# Thumbnails — YouTube, Shorts, Reels, TikTok covers Load this file when the user wants a **thumbnail, video cover, first-frame card, or channel art**: "thumbnail for my video", "YouTube cover", "Shorts cover", "make it clickable", "higher CTR". A thumbnail is not a nice image at small size. It is a different craft with a different success test: **it is judged at ~168px inside a scrolling feed, next to a hundred others.** Everything below follows from that. --- ## ⚠️ Two things to get right before advising anyone **1. The metric is watch time, not clicks.** YouTube's own A/B thumbnail test optimises "for overall watch time over other metrics, like click-through rate." A thumbnail that wins the click and loses the viewer *loses the test*. This is the mechanical reason the shock-face era ended. Never promise something the video does not deliver — it is both a spam-policy violation ("malicious clickbait" names thumbnails explicitly) and a test loss. **2. Most thumbnail statistics online are fabricated.** This topic is saturated with AI-generated SEO spam inventing authoritative-looking numbers ("47.3% of creators…", "9% vs 4% CTR study…", "70% higher CTR for dark thumbnails"). Traced individually, they have no source. **Do not quote a thumbnail statistic to a user unless it is in this file.** Everything below is graded: **[A]** real study/primary source · **[A?]** real study, but its page could not be opened directly — figures corroborated only via secondary quotes, so treat as directional, not exact · **[B]** credible practitioner claim · **[C]** craft convention, no data. Also useful to know: CTR *falling* as a video spreads is normal, not failure — early impressions come from subscribers, then Browse/Suggested reach cold viewers. Half of all channels sit between **2% and 10% CTR**. **[A]** --- ## The four layers ### 1. ONE hero subject A face with one **legible** emotion, or a single object caught mid-action. Crop tight. **Faces scale with performance.** In a study of 500 breakout videos: 69% used a face, rising to 75% of the top 100 and **80% of the top 50**. Face occupies about **one third of the frame** in that top cohort. **[A?]** — the direction (faces matter, and matter more at the top) is safe; the exact percentages are not verified at source. **The exaggerated shocked face is dead as a default.** Only ~5% of those breakouts used an exaggerated expression; 6% of the top 50. MrBeast's team A/B tested ~30 videos open-mouth vs closed-mouth — **every closed-mouth version produced higher watch time**, and they changed the channel's house style permanently. **[A]** So: **a clear face beats a wild face.** Direct the expression to *one* readable emotion — closed-mouth determination, a genuine restrained smile, focused concentration, real scepticism. Direct eye contact into the lens, unless the gaze is deliberately pointing at the subject. Forbid in the prompt: gaping "O" mouth, bulging eyes, strained forehead, raised chin, any look of discomfort or effort. **Legibility test:** at 168×94, can someone name the emotion in under a second? **Faces are not mandatory** — 31% of breakouts had none. If the subject is a tool, an interface, or a generated frame, the object can be the hero. Kill on sight: three competing focal points, a floating UI panel over an abstract gradient, "a person at a desk". If the concept has no surprise, wit, or impossibility in it, invent harder before generating. ### 2. Separation — luminance first, hue second **Greyscale test: desaturate the thumbnail. If the subject stops separating from the background, no palette will save it.** Complementary pairs (orange/teal, blue/orange) work mostly because they *also* carry a luminance gap. - **Three dominant colours maximum, text included.** **[B]** - **Restrain saturation.** The over-saturated HDR/clarity look — "exaggerated vibrancy, bright lighting and sharp contrast" — is now documented as the visual signature of AI-generated imagery, inherited from advertising imagery in training data. **[A]** Push *luminance* contrast hard; keep saturation on one or two accent elements only. - **Check on both YouTube themes.** The UI is near-white or near-black with red accents. A dark vignette that separates your subject in dark mode can vanish in light mode. Saturated red as a dominant field reads as UI chrome, not content. - A single-hue thumbnail is *not* automatically weak — Kurzgesagt and MKBHD ship near-monochrome successfully. What fails is a single hue with no luminance separation. ### 3. Text — often none at all The real distribution among breakout thumbnails: **28% had no text** (the largest single group), **24% had 1–3 words**, median among those that had any was 5. **[A?]** Over half carried three words or fewer. The ubiquitous "3–5 bold words" advice describes a minority. - **Drop text entirely** when the image already states the promise and the title carries the specifics. Text is a crutch for a thumbnail that hasn't found its image. - **Three words maximum** when used. Never repeat the title — the title is right next to it. Text should add the *second half* of an idea the image starts. - Quote the exact words in the prompt: `render only this text: "…"`, and forbid the rest: `no other text, no taglines, no watermark, no logo, no captions, no gibberish letters`. - Heavy-weight sans, one typeface. Cap height ≈ **12–15% of frame height**. - **Always** an outline, drop shadow, or solid colour block behind it. - Placement: top third or the side opposite the face. **Never the bottom-right quadrant.** - Let the subject overlap one letter slightly — instant depth. Long strings come back mangled from every image model. If words garble twice, generate the plate **text-free** and set type in the Canvas tool or in HTML — a clean plate plus real type beats a third mangled attempt. Logos are the same: **image models cannot render a real brand mark.** Generate the plate without it and composite the actual logo file. ### 4. Platform-safe composition | Zone | Rule | |---|---| | **Bottom-right corner** | Duration chip sits here. Nothing important. | | **Bottom edge, full width** | Red progress bar on partially-watched videos. Keep critical content ≥40px off the bottom (at 1280×720 scale). | | **Overall safe area** | Everything load-bearing inside the centre ~1100×620 of a 1280×720 canvas. | | **9:16 — Shorts/Reels/TikTok** | Must survive a **centre-square crop** (grids and cross-posts crop vertical media to the middle). Keep the top ~15% and bottom ~20% clear of platform UI. | | **Validate at** | **168×94 px** — the desktop suggested-videos render size. Not 1280×720. | **File spec (updated — most guides are stale):** YouTube now recommends **3840×2160**, minimum width 640px, file cap **50 MB** on desktop. Driven by TV overtaking mobile as the primary US YouTube device in Feb 2025. **Export at 4K; design for 168px.** **[A]** --- ## Model choice Text fidelity is the constraint. `gpt-image-2` is the strongest for type — use **quality: medium** (its sweet spot) or high, and it handles **Hebrew** notably better than the alternatives. `nano-banana-2` is strong on cinematic people but weak on text. For a text-free plate, either is fine and the cheaper one wins. **Field result (Aug 2026, head-to-head, same prompt/refs/1K):** `nano-banana-2` beat `gpt-image-2` on BOTH photorealism (looked like a real studio photo, not a render) and reference-mark fidelity — and rendered a short Hebrew headline + Latin badge cleanly. The "weak on text" caveat applies to long/dense copy, not a 3-word headline. When a thumbnail is a photographic person + short text + referenced logos, run both models once and pick — do not assume gpt-image-2 wins by default. Lock the host with a **character Visual DNA** (`workflows/visual-dna.md`) so every thumbnail in a series is the same person — and check you are using the DNA of the person *as themselves*, not a costumed character DNA built for a film shoot. ## Kolbo brand-asset kit — reuse these, do not rediscover them Learned the hard way (Time Machine tutorial thumbnail, Aug 2026): re-deriving logo files and the right Visual DNA from scratch every session, then patching AI mis-draws with HTML overlays, is slow and produces a worse result than just generating natively with the right references from the start. Check this list before generating anything with a Kolbo or model logo, or with Zohar's likeness, in it. | Asset | Where | Status | |---|---|---| | Kolbo K icon — **THE reference to use** | `Graphics\Logo\kolbo-ai-new-icon-black2.jpg` (white K on black) | ✅ verified real mark. **Always pass THIS file as the generation reference**, never a transparent cutout: the black background gives the model contrast to lock onto and it reproduces the K correctly. Proven tricks that keep the geometry exact fully in-model: (a) print it as a white chest logo on a black t-shirt, (b) render it inside a small dark-navy rounded chip next to a live-typed 'Kolbo.AI' wordmark — both mirror the reference's white-on-dark context | | Kolbo K icon, clean cutout | `Youtube\מכונת הזמן\Exports\Thumbnail\assets\k-icon-clean.png` | ⚠️ white-on-transparent — fine for HTML/PIL compositing, but as a *generation reference* on a light background it gives zero contrast and the model redraws the K wrong. Use the black-background source above instead | | Kolbo lockup (K + wordmark) | `Youtube\מכונת הזמן\Exports\Thumbnail\assets\kolbo-lockup.png` | ⚠️ this is the **stacked t-shirt lockup** (K on top, small wordmark below) — do not use for a horizontal corner mark, it misreads or gets redrawn wrong when scaled | | ByteDance icon | `kolbo-api\assets\Bytedance icon.png` | ❌ **wrong file** — a generic blue bar-chart icon, not ByteDance's real mark. Confirmed by hash, pre-existing bug (not from any recent edit). Filed as `task_f3fab040`. Do not composite this into a "real logo" claim; if a real ByteDance mark is needed, source and verify one first | | Zohar — real likeness Visual DNA | id `6a64b8fa5bd226f7e763367b`, name **`zohar`** (single token, so `@zohar` binds) | ✅ correct — use this whenever the ask is "me"/"my face" | | Zohar — costumed character DNA | `@zohar_salon` and similar | ⚠️ these are **film-character** DNAs from specific shoots, not his real likeness — never substitute for a "me presenting" thumbnail | **DNA naming rule that bit us:** the `@Name` prompt tag only binds if it exactly matches the DNA's stored `name` field as one token. A DNA named "Zohar (Copy)" can never bind via `@zohar` — rename the DNA (`update_visual_dna`) once, don't work around it per-prompt. ## Routing in Kolbo | Ask | Tool | |---|---| | One cover | `generate_image` | | Several options at once | The in-app **Thumbnail Generator** — fans out 4–8 art-directed variations | | A batch with a locked character or product | `generate_creative_director` with a Visual DNA | | Cover for a video the user already made | Never crop a frame out of the video — generate a fresh comp. A film frame is exposed for motion, not for a 168px grid | | A second aspect ratio (e.g. 9:16 Shorts after a 16:9 main) | **Generate it natively at that aspect ratio**, do not crop/recompose the other one in HTML. Reuse the same brief, DNA, and reference logo files; write a fresh detailed prompt sized for the new canvas | **Default workflow, one shot:** `generate_image` (or `_edit`) at **`quality: high`**, native target resolution (1024-class for GPT Image 2), passing every real logo file the comp needs as `reference_images` plus the matching `@Name`/`#Name` tags in the prompt text, and the correct Visual DNA id. Ask for **3 concept variations in the same call batch** per the ladder below rather than iterating one image through many small manual fixes. Reserve HTML-compositing patches for the rare case a specific mark still comes back wrong after 2–3 regenerations with references — it is a fallback, not the default path. **9:16 safe-zone discipline:** keep the hero content (face, gesture, headline, badge, logo) inside roughly the **centre 70% of the canvas width**, not edge-to-edge — verified by the centre-square-crop test below. State this explicitly in the prompt ("generous margin on both sides, nothing touches the left/right edge") rather than fixing it after generation. **Multi-element layout recipe that shipped (Aug 2026, nano-banana-2):** when a comp carries 4+ graphic elements (headline, badge, app tile, still cards, brand chip), three prompt clauses turned a cluttered mess into an approved final: - **Glassmorphism containers** — put every element in its own "frosted translucent glass container, soft blur, subtle white border, delicate shadow". Unifies the pile into one designed system; solid stickers read as clutter. - **STRICT NON-OVERLAP RULE** stated as its own sentence — "clear margin between every element; nothing touches the face or hair" — models honour it remarkably well. - **LIKENESS PRIORITY preamble** — face fidelity *drifts as element count grows*. With 4+ elements, open the prompt with "preserve his EXACT face, identical to the Visual DNA identity, do not drift the facial features" or the subject becomes a stranger. - **Pin each mark to its container** — with two logos in one comp the model WILL swap them; write "the tile shows ONLY the [X] mark; NEVER put [Y] on this tile; [Y] appears ONLY in the bottom chip". Verify every logo placement on delivery. ## Variation ladder Vary the **concept**, never the words: bold dynamic · clean minimal · vibrant saturated · cinematic wide · close-up dramatic · typography-forward · dark moody · flat illustration. Make **3 thumbnails and 10 titles** per video, and design them *before* shooting. Top creators spend ~30% of their effort on packaging versus ~5% for small channels. **[B]** --- ## What now reads as dated or "AI slop" **Correct the premise first: YouTube has NOT demoted AI thumbnails.** Its synthetic-content policy explicitly exempts "using generative AI tools to create or improve a video outline, script, **thumbnail**, title, or infographic." **[A]** Anyone claiming the algorithm penalises AI thumbnails is repeating a fabrication. The damage is **competitive and trust-based**, and it is real: - **The models converge — you disappear into sameness.** 700 generation trajectories through image-model feedback loops all collapsed to just **12 dominant motifs**; the authors call the result "visual elevator music." **[A]** - **The gloss is the tell** — see the saturation note above. **[A]** - **AI faces homogenise.** Diffusion models render same-demographic individuals as near-identical across professions. Your AI face looks like everyone's AI face — which is exactly why a real Visual DNA built from real photos matters. **[A]** **Avoid — evidence-backed:** exaggerated open-mouth shock · over-saturated HDR/clarity · plastic AI skin · piles of arrows and circles (one or two maximum) · **imagery not actually in the video** (policy risk *and* it loses the watch-time test) · AI likeness of real public figures (YouTube likeness detection is live for all YPP creators). **Avoid — credibility damage in tech/AI niches:** glowing brains, robot-hand-touching- human-hand, circuit boards, binary code, humanoid robots, the default blue "tech" wash. Each is sci-fi-derived and misleading about what AI actually is. For a channel whose credibility *is* the product, these are self-harm. **Avoid on taste, no data — don't cite numbers for these:** golden particle dust and bokeh, radial speed lines, neon collage, holographic UI overlays, floating tech icons, gradient-mesh purple/blue backgrounds, hexagon grids, generic fantasy creatures. --- ## Tech / AI-tool / filmmaking channels The grammar here is the **opposite** of the MrBeast formula, and that is the point. Restraint reads as authority: controlled product photography, matte dark grounds, one accent pulled from the subject itself, minimal or no text, a composed direct-to-camera expression rather than a reaction. **The absence of arrows and explosions is the brand signal.** For a channel selling expertise, the thumbnail is a claim about whether you know what you are talking about. What works specifically: - **The tool logo as the recognisable object** — high search intent, heavily used; differentiate through treatment (scale, lighting, physical staging), not by dropping it. - **Before/after** — genuinely strong for AI filmmaking because the transformation *is* the promise and it is honest. Skip the arrow; let the two images do the work. - **The interface as hero** — a real UI cropped tight to one striking element signals "actual tutorial, not hype." Under-used. - **Face + artefact** — face at ~⅓ frame, closed-mouth focused, beside the generated frame. Satisfies both the face finding and "show the thing." --- ## Hebrew and RTL > **No evidence base exists for any of this.** There is no measured data on Hebrew vs > Latin thumbnail text, Hebrew legibility at 168px, or RTL thumbnail composition. What > follows is typographic reasoning plus Israeli foundry commentary. For a Hebrew channel > this is worth settling empirically — ship a text-right/subject-left variant against a > mirrored one and let YouTube's own test decide. - **Hebrew has no ascenders or descenders and a uniform x-height.** Latin words have a ragged silhouette that aids word-shape recognition at small size; Hebrew words are near-rectangular blocks. **Hebrew needs more size, more weight and more letterspacing than Latin to hit the same legibility at 168px.** Budget for it. **[C]** - **Fonts:** **Ploni** (AlefAlefAlef) is purpose-built bilingual — Hebrew plus Latin designed to sit together without either overshadowing the other, which is exactly the problem when a tool name like `Seedance 2.5` sits inside a Hebrew phrase. **Heebo** and **Rubik** are strong free alternatives with real bold weights. - **Mirror the layout, not the logos.** RTL readers scan a mirrored F-pattern entering from the **top-right**, so the natural composition inverts: **text block right, subject left**, gaze pointing right-to-left toward the text. Latin brand marks and numerals stay LTR regardless. **[B]** - **Language split:** Hebrew for the emotional/promise word, Latin for the tool name — the tool name is the search-intent anchor and the audience already reads it in Latin. - **Per-language thumbnails exist** but are gated behind multi-language audio tracks: you need at least one added audio track before a localised thumbnail can attach to it. **[A]** --- ## Honest limits Thumbnail design is a craft with weak empirical foundations. A study of 2,400 news thumbnails across 21 visual features found **few statistically significant correlations with engagement at all** **[A]**. The breakout data above describes what winning thumbnails *look like*, not what *caused* the win. Genuinely unsettled: rule-of-thirds vs centred (no data either way), and saturation levels (creator orthodoxy vs the aesthetics literature). Do not present either as settled. A/B testing needs ~10k impressions per variant to mean anything. Below that, just replace the thumbnail outright. --- ## Checklist - [ ] One idea, nameable in under a second at 168×94 - [ ] Face ~⅓ of frame, **one legible emotion, closed-mouth**, eye contact - [ ] Greyscale test: subject still separates - [ ] ≤3 colours, restrained saturation, checked on light *and* dark themes - [ ] 0–3 words, outlined, ~12–15% frame height, off the face, never bottom-right - [ ] Nothing in the bottom-right quadrant or bottom 40px - [ ] Real logos composited from files, never model-rendered - [ ] Nothing in the image that isn't in the video - [ ] Exported 3840×2160; verified at 168×94 (and centre-cropped first, for 9:16)