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Chaining VEO 2's 8-second clips into 2-minute videos with sync audio

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Chaining VEO 2's 8-second clips into 2-minute videos with sync audio

The technical challenge: VEO 2 only generates 8-second clips with no audio. I needed to create longer marketing videos, so I built a system that chains multiple clips into cohesive 2-minute videos with synchronized narration. The core technical problems I solved: 1. Script Continuity Across Clips VEO 2 doesn't maintain context between generations. I built a script parser that breaks longer narratives into 8-second segments while maintaining visual and narrative continuity. Each segment gets contextual prompts that reference the previous clip's end state. 2. Seamless Visual Transitions To avoid jarring cuts between clips, I analyze the final frame of each segment and use it to inform the opening prompt of the next segment. This creates natural visual flow across the 8-second boundaries. 3. Audio Synchronization Pipeline Since VEO 2 is silent, I integrated Google's Gemini Flash audio model with a custom FFmpeg pipeline: Parse the script timing Generate audio segments that match video pacing Use FFmpeg to sync audio with video transitions Handle audio crossfades between clip boundaries 4. Cost Optimization At current VEO 2 pricing, a 2-minute video costs ~$60 in API calls. I built request batching and caching to minimize redundant generations. The system can theoretically generate longer videos, but I capped it at 2 minutes due to cost considerations. This powers the video generation feature in my marketing tool Smarketly, but the core chaining technique could work for any VEO 2 application needing longer content. Technical questions I'm still working on: Better frame-to-frame consistency algorithms More efficient prompt engineering for visual continuity Happy to share code snippets or discuss the technical implementation! But you can check what i'm building this on by visiting https://smarketly.lema-lema.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, context · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: efficient, calls · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, google · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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