue Engineer Do?
What a revenue engineer does: trace sales to their origin, diagnose leaks, build AI and automation systems, and prove results. With a sample weekly loop.
Read · 6 min →mithub.clubAI & AutomationWhat Does AI-Native Mean? Companies, Workers and Workflows
What AI-native really means for companies, workers and workflows, how it differs from simply using AI tools, and a checklist to see how AI-native your work is.
Read · 6 min →mithub.clubAI CareersWhat Does an AI Automation Engineer Do?
An AI automation engineer builds workflows and agents that run unattended in a business: the real scope, a production checklist, the stack and the path in.
Read · 7 min →mithub.clubAI CareersWhat Is a Forward Deployed Engineer?
The forward deployed engineer role explained: where the title came from at Palantir, what FDEs do, the skills required, and how to build proof for the job.
Read · 6 min →mithub.clubAI CareersWhat Is a RevOps Engineer?
A RevOps engineer builds the systems behind revenue operations instead of only administering them: the scope, the stack, the neighbouring roles and the path in.
Read · 7 min →mithub.clubAI & AutomationWhat Is AI Automation? A Practical Guide for Revenue Work
AI automation explained simply: rules vs AI-assisted steps, human in the loop, APIs, webhooks and JSON, architecture, process mapping and the real risks.
Read · 10 min →mithub.clubAI & AutomationWhat Is an AI Agent? LLMs, Tools, Memory and Their Limits
What an AI agent is in plain language: how LLMs use tools in a loop, memory and context, RAG, agentic workflows, human in the loop and where agents fail.
Read · 7 min →mithub.clubClayWhat Is Clay? Enrichment, AI Research Agents, Signals and Scoring Explained
What Clay is and how GTM teams use it: waterfall enrichment, Claygent research agents, signals, scoring, common architectures and when not to use it.
Read · 10 min →mithub.clubGTM EngineeringWhat Is GTM Engineering? The Complete Guide
GTM engineering explained: where the role came from, what GTM engineers build, tools, skills, job market data, how it differs from RevOps and how to learn it.
Read · 9 min →mithub.clubn8nWhat Is n8n? Workflows, Webhooks, AI Agents and Hosting Explained
What n8n is and how it works: nodes, triggers, webhooks, cloud vs self-hosted, AI agent nodes, revenue workflows and the basics of error handling.
Read · 9 min →mithub.clubRevenue EngineeringWhat Is Revenue Engineering? The Complete Guide
Revenue engineering explained: MitHub's definition, why it exists now, the follow-the-money method, the four families of systems, the roles and how to learn it.
Read · 12 min →mithub.clubFaculty of Revenue Reverse Engineering — videos
The new game
AI already does the repetitive work
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Follow the money
From the payment back to its origin
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Diagnose
Understand before you build
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Prove value fast
A small result you can measure
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Operate
Your role, iterating, the client
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Your case study
Verified proof on MitHub
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