edit(content): review batch-1 editorial pass across 24 posts (EN + ZH)

- voice: first-person throughout (we/our -> I/my), matching the post guideline
- privacy: internal LAN IPs replaced with placeholder hostnames
  (n8n.internal, gpu-node.internal); one compose volume path generalised
- ZH twins: Traditional -> Simplified characters (site serves zh-Hans),
  whole-file renormalisation where the twin was still Traditional
- dynamicCta: true on 12 posts so the contextual dual-track CTA renders
- long configs wrapped in <details class="tech-deep-dive"> with a summary
  naming the artefact and line count
- self-hosted-tts: token refresh now writes .tmp + atomic mv, with a note on
  the 401 race the old truncating redirect created

src/content/posts/best-ai-video-generators-2026.md

@@ -6,13 +6,14 @@ category: ai
 tags: [ai-video, video-generation, ltx, open-weights, pricing, veo]
 ogImage: /og/best-ai-video-generators-2026.jpg
 banner: /banners/best-ai-video-generators-2026.jpg
+dynamicCta: true
 ---
 
 Which AI video generator should you actually use in 2026 — and how much of it can you really get for free?
 
 AI video has changed faster than almost any other creative tool I've used. A year ago, decent video generation meant a paid subscription and hoping for the best. Today you can generate synchronized video *and* audio with an open-weight model on your own GPU, or get genuinely useful free credits from half a dozen platforms.
 
-But there's a catch hiding in the word "free". It means at least four different things in this market, and most articles don't tell you which one they're talking about. This guide compares the options that are actually usable in 2026, separates marketing from reality, and ends with a hands-on test: I generated a short video with the open-weight model **LTX-2.3** through PinkCherry, and I'll show you the result.
+But there's a catch hiding in the word "free". It means at least four different things in this market, and most articles don't tell you which one they're talking about. This guide compares the options that are actually usable in 2026, separates marketing from reality, and ends with a hands-on test: I generated a short video with the open-weight model **LTX-2.3** on my self-hosted ComfyUI production node (internal workflow code: **PinkCherry**), and I'll show you the result.
 
 *Pricing and free quotas below were checked on September 11, 2026. This market moves monthly — always verify against the provider's current pricing page before you commit.*
 
@@ -23,7 +24,7 @@ Before comparing tools, you need to decode the word. There are four different de
 1. **Free daily or monthly credits** — you get a set number of credits that refresh. Pika (80/month), PixVerse (daily), and Kling (66/day) work this way.
 2. **A one-time free trial** — you get an allocation once, and it never comes back. Runway's 125 credits are the classic example.
 3. **Open weights** — the model itself is free to download and run. But "free model" doesn't mean free *compute*: you still need a GPU, electricity, and setup time.
-4. **A hosted demo or community platform** — someone else runs the model for you (like PinkCherry, where I tested LTX-2.3). Quotas, queues, and availability can change without notice.
+4. **A hosted demo or community platform** — someone else runs the model for you (like Hugging Face Spaces or fal.ai's playground). Quotas, queues, and availability can change without notice.
 
 Same word, wildly different economics. A platform advertising "free AI video" is usually meaning #1 or #2. A model card advertising "free" is meaning #3 — and it quietly shifts the cost to your hardware.
 
@@ -146,7 +147,7 @@ Why this is a big deal: an open-weights model with synchronized audio changes th
 
 ## My LTX-2.3 test
 
-I generated this short clip with **LTX-2.3** in my own ComfyUI setup — a local install I've nicknamed **PinkCherry**. It's a real-world test, not a benchmark — no claims about speed or quality leadership here, just what the model did when I pointed it at a prompt and let it work.
+I generated this short clip with **LTX-2.3** in my own ComfyUI setup — a local production install with the workflow nicknamed **PinkCherry** on my RTX 3060 12GB workstation. It's a real-world test, not a benchmark — no claims about speed or quality leadership here, just what the model did when I pointed it at a prompt and let it work.
 
 <video controls playsinline preload="metadata" poster="https://content.hoelee.com/file/hoelee/video/LTX23-Demo.jpg">
   <source src="https://content.hoelee.com/file/h_480/hoelee/video/LTX23-Demo.mp4" type="video/mp4" />
@@ -159,7 +160,7 @@ What made this experiment interesting wasn't that LTX beats the commercial model
 
 - **Different cost structure.** No subscription, no per-clip API bill. The cost moves to GPU hardware, electricity, and setup time — which is a *capital* cost you can amortize, not a recurring one.
 - **More control.** You can run it in ComfyUI, swap checkpoints, quantize to fit your VRAM, and fine-tune under the community license.
-- **It's the model's job to evolve fast.** I tested 2.3; 2.5 shipped a month later with multishot and 4K HDR. Open weights mean you don't wait for a company to upgrade you.
+- **It's the model's job to evolve fast.** I tested 2.3; five months later in August, 2.5 shipped with multishot and 4K HDR. Open weights mean you don't wait for a company to upgrade you.
 
 The honest caveats: your results depend heavily on hardware, workflow, model version, and quantization. A hosted playground (like a Hugging Face Space or ComfyUI.cloud) is meaning #4 of "free" — great for a first taste, but queues and quotas can change, and serious local use means learning ComfyUI.
 
@@ -173,7 +174,7 @@ Here's how the test above was actually made — the workflow behind the clip. No
 - **Separate video and audio VAEs.** LTX-2.3 generates synchronized audio in the same pass, and it needs its own audio VAE alongside the video VAE — that's the piece that makes the sound *part of* the generation instead of an afterthought.
 - **Gemma 3 12B text encoder.** The LTX line uses Gemma as its language backbone, loaded with the LTX text projection.
 - **Distilled checkpoint + LoRA.** The fast distilled variant with the distilled LoRA at 0.6 strength, at **24 fps and 5–7 second clips** — the practical sweet spot for LTX.
-- **GGUF quantization + Chunk FeedForward.** The model loaded GGUF-quantized, with chunked feed-forward layers — that's how a 22B model fits on consumer VRAM.
+- **GGUF quantization + Chunk FeedForward.** The model loaded GGUF-quantized, with chunked feed-forward layers — that's how a 19B/22B parameter model fits on my RTX 3060 12GB workstation.
 - **Negative audio guidance (NAG).** Separate negative prompts for the video track and the audio track (e.g. "voice over, narration, off-camera speech" for audio), which keeps the generated sound clean.
 - **A tiny preview VAE** for fast in-sampler previews instead of full decodes.
 
@@ -185,7 +186,7 @@ The prompt matters more than any single node. LTX rewards prompts that describe
 4. **Consistency:** avoid instructions that don't match the reference image — they degrade results.
 
 <details>
-<summary>The exact prompt from my workflow (tap to expand)</summary>
+<summary>The exact prompt from my workflow (195 words — tap to expand)</summary>
 
 > A cinematic futuristic night scene in a dense neon-lit city alley during a heavy rainstorm. A young Asian female courier in a dark waterproof jacket, black cargo pants and a compact glowing backpack runs quickly toward the camera, water splashing from her boots with every step, loose strands of wet hair moving naturally in the wind. The shot begins behind and slightly above her as she runs through the narrow alley, then the camera smoothly tracks alongside her and arcs around to the front, revealing her focused face as she looks briefly toward the camera while continuing to run. A small sleek hovering surveillance drone follows several meters behind her, its white searchlight sweeping through the rain. Neon signs reflect vividly across the wet pavement, puddles ripple from raindrops, mist drifts through the alley, and colored light flickers across her face and clothing. The camera movement remains smooth and cinematic with realistic handheld micro-motion, shallow depth of field, natural motion blur and strong foreground-to-background parallax. The final moment shows her rushing past the camera while the drone flies overhead, leaving the camera facing the glowing rainy alley. Realistic cinematic lighting, physically believable rain and water interaction, detailed skin, fabric and wet surfaces, high environmental detail, dramatic science-fiction atmosphere. Audio: heavy rainfall, footsteps splashing through puddles, distant city traffic, subtle electrical ambience and the quiet mechanical hum of the hovering drone.