How to Use AI to Make Software Development Accessible

My Suggestion to Young Computer Science Undergrad Students in Nepal When these young students graduate in a year or two, there will be very few jobs available to them. This is because the entry-level jobs traditionally given to fresh software engineers will increasingly be done by AI. This change is happening globally, and Nepal won’t be an exception. So, young students must prepare themselves to be productive at the level of a senior engineer — with the help of AI. If you rely only on what you’re learning in college and expect a company to train you after hiring, you will be left behind. Here are stepwise suggestions tailored for the Nepali context: 1. Learn to Communicate with GenAI Tools Like ChatGPT, Gemini, and MetaAI You should know how to ask the right questions, and then refine those questions again and again until the tool gives you something meaningful. Most students in Nepal still don’t know how to use these tools well — mastering them early will give you a serious edge. 2. Use AI to Prepare Real Work Artifacts Start using AI to write technical documentation, QA plans, and code. Even if the output is not always perfect, you will learn by reviewing and fixing it. Have the patience to debug AI-generated code and edit AI-written documents. That skill will be more important than memorizing syntax. 3. Treat AI as Your Personal Tutor In Nepal, not everyone has access to high-quality teachers or mentors. That’s where AI can fill the gap. You have a 24/7 assistant that can help you understand concepts, debug code, and explain things clearly. Learn how to use it well. 4. Present Yourself as “AI-Powered” When Applying for Jobs When you graduate, you should be able to show that you can work like a team of three people — because you know how to use AI. Employers in Nepal, especially in startups or outsourcing companies, want efficiency. They will hire someone who can do more with less — and AI makes that possible. 5. Build a Portfolio that Reflects AI-Augmented Development Don’t just say you can use AI — show it. Build projects (websites, automation tools, apps) using AI tools, and publish them on GitHub or your own portfolio. Make it clear that you know how to use AI to speed up development and produce quality output. Final Note for Nepali Students: AI is not here to replace you — it’s here to work with you. If you stay passive, you will fall behind. But if you embrace AI early and smartly, you can leapfrog many of your peers — not just in Nepal, but globally.

90% of engineers using AI coding tools are doing it wrong. They're treating AI like a code monkey. Fire prompt → Get code → Accept all changes → Ship. That's why we see 128k-line AI pull requests that became memes (look this up, it's a fun read). After spending quite a bit of time using AI dev tools, I discovered the real game isn't about generating more code faster. It's about rapid engineering while managing cognitive load. My workflow now: 1. Start with AI-generated system diagrams 2. Ask questions until I understand the architecture 3. Create detailed change plans 4. Break down into AI-manageable chunks 5. Maintain context throughout This isn't coding. It's orchestration. The best engineers aren't typing anymore. They're conducting symphonies of AI agents, each handling specific complexity while the human maintains the vision. Think about it → We're moving from IDEs to "Cognitive Load Managers." Tools that auto-generate documentation, visualize dependencies in real-time, and explain impact before you commit. The future isn't AI writing code. It's AI helping you understand what code to write. The billion-dollar opportunity? Build the tool that turns every engineer into a systems architect who happens to code. We're not being replaced. We're being promoted. Who else sees this shift? #AI #SoftwareEngineering #DevTools #FutureOfCoding #TechLeadership

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.