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TierOnePrep

Interactive system design prep for FAANG & AI-era companies

Education
Developer Tools
Career
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Hunted byArun SinghArun Singh

Interactive interview prep for FAANG and AI-era companies (Meta, Anthropic, Nvidia, Google, OpenAI, SpaceX). System design questions are 4-phase simulations of real interviews — vague brief, clarifying questions, architecture, hard follow-ups. Coding questions show algorithms running live with step-by-step visualizations. Written by engineers who've conducted hundreds of interviews at top companies. 12 free questions, no credit card.

Top comment

Hey Product Hunt! 👋 TierOnePrep was born from a pattern we kept seeing on both sides of the interview table: strong engineers grinding hundreds of LeetCode problems, then freezing in the system design round. Not from lack of ability — but because nothing prepares you for how those interviews actually flow: the deliberately vague prompt, the clarifying questions that matter, the follow-ups that separate a hire from a strong-hire. So we built the platform around that exact flow. Every system design question is a four-phase interactive simulation of a real interview, and every coding question shows the algorithm running live — data structures updating step by step, not buried in a wall of code. We also focused on where interviews are going, not where they were. AI-era companies are asking about LLM inference, GPU scheduling, and vector databases — and almost no quality prep material exists for that yet. Our questions are written by engineers who've conducted hundreds of real interviews at top tech companies. There are free questions live right now, including full system design guides — no credit card needed. We'd love your honest feedback, especially: which system design topic do you wish had a proper deep-dive guide? We ship weekly, and that's genuinely what we'll build next. Thanks for checking it out! 🙏

Comment highlights

The system design simulations actually feel closer to a real interview than anything else I've tried, especially the way it forces you to ask clarifying questions first. Live algorithm visualization is a nice touch too.

The 4-phase system design format is genuinely smart, treating it like a real interview instead of just tossing a prompt at you. Really wish more prep tools approached it this way instead of just grading a single answer.

The system design simulations feel closer to a real interview than any Leetcode-style prep I've tried. Wish the coding visualizer ran a bit faster on bigger inputs, but the clarifying question phase alone is worth the sign up.

About TierOnePrep on Product Hunt

Interactive system design prep for FAANG & AI-era companies

TierOnePrep was submitted on Product Hunt and earned 0 upvotes and 4 comments, placing #152 on the daily leaderboard. Interactive interview prep for FAANG and AI-era companies (Meta, Anthropic, Nvidia, Google, OpenAI, SpaceX). System design questions are 4-phase simulations of real interviews — vague brief, clarifying questions, architecture, hard follow-ups. Coding questions show algorithms running live with step-by-step visualizations. Written by engineers who've conducted hundreds of interviews at top companies. 12 free questions, no credit card.

TierOnePrep was featured in Education (78.8k followers), Developer Tools (515.4k followers) and Career (2.1k followers) on Product Hunt. Together, these topics include over 111.4k products, making this a competitive space to launch in.

Who hunted TierOnePrep?

TierOnePrep was hunted by Arun Singh. 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.

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