Different times.
New skills.
Guides, articles and faculty videos on AI, automation, revenue and careers: useful, sourced, and connected to something you can build.
A Two Hour Audit of Your Revenue Process
A self audit any leader can run without tools or consultants: ten leads, six questions, and the leak becomes obvious.
Read · 2 min →mithub.clubAI & AutomationAI Agents for CPA Firms: What Works Outside Season
Where AI agents earn their place in an accounting practice: reactivating past filers, qualifying enquiries the moment they arrive, and removing the manual steps that eat the off season.
Read · 2 min →mithub.clubAI & AutomationAI Calling for Consumer Lenders with Branches
How a multi branch lender puts AI on the phone without breaking compliance or the branch relationship: which lists to call, what the agent should and should not do, and how each branch sees its own numbers.
Read · 2 min →mithub.clubAI & AutomationAI for Staffing Agencies: Where It Helps and Where It Hurts
Sourcing, screening, scheduling and redeployment: which parts of an agency workflow AI actually improves, and the one place it damages the relationship you sell.
Read · 2 min →mithub.clubAI & AutomationAI for Ticket Resolution and Documentation: Demo vs Production
The tools demo well and stall in production. What actually works, what does not, and the setup that keeps a documentation assistant from becoming shelfware.
Read · 2 min →mithub.clubRevenue EngineeringAI Receptionist for Home Services: What It Has to Do
In home services the job goes to whoever answers first. What an AI receptionist must capture, how dispatch rules work, and how to size the problem before buying anything.
Read · 2 min →mithub.clubAI & AutomationAI SDR vs Human SDR: What Each One Is Actually For
A practical comparison for revenue leaders: what an AI SDR does well, what it does badly, the hybrid that works in production, and the failure mode that wastes the investment.
Read · 2 min →mithub.clubAI & AutomationAI Voice Compliance Checklist Before You Dial
What to have in place before an AI agent calls or texts your customers: registration, consent, disclosure, recording, calling windows and the records that prove it.
Read · 2 min →mithub.clubRevenue EngineeringAt What Point Do Spreadsheets Stop Working for Sales?
The specific failures that mean a spreadsheet has stopped being enough, what usually breaks first, and how to move without losing the thing that made it work.
Read · 2 min →mithub.clubAI & AutomationAutomating User Onboarding and Offboarding Across Clients
Provisioning and deprovisioning across many client environments, without a pile of custom scripts nobody maintains. What to standardise, what to keep per client, and the offboarding risk nobody prices.
Read · 2 min →mithub.clubGTM EngineeringBuild or Buy: GTM Automation Decision Guide
When to buy a platform, when to build on an automation layer, and when to hire someone to own it. The costs each option hides and the questions that decide it.
Read · 2 min →mithub.clubRevenue EngineeringCase Study: A Reactivation System That Closed the First Contract
A tax services company connected cold list reactivation, outbound, AI calls and CRM handoffs into one system, and traced its first signed contract back to it.
Read · 2 min →mithub.clubAI & AutomationCase Study: From a 10 Branch Pilot to 28 Live Branches
A consumer finance network put an AI agent on its dormant borrower list. What was built, the numbers it produced, and the four things that made the rollout possible.
Read · 2 min →mithub.clubRevenue EngineeringCase Study: Replacing One Company Average with 28 Branch Reports
A calling platform reported a single number for the whole network. Pulling its API into per branch reporting changed what managers could do about it.
Read · 2 min →mithub.clubn8nChoosing an Automation Platform: n8n, Zapier or Make
A buyer's comparison of the three automation layers most revenue teams choose between, the cost model that decides it, and when self hosting is worth the operational burden.
Read · 2 min →mithub.clubRevenue EngineeringCRM Data Quality Checklist Before You Automate
Twelve checks to run on your CRM before automating anything on top of it, why each one breaks automation downstream, and the order to fix them in.
Read · 3 min →mithub.clubAI & AutomationDesigning the Moment an AI Agent Hands to a Human
The escalation rules that decide whether an AI channel helps your team or embarrasses it: what triggers a handover, what travels with it, and what happens when nobody is available.
Read · 2 min →mithub.clubAI & AutomationForward Deployed Engineer vs Consultant vs Professional Services
Three ways to buy implementation help, told apart by what each is accountable for, what you own at the end, and which one fits a rollout that has already stalled once.
Read · 2 min →mithub.clubRevenue EngineeringFractional RevOps or a Full Time Hire?
When a fractional RevOps arrangement works, when it quietly becomes a dependency, and the hiring timeline that usually decides it for you.
Read · 2 min →mithub.clubGTM EngineeringGTM Agency vs In House vs Dedicated Talent: The Real Comparison
Three ways to buy go to market execution, what each actually costs once you include the parts nobody quotes, and the third option most comparisons leave out.
Read · 2 min →mithub.clubGTM EngineeringGTM Engineer Job Description (Template You Can Copy)
A job description for a GTM engineer written around accountability instead of tools, with the sections to keep, the ones to delete and a version you can paste and edit.
Read · 3 min →mithub.clubGTM EngineeringGTM Engineer vs Forward Deployed Engineer: Which One Do You Need?
Two roles that overlap and solve different problems. What each owns, the question that tells them apart, and when a company needs both.
Read · 2 min →mithub.clubGTM EngineeringGTM Engineer, RevOps or SDR: Who You Actually Need
Three roles that get confused in hiring plans, told apart by what each one is accountable for, the symptoms that point to each, and the order to hire them in.
Read · 3 min →mithub.clubGTM EngineeringHiring a GTM Engineer Remotely: The Practical Guide
How to hire a GTM engineer remotely: what the role is accountable for, how to write it around one number, what to ask, and the paid trial that avoids a bad contract.
Read · 4 min →mithub.clubAI & AutomationHow Long Does an AI Implementation Actually Take?
Realistic timelines for GTM and AI automation projects, what makes them slip, and the milestones to agree before anyone starts building.
Read · 2 min →mithub.clubAI & AutomationHow Long Should Onboarding a New Client Take?
Onboarding that runs six to eight weeks is usually discovery disguised as setup. What to standardise, what genuinely varies, and where the weeks actually go.
Read · 2 min →mithub.clubAI & AutomationHow Much Should AI Be Allowed to Write to Your CRM?
A five level permission ladder for AI in a system of record, which actions belong at each level, and the audit trail that makes any of it defensible.
Read · 3 min →mithub.clubRevenue EngineeringHow to Automate Follow-Ups to Old Leads Your Reps Never Contacted
There are leads in your CRM that nobody ever called. How to find them, decide which are worth reviving, and build the follow up without burning the list or your domain.
Read · 2 min →mithub.clubRevenue EngineeringHow to Choose a CRM Implementation Partner
What to ask an agency, consultant or contractor before signing: the four questions that predict the outcome, the pricing shapes and the clause that protects you if it ends badly.
Read · 2 min →mithub.clubAI & AutomationHow to Choose an Agentic AI Implementation Partner
Twenty questions that separate a partner who will reach production from one who will stall in your company, grouped by what each protects you from.
Read · 2 min →mithub.clubGTM EngineeringHow to Hire a GTM Engineer for a Non-SaaS Company
Most advice about this role assumes a venture backed SaaS company. If you run branches, clinics, agencies or field teams, here is how the role changes and how to tell whether you need it.
Read · 4 min →mithub.clubRevenue EngineeringHow to Migrate a CRM Without Breaking What Runs on It
The sequence that keeps a CRM migration from taking the automations, the reporting and the team's trust with it.
Read · 2 min →mithub.clubRevenue EngineeringHow to Run a Database Reactivation Campaign
The revenue sitting in your existing customer list, how to segment it, what to say, which channel to use first and how to prove the result.
Read · 2 min →mithub.clubRevenue EngineeringHow to Standardize Lead Follow-Up Across Multiple Locations
Every branch does it differently and each one is sure theirs is right. The configuration model that makes follow up identical everywhere without taking local judgment away.
Read · 2 min →mithub.clubAI & AutomationIn House or Fractional: How to Buy Forward Deployed Engineering
The work is intense and finite, which is exactly the shape a full time hire fits badly. What each model costs, and the handover clause that decides whether fractional works.
Read · 2 min →mithub.clubRevenue EngineeringIs Low CRM Adoption a User Problem or a System Design Problem?
Teams get blamed for not using the CRM. Usually the design asked them to do work with no return. How to tell which one you have and what actually changes it.
Read · 2 min →mithub.clubRemote WorkNearshore Staff Augmentation for GTM and Implementation Roles
The trade offs that matter when augmenting a team with remote revenue talent: overlap hours, communication load, retention and what each model costs in practice.
Read · 2 min →mithub.clubAI & AutomationOur Vendor's Implementation Stalled. Who Finishes It?
The software is paid for and the rollout stopped. Why it happens, what is usually left undone, and the four ways out with what each one costs.
Read · 2 min →mithub.clubAI & AutomationPatient Intake Automation for Clinic Networks
Where automation helps a multi site clinic and where it must not go: missed calls, recalls and no shows, with the privacy and escalation rules that come first.
Read · 2 min →mithub.clubRevenue EngineeringReactivating an Insurance Book Without Annoying the Book
An agency's renewal and cross sell list is its most valuable asset and the easiest to damage. How to work it with automation while keeping the producer relationship intact.
Read · 2 min →mithub.clubAI & AutomationRolling Out AI Calling Across a Branch Network
What changes when an AI calling pilot has to work in thirty locations: treating a branch as a configuration, the compliance gate, branch level reporting and the QA that keeps it honest.
Read · 3 min →mithub.clubClaySignal-Based Account Scoring Without Runaway Enrichment Costs
Enrichment is priced per row, so the cost scales with your list rather than with your results. How to order the steps so you pay for signal instead of for volume.
Read · 2 min →mithub.clubRevenue EngineeringSpeed to Lead: What Good Looks Like in 2026
What response time actually does to conversion, realistic targets by channel, and the four things that make a company slow.
Read · 2 min →mithub.clubGTM EngineeringThe First 90 Days of a GTM Engineer
A month by month plan for the company hiring one: what should exist at day 30, 60 and 90, what to protect them from, and the signals that the hire is not working.
Read · 2 min →mithub.clubAI & AutomationThe Pilot to Production Gap in AI Projects
The pilot worked and the rollout did not. The six failures that sit between a demo and a production system, and what has to exist before the second unit goes live.
Read · 3 min →mithub.clubRevenue EngineeringThe Sales Handoff Checklist
What has to travel with a lead when it moves from marketing, an agent or an SDR to the person who closes, and what happens when it does not.
Read · 2 min →mithub.clubGTM EngineeringWe Are Hiring a GTM Engineer. What Should We Look Out For?
What to look for when someone shows you their work: the four questions that separate a real deployment from a demo, and the answers that should make you pause.
Read · 2 min →mithub.clubRevenue EngineeringWe Do Not Want Another Platform. Can You Work With What We Have?
The most common question buyers ask implementation help, answered honestly: when your existing stack is enough, when it is not, and how to tell the difference before spending anything.
Read · 2 min →mithub.clubAI & AutomationWhat AI Automation Actually Costs to Run
The cost lines nobody models before launch: model usage, platform execution, telephony, enrichment, and the human time that does not disappear.
Read · 2 min →mithub.clubRemote WorkWhat GTM and Forward Deployed Talent Costs in Latin America
Cost for the specific roles, not for developers in general: what moves the number for GTM engineers and forward deployed engineers, what the quote leaves out, and the comparison that decides it.
Read · 3 min →mithub.clubRevenue EngineeringWhat Should You Pay Someone to Make Cold Calls?
Per hour, per call, per conversation or per meeting: what each payment model actually buys, which one produces the behaviour you want, and what nobody quotes.
Read · 2 min →mithub.clubRevenue EngineeringWhat to Automate First in a Revenue Team
A prioritisation method that ranks automation candidates by hours returned, error cost and effort, plus the five that pay back fastest in most companies.
Read · 2 min →mithub.clubRevenue EngineeringWhat to Measure in GTM Automation (and What to Ignore)
The metrics that tell you whether an automated revenue system is working, the vanity numbers that hide failure, and the one check that catches silent breakage.
Read · 2 min →mithub.clubRevenue EngineeringWhen to Automate a Process (and When Not To)
Five tests that tell you whether a process is ready to automate, the three that mean wait, and why automating too early is more expensive than doing nothing.
Read · 2 min →mithub.clubAI & AutomationWhen to Hire a Forward Deployed Engineer
The buyer's view of the forward deployed engineer: the problems that need someone embedded in your systems, how the engagement is scoped, and how to tell a real one from a consultant with slides.
Read · 3 min →mithub.clubRevenue EngineeringWhen to Stop Buying SaaS and Build the Integration Layer
You bought five products and now you are building all the connections anyway. The four signals that it is time to own the layer between them, and what owning it actually means.
Read · 2 min →mithub.clubRevenue EngineeringWho Owns an Automation After It Goes Live?
The question nobody answers when the build finishes: who monitors it, who fixes it when an API changes, who approves changes, and what happens when the person who built it leaves.
Read · 2 min →mithub.clubAI & AutomationWhy Automations Fail Silently in Multi-Branch Operations
A workflow that stops running produces no error. In a single office someone notices. Across thirty locations nobody does, for weeks.
Read · 2 min →mithub.clubRevenue EngineeringWhy Your Outbound Email Stops Landing
The technical and behavioural reasons cold email quietly stops reaching inboxes, in the order they usually happen, and what to fix first.
Read · 2 min →mithub.clubRevenue EngineeringWhy Your Reps Still Forecast in a Spreadsheet
You bought the CRM and the conversation intelligence tool, and the forecast still gets built by hand. The reasons that is rational, and what actually fixes it.
Read · 2 min →mithub.clubRemote WorkWorking With Nearshore Teams on Healthcare Data
HIPAA is about safeguards and agreements, not about geography. What has to be in place before a remote team touches protected health information, and what to ask before signing.
Read · 2 min →mithub.clubAI & AutomationAgentic 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 · 9 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 · 7 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 · 8 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.clubNothing matches that.
Faculty 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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