← Anthropic Interview Insights
I came in expecting a concurrency question, recruiter literally told me that's what they'd assess.
Start by clarifying requirements (scope, politeness, data extraction) and then design a single-threaded crawler with a clear pipeline: URL frontier, fetcher, parser, and storage. Walk through the implementation, emphasizing correctness, robustness, and extensibility to multi-threading.
Pro tip: Mention that you would use a politeness policy (e.g., robots.txt and rate limiting) even in the single-threaded version, as it shows production awareness and sets the stage for scaling.
Ask about scope (domain restrictions, depth), politeness (robots.txt, rate limits), and data to extract. Confirm single-threaded for now but note future scaling.
Outline the main components: URL frontier (queue), fetcher (HTTP client), parser (HTML parser), and storage (visited set, output). Explain how they interact.
Describe the crawl loop: dequeue URL, check if visited, fetch page, parse for links and data, enqueue new URLs, store results. Handle errors and retries.
Incorporate robots.txt checking, rate limiting, and error handling (timeouts, HTTP errors). Discuss deduplication and normalization of URLs.
Explain how to extend to multi-threading (e.g., thread-safe queue, worker pool) and mention trade-offs like concurrency control and politeness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I skipped most of this because I was nervous about looking slow or like I'd just memorized a template answer for a classic problem.
Start by framing the problem: clarify the crawler's purpose, scope, and constraints with the interviewer. Then systematically walk through key dimensions—URL normalization, fragment handling, scheme filtering, and stopping conditions—proposing sensible defaults while noting trade-offs and asking for confirmation.
Pro tip: Demonstrate pragmatism by suggesting a 'crawl frontier' with a visited set and robots.txt compliance, and mention that you'd log decisions for observability. This shows you balance correctness with operational concerns.
Ask what the crawler is for (e.g., indexing, monitoring, data extraction) and its boundaries (domains, depth, rate limits). This determines which edge cases matter most.
Discuss standardizing URLs: lowercasing scheme/host, removing default ports, resolving dot-segments, sorting query parameters, and handling percent-encoding. Propose a canonical form to avoid duplicates.
Decide whether to strip fragments (usually yes, as they don't affect server content) and which schemes to allow (http/https only, or also ftp, mailto). Explain the rationale.
Define when to stop: max pages, max depth, time limit, no new links, or error thresholds. Discuss how to detect and handle cycles and traps.
Summarize assumptions and ask for feedback. Suggest starting with a minimal viable crawler and adding complexity as needed, emphasizing testability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge the interviewer's language preference gracefully and adapt immediately, while briefly noting your own preference for clarity. Frame it as a collaborative adjustment that ensures mutual understanding and keeps the focus on problem-solving.
Pro tip: Proactively ask which language the interviewer prefers at the start of the interview, or if the switch happens mid-way, mirror their choice and then confirm key points in both languages to avoid miscommunication.
Immediately accept the interviewer's language preference without resistance. Say something like, 'Of course, I'm happy to continue in [their language].'
Briefly restate the current topic or question in the new language to ensure you understood correctly. Ask for confirmation if needed.
Continue explaining your backend engineering concepts clearly, using terms that are standard in the new language. If unsure of a term, describe it.
Periodically ask if the interviewer is following your explanation, and invite them to correct or guide you if needed.
After the interview, note the experience as a lesson in adaptability. Consider practicing technical interviews in multiple languages.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I did send a note to the recruiter, kept it factual: the prep I'd done based on their guidance, what actually happened in the room, and that I'd have liked a chance to show the concurrent implementation.
Acknowledge the frustration but emphasize a professional, objective approach: first seek clarification on the misalignment, then decide whether to provide feedback or appeal based on the response and your goals. Focus on maintaining relationships and learning, not on reversing the decision.
Pro tip: If you do send feedback, frame it as a request for clarification or a desire to improve, not as a complaint. This shows emotional intelligence and keeps the door open for future opportunities.
Determine if the misalignment was a genuine misunderstanding or a deliberate change. Consider if providing feedback could benefit you or the company.
Reach out to the recruiter or hiring manager to politely ask for clarification on the interview process and any discrepancies. This can resolve issues without formal feedback.
If clarification doesn't resolve concerns, decide whether to provide constructive feedback or a formal appeal. Appeals are rarely successful but may be warranted if there was a clear procedural error.
If you proceed, write a concise, respectful message focusing on facts and your interest in the role. Avoid blaming or emotional language.
Regardless of outcome, reflect on the experience and use it to improve future interviews. Maintain a positive relationship with the company.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.