DEV Community: Tanya Melnikova

From Mid-Level to Senior: Why I Built a Product Thinking Simulator

Tanya Melnikova — Wed, 27 Aug 2025 19:58:27 +0000

Not long ago I felt stuck as an engineer. I shipped features, closed tickets, met deadlines… but nothing I did really changed the product. Meanwhile, friends around me were already seniors and principals — and I couldn’t shake the feeling that I was missing something.

At first, I thought it was about Leetcode or grinding system design. But then I realized: the people growing fastest weren’t just better coders. They were asking the harder questions:
Why are we building this? Who is it for? How will we know it’s working?

That’s when it hit me — what I lacked was product thinking.

So I built a small Product Thinking Simulator. It’s like a comic you play: short, story-driven scenarios where you face real dilemmas every engineer knows — shipping fast vs. building right, hacking vs. scaling, UX polish vs. technical debt

This is still at a very early stage, and right now I need as much feedback as possible to make it better.

🔗 Try the simulator (beta)

I’d love to know:

Would you actually use something like this?
What kinds of dilemmas should I add?
Do you think product thinking is the real leap from mid-level to senior?

❤️ Any feedback means a lot at this point!

PS: picture to grab attention

Engineers Are Becoming Product People. Here’s How to Prepare

Tanya Melnikova — Tue, 08 Jul 2025 12:50:02 +0000

I’ve been a full-stack engineer for 4 years, and like many of you, I’ve watched AI tools—Copilot, Bard, Gemini—rapidly take over boilerplate coding tasks. At first, I thought, “Great, more time for deep technical work!” But then I noticed my role shifting: I wasn’t just writing code anymore, I was being asked to define what to build and why.

After digging into recent posts from Google, Microsoft, OpenAI, Meta, and Amazon, I realized this isn’t a temporary fad. It’s a fundamental change: engineers are becoming product people. If you want to stay in demand—especially in the AI era you need to sharpen your product-thinking skills. Here’s what I learned and how I’m preparing.

The “AI Does the How” Mandate

“The job is shifting from writing code to asking the right questions and defining the ‘what’ and ‘why’ of a problem.”

— Srinivas Narayanan, VP Engineering, OpenAI

At OpenAI’s Sangam 2025 panel, Srinivas Narayanan called on engineers to act like CEOs of their feature areas. AI handles the implementation details; we must define the right problem to solve and ensure our solution delivers real user value.

Action Steps:

Write clear problem statements. Use templates like


If we build X, then Y user will achieve Z outcome.

Frame testable hypotheses. Before coding, ask: “How will we measure success?”

2. Prototype & Validate Early

“Building enterprise-quality AI applications requires much more than a model.”

— Hernandez & Castille, Google Cloud Blog, Jan 2024

Google Cloud’s team emphasizes rapid prototype-and-test cycles. Don’t wait for perfect data or a polished ML model—build a clickable UX prototype or a Wizard-of-Oz simulation, then get user feedback.

Action Steps:

Paper prototypes or Figma mockups to validate flows in under a day
No-code demos (Glide, Bubble) to test core logic with 5–10 users
AI simulations: prompt ChatGPT, “Act like a time-pressed user—what’s confusing on this screen?”

3. Adopt a Product Engineering Mindset

“Great experiences come from engineers who are customer-obsessed, data-driven, speed-oriented, and quality-focused.”

— Microsoft Inside Track Blog, Jan 2025

Microsoft’s modern engineering initiative rewrote team OKRs around business outcomes instead of tickets closed. Engineers now start each sprint with a product vision and continuous user feedback loops.

Action Steps:

OKR your sprint. Define one user-centric objective (e.g., “Increase 7-day retention by 5%”)
Telemetry–driven decisions. Use data (Mixpanel, Amplitude) to guide your next steps
Customer obsession. Read support tickets or chat logs weekly to keep empathy front and center

4. Blend Domain-Driven Design with AI

“Even in AI applications, maintain a clear domain model and separation of concerns.”

— Liam Connell, Google Cloud Community, May 2025

AI code assistants can generate prototypes in minutes, but without a solid domain model, you end up with unmaintainable code. Connell recommends anchoring AI-driven development in user and business logic.

Action Steps:

Map your domain. Sketch entities, workflows, and business rules before prompting AI
Iterate with modules. Generate and review one component at a time, then integrate

5. Prepare for Role Convergence

“Prompt engineering makes PMs more like engineers, and AI assistants make engineers more like PMs.”

— Raza Habib, Humanloop Blog, Feb 2025

As Habib observes, AI blurs the lines between product managers and engineers. PMs craft prompts, and engineers must think in user stories and metrics.

Action Steps:

Learn prompt engineering. Practice writing clear, goal-oriented prompts for LLMs
Master user stories. Translate technical tasks into story cards with acceptance criteria
Build cross-functional fluency. Participate in discovery workshops or design critiques

6. Invest in Product-Thinking Training

All these insights point to one thing: you need structured practice. I found the Product-Thinking with AI for Software Engineers course invaluable.

If you’re ready to step into the AI-era engineer role—one that blends coding with product leadership—check out the course here: Product-Thinking with AI

Let’s embrace the future where writing the right software is as important as writing software right.

Did these tips resonate? I’d love to hear your experiences: comment below or reach out!

References

OpenAI Sangam 2025 Panel Summary
Microsoft Inside Track: “Transforming modern engineering at Microsoft”
Google Cloud Community: “Applying AI to the craft of software engineering”
Humanloop Blog: “AI Is Blurring the Line Between PMs and Engineers”

Is This Happening at Your Company Too?

Tanya Melnikova — Sat, 07 Jun 2025 19:12:53 +0000

Lately, things have been changing at work. Our teams are now smaller and faster, and there’s no product manager in sight. As engineers, we’re the ones deciding what to build, why it matters, and how we’ll know if it’s working.

It’s a big shift. The expectation isn’t just to write solid code anymore. It’s to think like a product person: to weigh trade-offs, prioritize ruthlessly, and understand the humans on the other side of the screen. Honestly? It’s exciting. But also intimidating.

Some days I feel energized by the autonomy. Other days I wonder if I’m missing something crucial, like an insight a PM would’ve caught. Or I second-guess whether I’m thinking "product" enough to keep up.

👀 So I’m wondering:
Is this happening where you work, too?
Are engineers around you stepping deeper into product decisions?
How are you making that transition, and staying sharp as the role evolves?

P.S. A friend and I are playing around with a tiny tool to help devs strengthen their product thinking through quick, low-lift prompts. It’s still early, but if you’re curious, here’s a 5-minute preview: https://inmindlab.hyperskill.org
(Not a pitch — just sharing.)
But more than anything, I’d love to hear your take. Does this resonate?