Fast Company

Zapier

Buffer

Anna Burgess Yang

Solopreneur Operations Expert

The Solopreneur Tech Stack: What You Need, What You Don't

Walk through a simple audit to see your real spend and find the tools that overlap or aren't worth the cost

Learn the evaluation framework

Identify the signs that a tool has stopped meeting your business needs and compare alternatives

Know when to switch tools and how to pick the right one

Calculate what your time is actually worth to decide when paying for a tool saves you money

Stop letting free plans cost you hours every week

AI capabilities are changing constantly. Learn to take advantage of what they can do, without getting overwhelmed.

How AI fits into your tech stack

In this course, you'll build real AI systems for your actual work - the kind you open on Monday morning and use.

Marketing Consultant. Founder. Teacher.

Silvi Specter

Marketing consultant. Founder. AI systems builder. Previously: Early Lemonade.

Cohort 2

Cohort 3

AI is supposed to save you time. So why does it still feel like you're doing everything yourself?That's the difference between "using AI" and having AI systems. This course gets you there.In five weeks, you'll build custom voices, AI projects, and automation workflows you'll use every day. I used these systems to go from 40 to 20 hours/week on marketing work while growing from $18k to $25k MRR.Here's what you'll gain from this course:→ Know exactly when to use Claude, ChatGPT, and Gemini - and which other tools are worth your time→ Stop rewriting AI drafts - get content that sounds like you out of the gate→ Create content and campaigns faster with AI projects that have your briefs, audience research, and brand guidelines→ Stop losing afternoons to follow-ups, meeting notes, and reporting - build workflows that handle repetitive stuff automatically→ Turn transcripts and research into clear customer insights in under 1 hour→ Give your team an AI system to work from so you're not fixing everything yourselfYour AI tools will know your brand, audience, and how you like to work. What used to take half a day will take an hour. And the time you lost to execution will finally be free.

Stop drowning in execution. Build AI systems that save you 5-10 hours per week.

Reclaim 5-10 hours weekly by systematizing the executional work that drowns you

Stop wondering if you're using AI the right way - and get ahead of it

Finally cut through AI tool FOMO and build your focused tech stack

Ship customer-centric content that actually sounds like your brand (not ChatGPT)

Get customer insights & strategy feedback you couldn't get on your own

Build delegation systems so others execute at your quality level

You should be able to apply your projects to the day-to-day work you need to save time on

Active work to apply AI systems to

In-House Startup Marketerswho are doing the work of a full team, and leadership assumes AI can fix that. You need systems to make it true.

Consultants &amp; Fractional Executiveswith clients who want faster &amp; cheaper work because "AI can do it." You need systems to make it happen.

Solo Operators Wearing Multiple Hatswho handle everything with no team to hand off to. You need infrastructure that works when you're not.

Master the systems to stop micromanaging and transition from a referee to an orchestrator of a self-running team.

Founder at The Strength Hack

Daimler AG

Airbus

Audi

Toll Collect GmbH

Murk Creusen

Founder & Leadership Coach at Strength Hack

Founding Cohort

You cannot fix burnout by working harder. You have a matching problem.AI is making your team's hard technical skills obsolete faster than they can learn them. You cannot fix this "AI anxiety" or burnout by asking them to work harder. You don't have an effort problem; you have a matching problem.Most Engineering Managers spend 70% of their week trapped in the "Mediocrity Loop" - refereeing personality clashes, fixing broken handoffs, and watching their team's shared knowledge collapse as junior roles vanish. You're bleeding runway and morale.This 30-day accelerator gives you the system to break out. Using the Big Five (or CliftonStrengths) assessment, a proven framework, and the Strength Hack AI, you'll map your team's cognitive architecture and engineer the conditions for psychological safety - not just hope for it.In 4 weeks, you will:<ul><li>Stop Generalist Exhaustion: Identify energy leaks and delegate based on Cognitive Fit, not calendar space. </li><li>Engineer Swift Trust: Use the 6-dimension Matching Compass to translate friction into synergy.</li><li>Drive ROI: Transition from a reactive referee to a strategic orchestrator</li></ul>

Engineer Psychological Safety. Stop Refereeing Your Team.

Shortcut the "Storming" Phase to Engineer Swift Trust

Reclaim 70% of Your Week (Plug the Energy Leak)

Turn Personality Clashes into Solvable Data

Restore the "Who Knows What" Architecture Your Team Lost

Walk Away with a "Plug-and-Play" Facilitation Kit

To get ROI from this course, a real-world team (direct reports or agile squads) is recommended to apply the Matching Compass frameworks.

Team Leadership or Management Role

You must take the free Big Five test (or CliftonStrengths) before to map your personal psychological operating system.

Completion of a Baseline Assessment

The Overwhelmed Engineering Manager / Tech LeadYou lead a hybrid or remote team. You want a data-driven system, not another workshop.

The Ambitious Senior / Staff Engineer<blockquote>You're moving to people manager. You want proven frameworks to lead a team without micromanaging.</blockquote>

The People-Conscious CTO / CPO<blockquote>You own culture and delivery for 10–200 people. Your board wants AI readiness. You need L&amp;D that's credible.</blockquote>

Evals aren't your product moat. A continuous improvement flywheel is.

AI Founder & Advisor to F500s | Ex-AWS

Amazon Web Services

Microsoft

University of Oxford

Aishwarya Naresh Reganti

June Cohort

Check out our free AI Evals Certification <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/ai_evals_for_everyone/README.md">here</a> (5000+) Learners!Most teams building AI products today know about evals and LLM judges, but they’re deeply confused about how to actually turn them into actionable improvement signals post production. Just throwing in LLM judges won’t magically fix your product. It won’t.<ul><li>This course shows how to use offline evals, online evals, and production monitoring to drive continuous improvement, based on real production experience.</li><li>We believe that going forward, a thoughtfully designed data flywheel will be the moat for AI applications. This course shows you how to build it.</li><li>This course is for people who’ve already built AI systems and are stuck with evals that are noisy, expensive, or uselessIf you need something more foundational, check out our top-rated flagship<a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design"> enterprise AI course</a> taken by over 1500+ builders and leaders.</li><li>For questions or bulk enrollments please email problemfirst.ai@gmail.com</li><li>We follow a flipped-classroom format with async lecture content and meet twice a week for office hour style sessions to optimize two-way/interactive live time.</li></ul>

Move beyond one-time evals to continuous improvement flywheels for AI products

Understand what evals actually mean and why it’s misunderstood

Build a pre-deployment evaluation foundation from scratch

Choose the right types of metrics for the system you’re building

Monitor in production and close the improvement loop

You should have built small AI applications before and be familiar with concepts like RAG, MCP, and tool use.

A good understanding of AI system design

The course includes optional Python assignments, you're free to use coding agents, but basic coding skills are recommended

Working knowledge of Python/Coding (Optional)

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants looking for systematic evaluation practices

Business Leaders and Product Managers seeking to gain the technical understanding of AI Product Lifecycle and intentional iteration

Founders &amp; teams stuck at systematic evaluations and monitoring, unsure how to turn signals into real product improvements.

Stop babysitting AI. Build a workforce that learns and acts on its own.

Non-Technical Founder w/ 8 AI Employees

PwC UK

Amazon

Capital One

Coda

PayPal

Brennan Collins

Non-technical founder running an 8-agent AI workforce. Coached 500+ PMs.

Founders

You're paying for ChatGPT, Claude, and three other AI tools with "agents" built in. Yet you're still working 60-hour weeks, micromanaging what happens in each tool.Every session starts with a fresh prompt. Every output needs a rewrite. Every "AI agent" you trained sat there waiting for instructions like a temp on day one.Most AI workshops teach you to hand out tasks to AI. None teach you to build a workforce that operates without you.This 2-hour hands-on session is different. You'll "hire" the first employee of an AI workforce that wakes up with goals, learns its own skills, experiments inside your business context, logs lessons, collaborates with agents you add later, and moves work forward while you sleep.You leave with: • An installed (and free) agent operating system • A completed Agent Role Charter • Your first agent live and running a real task from your business • A self-learning loop so the agent gets smarter weekly • A one-page roadmap for agents two through fiveThis is the same system Brennan uses to run Unabated Products with a workforce of 8 agents handling content, sales, course design, and operations, all without daily babysitting.Stop using AI. Start hiring it.

Solo founder doing all the thinking. AI tools help but you're still the bottleneck on every output your business produces.

PMs tired of AI that needs babysitting. You want agents that run research, draft specs, and update stakeholders without you driving them.

Small business operator allergic to hiring overhead. You'd rather build an AI workforce than manage payroll, onboarding, and human issues.