THE INTERVIEW PHANTOM BLOG
Coding interview guides, LeetCode patterns, system design, interview tools, and the skills that help you explain your work.
Archive months identify the periods covered. Each article shows its publication date; republished articles identify their earlier publication and current update.
Practice speaking naturally in job interviews with personal coaching: clear answers, comfortable pacing, eye contact, thoughtful pauses, and unscripted follow-ups for tech and sales roles.
Read practice guide →Compare hardware-level, software-level and OS-level stealth interview copilots. See documented competitor limits and how complete hardware separation changes the guidance path.
Read practice guide →Practice explaining coding solutions aloud with a worked duplicate-detection example, AI follow-up prompts, and a checklist for reasoning, complexity, and edge cases.
Read practice guide →Rehearse a sales engineer discovery interview with a 20-minute practice plan, an AI buyer prompt, and a checklist for human feedback.
Read practice guide →Practice account executive STAR interview answers with a worked missed-target example, five sales questions, AI follow-ups, and a human coaching scorecard.
Read practice guide →Explore a hardware-separated technical interview assistant for coding and system design practice, with dedicated human coaching for candidates targeting U.S. and Australian roles.
Read practice guide →An opening-of-month reflection on the tools now available and the different problems each can help you practice.
Two friends reported being detected on HackerRank. We examine InterviewMan's background app, Interview Monster's Chrome extension, and why fully separate hardware changes the software-detection boundary.
September’s Google AI plan update added voice-driven work across familiar apps, making an oral first draft a practical starting point.
An updated speech-following engine and script editor offered a useful rehearsal question: how much prompting do you really need?
Prepare for azure interview questions with concise answers, architecture scenarios, code examples, and a practical framework for explaining trade-offs.
Compare the best coding interview books by skill level, topic, and study use, then build a practical reading plan with problems to solve and review.
Research the Cluely founder through primary sources, with a dated company timeline, claim-checking method, and links readers can verify directly.
Cluely valuation claims can vary by source. Learn how funding announcements, reporting dates, and valuation terms shape what those claims mean.
Learn how a CoderPad interview works, what the shared coding environment changes, and how to prepare with a practical coding workflow and example.
Learn the CodeSignal assessment format, task workflow, preparation strategy, and a worked coding example with code and clear complexity analysis.
Practice coding interview questions by pattern, then study worked Python answers, explanation prompts, edge cases, and time and space complexity.
Looking for a Cracking the Coding Interview PDF? Find legitimate access options, edition guidance, and a practical way to use the book for focused prep.
Learn how to explain your approach in a coding interview with a clear talk-through framework, worked example, complexity analysis, and recovery phrases.
Use this grokking the coding interview guide to turn pattern lessons into a repeatable study plan, with a worked interval-merging problem and code.
A practical guide to Grokking the System Design Interview: its core framework, study trade-offs, and a worked URL shortener design you can reuse.
Practice hirevue interview questions with adaptable answer frameworks, sample responses, recording tips, and a preparation checklist for candidates.
Prepare for a machine learning system design interview with a reusable framework, worked recommender example, trade-offs, and evaluation questions.
Prepare for node js interview questions with concise explanations, runnable examples, event-loop reasoning, API design scenarios, and follow-up prompts.
Learn the OpenAI interview process stage by stage, what each conversation may assess, and how to prepare coding, system design, and behavioral examples.
Learn how to practise LeetCode without memorising solutions using pattern recall, spaced review, fresh variants, and a readiness test for interviews.
Learn how many LeetCode problems before interviews you actually need, with readiness tests, pattern coverage, review rules, and a practical stopping point.
Prepare for react js interview questions with clear explanations, practical exercises, common pitfalls, and a focused plan for technical interviews.
Searching Roy Lee Cluely? Learn who the founder is, what he has said publicly, what the desktop assistant does, and which claims you can verify.
Learn how to spot a dynamic programming problem using repeatable clues, state design, recurrence tests, and a worked House Robber solution in Python.
Use these technical interview questions to practice concise answers across coding, debugging, system design, computer science, and project discussions.
Use this video interview setup checklist to test your camera, audio, lighting, network, workspace, backup plan, and screen-sharing workflow.
August’s transcription news brought attention back to the recording: the source material for any critique of a spoken answer.
This leetcode 75 guide reorganizes the official list by pattern, shows how to study each group, and works through a representative problem in Python.
A practical guide to the Amazon online assessment: its sections, what the coding problems really test, a two-week plan, and an honest read on the rest.
The Blind 75 explained: where the list came from, what it leaves out, and a six-week schedule for working through all seventy-five problems.
A coding interview cheat sheet worth keeping open: complexity budgets, data structure costs, pattern triggers, Python idioms and a pre-submit checklist.
A map of the LeetCode patterns worth learning: what triggers each one, a short Python template you can write from memory, and three problems to practise on.
The monotonic stack explained: why a sorted stack answers next-greater queries in one pass, how to pick the direction, and six worked problems in Python.
NeetCode vs LeetCode, compared honestly: one is the judge and problem source, the other a curriculum built on top of it — and how to divide your time.
Explore ShadeCoder's documented features, product shape, workflow, trade-offs, and alternatives, plus a concrete checklist for comparing interview assistants.
The sliding window algorithm explained: fixed versus variable windows, one Python template covering both, and eight worked problems with complexity.
Thirty SQL interview questions with worked, runnable answers: joins, NULL semantics, window functions, dedupe, dates, pivots and query plans.
Topological sort in Python: Kahn's algorithm and the DFS variant side by side, why cycle detection comes free with both, and four worked LeetCode problems.
Google’s July how-to showed another everyday use for visual assistance. A demonstration still needed a beginning, a decision, and a result.
June’s speech-translation development suggested a useful practice distinction: translating the words and explaining the idea are separate skills.
May’s I/O announcements broadened AI workflows. For candidates, the useful response was to ask how the work of the role might change.
Google’s April roundup included Learn Mode in Colab, a useful prompt to practice understanding before polishing technical answers.
March’s professional-work release offered another reason to practice explaining how you checked an AI-assisted result.
A private interview copilot needs more than a hidden overlay. Evaluate separate hardware, resume handling, audio retention, and realistic privacy promises.
A model update is a useful occasion to revisit how you connect past experience to the role you want next.
January’s connected-app announcement made personalized assistance more concrete. Useful interview context still needed careful selection.
Understand the difference between screen-share invisibility, device privacy, and physical visibility before relying on an undetectable AI interview assistant claim.
Long-context work offered another preparation use: finding contradictions across the examples you plan to discuss.
A release focused on complex work offered a timely reason to rehearse the questions that a polished project story leaves unanswered.
October’s Camera Hub release supported a larger prompter. Display size is useful only when the whole speaking setup works together.
Compare separate-hardware assistance with software overlays by where answers are generated, displayed, and captured. Hardware separation is useful, but has limits.
More capable agents could produce more preparation material. The important step was checking the brief before practicing from it.
The API’s general availability was an infrastructure milestone; a useful practice session still required clear questions and review.
A repeatable method for the 45-minute coding interview: how to budget the clock, pick the pattern from the constraints, narrate while you type, and defend your complexity.
Meta has piloted coding interviews where candidates may use an AI assistant. Here is what an AI-allowed loop really tests, how to prepare for it, and why most companies still say no.
Vibe coding means accepting AI-generated code without reading it. That is a real technique with a narrow safe range — here is where it works, where it fails, and how to review what it produces.
A guided learning experience offered a useful model for interview practice: work through the problem instead of collecting finished responses.
A family of fast and deeper models suggested matching the assistant to the exercise rather than defaulting to the biggest option.
Concerned about a copilot being discovered? Learn why a hidden screen-share window is a narrower promise than complete stealth, and what evidence to request.
May’s coding-focused release made a familiar distinction sharper: producing code and explaining engineering judgment are different tasks.
A visual conversation could follow what you were showing. A good walkthrough still needed a deliberate route through the work.
March’s audio releases highlighted the link between what you say, what a system hears, and the feedback you receive.
Extended reasoning is useful for preparation. Interview delivery needs the conclusion and the decisions that support it.
January’s reasoning-model release made it timely to distinguish a model’s worked answer from an explanation you can give yourself.
December’s advanced voice update made visual conversations more tangible, opening another route for rehearsing explanations.
Connecting assistants to data raised a practical question: what should a coach know before suggesting your next answer?
October’s speech-to-speech API showed why response timing belongs in a conversation design, not just a speed benchmark.
Elgato’s September release brought speech-following scrolling to Camera Hub. Rehearsal could now focus more on pacing.
A free-flowing voice interface made it easier to imagine a mock interview that changes direction with you.
July’s Gemini update is a useful reminder that quick, focused feedback can be easier to act on than a long report.
A new model and an editable workspace suggested a better way to organize examples without turning them into rigid scripts.
The launch included an interview-preparation demonstration. A convincing demo still needed a repeatable practice routine.
A research demonstration put facial motion in the spotlight, while interview preparation still depended on your own clear explanation.
March’s model release invited a better feedback loop: less rewriting, more questions that test the substance of your story.
A larger context window could hold more preparation material. The useful part was deciding which material mattered.
January 2024 brought a directory of specialized assistants. Choosing a useful practice partner still required a clear brief.