Launching today
EasyEnv
Interview Engineers in Real Work Environments
73 followers
Interview Engineers in Real Work Environments
73 followers
EasyEnv helps companies hire engineers who can actually ship. Candidates solve real-world engineering problems with access to machines, databases, services, logs, and job-like tools. Teams can optionally allow AI chatbots or agents, then evaluate how candidates prompt, verify, debug, and solve problems in practice. Every session is recorded, scored, and easy to review, so hiring decisions are based on real evidence.
Hey Product Hunt 👋
I’m Mo, co-founder of EasyEnv.
In EasyEnv, companies can give candidates real-world problems they might face day to day. Candidates get access to the whole environment, including machines, databases, services, logs, and tools, so they can investigate, debug, and solve problems the way they would on the job.
Companies can also choose to let candidates use AI chatbots or agents during the interview.
Instead of treating AI as cheating, teams can see how candidates actually use it: how they think, prompt, verify, debug, and move faster without losing judgment.
Our goal is simple: help companies hire engineers who can actually ship in the AI era.
We built EasyEnv because engineering interviews should reflect how people actually work today.
I’d love your feedback: how should companies evaluate AI skills during technical interviews?
@efazati
Having a history of AI usage during interview seems a good idea, maybe tracking their token usage would be interesting as companies started to see the efficiency of using AI!
Overall, Seems very nice product, I like it till here!
Seem like a nice product. One question, when a candidate uses AI during the interview, how do you separate "strong engineering judgment" from "fluent with this specific AI tool"? A candidate who lives in Claude Code or Cursor every day will move very differently from someone equally skilled who just hasn't built the muscle memory, even if their underlying judgment is identical. How does EasyEnv score the thinking (how they prompt, verify, and catch the AI being wrong) without accidentally rewarding tool familiarity or penalizing it?
@arash_mousavi That’s a great question, and honestly one of the reasons we built EasyEnv this way.
We don’t want to score candidates just on how fast they move with a specific AI tool. Tool familiarity can definitely create noise. What we care about is the engineering process around AI: how they break down the problem, what they ask AI, how they verify the output, how they react when AI gives a wrong or incomplete answer, and if they can still reason through the system themselves.
So the AI usage is not scored in isolation. It is evaluated together with the full session: terminal activity, code changes, logs, debugging steps, final result, and the decisions they made along the way.
A candidate who is very fast with Cursor or Claude Code may look smoother, but if they copy blindly or fail to validate, that should show up. On the other hand, someone less familiar with the tool can still score well if they show strong debugging, good judgment, and careful verification.
Our goal is not to reward “AI power users” by default. It is to help teams see how candidates use AI as part of real engineering work.
Huh! Hopefully interviewing will be less stressful if more companies adopt this solution, instead of having to go refresh l33t coding skills every couple of years :P
@nickrossolatos Haha exactly 😄 That is a big part of the idea.
Most engineers don’t spend their day solving LeetCode problems. They read logs, debug systems, understand services, use tools, ask good questions, and figure things out.
We want interviews to feel closer to the real job, and hopefully less stressful for candidates too.
Hi Product Hunt!
I'm one of the co-founders of EasyEnv.
Building EasyEnv has been an interesting journey because we kept asking ourselves one question: Why don't technical interviews look more like the actual job?
Instead of whiteboard exercises or isolated coding challenges, we wanted candidates to work in real environments, investigate real issues, collaborate with AI when appropriate, and demonstrate how they think under realistic conditions.
Seeing the first teams use EasyEnv has been incredibly rewarding, and we're just getting started.
Thanks for checking us out, and we'd love to hear your feedback. What would make technical interviews feel more representative of real engineering work?
This is incredible, far superior to offline programming tasks or basic technical QA. you get to witness candidates in action firsthand.
@akira_asimov Thank you, really appreciate that 🙏
That’s exactly what we’re trying to improve. Offline tasks and basic technical Q&A can miss a lot of signal, because you don’t really see how someone works.
With EasyEnv, teams can watch candidates in action: how they investigate, debug, use tools, make decisions, and solve real problems. That gives a much clearer picture than just reviewing a final answer.
This is awesome, it's solving a real problem for us. We've had exactly this challenge and tried adapting Coderpad and other coding interview tools, but none of them felt built for it. We also tried a more handmade setup with Codespaces, but it took a lot of boilerplate config and ended up clunky.