AI, Web3 &You:Building Smarter, Decentralized Solutions for the Future

The Spaces convened a mentorship webinar for the Tech Africans Scholarship (Tech Crush Bootcamp), focusing on breaking into Artificial Intelligence and Web3. Host Sabrina guided speakers Abdullaziz Jimo (Full‑stack & AI Engineer), Miracle (AI/ML Engineer in insurance), and Ola Hamid (Smart Contract Engineer & Security Researcher) through practical pathways, learning strategies, and career outlooks. They emphasized self-awareness in choosing paths (AI research vs. engineering), the primacy of fundamentals, the power of community, and building and showing projects early. Abdullaziz outlined AI’s role as an enabling technology, its likely persistence and evolution over 5–10 years, and cautioned against “tutorial hell.” Miracle stressed foundations first, then project work to reveal gaps and deepen skills. Hamid demystified Web3 as the decentralized internet enabled by blockchain, detailed diverse roles (beyond coding), and advocated mentorship-driven bootcamps. Q&A covered UI/UX with AI (use AI as a tool), fairness and bias (data is the main source), reliability challenges (data, compute, deployment), and Web3 security learning paths. The session closed with candid advice on purpose, resilience, and leveraging cohort learning and capstones to accelerate progress.

Mentorship Webinar: Getting Started and Growing in AI and Web3

Who spoke

• Host: Sabrina (opening); Abisula (main host and moderator)
• Steven (Process Engineer using AI for systems)
• Miracle (AI/ML Engineer in the insurance industry)
• Abdullaziz Jimoh (Full‑stack Software and AI Engineer)
• Ola Hamid (Smart Contract Engineer and Security Researcher; co‑founder of Bubble Fire on Monad; contractor at Salamanda)

Why this session

A mentorship webinar for applicants and beginners evaluating AI and Web3 tracks under the Tech for Africans Scholarship/Tech Crush Boot Camp. The goal: demystify AI and Web3, share real journeys, outline how to start, common pitfalls, and provide clear next steps. The boot camp emphasizes live classes, structured curriculum, community learning, and project building (including capstones). Scholarship results are targeted for Dec 1; applications close Friday.

The case for AI and how it’s evolving

• Abdullaziz’s view: Technology is an enabler that changes how we solve problems across domains. AI has moved from simple systems to complex ones; it disrupts but also creates new opportunity tracks. Media can distort perceptions, but the core is: AI enables new ways of doing things and will remain central in 5–10 years, with improved approaches and fresh paradigms.
• Host’s reinforcement: No matter the path, expect continuous evolution. Your edge is learning to use AI to enable your work, staying adaptable, and evolving your skills.

Should beginners start with AI as a first tech skill?

• Abdullaziz: It depends on you—your strengths, weaknesses, motivations, and the sub‑field you choose.
  • If math is a weak point, the research/theory side may be tougher; the engineering side (serving models, applied systems) might fit better.
  • There is no single “correct” answer; self‑awareness and commitment are key.

AI journeys and lessons learned

• Abdullaziz: Explored web, then AI, back to web (due to math burden and compute constraints), then returned to AI on the engineering side (serving models and building AI solutions to business problems).
  • Key lessons:
    • Choose a foundation (language, tooling; start small with scripts and projects).
    • Don’t learn in isolation—join communities for guidance and momentum.
    • Avoid “tutorial hell.” After a structured course, build things (however small or imperfect) and iterate.
    • Share your work; visibility invites feedback and opportunities.
    • Master fundamentals; don’t treat everything as a black box.
    • Don’t chase every buzzword; go deep on one focus before broadening.
• Miracle: Engineering background → Data analysis → Backend/Full‑stack → Returned to AI/ML (Python) after ~3 years because of opportunity and enduring interest. Still builds applications while specializing in AI.
  • Advice to beginners:
    • Prioritize foundations first. Projects will always be there.
    • Once basics are solid, replicate projects and build your own; projects reveal gaps and accelerate learning.
    • Domain specialization pays off—apply AI to specific verticals (e.g., insurance).

Web3 in plain terms and why it matters

• Hamid’s framing: Blockchain’s core promise is decentralization—returning control (e.g., over data) to users, versus centralized platforms that track and monetize behavior. Web3 is the internet layer powered by blockchain that enables this shift.
• Day‑to‑day (Hamid): Security research and smart contract auditing—finding bugs, researching vulnerabilities, and improving protocol safety.

How to start in Web3

• Best path for beginners: Structured learning with mentorship (e.g., this boot camp). Learning together reduces isolation and accelerates progress.
• For builders: You will write code (smart contracts), learn token mechanics, and understand blockchain fundamentals. Foundations also help non‑coding roles.
• Opportunities span many roles beyond engineering:
  • Developers (smart contracts, protocol, dApp frontends)
  • Security/auditing/research
  • NFT artists/creatives, product/design, legal (blockchain law), marketing, community moderation, and more
• Skills required to begin: Curiosity; you can start from zero and build up.
• Outlook: Web3 is early; despite noise (e.g., scams), core innovation and real solutions will endure over the next 5–10 years.

Handling self‑doubt and staying motivated

• Hamid: Have a clear “why” (money, impact, curiosity—any is valid). Show up consistently; momentum compounds.
• Host: The boot camp is demanding (3 months, live classes, assignments, projects). Your “why,” resilience, and community support will get you through.
• Abdullaziz: Don’t wait for a perfect plan. Pick a path, build small things, and surround yourself with supportive people.

Q&A highlights (selected)

• Web3 cybersecurity path (Starboy → Hamid):
  • First learn to build: smart contracts, deployment, and core patterns.
  • Then specialize in security research and auditing. Understanding how things are built is prerequisite to securing them.
• Cybersecurity basics (Amelia): Deferred—dedicated cybersecurity webinar is scheduled for Saturday.
• Bringing AI into UX (Henry → Abdullaziz):
  • Treat AI as an enabler within your domain (e.g., design ideation, tooling like Figma plugins, structured prompting for planning/flows).
  • You don’t need to switch careers—use AI to enhance your design process and outcomes.
• AI learning paths—math vs engineering (Audience → Abdullaziz):
  • Math/research‑heavy: AI researchers, many data scientists (optimization, theory, new architectures).
  • Engineering side: Serving models, applied systems, fine‑tuning, MLOps, productization (e.g., LLM fine‑tuning for NLP tasks).
  • Choice depends on your background, interests, and tolerance for math.
• Where bias/unfairness enters AI (Suki → Abdullaziz):
  • Primarily at the data stage. Biased/imbalanced data leads to biased models; models reflect their training data.
  • Mitigate with better data practices (diversity, representativeness, evaluation for fairness).
• “Hidden” attributes for progress (Suki → Abdullaziz):
  • Community, focus, consistent building, and visibility (show your work) to attract feedback and opportunities.
• Basics and monetization in Web3 (Suleman → Hamid):
  • Boot camp covers fundamentals through smart contract development; monetization follows from roles (dev, auditing, creative, product, etc.).
• Is Web3 “coding”? (Philip → Hamid):
  • For developers, yes—contracts and algorithms are code. But many valuable non‑coding roles exist; foundations help you pivot intelligently.
• Challenges in building reliable/interpretable AI systems (Starboy → Abdullaziz):
  • Data: quality, quantity, representativeness.
  • Compute: sufficient resources to train/serve.
  • Model selection/experimentation: try, measure, iterate; no one “best” template.
  • Deployment/serving: MLOps, reliability, monitoring.

Boot camp notes and logistics

• Who it’s for: Complete beginners and up; some learners already have programming background in adjacent areas.
• Structure: Live classes with tutors, community support, projects throughout, capstones bringing tracks together.
• Proof of growth: A prior cohort student built 4 projects in one quarter; regular public sharing of work is encouraged to build portfolio and attract feedback.
• Timeline: Applications close Friday; scholarship announcements targeted for Dec 1. Not everyone will get a scholarship; if you do, maximize the opportunity.
• Ongoing mentorship series: Additional webinars throughout the week, including a “non‑code” day (virtual assistants, technical writers) and a dedicated cybersecurity session on Saturday.

Closing advice from speakers

• Miracle: Follow your passion—choose work that sustains your curiosity and joy through challenges. Specialize in a domain for compounding rewards.
• Hamid: Have a strong “why,” pick a track, and commit. Bootcamps and mentorship put you on the right path.
• Abdullaziz: Don’t wait for perfect plans. Build small, share imperfect work, and stay close to good people.

Practical guidance for beginners

• AI
  • Start with foundations: Python, data handling, core ML concepts, evaluation metrics.
  • Build incrementally: replicate tutorials → small projects → domain‑specific problems.
  • Pick a side: research/math (theory, new models) vs engineering (serving, integration, MLOps). Choose based on strengths and interests.
  • Join a community; avoid learning alone.
• Web3
  • Learn blockchain fundamentals, smart contracts, patterns, and security basics.
  • Code if you aim to build; otherwise apply foundations to non‑coding roles (product, legal, design, marketing, moderation).
  • Seek mentorship; practice via audits, CTFs, or open‑source contributions if leaning into security.

Pitfalls to avoid

• Learning in isolation—leverage community for guidance and momentum.
• Tutorial hell—consume, then build; don’t get stuck in endless courses.
• Chasing buzzwords—go deep on one area before broadening.
• Hiding your work—share early and often to gain feedback and visibility.
• Skipping fundamentals—underpinning concepts make advanced work tractable.

Key takeaways

• AI and Web3 are transformative and enduring; both will evolve rapidly—so must you.
• Your “why,” not hype, should drive your choices.
• Foundations first; then learn by building and sharing.
• Community accelerates clarity, resilience, and opportunity.
• Both coding and non‑coding paths exist—align with your strengths and interests.