Roadmap · Earn independently
AI video and design for clients (AI images and design)
Opens with creditsFor students and early-career creatives who want to earn by making ads, explainers, shorts, product shots, thumbnails and brand visuals with AI tools. By the end you can plan work that sells, direct the tools with consistent results, stay inside each tool's terms and each platform's rules, and package, sell and deliver the work like a professional.
Curated by Tauseef Fayyaz8 stages, 43 topics~6 months at a few hours a weekBy the end, you will be able to
- Name what businesses actually buy in AI video or AI imagery, and read the demand evidence without hype
- Apply the creative basics AI does not do for you: hooks, story, pacing, composition, colour, type and brand consistency
- Pick the right tool for each job and keep characters, products and styles consistent across a whole set of assets
- Check commercial-use terms, consent and platform labelling rules before anything you make is published
- Offer clear packages, find and keep clients, and deliver the right files in the right formats for every platform
The path
8 stages, in the order to learn them. Open one to start.
1What clients actually buyBefore any tool: the kinds of images businesses pay for (product shots, ad creatives, thumbnails and brand kits), why AI changed the price and speed of each, and where demand is heading.5 topics · ~2 weeks
- 1What the demand data saysUpwork's In-Demand Skills 2026 report, published in February 2026, measured freelancer earnings on completed jobs for US clients in 2025 against 2024.
- 2What AI changed and what it did notAI tools cut the cost of making a first version.
- 3Image work clients pay forFour kinds of image work make up most paid AI design jobs.
- 4Product images that match the productAI can place a product on a beach, in a kitchen or in a model's hand in minutes.
- 5Choosing one service to startA service you can sell is one format, for one kind of client, with one clear result.
5 in this stageOpen this stage2Design fundamentals AI does not replaceThe skills that decide whether an AI image is usable: composition, visual hierarchy, colour, typography, contrast at small sizes and brand consistency.6 topics · ~4 weeks
- 1Hooks and the first secondA hook is whatever stops someone scrolling.
- 2A simple story structureEven a 15-second ad or a single image tells a story.
- 3Composition and visual hierarchyComposition is where things sit in the frame.
- 4Typography on imagesTypography is how text looks: the typeface, size, weight, spacing and placement.
- 5Colour and contrastColour sets mood and guides attention.
- 6Brand consistencyA brand is recognised by repetition: the same colours, fonts, logo placement, tone of voice and image style every time.
6 in this stageOpen this stage- The AI image tool stackImage generators (such as Midjourney, Ideogram, FLUX models and Adobe Firefly), editing with generative fill, and the design tools where final layouts are built (Canva, Photoshop or Figma).4 topics · ~5 weeksOpens with credits
- Prompting and consistencyTurning a brief into prompts that keep the same character, product and style across a whole set: structured prompts, style and character references, iteration and upscaling to print or ad sizes.5 topics · ~4 weeksOpens with credits
- Rights, ethics and platform rulesWhat you may do with what you make: each tool's commercial-use terms, who owns AI output, consent for real people's faces and voices, deepfake rules and the AI labels platforms require. This is not legal advice; it is the checklist that tells you when to read the terms or ask a lawyer.8 topics · ~2 weeksOpens with credits
- Building a portfolio and offersProof first, then packages: a small portfolio aimed at your service, and offers with clear deliverables, turnaround times, revision rounds and usage rights.5 topics · ~3 weeksOpens with credits
- Finding and keeping clientsWhere AI creative clients come from: marketplace gigs and proposals, direct outreach to businesses already running ads, and turning one-off jobs into monthly retainers. Platform basics live in the Freelancing roadmap; this stage is about the creative angle.5 topics · ~3 weeksOpens with credits
- Delivering like a proThe professional half of the job: a clear brief, versions and review rounds, the right formats and aspect ratios for every platform, and a clean handover the client can use without you.5 topics · ~3 weeksOpens with credits
6 more stages, 32 more topics, free with an account.
You have seen the open stages of the AI video and design for clients roadmap. Every topic it covers is listed above, and an account opens the rest: what each topic means, a question to check yourself, the courses, the practice and Tauseef's advice for every stage. Early access is open: sign up and your account is active shortly after.
Everything here is free to learn from. A few things, like adding a roadmap, keeping a cheat sheet or asking Tauseef a question, use credits. You get 15 when you join, your first roadmap is free, and if you are learning seriously and run out, you can ask for more. Credits only make sure the platform goes to people who are actually learning.
Build a project
A finished project proves more than a certificate.
- Start with this onebeginner1 semester · 1 to 2 studentsServerless Product Photo Pipeline for Small Online Sellers
If you can code a little, automate the product photo workflow from this roadmap: background clean-up, sizes per marketplace and a review step before anything goes live
- AWS, Azure or Google Cloud
- Object storage events
- Lambda, Azure Functions or Cloud Run
- CDN
- Python or Node.js
- intermediate1 semester · 2 studentsContent Credentials Checker for AI-Generated Images in Newsrooms
See provenance from the other side: read Content Credentials and explain what can and cannot be concluded about an image
- Python
- c2pa-python or c2patool
- ExifTool
- FastAPI
- PostgreSQL
Related on Sefism
Picked from the other libraries by shared topics.
Best practices
See all- Best Practices for Building LLM AppsHow to build LLM apps and agents that hold up in production: prompts, evals, prompt injection, tool permissions, cost, logging and privacy.
- Prompt Engineering Best PracticesHow to write prompts that work the same way on the hundredth input as on the first: clear instructions, structure, examples, reasoning, output control and testing.
- Best Practices for AI Coding AssistantsHow to get reliable code from AI coding assistants and agents: instruction files, task prompts, verification, review, security, dependencies and session hygiene.
Checklists
See all- RAG Production ChecklistA RAG production checklist: chunking, embeddings, hybrid search, reranking, per-user permissions, citations, evals, re-indexing and cost, with how to verify each check.
- LLM Evaluation ChecklistAn LLM evaluation checklist: success criteria, eval datasets, code and model graders, judge calibration, agent evals, safety tests and production monitoring, with how to verify each check.
- AI Feature Launch ChecklistAn AI feature launch checklist: use case fit, evals, UX for errors, disclosure, human override, abuse filters, rate limits, cost caps, prompt privacy, monitoring and rollback.
Project structures
See all- GenAI RAG app (FastAPI) project structureFor developers turning a "chat with your documents" demo into an app people can rely on. A FastAPI backend where every layer has a home: ingestion workers, retrieval, prompts as versioned files, model clients with fallbacks and cost caps, guards, evals that gate CI, and tracing, so a wrong answer can be traced to the step that caused it.
- AI agent with an MCP server project structureFor developers who want to give AI assistants safe access to their own data or API. A TypeScript MCP server built on the official SDK, organised by feature (tasks, projects) with each feature owning its tools, resources and prompts, served over stdio for local hosts and Streamable HTTP with OAuth for remote ones, plus a small agent and the evals that prove a model can use it.
- Machine learning project project structureFor students and analysts whose model works in a notebook and now needs to be reproducible, reviewable, served and watched. It starts from the Cookiecutter Data Science v2 layout (immutable raw data, numbered notebooks, a real Python package) and grows into versioned data with DVC, typed parameters, data validation with pandera, experiment tracking and a model registry with MLflow, a FastAPI prediction service, drift monitoring and, for a team, scheduled retraining, model cards, fairness checks and shadow deployment.
Career questions
See all- Which freelance skills is AI replacing, and what should I switch to?AI is squeezing basic, repetitive freelance work first: plain data entry, simple transcription and translation, generic articles, basic logos and simple product descriptions. Work that combines a skill with judgement, client context or systems is holding up better, including automation, AI integration, analytics, specialist writing and full projects that own a business outcome.
- Which AI tools do employers expect junior developers to know in 2026?Employers rarely require a specific brand. They expect you to use an AI coding assistant well (in the editor and in the terminal), to review and test what it produces, to call an LLM API in a small feature, and to explain when you would not use AI. Pick one assistant, learn it properly, and show judgement in your projects.
- How do I use AI to become a better engineer instead of becoming dependent on it?Decide the job AI does before you open it. Use it as a tutor, reviewer and sparring partner that explains and challenges, not a vending machine that hands you finished code. Attempt first, ask second, and always be able to explain and defend what you ship.
Cheat sheets
See allWork with me
Stuck on something specific?
Writing only gets you so far. If you want an answer to your situation rather than the general case, book a session and we will work through it together. Sessions are free for approved Sefism members, and a few slots open each week.
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