AI Interview Assistant in 2026: What They Are, The 4 Categories, How Detection Works, and Who Actually Needs One

Most candidates land on this page after typing "ai interview assistant" into Google because they have an interview on Friday and a job-search runway that is shorter than they'd like. They want a tool that moves the needle. Inside that one search are four very different questions, and the marketing copy in this category blurs them on purpose: "what's the best tool for my Friday round?", "what's the best tool for the next six months of OAs?", "what's the best tool that won't get my offer rescinded?", and "what's the best tool I can use without telling anyone?". This guide is the opinionated walkthrough of every category in 2026, written from the inside of the prep market.

We build a practice-mode AI interview assistant (InterviewChamp.AI) so this is not a disinterested survey. It is an opinionated one, and the bias shows up explicitly each time it matters. The first lesson of the 2025 to 2026 cycle has been that the honest answer about this category is more useful than the marketing answer.

What an AI interview assistant is in 2026

An AI interview assistant is software that uses generative AI to help a candidate prepare for or perform during a job interview. The phrase covers four meaningfully different products, and the difference between them determines whether the tool builds your career or ends it.

The clean four-way split:

Real-time speech-to-AI-answer overlays. Run during the live interview. Capture the interviewer's audio through your laptop microphone. Stream it through production-grade speech recognition. Send the transcribed question to a frontier reasoning model. Render the generated answer in a translucent window positioned above the video-call surface but invisible to the screen-share layer. The category that has gone viral, gone venture-funded, and gone wrong for a documented cohort of candidates across 2025.

Screen-capture stealth tools. Run during coding rounds on platforms where the question lives inside a browser tab. Take a screenshot of your screen at intervals. Run optical character recognition on the screenshot to extract the problem statement. Send the extracted text to a frontier reasoning model. Surface the solution in a translucent overlay. The category most aggressively positioned as "undetectable" in 2025 to 2026 marketing, which is the language that should make you suspicious.

Mock-interview practice tools. Run before the interview. The AI plays the interviewer; you answer out loud, under realistic time pressure; the AI feeds back on structure, content, hesitation, and pacing after each session. The market leaders in this category have invested heavily in role-specific question banks, structured feedback rubrics, difficulty calibration, and platform-specific drill. This is the category we build in. It is the only category that compounds skill across a career.

Async coding assistants. Sit in the background during take-home assignments or live coding rounds. Some are full IDEs with AI baked in. Some are browser extensions that surface hints. Some are clipboard monitors that pop up answers when you copy a problem. The category most likely to get caught by the basic anti-cheating layer on a take-home assignment, because most platforms now compare your submission to GitHub history and detect suspicious paste behavior.

The first question to ask of any AI interview assistant is which of these four categories it belongs to. The second is whether the category survives a career arc. Most marketing copy blurs the first question and ignores the second. We walk through both below.

The 4 categories, side by side

The four categories solve different problems with different risk profiles. The table is the fastest way to see the tradeoffs.

Two things to notice. First, three of the four categories run during the interview. Only one runs before. The three-to-one ratio reflects where the venture money has gone, not where the safe outcomes live. Second, the only category with zero detection risk is also the only category with zero career-arc risk, which is not a coincidence. The categories that promise the short-term boost are the same categories that carry the long-term cost.

If you only read one paragraph of this guide, read this one: the AI interview assistants that get sold as "the tool that will get you through Friday" are not the same as the ones that build the career-long skill of interviewing well. The first category is a short trade. The second category is the durable asset.

How AI interview assistants work under the hood

For the technically curious, all four categories follow variations of the same four-stage pipeline. Knowing the architecture helps you spot the marketing language that papers over the limitations.

Stage 1: Capture. The tool takes input from one or more sources. Live overlays capture system audio (the interviewer's voice through your speakers or microphone). Stealth tools capture screenshots at intervals. Practice tools capture your microphone audio plus typed input. Async tools tap into your IDE or browser extension.

Stage 2: Transform. The captured input becomes structured text. Audio gets transcribed through a streaming speech-to-text model with latency in the 200 to 500 millisecond range for production-grade providers. Screenshots get OCR'd through a vision-language model. The transformation stage is where most of the engineering complexity sits, because the quality of the downstream answer depends entirely on the quality of the input transcription.

Stage 3: Generate. The structured text gets sent to a frontier reasoning model along with any context the tool has (your resume, the job description, the role type, the platform). The model generates an answer. The 2026 generation latency for frontier models is typically 800 milliseconds to 2 seconds for a short answer and 3 to 8 seconds for a long structured response.

Stage 4: Surface. The generated answer reaches you through whichever interface the tool uses. Live overlays render it in a translucent window. Stealth tools paint it on top of the captured screen region. Practice tools speak it through text-to-speech or show structured feedback. Async tools push it through IDE annotations.

The four-stage pipeline matters because each stage introduces a constraint the marketing copy usually hides. Capture limits what the tool can hear or see. Transform introduces transcription errors that cascade into wrong answers. Generate introduces hallucination risk and latency. Surface introduces the detection risk that is the whole story of the live-mode category. A tool that wins on stage 3 (best model) can still lose on stage 1 (poor audio capture) or stage 4 (visible overlay), and most of the disappointed-customer reviews trace to mismatches across the stages.

A specific detail worth knowing: the most common failure mode in the live-mode category is that the AI is confidently wrong because the transcription dropped a critical word in stage 2 and the generation in stage 3 didn't know it. The candidate reads the wrong answer out loud. The interviewer's face changes. The candidate has now paid the price of the cheating trade without getting any of the benefit. The mature tools in this category surface uncertainty when transcription confidence drops. Most of the venture-funded entrants in the category do not.

How detection works in 2026

The conversation about detection has shifted between 2024 and 2026. Two years ago the answer was "probably not detected unless you do something obvious." Today the answer is "detected often enough that the cohort outcomes are bad."

Five detection paths matter in 2026:

Time-to-answer rhythm analysis. The AI introduces a sub-second delay between the interviewer's question and your answer. Across a 45-minute round with 15 to 20 questions, the delay creates a pattern. Internal recruiter tools now flag candidates whose response timing fits the AI-assisted pattern, especially when the answers are unusually polished for the timing. Sixty seconds of thinking visible on your face produces a different signature than 0.8 seconds of micro-pause followed by a fluent answer.

Gaze tracking for overlay-window eye movement. Async video platforms have logged gaze patterns since 2019. Live video platforms increasingly add the same instrumentation for paid enterprise tiers. The overlay window sits in a specific screen region. Your eyes track to that region. Repeated micro-glances to the same off-center region pattern-match against the cohort of overlay users.

Screen-share plus webcam room scans during proctored rounds. The most common proctored format in 2026 is a full screen-share plus a 360-degree webcam pan at the start of the round. The screen-share catches any overlay running outside the proctored browser. The room scan catches second monitors, second laptops, and phones. Some platforms also require you to share both your screen and a phone-camera view of yourself simultaneously.

Curveball clarifying questions that break the AI's context. The interviewer asks an off-topic clarifying question mid-answer ("wait, when you said the database fails over to a replica, did you mean primary-replica or peer-to-peer?"). The AI's context window has moved on. The generated answer to the original question now includes the wrong follow-up. The candidate either reads the wrong answer or stalls awkwardly while waiting for the AI to catch up. Either way, the interviewer sees a tell.

Post-hire performance review at the 30-to-90-day mark. The catch-all. Even if all four live-round detection paths miss, the candidate now starts the job. Day three someone hands them a real ticket. Day 30 they're underperforming by enough that the manager schedules a check-in. Day 90 they're either on a PIP or they've been let go. The post-hire performance signal catches everything that the live-round detection missed, which is why the cohort outcomes for live-mode use are bad on a longer horizon than the candidate originally calculated.

The honest read is that the first four detection paths catch some live AI use, with the rate climbing in 2026 because the recruiter tools are getting better. The fifth path catches almost all of it eventually. The career-arc question is the one that should drive the decision, not the round-one question.

The honest-prep counter-positioning

The cleanest position in the 2026 AI-interview-assistant market is the one that the marketing copy of most competitors actively avoids: use the AI heavily in prep, close it before the round, walk in with the reps in your head.

This position works because the interview tests a real skill: recall-and-articulation under live observation. The skill is buildable. You can be bad at it at 23 and excellent at it at 26 if you put the reps in. The reps are exactly what an AI interview assistant excels at delivering, because the AI can run 25 mocks in a week without scheduling friction, get tired, or charge you per session.

What honest-prep specifically means in practice:

Treat the AI like a sparring partner, not a stand-in. A sparring partner pushes you, surfaces your weak points, and makes you better. A stand-in does the work for you. The AI is good at the first job and a liability at the second.

Run mocks on the role-type and platform you're facing. Generic prep is less useful than platform-specific prep. If you're interviewing at a Series B fintech on HackerRank in 10 days, your prep should be 20 HackerRank-style mocks on fintech-adjacent problems, not 50 general behavioral mocks.

Review session history the next morning. The first pass through a mock is the practice. The second pass, with fresh eyes, is the learning. Most AI interview assistants keep 30 days of session history. Use it. The gap between what you said and what you should have said is your next prep cycle.

Drill the gap, not the comfort zone. The temptation is to keep running mocks on the questions you nail. The compounding move is to keep running mocks on the questions you bomb. The mocks you hate are the ones building the skill fastest.

Close before the round. The whole point of prep is to walk in with the answers built into your reflexes. If you bring the AI into the round, you've made the prep optional, which means you didn't actually do it.

The honest-prep position is not a moral high ground or a sales pitch. It's the position that survives a 40-year career. Live-mode use is a short trade with a wide adverse tail. Prep-mode use is a long position on a skill that gets you the next 40 jobs. The math is asymmetric in favor of prep, and most candidates who do the math choose prep once they see the framing.

When an AI interview assistant helps vs when it backfires

Not every interview needs an AI assistant. Some formats benefit heavily from prep volume. Others reward muscles that prep volume doesn't build. The decision framework matters because pouring 30 hours of prep into the wrong format is more expensive than not prepping at all.

Helps most:

• Behavioral rounds with structured frameworks (STAR, SOAR, CAR, PAR). The format is repeatable, time-boxed, and pattern-heavy. 20 mocks builds reflex.
• Technical phone screens with predictable question banks. The 30 to 50 questions a phone screener might ask are well-documented. Drilling the bank works.
• System design rounds on common patterns. URL shortener, social feed, distributed cache, ride-share matching. The patterns are finite. Reps build pattern recognition.
• Async video interviews with time-boxed questions. Practice talking to a camera under a 90-second budget. The muscle is specific and the AI can simulate it.
• Coding OA prep on a specific platform. If you're facing a HackerRank OA on Tuesday, 15 HackerRank-style mocks in the right pattern category is the single highest-impact prep move.

Backfires most:

• Panel interviews with three or more interviewers. Multiple sets of eyes on your face. The AI lag is visible. Overlay use is the most exposed in this format.
• Whiteboard rounds in person. No laptop, no overlay. The prep transfers; the live-mode tool doesn't.
• Pair programming rounds with live observation. The interviewer is watching your IDE, your hands, your face, your code in real time. AI assistance is detectable and the format is conversational enough that the AI lag breaks it.
• Hiring-manager rounds with senior leadership. The questions are unpredictable, the format is conversational, and the interviewer has seen 200 candidates and pattern-matches faster than you'd think.
• Reference checks and informal coffee chats. These are not formal interviews but they're scored. AI use here is both pointless and reads as desperate.

A working decision rule: if the format involves a single interviewer on a video call with screen-share, the prep-vs-live tradeoff is the active question. If the format involves multiple interviewers, in-person elements, or pair programming, prep is the only viable mode. The candidates who get burned the most are the ones who used a live tool for prep and then couldn't shake the habit when the format shifted to panel or in-person.

The 5-question vetting checklist before paying

Before you put money down on an AI interview assistant, run the checklist. A tool that fails three of these five is not worth paying for.

1. What's the cancel flow? One-click cancellation from a dashboard you can find without contacting support, or buried in a 4-step process that requires an email to support@? The cancel flow is the single best signal of whether the company respects you as a customer. The 2026 market has matured enough that one-click cancel is table stakes. Anything else is a red flag.

2. What's the data-retention policy? Where do your audio recordings, transcripts, and resume data go? Are they deleted after 30 days, 90 days, never? Is the data used to train models? Are third-party vendors involved in the pipeline (most are, but the transparency about which vendors and which data flows matters)? A tool with no privacy policy or with a privacy policy that gives the company unlimited rights to your data is a tool you should not use.

3. What's the parent company, and how long have they been around? A tool from a venture-funded company with 50 employees and 2 years of history is a different risk profile than a tool from a single-founder operation that launched 6 months ago. Both are valid, but the failure modes differ. The single-founder operation is more likely to disappear next quarter. The venture-funded company is more likely to pivot or get acquired and break the workflow you depended on. Knowing the answer lets you price the risk.

4. What's the refund policy, and is it advertised? A 7-day money-back guarantee that's prominently displayed on the pricing page is a different signal than a refund policy buried in the terms of service that requires you to argue with support. The advertised refund policy is the company's bet on the product. The unadvertised one is the company's bet that you won't try.

5. What does the founder say publicly about live-mode use? Search for the founder's name plus "interview ai cheating" or "is this detectable" on YouTube, Reddit, X, and LinkedIn. Defensive, evasive, or absent answers are a red flag. Transparent, candid, or even self-critical answers are a green flag. The founder's public posture is the closest thing you have to the company's actual ethics.

A specific example of how to use the checklist: a candidate I talked to last month was deciding between three tools at $15 to $25 per month. Running the checklist eliminated two of them inside 20 minutes. One had no cancel flow on the dashboard (failure on question 1). One had a founder who'd deleted multiple posts about detectability (failure on question 5). The third passed all five questions, costs the same, and the candidate paid for it. He'd have been just as fine with not paying, but at least he didn't pay for the wrong one.

Edge cases by interview format

The general framework above covers the modal cases. The edge cases are where the wrong category choice costs the most.

Panel interviews. Three to five interviewers, usually a mix of engineers, hiring manager, and a cross-functional partner (PM, designer, recruiter). The format is rotating: each interviewer takes the lead for 10 to 15 minutes, the others observe and pile on follow-ups. The AI assistant's lag is most visible here because at least one of the four people watching you is going to notice the micro-pause. Prep mode is the only viable approach. Drill rotating-question formats with an AI assistant in the two weeks before, then go in with the reflexes built.

System design. Conversational and branching. The interviewer probes your design with follow-ups that depend on what you just said, and the depth of the conversation is the whole point. An AI assistant cannot keep up with the branching because the context window resets every few exchanges. Use practice mode to drill common designs (URL shortener, distributed cache, social feed, ride-share, video streaming) until you can sketch each one cold. Live-mode use here is more of a liability than an asset, because the AI's confident-but-wrong answer to a probing follow-up is worse than your uncertain-but-thoughtful answer.

Behavioral rounds. The category where AI prep helps the most and live AI use is the most exposed. The interviewer is watching your face, asking follow-ups based on your specific story, and grading whether you're talking about a real experience or a fabricated one. Drill 30 to 50 STAR-formatted stories from your own resume with an AI assistant. Walk in with the stories so cemented that you don't need notes. Live AI use in a behavioral round is the easiest category to detect because the AI fabricates specifics that contradict each other across follow-ups.

Coding rounds on assessment platforms (HackerRank, CodeSignal, CoderPad). Platform-specific prep matters more than feature breadth. If you're facing a HackerRank OA, drill 20 HackerRank-style problems in the role-relevant pattern category (dynamic programming for backend, graphs for systems, two-pointers for SWE-general). Live-mode stealth use on these platforms gets detected at increasing rates in 2026 because the platforms now log paste behavior, keystroke timing, and screen recordings for the proctored tiers.

Take-home assignments. The format where the temptation to use AI is the highest and the catch rate is also climbing fast. Most take-home platforms in 2026 compare your final submission to GitHub history (does the code style match your prior public work), check for paste behavior (a 200-line function pasted in one go reads differently than typed across 20 minutes), and run plagiarism comparisons against AI output samples. Use AI to plan the architecture and draft small pieces. Write the bulk of the code yourself. Submit at a pace that reads like you wrote it, not pasted it.

Phone screens. Less detection risk because there's no video. Higher temptation to run a live overlay because the format feels casual. Same career-arc problem as every other live use, with one additional twist: phone screens are usually the first round of a multi-round loop, so a fake performance on the phone screen sets up a much harder gap to bridge in the in-person rounds. The compounding cost of the trade is worse for phone screens than for any other single round.

The 2026 pricing landscape, generically

The market in 2026 has settled into three pricing tiers. Knowing the tier helps you position the value against your actual constraint.

Free tiers. Capped at 30 minutes per month or 5 to 10 mock interviews. Sufficient for casual users running 1 or 2 mocks per round. The free tier is the right starting point for most candidates because it lets you calibrate fit without commitment. Most quality tools offer a free tier; the ones that don't usually have a 7-day trial in the $1 to $9 range that functions the same way.

Paid monthly plans. $9 to $50 per month, with most quality tools landing in the $15 to $25 range. The middle of the band is where the volume of features sits. Below $15 per month is usually a single-feature tool or a heavily-capped free-tier-plus. Above $25 per month is usually a stealth-positioned tool or an enterprise-aimed product that's not optimized for individual candidates.

Lifetime or annual deals. $150 to $300 for the year, $200 to $500 for lifetime access. Typically discounted aggressively on Black Friday, end of quarter, or in response to a competitor launch. The lifetime tier is the best value if you trust the company to be around for the next 5 years, which is the only horizon where lifetime math beats monthly. The single biggest risk is paying for lifetime access and the company shutting down in 18 months, which has happened to multiple entrants in the 2024 to 2025 cohort.

The price band to be suspicious of. $100 to $150 per month, which is where most stealth-only tools sit. The pricing signals positioning as a cheating tool rather than a prep tool, because nobody pays $1,800 per year for mock interviews; they pay it for invisibility. The career-arc math on this band is brutal because the people paying it are concentrated in the cohort that gets caught.

A note on category turbulence in 2026. The category is consolidating fast. One of the original stealth-overlay vendors (Final Round AI) saw its paid program contract sharply in May 2026, ~96% MoM. Another large name (Cluely) saw its organic keyword footprint shrink ~16% over the same period as the category narrowed. Smaller entrants (Beyz, Parakeet) keep launching at near-zero brand defensibility. The honest read for a candidate paying for one of these tools is that the company you sign up with today might not be around in 18 months. That is the durable reason to favor monthly pricing over lifetime here unless the brand has at least one full hiring cycle of demonstrated runway. Our InterviewChamp pricing details (the current rates, the hour packs, and the trial structure) sit at our pricing page rather than getting quoted in this guide, because guide prices go stale and the pricing page is the source of truth.

A working frame: if you can afford $20 to $25 per month for 2 to 4 months around your active interview cycle, that's the right spend level for honest prep. The free tier covers calibration. The monthly tier covers the active prep period. The lifetime tier only makes sense if you're either in a multi-year career-transition phase or you're a power user who interviews every 18 months and the tool stays useful across that horizon.

What Jordan Patel actually does at 487 apps, 14 interviews, 0 offers

A hypothetical-but-composite walkthrough of how a CS new grad in the worst-case shoes uses this category honestly. Specifics drawn from the canonical avatar in our positioning doc.

Jordan is 23. CS degree from May 2025. 487 applications across 11 months. 14 phone screens, no second rounds. Part-time at a warehouse. Student loans active again last quarter. He's been doing the ChatGPT-in-another-tab trick on his last 6 phone screens. It hasn't worked. He bombed the Meta phone screen 8 minutes in. The engineer was nice about it. He has a Series B fintech OA on Tuesday on a HackerRank-style platform he's never used.

What he should do across the next 14 days, in this order:

Days 1 to 2: Diagnose the category. Read this guide. Realize he's been mixing prep-mode and live-mode use in ways that have given him neither benefit. Pick a practice-mode tool and commit to using it as prep, not as a stand-in.

Days 3 to 5: Platform-specific drill. Run 15 mocks on HackerRank-style problems in the role's pattern category (the fintech is hiring backend, so dynamic programming and graphs are likely). Time-box each mock to the real time budget the platform uses. Review session history each morning. Identify the patterns he's slowest on.

Days 6 to 8: Behavioral drill. Run 10 to 15 STAR-formatted behavioral mocks on his own resume stories. Specifically drill the "tell me about a time you failed" and "why are you leaving your current role" prompts that have been the failure modes on his last 6 phone screens. Update his stories to address the consistency issues the AI surfaces.

Days 9 to 11: System design familiarity. The fintech almost certainly won't ask system design at this stage, but he should rehearse 4 common designs (URL shortener, payment processing, distributed cache, user authentication) anyway because the pattern recognition compounds.

Day 12: Full mock loop. Run a 90-minute end-to-end mock with the AI playing a fintech engineer. Behavioral plus coding plus a brief design conversation. Watch the recording back the next morning.

Day 13: Rest day plus warmup. No new prep. 30 minutes of light review on the 3 patterns he's still slow on. Sleep early.

Day 14: The OA. Close the AI assistant before opening the OA. Go in with the reps in his head. Do the work himself.

The Tuesday OA result is unknowable. What is knowable is that the prep loop above is the highest-impact 14 days he can spend, and that the compound effect builds the skill he needs for every interview in the next 40 years of his career. The trade he's been making (live AI use during the call) has produced 14 failed phone screens. The trade above has a meaningfully better expected outcome and an unbounded upside on the skill it builds in him.

The honest call here: this is what I would do in his shoes. Not because it's the moral high ground. Because the math on the 40-year career is asymmetric and the 14-day prep loop is the highest-EV move he can make right now.

Key terms

AI interview assistant

Software that uses generative AI to help a candidate prepare for or perform during a job interview. The umbrella term covers four meaningfully different categories: real-time speech-to-AI-answer overlays, screen-capture stealth tools, mock-interview practice tools, and async coding assistants.

Practice mode

The category of AI interview assistant that runs before the live interview, simulating the round with role-specific question banks, structured feedback rubrics, and session history. The only category with zero detection risk because it never runs during the actual interview.

Live mode

The category that runs during the live interview. Captures audio or screenshots, generates answers in real time, surfaces them through a translucent overlay invisible to the screen-share layer. The category with the highest documented career-arc risk in 2026.

Streaming speech-to-text

The transcription technology that converts the interviewer's audio into text in real time, typically with a 200 to 500 millisecond latency for production-grade providers. The capture stage of every live-mode AI interview assistant depends on streaming STT quality.

Frontier reasoning model

The class of large language models that powers the answer generation in every AI interview assistant in 2026. The "frontier" tier shifts every 6 to 12 months as new model generations release. The quality of the assistant's answers tracks the quality of the underlying model more than the quality of the wrapper around it.

Stealth overlay

The user-interface pattern used by live-mode AI interview assistants: a translucent window that appears on the candidate's screen but is filtered out of the screen-share feed and the recording layer. The technical implementation varies by platform; the marketing claim of "100% undetectable" is uniformly overstated.

Detection rhythm

The time-to-answer pattern that internal recruiter tools use to flag candidates whose response timing fits the AI-assisted profile. A 0.8-second pause followed by a fluent answer reads differently than a 60-second pause followed by a thoughtful answer, and the pattern across 15 to 20 questions in a round forms a signature.

Honest-prep

The positioning frame that uses an AI interview assistant in prep mode only, closes the tool before the live round, and builds the recall-and-articulation skill through volume reps. The only positioning that survives a 40-year career arc with positive expected value.

Cohort outcome

The aggregate performance of the candidates using a particular category of AI interview assistant over a defined window (90 days, 12 months). Live-mode cohort outcomes across 2025 to 2026 have included rescinded offers, 30-to-90-day terminations, and in proxy-interview cases, criminal charges. Practice-mode cohort outcomes have been net-positive across the same window.

Session history

The recorded archive of your prep sessions that quality AI interview assistants maintain for 30 days or more. The compounding asset of prep-mode use, because the gap between what you said and what you should have said is your next prep cycle. Tools without session history are usually missing a feature that matters more than they realize.

Related guides

• The AI interview helper mega-guide: the deeper walkthrough of the 7 sub-categories inside the helper market, with the same honest-prep voice and a wider category map.
• Can interviewers detect AI during a Zoom interview?: the companion guide on detection paths across video-conference platforms, with the specific signals each platform logs.
• Honest interview prep vs cheating: the ethical and career-arc framework behind the prep-mode positioning, with the full decision tree on where the line sits.
• The CS interview cheating economy in 2026: the market-side analysis of the live-mode category, including the cohort outcomes and the venture-funding dynamics.
• AI interviewer in 2026: the companion guide on the other side of the table, covering the AI-graded async video platforms and how their scoring layers work.
• Mock interview practice for CS new grads: the deep dive on practice-mode use, with the specific drill loops for behavioral, coding, and system-design rounds.
• HackerRank tech interview guide: platform-specific prep for the assessment surface most CS new grads hit first in 2026.
• Online interview assessment platforms: the umbrella reference for all the coding, video, and hybrid platforms you'll encounter across a typical interview loop.

About the author: Alex Chen is the founder of InterviewChamp.AI, building AI interview prep for the new-grad CS market and writing about the modern interview gauntlet from the inside.