This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
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.
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?
This is a really strong direction for technical hiring. As someone who works more on infrastructure/SRE/cloud than pure algorithms, I’ve always felt realistic debugging and system investigation tasks give a much better signal than LeetCode style questions.
Curious how EasyEnv handles scoring. does it mainly evaluate the final solution, or also the candidate’s investigation path, tool usage, and decision making during the session?
The part that gets me is being able to actually watch how someone works instead of just grading the end result. Has a session ever flipped your read on a candidate? Like someone who looked great on paper but kind of fell apart once things got messy, or the opposite - someone you weren't sure about who turned into a total debugging machine the second they had real logs in front of them. Curious if you've seen that yet.
Love this! It's so hard to get meaningful signal during interviews. The closer you can get to an actual prod scenario, the more valuable is the signal you get from the candidate.
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.
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?
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
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?
This is incredible, far superior to offline programming tasks or basic technical QA. you get to witness candidates in action firsthand.
About EasyEnv on Product Hunt
“Interview Engineers in Real Work Environments”
EasyEnv was submitted on Product Hunt and earned 40 upvotes and 19 comments, placing #28 on the daily leaderboard. 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.
EasyEnv was featured in Hiring (15.4k followers), Artificial Intelligence (473.1k followers) and Career (2.1k followers) on Product Hunt. Together, these topics include over 120.4k products, making this a competitive space to launch in.
Who hunted EasyEnv?
EasyEnv was hunted by Mo Efazati. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how EasyEnv stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Mo Efazati
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?