Develop Yourself

Free interview prep guide: ⁠https://www.parsity.io/interview-prep⁠Free career roadmap for developers: ⁠https://www.parsity.io/career-roadmap⁠Carter Nadain spent four years trying to break into software development. He started with a bootcamp in 2022, got his CS degree, worked at Amazon delivering packages, sent out thousands of applications, and went through every kind of setback you can imagine — including having to move back in with his mom at 25 and completely restart his life.In this episode, Carter shares what actually worked: the interview tip that landed his current job, why recording yourself talking through problems is the most underrated prep strategy, and how he stayed consistent for four years when most people quit after three months.We also get into imposter syndrome, what it's really like walking into your first dev job, and why this career rewards people who refuse to stop.

Join the free weekly live session: parsity.io/aiHope I don't regret this.I'm giving away the exact AI engineering curriculum I teach at Parsity. The same stuff that helped change my own career trajectory and has recruiters sliding all in my DMs.How LLMs actually work (and why knowing this helps you push back on the hype)RAG from scratch — embeddings, vector databases, chunking strategiesBuilding with Pinecone, Weaviate, or QdrantStructured outputs with Zod + OpenAI/Anthropic SDKsObservability with LangSmithLLM-as-Judge evals so your agents don't silently degradeThere's a free project linked below where you build a LinkedIn writing clone using my actual posts and articles as training data. No fluff, no theory. Just build the thing.💅 Free RAG project: https://www.parsity.io/ai-with-rag

A practical playbook for landing a junior developer role in 2026. Topics include modern interview formats, which languages and frameworks to learn, and building one over‑engineered side project with AI and analytics. Tactical LinkedIn posting and authentic networking strategies are covered. Advice on applying smartly and presenting yourself as a problem‑solving developer rounds out the conversation.

A few years ago, my advice to developers would've been simple: learn the fundamentals, get good at system design, master your language of choice. And honestly? That advice still feels good to give. But it doesn't feel right anymore.Knowing how to code is table stakes now. It's not enough to get you hired.In this episode, I break down the three levels of AI skills I'd be investing in right now if I was a new grad, a bootcamp grad, or even a senior engineer looking to transition — and why the bar for "hireable" has shifted dramatically in the last few years.Level 1: Actually getting good at the tools. Not being a prompt monkey — having opinions on Claude, Cursor, git worktrees, and knowing why you accept or push back on what the agent gives you.Level 2: Building on top of AI. MCP servers, RAG pipelines, agents. This is where the biggest career opportunity is right now, and it's where the smallest pool of people actually know what they're doing.Level 3: The deep end: data engineering, pipelines, model hosting, fine-tuning. Less sexy, fewer positions, but massively defensible if you get in early.If you're trying to figure out where to put your time right now, this one's for you.

Discover how to move heavy browser work off the main thread with web workers to keep interfaces responsive. Learn about generator functions that pause and resume code for pagination and streaming data. Explore web components to package isolated UI pieces and build framework-agnostic micro frontends. Hear why learning niche JavaScript tools sharpens your developer toolkit.

Funny and frank stories about crashing production with a triple nested loop and shipping UI regressions that disabled buttons. Honest takes on craving to stop being seen as junior and choosing risky tasks to learn. Reflections on feeling like the weakest engineer in the room and how being near stronger teammates becomes a learning opportunity.

A hands-on walkthrough for building an AI advisory board that answers questions in the style of real influencers. Steps include calling an LLM programmatically, adding system prompts and guardrails, and creating a simple backend and frontend. Learn how to fetch and organize YouTube transcripts, inject relevant data with naive RAG, and know when to add vector databases.

Resources mentioned 👇parsity.io/ai-with-ragThe Router Pattern – A Smarter WayI mentioned that I got a lot more requests for interviews. Here's the proof: Da Proof3 Ways We Work with Software Developers (and noobs) 👇AI Dev Program – parsity.io/ai-devApply for our full stack software engineering programLearn JavaScript in 30 days... the hard way

I created a free project for developers who want to learn AI the hard way: https://parsity.io/ai-with-ragI don't envy new coders. AI is being jammed down your throat and at the same time, you're being told you must understand the code it generates.It's a confusing time.Using AI too early comes with a cost. More importantly, we'll look at some proven ways to make sure you are learning deeply and can work efficiently with your little code bot.You can read the study from Anthropic here: https://www.anthropic.com/research/AI-assistance-coding-skills

A developer recounts how 58 targeted applications produced 16 interviews across nine companies. They discuss why most resumes get filtered out and what hiring managers actually look for. Behavioral STAR stories and choosing the right level get highlighted as early gates. The conversation covers DSA interview patterns, the importance of speed, and why focused applications beat blasting hundreds.