Notes About Screening

Host: notes.addict.best

File: notes-about-screening.md

Scope: Screen capture quality, images→video, re-encode size math, browser publishing patterns, growth/viral mechanics, shot-list review notes.

Updated: 2026-08-03

Table of contents

1. Mac screen recording (aliases & codecs)

2. H.264 vs H.265 for screen content

3. CRF / quality settings (and videotoolbox reality)

4. Why re-encodes got tiny but still looked good

5. Images → video (OpenCV review + better practice)

6. Browser automation / no-API publishing (use carefully)

7. Content & viral mechanics (retention math)

8. High-retention video structure

9. Actions on media platforms (ops checklist)

10. Platform playbooks from zero

11. Shot-list / AI production order review

12. Quick reference tables

1. Mac screen recording (aliases & codecs)

Baseline: rec-screen (current default)

Quality take: At 18 Mbps CBR, native res, quality is close to visually lossless for screen content. CBR still wastes bits on static frames → fat files.

Added aliases (recommended daily drivers)

When to use which

• Archive / master capture: native rec-screen (or 2K HEVC if storage matters).
• Screening / share / app demos: rec-screen-1080 (sharp enough, ~40%+ smaller than fat H.264).
• Retina-ish but controlled: rec-screen-2k with tighter -q:v 35.

2. H.264 vs H.265 for screen content

Rule of thumb

• Keep / edit yourself on Mac → prefer HEVC.
• Must play everywhere without codecs → stick to H.264 (or export a second H.264 deliverable).

3. CRF / quality settings (and videotoolbox reality)

Software x264 / x265 (CRF)

CRF idea: quality-targeted — hard scenes get more bits; flat UI gets few. Better than fat fixed bitrate for screen demos.

Hardware videotoolbox (important)

hevc_videotoolbox / h264_videotoolbox do not support true CRF.

Use quality mode -q:v (lower number = better quality).

Practical defaults already set

• rec-screen-1080 → -q:v 40
• rec-screen-2k → -q:v 35

4. Why re-encodes got tiny but still looked good

Rough impact order (screen / app demos):

1. Resolution drop (biggest lever)

Example: 2880×1800 → 1920×1200 ≈ 0.44× pixels. Half-ish the pixels → far fewer bits for the same perceived sharpness on laptop/phone. For UI demos, 1920 is often enough; 2880 is frequently overkill for screening.

2. Original bitrate was wasteful

Sources ~6.7–7 Mbps at high res vs outputs ~0.5–0.7 Mbps at 1920 with CRF ~20. Originals were “fat” relative to flat UI complexity.

3. CRF / quality mode, not fixed high bitrate

Avoids paying full bitrate for static frames.

4. Content type loves compression

Flat colors, UI panels, text, slow pans, little grain/noise → encoders crush size without looking soft. Grainy outdoor cinema would not shrink the same way.

5. Modern re-encode efficiency

Current libx264 + medium (or HEVC HW) often beats older near-lossless screen dumps.

6. No audio

100% of bits go to video (audio only a few MB, but simplifies the story).

What *not* to do (protects quality)

• Don’t jump to CRF 28–32 H.264 as default (blocky risk).
• Don’t force 720p for clinical UI demos unless mobile-only.
• Don’t heavy denoise / slash FPS in a way that kills motion.
• CRF 20 = clean zone; 23–24 still good; 28+ = “small first.”

Mental model

Size ≈ pixels × motion/detail × encoder efficiency × “safety margin” bitrate

You win size by cutting pixels + safety margin (CRF/q-mode) + content that needs little bitrate (UI screens).

FAQ

5. Images → video (OpenCV review + better practice)

What the common OpenCV tutorial does (summary)

1. Install opencv-python + pillow

2. Count .jpg/.jpeg/.png

3. Compute mean width/height, resize all frames with LANCZOS

4. cv2.VideoWriter with DIVX fourcc, 1 fps, write each frame → .avi

Review — what is solid

• Same frame size for all images (required for most writers).
• LANCZOS (not deprecated ANTIALIAS) for downscale.
• Explicit release of VideoWriter.

Review — problems / upgrades for real screening work

Preferred pipeline (production-aligned)

images (sorted)
  → optional scale to even WxH (or fixed 1080×1920 / 1920×1080)
  → FFmpeg image sequence or concat demuxer
  → -framerate 30 on image inputs (VFR trap)
  → H.264 social default or HEVC archive
  → -ar 48000 if audio
  → QA: duration, non-black frames, mute-readability if text-on-screen

OpenCV is fine for prototypes / research; for screening + social delivery, FFmpeg (and editor.addict.best enhance templates) is the durable path.

6. Browser automation / no-API publishing (use carefully)

Policy: Browser/cookie automation only for your own accounts, conservative pacing, no ban-loop retries. Prefer existing Hybrid/Omni patterns over random GitHub clones.

Useful patterns (reference only)

How-to sketches

• AutoSocial: local dashboard → first-run login per account (.profiles/) → queue → schedule/instant. Extend with face-blur / OpenCV offline.
• auto-instagram: content folder → import cookies → CLI; add residential proxy + human delays.

Stealth helpers

• cloakbrowser (npm) — stronger stealth Chromium for Playwright.
• Self-hosted anti-detect profiles (AdsPower-class) for one account → one profile/IP.

Simplified CLI (API side note)

• https://github.com/celeryhq/simplified-cli
• npm i -g simplified-cli → SIMPLIFIED_API_KEY → simplified auth:login
• Commands: posts:create, ai-image:generate --wait, analytics — JSON stdout, non-zero exit codes (agent-friendly).

Not a substitute for cookie vault + watchdog hygiene on browser-first stacks.

7. Content & viral mechanics (retention math)

Grounding facts (2026-style)

• Platforms measure hook strength in ~1–2 seconds.

- TikTok: aim hook intent ~1.0–1.3s

- Reels: ~1.5–2.1s

• Keeping ≥60% of viewers past ~3 seconds strongly improves push odds.
• Negative / mistake framing (“you’re doing this wrong”) often 1.3–1.8× higher hook rate than pure positive framing on short video — especially feature demos (“old slow way vs app way”).

Five content archetypes that spread

1. Contrarian — “Why [popular thing] is actually hurting you”

2. Transformation / reveal — before/after, process, “X → Y”

3. Relatable pain — “Does anyone else…”, “POV:…”

4. Information gaps — “The real reason…”, “What nobody tells you…”

5. Authority by association — react to big names / viral niche moments

Golden hour & distribution

• First 60 minutes can decide a large share of reach.
• After publish: share to relevant public/private groups, Stories, and platform-native cross-posts (adapt, don’t clone).
• Post when audience is online (analytics), not only when you are free.
• Recycle winners every 60–90 days with slight variations.
• North-star metrics: saves + shares, early velocity, completion/retention — not vanity likes alone.

What kills growth

• Bought followers/engagement
• Watermarked or copy-paste reposts
• Engagement bait (“comment YES if…”)
• Ignoring comments in the first hour
• Same file + caption on every app (non-native)
• Quitting after a quiet first week

Hub-and-spoke (default system)

One hub piece → many spoke adaptations (Reels cut, Threads text, X post, carousel). Don’t invent six full originals from zero.

8. High-retention video structure

Editing techniques that help

9. Actions on media platforms (ops checklist)

Use with ethics / account age caps elsewhere (Hybrid rules). This list is what to do, not “spam harder.”

• Comment on niche influencers with high-value replies (not empty praise).
• Publish + engage + auto-respond (don’t ignore comments/DMs).
• Scrape/target only high-intent users (competitors’ recent commenters, niche groups, hashtag engagers) — with filters.
• Grow groups/pages carefully; invite with warm-up, not cold blasts.
• Choose post times from analytics (and ads data when available).
• First 60 minutes after post: public share, groups, cross-platform native variants, reply hard.
• Recycle top performers 60–90 days with variations.

Recommended rapid-spread system

1. Choose 2–3 primary platforms.

2. Weekly batch: core idea → native adaptations.

3. Publish → golden hour engage → next-day retention review.

4. Double down on formats above your baseline.

5. Cross-pollinate winners by adapting, not cloning.

6. Track saves/shares + early velocity.

High-leverage “treasure” edges

• First-hour system: prime via Stories/groups → post at peak → stay online 60 minutes.
• Author replies multiply distribution (esp. X / LinkedIn / YouTube early window).
• Serial/episodic content trains the algorithm better than pure one-offs.
• Platform-native always beats watermarked crossposts.
• Emotion + social utility drives shares.
• One idea = a content system (hooks, lists, stories, contrarian cuts).
• Consistency compounds (3–5× distribution over time vs sporadic).

10. Platform playbooks from zero

First 30 days

1. Lock niche, pillars, handle, keyword bio; pick TikTok or Reels as lead; outline 10 hubs.

2. Batch film; one hub → many spokes; post lead daily-ish; Threads/X 1–2×/day.

3. Reply every comment ~first hour; track retention, saves+shares, follows-per-view.

4. Double down on winners; drop platforms with zero return — 2–3 done well > 6 thin.

30-day blitz (optional intensity)

11. Shot-list / AI production order review

Review of a ~48s / 14-shot Promedic-style order:

Discipline that matters most

• Locked Style Block + Master Negative Prompt as unchanged copy-paste strings every shot. Drift starts when people paraphrase from memory.

Consistency workflow highlights

• Reference propagation: after early shots approve a clean frame from Shot 3 as the anchor for later shots (not only the master sheet).
• Performance close-ups (e.g. Shot 13): prefer video-reference / Act-Two-style performance over pure text-prompt lip sync (esp. Arabic phonemes).
• LLM shot-list meta-prompt: force escape hatch [NEEDS INPUT: …] instead of inventing props/characters.

9:16 safe zones

• Example hard numbers on 1080×1920: ~200px top / 300px bottom / 60px sides for TikTok/Reels chrome.
• Hard rule: compose AI keyframes at 16:9 or wider with subject centered, then crop — never generate critical subject on the edge.

Tool notes

• Kling subject binding / Runway references are solid anchors.
• Verify subscription-gated “video reference / Omni” features are actually enabled before building a shot around them.

Expert non-negotiables

1. Render textless master + captioned version separately (cheap at export; saves EN/AR rework).

2. Never let AI invent medical numbers on-screen — generate abstract UI; burn real numbers as post graphics. Liability + accuracy.

12. Quick reference tables

Screen capture decision

Size vs quality levers (screen)

Viral ops (one screen)

Hook ≤2s (mistake-frame preferred)
→ ≥60% past 3s
→ visual change ~2–3s
→ publish
→ 60-min golden hour (reply/share/groups)
→ next day: retention + saves/shares
→ recycle winners 60–90 days

Related hosts

*End of notes-about-screening.md*