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The First How I AI Personal Agent Benchmark: Grok vs Muse vs Dot vs Claude

Outlier score6.0×Overperformer

156,478 views vs a median of 25,879 across the channel's last 8 long-form uploads · as of Oct 30 · 1 day old, early

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Claire Vo conducts the first How I AI personal agent benchmark, pitting Grokbot, Muse, Dot, and Claude Duet against nine real-world personal and professional tasks. Operated by background computer-use agents, each assistant was tested on everything from video editing and code debugging to shoe shopping and family newspaper generation. Grokbot took first place overall with strong work and Figma workflows, while Muse dominated creative personal tasks and Claude excelled at product launch video generation.

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17chapters24on-screen captures3people11claims and stats17references in all
Source: I tested 4 AI agents on 9 real tasks. Here’s who won. · How I AI
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    The explainer

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    In this episode of How I AI, host Claire Vo sets out to solve personal agent fatigue by creating the very first personal agent benchmark0:08. To evaluate which AI assistant performs best across practical everyday demands, she tests four prominent agents—Dot, Grokbot, Muse, and Claude Duet—across nine distinct personal and professional tasks. To run the benchmark in an unbiased and parallel fashion, she designed an operator agent architecture2:57, having Codex control Claude, Grokbot, and Muse, while Anthropic's Claude computer use ran Dot.

    The benchmark tasks spanned realistic everyday workflows: drafting a Slack response10:12 to an invitation from Lovable, cutting a vertical video clip11:49 from raw footage of Reddit's Blair, fixing a production bug14:58 reported in Sentry, designing YouTube thumbnails17:27 in Figma, triaging email into actionable to-dos19:29, generating a weekly family newspaper21:11, explaining 4th-grade physics homework23:40, bargain shopping for Adidas sneakers24:58, and producing a ChatPRD product intelligence launch video26:48.

    quote
    TASK 01 · DRAFTINGREPLY AS ME· Draft a reply to Ceci's Slack about Lovable this day. Draft only
    ▶ 10:07
    On screen at 10:07

    Across the speed and execution metrics, distinct strengths emerged. Muse proved lightning fast, recording the fastest completion times in multiple tasks such as explaining homework graphs and formatting the newspaper, but suffered in work contexts because it lacked connectors to GitHub and Slack. Claude Duet was notoriously slow and overly cautious, requiring multiple permission prompts8:44 and failing basic web browsing, yet it delivered an exceptional performance on complex code bug identification15:47 and crafted the highest quality scripted product launch video28:50 using ElevenLabs audio.

    When scoring all nine challenges, Grokbot claimed first place31:18 with an overall score of 93%, winning three tasks including Figma thumbnail creation, to-do list synthesis, and shopping comparison. Muse finished second at 89%, winning personal creative tasks, followed by Dot at 88% and Claude Duet at 79%. Claire concludes with actionable takeaways for AI users32:39: select Grokbot for versatile daily productivity and Figma tasks, rely on Muse for rapid personal and creative domestic workflows, and utilize dedicated underlying models like Codex and Claude for serious software engineering rather than personal agent wrappers.

    framework
    WHAT I LEARNEDTHE TAKEAWAYS1 PICK THE AGENT BY THE JOB2 ACCESS DECIDES THE RESULT3 SPEED MATCHED THE REVIEWS4 THE FORMAT IS PART OF THE ANSWER5 SAFETY STYLES DIFFER6 CODING IS STILL A TWO-AGENT RACE
    ▶ 32:38
    On screen at 32:38
    Highlights
    1. 2:19Operator agent architecture and methodology
    2. 8:04Initial scoreboard and speed stats
    3. 11:49Task 2: Cutting short-form video
    4. 14:58Task 3: Fixing a Sentry bug
    5. 31:18Final standings: Grok takes first place
    6. 32:39Six core takeaways on personal AI agents
    Chapters across the 35:10 runtime
    How it's told

    A multi-task benchmark comparing four AI agents

    Format: Review. Claire Vo conducts a controlled benchmark comparing Dot, Grok, Muse, and Claude across nine real-world productivity and personal tasks. Each agent is operated blind via computer use, culminating in task-by-task evaluations, final standings, and operational takeaways.

    Story arc

    1. 0:00IntroductionClaire introduces personal agent fatigue and establishes the goal of running a realistic benchmark across four top agents.
    2. 1:04Sponsor Segment: WorkOSHost discusses enterprise system integration challenges and presents WorkOS features.
    3. 2:14Methodology & SetupClaire outlines the benchmark run sheet, explaining how Codex and Claude operated the four agents blind across nine real tasks.
    4. 5:52Pre-Evaluation ObservationsHost details early setup issues, connectors across apps, and computer-use latency.
    5. 6:57Scoreboard & Speed MetricsClaire presents the overall completion stats, completion time charts, and steering metrics.
    6. 10:07Tasks 1–3 ReviewAnalysis of the agents' performance on drafting a Slack reply, cutting a short video from Dropbox footage, and fixing a real Sentry bug.
    7. 16:47Sponsor Segment: FirecrawlSponsor breakdown highlighting Firecrawl's web scraping and indexing infrastructure for AI agents.
    8. 17:31Tasks 4–8 ReviewEvaluations covering Figma thumbnail generation, inbox to-do triage, family newspaper broadsheets, physics homework explanations, and Adidas shoe shopping.
    9. 26:45Bonus Task: Launch VideoEvaluation of generated marketing videos for ChatPRD Product Intelligence, featuring Claude's ElevenLabs voiceover and Dot's motion graphics.
    10. 30:15Final StandingsClaire reveals the blind scoring results, crowning Grok first place at 93% followed by Muse, Dot, and Claude.
    11. 32:38Key TakeawaysClaire synthesizes six core lessons regarding agent specialization, access limits, speed, UI formats, safety styles, and software engineering capability.
    12. 34:40OutroFinal recommendations and closing podcast call to action.

    Setups and payoffs

    1. 0:15Setup Benchmark across four agents and nine tasks announced
      ↳
      30:15Payoff Final standings and full scoreboard revealed with Grokbot winning at 93%
    2. 2:46Setup Claire notes using different agents for different tasks in real life
      ↳
      32:41Payoff Takeaway 1 explicitly advises choosing specific agents tailored to work versus personal tasks
    3. 3:49Setup Codex checking connectors and Muse missing Slack access
      ↳
      10:24Payoff Muse fails Task 1 due to lack of Slack connectivity
    4. 4:05Setup Setting up Blair's podcast footage and Linear tasks in the run sheet
      ↳
      11:40Payoff Task 2 evaluates agents pulling footage from Linear to cut a short video
    5. 6:21Setup Claire notes forgetting to connect Muse to business systems
      ↳
      15:05Payoff Muse is completely blocked on Task 3 bug fix due to no GitHub access
    6. 6:40Setup Claire criticizes Claude's slow computer use
      ↳
      8:31Payoff Claude ranks slowest overall and asks for permission four times on the scoreboard
    7. 19:00Setup Claire hypes Grok's Figma skills from previous experience
      ↳
      18:33Payoff Grok wins Task 4 by producing the highest-quality thumbnail layouts

    Devices

    • On-screen Scoring GraphicsFullscreen comparative slides display side-by-side prompt responses, timestamps, steering counts, and PR diffs10:0715:30
    • Video Clip PlaybackDirectly playing back generated video shorts and launch videos side by side allows direct sensory verification of output quality12:2027:24
    • Personal Real-World PromptsUsing authentic personal tasks (family newspapers, kids' homework, Slack replies) anchors the benchmark in daily utility rather than synthetic tests21:3124:00
    • Humorous CommentaryDeadpan critiques of bad AI outputs, such as 'creepy curtains' on thumbnails and PowerPoint-style launch videos18:0527:20
    • Structured Takeaway FrameworkSix numbered takeaways summarize broader operational principles derived from empirical testing32:3834:00

    From the video's own structure, matched to the transcript and on-screen text.

    Teardown

    This video overperforms by turning overwhelming AI tool hype into a rigorous, side-by-side empirical showdown using authentic personal tasks rather than synthetic benchmarks.

    A real overperformer: 6.0× its typical views (156,478 vs a median of 25,879 across the channel's last 8 long-form uploads, as of Oct 30) · Long-form review · 1 day old, early

    The promise

    A direct comparison revealing which of four AI agents actually performs best across realistic daily tasks.

    Packaging

    How it looks in the feed

    Show the feed as
    35:10

    I tested 4 AI agents on 9 real tasks. Here’s who won.

    How I AI156K views · 1d agoI have personal-agent fatigue, so I ran the first How I AI personal-agent benchmark: Grok Bot, Dot, Muse, and Claude Duet across nine real tasks, from a Slack…

    Views and age as of the teardown, Oct 30, 2026. Other videos are blanked out.

    • 5/5Thumbnail imageThe creator is centered with open palms presenting four clear app icons against high-contrast purple lighting. The visual layout communicates an agent showdown instantly even at small phone sizes.Creator in a bright blue hoodie with open hands surrounded by four floating app icon badges.
    • 4/5Thumbnail text“WHICH AGENT WINS?”Three bold words with a boxed highlight stop the eye immediately. It drives a single question without cluttering the frame.'WHICH AGENT WINS?' displayed in large white sans-serif text with 'WINS?' enclosed in a red-bordered box.
    • 5/5TitleUses specific numbers to signal thorough, practical testing rather than surface-level hype. The second sentence sets a sharp resolution hook.'I tested 4 AI agents on 9 real tasks. Here’s who won.'
    • 4/5Title + thumbnailThe image establishes the visual competition and shows the exact tools, while the title delivers the testing scope and promise of a definitive winner.The thumbnail displays the tool icons and asks 'WHICH AGENT WINS?' while the title specifies testing four agents across nine real tasks.

    Curiosity gap: Viewers see the specific tools tested across nine real tasks but must click to find out which agent took first place.

    The three hooks

    The title, the thumbnail text and the first words spoken each make a promise, and each does a different job.

    • Title“I tested 4 AI agents on 9 real tasks. Here’s who won.”ResultsProofOutcomeCuriosityIt makes the viewer want to see practical benchmark results and learn the ultimate winner.
    • Thumbnail text“WHICH AGENT WINS?”Curiosity gapCuriosityOutcomeIt makes the viewer want to find out which tool beat the competition.
    • First words spoken“I told you all I have personal agent fatigue, but I am here for you.”ReliefReliefCuriosityIt validates the viewer's exhaustion with the flood of AI tools and promises to do the heavy testing work for them.
    • PairingDifferent jobsThe thumbnail poses the overarching contest question while the title supplies the exact testing methodology and scope.
    • StakesFitsThe promise of testing four agents across nine real tasks matches the exhaustive, task-by-task empirical breakdown delivered in the video.
    • UrgencyNoneThe video focuses on evaluating tool maturity and establishing practical benchmark results rather than manufacturing immediate FOMO.

    The first 20 seconds

    TimeWhat you seeWhat you hearWhat it does
    0:00Host sits at studio mic; blue app badge appears lower right.I told you all I have personal agent fatigue...Acknowledges shared overwhelm with AI tools, building immediate empathy.
    0:01Four app icons pop up around host's head with neon glowing rings....fatigue, but...Visually represents the chaotic clutter of competing AI assistants.
    0:02Host gestures outwards while framed by floating app logos....fatigue, but...Builds tension before pivoting into the purpose of the video.
    0:03Host raises right hand emphatically....Signals a deliberate shift from exhaustion to action.
    0:04Host presses hand to chest with direct eye contact....I am here for you. So...Positions the testing effort as a service to spare the audience tedious work.
    0:05Host points upward and speaks with rising enthusiasm....today...Directs viewer attention to the immediate mission.
    0:06Host lowers hand and delivers line straight to camera....I am going into the personal agent mines...Uses vivid language to dramatize the deep-dive research process.
    0:07Host looks directly into lens, leaning forward slightly....and I am running...Prepares the audience for a structured methodology.
    0:08Host opens hands wide in front of mic....the very first...Claims novelty and authority for the test.
    0:09Host maintains steady eye contact....How I AI...Branded framework introduction.
    0:10Host gestures to punctuate words....personal agent...Specifies the exact category being evaluated.
    0:11Host pinches fingers together deliberately....benchmark...Promises objective, systematic evaluation rather than subjective opinion.
    0:12Bold white text overlay appears: 'PERSONAL AGENT BENCHMARK'....which goes across...Graphically anchors the exact premise and confirms the title.
    0:13Text remains bold and centered over host....four agents...Introduces the first concrete scope parameter.
    0:14Text remains on screen as host prepares hand gesture....four agents...Emphasizes comparative depth.
    0:15Text clears; host holds up 4 fingers....four agents...Visually reinforces the exact count of tools tested.
    0:16Host switches hand gesture to indicate nine....and nine tasks...Quantifies the rigorous scope of practical test cases.
    0:17Host points index finger toward camera....to tell...Builds anticipation toward the ultimate verdict.
    0:18Host points straight into lens with confident smile....you...Frames the answer as a direct personal recommendation.
    0:19Host holds point; musical riser swells in background.(musical riser swells)Creates an audio-visual cliffhanger into the first test.
    0:20Host transitions to introduction of the test setup.(riser hits transition beat)Pushes the viewer straight into the content without hesitation.
    Click confirmation
    Within the first 15 seconds, the host explicitly confirms the title by announcing a structured benchmark comparing 4 personal agents across 9 tasks.
    Time to the point
    0:16
    Hook
    personal confession, bold claim
    Reason to stay
    Discovering which of the 4 tested AI agents won the 9-task benchmark so the viewer knows which one is worth using.
    Missing on purpose
    No channel intro bumper, no 'hey guys welcome back', no sponsor plug, and no subscribe request.
    Pace and texture
    Fast and punchy voice energy, punctuated by floating app graphics, big bold on-screen text, physical finger counting, and an escalating music riser.

    Story map

    Versus/comparison. A tech founder overwhelmed by AI agent fatigue tests four top agents across nine real-world personal and professional tasks to determine which tool genuinely delivers practical utility. The payoff reveals the objective winner and exposes where current autonomous agents break down.

    TimeSectionNew tensionStakesTurnLoops
    0:00 –1:04Hook & Benchmark PremiseWhich personal AI assistant actually works across real daily tasks?RiseClaire has severe personal agent fatigue, but instead of giving up, she builds a 4-agent, 9-task benchmark to settle the debate.Opens: Which agent will win the ultimate benchmark?
    1:04 –2:14Sponsor: WorkOSHow do developers connect enterprise systems securely?FallSag: no turn
    2:14 –5:52Methodology & SetupCan an automated test between AI agents be truly fair and unbiased?HoldClaire wanted parity across tools, but configuring connectors proved messy, requiring operator agents to run them blind.Opens: Will operator agent execution create unexpected failures?
    5:52 –8:05Execution ObservationsDid the testing setup hold up under autonomous computer use?HoldAutonomous parallelism worked, but Claude computer use was painfully slow and Muse lacked business connectors.Opens: How heavily will missing integrations penalize Muse and Claude?
    8:05 –10:07Speed & Friction ScoreboardWhich agent executes tasks fastest without constant human handholding?RiseMuse proved fastest across most tasks, but Claude demanded four separate permission interruptions.Opens: Does speed translate to higher output quality?Closes: Will Claude's slow execution hurt its baseline score?
    10:07 –11:39Task 1: Slack ReplyCan an agent draft a sensitive business response that sounds authentic?HoldAll models drafted acceptable text, but Dot captured Claire's exact personal fee preferences.Opens: Can Dot maintain this personalized edge in technical tasks?Closes: How Muse handles Slack without native integration.
    11:39 –14:57Task 2: Video EditingCan an AI agent download raw footage from cloud storage and edit a finished vertical clip?RiseClaude completely refused to download files, whereas Muse created clean cuts with branded captions.Opens: Can Claude recover from a complete zero on video tasks?Closes: Whether agents can autonomously handle video post-production.
    14:57 –16:47Task 3: Bug FixingWhich agent can inspect a real Sentry bug report and submit a working pull request?RiseMuse lacked GitHub access, but Claude Duet found and fixed an additional hidden bug.Opens: Will Claude's coding strength balance its operational slowness?Closes: Which model is the most dependable code debugger.
    16:47 –17:31Sponsor: FirecrawlHow do agents pull clean unstructured data from the live web?FallSag: no turn
    17:31 –19:26Task 4: Episode ThumbnailsCan agents use Figma templates to create appealing YouTube thumbnails?HoldMuse produced an unusable distorted design, but Grok generated novel layouts that outperformed the standard templates.Opens: Can Grok sustain design leadership into productivity tasks?Closes: Which agent performs best inside Figma via MCP.
    19:26 –21:07Task 5: Inbox TriageHow cleanly do agents synthesize messy unread emails into actionable to-dos?HoldDot generated an unreadable plain text dump, while Grok organized action items into an interactive native document.Opens: Does Grok's formatting advantage give it the overall lead?Closes: How agents structure personal inbox triage.
    21:07 –23:30Task 6: Family NewspaperCan AI format domestic calendar events and news into a charming printable broadsheet?HoldClaude produced a plain Markdown file, but Muse generated a complete two-page illustrated family paper.Opens: Can Muse catch up to Grok on purely personal workflows?Closes: Which agent handles multi-page creative layout generation.
    23:30 –24:55Task 7: Physics HomeworkCan an agent visually parse a school workbook problem and explain the physics concept instantly?HoldAll four models identified the correct answer, but Muse answered almost instantaneously.Opens: Will response latency determine the top spot between Grok and Muse?Closes: Whether agents can explain homework from raw photos.
    24:55 –26:45Task 8: Shoe ShoppingCan agents navigate ecommerce sites to locate specific shoes at the best price?HoldClaude was blocked by store anti-bot protections and found overpriced variants, but Grok navigated and compared prices accurately.Opens: Is Grok's shopping win enough to secure first place overall?Closes: Which agent can perform autonomous live web shopping.
    26:45 –30:15Task 9: Product Launch VideoCan an agent autonomously script, voice, and composite a SaaS marketing video?RiseDot built an unappealing slide deck, while Claude integrated ElevenLabs voiceover to produce a production-ready video.Opens: Who wins the overall benchmark after factoring in Claude's video win?Closes: Whether agents can execute complex end-to-end multimedia creation.
    30:15 –32:38Final Standings & WinnerWhat are the final cumulative standings across all nine tasks?RiseClaude fell to last place due to speed and permission blocks, leaving Grok as the clear benchmark winner at 93%.Opens: What practical lessons should viewers take from these scores?Closes: Which agent won the How I AI Personal Agent Benchmark.
    32:38 –34:40Key TakeawaysHow should users divide tasks between competing agents in daily life?HoldInstead of picking a single winner, Claire advises routing work tasks to Grok and personal tasks to Muse.Closes: How to handle agent selection across work versus personal life.
    34:40 –35:10OutroNone.FallSag: no turn

    Payoff: delivered fully at 30:15. Claire reveals the final ranked scoreboard with Grok taking first place at 93%, backed by specific task win totals and score tallies. Ending: A concise six-part synthesis of operational rules for using AI agents, concluding with a standard podcast subscription and platform outro.

    Leaks

    Where a viewer might leave. Guesses from watching, not retention data.

    • 1:04 A 70-second enterprise auth sponsor segment starts just one minute in, halting viewer momentum before any test data appears.
    • 2:14 A four-minute technical breakdown of browser operators and connector baselines delays the task showdown viewers clicked to see.
    • 16:47 A second mid-roll sponsor ad interrupts right after Task 3, stalling pace after an engaging bug-fixing test.
    • 32:38 The final leaderboard was already revealed at 30:15, encouraging viewers only interested in the winner to leave before the conceptual takeaways.

    Steal this

    1. The Autonomous Operator Benchmark Running test prompts through an operator agent using computer-use eliminates subjective human typing bias and tests real execution speed. 2:56
    2. Side-by-Side Sensory Playback Playing raw generated video shorts and voiceovers sequentially lets viewers evaluate real quality differences instead of relying on arbitrary numbers. 12:20
    3. Friction and Latency Heatmap Showing total execution minutes and permission request counts before diving into individual tests creates an objective foundation for subsequent critiques. 8:05
    4. Domestic and Utility-Anchored Tasks Testing edge AI tools on kids' physics homework, family newspapers, and shoe shopping grounds high-tech reviews in relatable daily utility. 21:31

    Skip this

    • Pre-built proprietary workspace infrastructure Claire relies on pre-existing Figma design systems, Linear ticketing workflows, and ElevenLabs API integrations that typical users will not have configured.
    • Dense internal jargon without on-screen definitions Casually referencing MCPs, PR diffs, Sentry logs, and Browser Use works because her channel audience consists largely of software operators.

    Open questions

    • How much did Claude's performance penalty stem from running inside a nested computer-use operator rather than a native desktop session?
    • What exact percentage weighting was applied to the qualitative audio transcript inside Claude to produce the final numerical percentages?

    Teardown by Avail.video from the thumbnail, the title, the first 20 seconds and the report's notes on the whole video.

    On screen

    Everything the video showed you

    Slides, frameworks, quotes and formulas, captured verbatim with the moment they appeared. Tap any card to jump there.

    Reference shelf

    People, ideas and things mentioned

    Said means mentioned aloud; Shown means it appeared on screen.

    People3

    Organizations1

    • WorkOSEnterprise platform providing authentication, Single Sign-On, and SCIM APIs1:04Said + shown1:05Shown2:06Shown

    Things on screen13

    • DevinAutonomous AI software engineer agent15:15Said15:18Said15:30Said + shown
    • GrokAI agent by xAI / Grok Bot tested in the benchmark0:01Shown2:20Said + shown8:58Said + shown10:07Said + shown11:39Said + shown14:57Said + shown15:30Said + shown19:00Said21:31Said + shown23:29Said + shown24:55Said + shown26:45Said + shown30:15Said + shown
    • MusePersonal AI assistant app developed by Meta0:01Shown2:20Said + shown8:58Said + shown10:07Said + shown10:24Said11:39Said + shown14:57Said + shown15:05Said15:30Said + shown21:31Said + shown23:29Said + shown24:55Said + shown26:45Said + shown30:15Said + shown
    • DotPersonal AI assistant application developed by New Computer0:01Shown2:20Said + shown8:58Said + shown10:07Said + shown11:39Said + shown14:57Said + shown15:30Said + shown21:31Said + shown23:29Said + shown24:55Said + shown26:45Said + shown30:15Said + shown
    • Claude DuetAnthropic's Claude agent environment running computer use0:01Shown2:20Said + shown8:31Said8:58Said + shown10:07Said + shown11:39Said + shown12:05Said14:57Said + shown15:30Said + shown15:47Said21:31Said + shown23:29Said + shown24:55Said + shown26:45Said + shown30:15Said + shown
    • CodexOpenAI agent used to drive Grok, Muse, and Claude during the benchmark2:20Said + shown3:17Said3:49Said
    • FirecrawlWeb data scraping and index tool designed for AI agents16:47Shown16:48Said + shown17:26Shown
    • ChatPRDAI-powered product management application created by Claire Vo14:57Shown26:45Said + shown27:24Shown
    • FigmaCloud-based collaborative design and thumbnail editing platform13:42Said17:31Said + shown18:55Said
    • LinearIssue tracking and project management tool connected to benchmark agents1:49Shown4:32Said11:39Said + shown17:31Said + shown
    • SentryApplication monitoring and error tracking service3:49Shown14:57Said + shown14:58Said
    • DropboxCloud file storage platform hosting source video footage3:49Shown11:55Said12:05Said
    • ElevenLabsAI voice generator integrated via MCP to produce narration3:49Shown29:18Said29:19Said
    Video description

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    By the numbers

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    The video

    Views
    156.5K
    Likes
    398
    Comments
    56
    Published
    Oct 28, 2026
    Length
    35:10
    Category
    People & Blogs
    YouTube's topics
    —
    Captions
    No
    HD
    Yes
    Language
    American English
    Paid promotion
    None declared
    Made for kids
    No
    License
    Standard YouTube

    Creator's tags

    No tags

    The channel

    How I AI@howiaipodcast
    Subscribers
    126K
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    491
    Total views
    7.4M
    Channel age
    1 year (since Mar 2025)
    Country
    United States
    Uploads
    about every 3 days
    Across the last 8 long-form uploads
    Before this video
    2 days after the previous long-form upload

    Calculated by Avail.video from YouTube's public numbers

    Outlier score
    6.0× · Overperformer
    Views against the median of the channel's last 8 long-form uploads
    Views per subscriber
    1.24
    Likes vs the channel's usual
    0.2× the channel's usual
    0.25% of views vs a usual 1.2%
    Comments vs the channel's usual
    0.3× the channel's usual
    0.036% of views vs a usual 0.10%
    Rank among recent uploads
    #1 of 9
    By views: this video and the channel's last 8 long-form uploads
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    Watched by Avail.video · 2026-10-30