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Lintic: Open Source AI Coding Assessment
Everything you need to hire and assess AI-native engineers
Lintic is an open-source platform for evaluating AI workflows. Instead of LeetCode or HackerRank, candidates use a browser IDE and AI agent to solve real tasks. Measure how engineers direct AI and decompose problems under constraints. With zero SaaS dependency and total privacy, Lintic assesses the skills that matter for modern teams. It uses WebContainers to run code in the browser, cutting your compute costs to zero.
Hey Product Hunt! I’m Oleg, the creator of Lintic.
If your engineers don’t write code without AI, why are you still interviewing like they do? LeetCode feels like a ghost of the past. Modern engineering is about direction, decomposition, and iteration. I built Lintic to assess how people actually work in 2026.
Features I’m excited about:
The Constraint System: This is the core signal. You can limit tokens and interactions to see whether a candidate uses AI strategically or just brute-forces prompts.
Simulated Infrastructure: We already support a mock Postgres service that runs in the browser, with realistic behavior designed to test judgment under pressure. The broader vision is to simulate more real-world infrastructure over time.
WebContainers Runtime: I’m excited that we can provide a full Node.js environment in the browser. That means zero server-side compute costs for you.
Agent Interface Protocol: Lintic is agent-agnostic. You can plug in OpenAI, Anthropic, or your own custom models to see how candidates handle different AI personalities.
Adversarial Profiles: You can toggle settings that stress-test code with slow queries or queue backpressure to see how a candidate recovers from errors.
AI-Native Review Process: Reviewing is AI-forward too. For each session, you can inspect what happened and chat with the agent about the candidate’s approach, tradeoffs, mistakes, and recovery. It gives you a much richer signal than just reading the final code.
Lintic is entirely self-hostable via a single Docker image, so you keep full control over your data and your API budget.
I’d love to hear your thoughts.
What signals would you use to evaluate someone in an AI-native coding interview?
Looking forward to your feedback!
About Lintic: Open Source AI Coding Assessment on Product Hunt
“Everything you need to hire and assess AI-native engineers”
Lintic: Open Source AI Coding Assessment was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #154 on the daily leaderboard. Lintic is an open-source platform for evaluating AI workflows. Instead of LeetCode or HackerRank, candidates use a browser IDE and AI agent to solve real tasks. Measure how engineers direct AI and decompose problems under constraints. With zero SaaS dependency and total privacy, Lintic assesses the skills that matter for modern teams. It uses WebContainers to run code in the browser, cutting your compute costs to zero.
On the analytics side, Lintic: Open Source AI Coding Assessment competes within Hiring, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Lintic: Open Source AI Coding Assessment performed against the three products that launched closest to it on the same day.
Who hunted Lintic: Open Source AI Coding Assessment?
Lintic: Open Source AI Coding Assessment was hunted by Oleg Mrynskyi. 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.
For a complete overview of Lintic: Open Source AI Coding Assessment including community comment highlights and product details, visit the product overview.
Oleg Mrynskyi
Hey Product Hunt! I’m Oleg, the creator of Lintic.
If your engineers don’t write code without AI, why are you still interviewing like they do? LeetCode feels like a ghost of the past. Modern engineering is about direction, decomposition, and iteration. I built Lintic to assess how people actually work in 2026.
Features I’m excited about:
The Constraint System: This is the core signal. You can limit tokens and interactions to see whether a candidate uses AI strategically or just brute-forces prompts.
Simulated Infrastructure: We already support a mock Postgres service that runs in the browser, with realistic behavior designed to test judgment under pressure. The broader vision is to simulate more real-world infrastructure over time.
WebContainers Runtime: I’m excited that we can provide a full Node.js environment in the browser. That means zero server-side compute costs for you.
Agent Interface Protocol: Lintic is agent-agnostic. You can plug in OpenAI, Anthropic, or your own custom models to see how candidates handle different AI personalities.
Adversarial Profiles: You can toggle settings that stress-test code with slow queries or queue backpressure to see how a candidate recovers from errors.
AI-Native Review Process: Reviewing is AI-forward too. For each session, you can inspect what happened and chat with the agent about the candidate’s approach, tradeoffs, mistakes, and recovery. It gives you a much richer signal than just reading the final code.
Lintic is entirely self-hostable via a single Docker image, so you keep full control over your data and your API budget.
I’d love to hear your thoughts.
Looking forward to your feedback!