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LemonSlice

Upgrade your voice agents to real-time video

Details

External ID
46783600
Source
HN
Company
—
Product
—
Website domain
—
Launched
Jan. 27, 2026
Cohort
—
Upvotes
133
Upvotes percentile
0.924901185770751
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

Hey HN, we're the co-founders of LemonSlice (try our HN playground here: https://lemonslice.com/hn). We train interactive avatar video models. Our API lets you upload a photo and immediately jump into a FaceTime-style call with that character. Here's a demo: https://www.loom.com/share/941577113141418e80d2834c83a5a0a9Chatbots are everywhere and voice AI has taken off, but we believe video avatars will be the most common form factor for conversational AI. Most people would rather watch something than read it. The problem is that generating video in real-time is hard, and overcoming the uncanny valley is even harder.We haven’t broken the uncanny valley yet. Nobody has. But we’re getting close and our photorealistic avatars are currently best-in-class (judge for yourself: https://lemonslice.com/try/taylor). Plus, we're the only avatar model that can do animals and heavily stylized cartoons. Try it: https://lemonslice.com/try/alien. Warning! Talking to this little guy may improve your mood.Today we're releasing our new model* - Lemon Slice 2, a 20B-parameter diffusion transformer that generates infinite-length video at 20fps on a single GPU - and opening up our API.How did we get a video diffusion model to run in real-time? There was no single trick, just a lot of them stacked together. The first big change was making our model causal. Standard video diffusion models are bidirectional (they look at frames both before and after the current one), which means you can't stream.From there it was about fitting everything on one GPU. We switched from full to sliding window attention, which killed our memory bottleneck. We distilled from 40 denoising steps down to just a few - quality degraded less than we feared, especially after using GAN-based distillation (though tuning that adversarial loss to avoid mode collapse was its own adventure).And the rest was inference work: modifying RoPE from complex to real (this one was cool!), precision tuning, fusing kernels, a special rolling KV cache, lots of other caching, and more. We kept shaving off milliseconds wherever we could and eventually got to real-time.We set up a guest playground for HN so you can create and talk to characters without logging in: https://lemonslice.com/hn. For those who want to build with our API (we have a new LiveKit integration that we’re pumped about!), grab a coupon code in the HN playground for your first Pro month free ($100 value). See the docs: https://lemonslice.com/docs. Pricing is usage-based at $0.12-0.20/min for video generation.Looking forward to your feedback!EDIT: Tell us what characters you want to see in the comments and we can make them for you to talk to (e.g. Max Headroom)*We did a Show HN last year for our V1 model: https://news.ycombinator.com/item?id=43785044. It was technically impressive but so bad compared to what we have today.

Enrichment

Theme
ai video creation and repurposing
Vertical
Horizontal
Function
Agent / copilot
Audience
B2B
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
video capabilities for voice agents
Manually corrected
False

Could you build this?

No Training and serving proprietary low-latency interactive video avatar models capable of real-time bidirectional audio-video streaming requires frontier generative video research and massive GPU infrastructure.

What it would actually take: Building this requires custom diffusion or autoregressive neural video generation models conditioned on audio phonemes/latents, optimized for sub-200ms latency. The infrastructure demands specialized low-latency WebRTC pipelines, TensorRT/vLLM-style custom model serving kernels, and massive compute clusters. Deep generative video AI research, high-performance computing, and real-time streaming infrastructure expertise are strictly required.

Discussion

20 comments analyzed.

Competitors mentioned: anam.ai, LLM chatbots, text-to-speech voice synthesis

Concerns raised: Potential for deception and fraud at scale with realistic human avatars, Ethical issues with creating photorealistic avatars designed to fool people, Risk of impersonation and illegal use cases, Future misuse potential (e.g., explicit content generation), Insufficient consideration of harm mitigation during design

Feature requests: Provide LLM context on avatar appearance from initial image and updates, Improve voice assistant awareness of its own appearance and surroundings, Better VAD (voice activity detection) to reduce perceived impatience, Real-time lip sync and full body movement options, On-device real-time processing capability

Competitors

Other products that read as similar to this one — 267 launches clear the similarity bar, closest 8 shown.

Attention rank: #27 of 268 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 90 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.