Without an agent:
npx hyperframes init demo
cd demo
npx hyperframes preview
npx hyperframes render
Timing lives in HTML attributes: data-start, data-duration and data-track-index on each clip.
The install lines, from the HyperFrames README.
In Claude Code:
claude plugin marketplace add heygen-com/hyperframes
claude plugin install hyperframes@hyperframes
Then run /hyperframes:hyperframes. For other agents: npx skills add heygen-com/hyperframes
HeyGen just open sourced HyperFrames, an Apache 2.0 framework that lets AI coding agents write HTML and render it into MP4 videos, and it sits at 62.7K GitHub stars after adding 5.1K in a week.
It's inspired by Remotion but runs on plain HTML instead of React
Headless Chrome and FFmpeg turn HTML, CSS, media and seekable animations into deterministic MP4
You can install it as a Claude Code plugin, an agent skill or a command-line tool
It needs Node.js 22 or later and FFmpeg
Source: https://t.co/uWikFN3UIg
It ships 21 skills. /hyperframes reads the request first and picks a workflow:
/product-launch-video from a site URL
/pr-to-video from a GitHub PR
/embedded-captions on a talking-head video
/remotion-to-hyperframes to port a Remotion project
To run it, clone the repo, then:
cp .env.example .env
docker compose up --build
Open localhost:5175. It also exposes an MCP endpoint, so Claude Code can call it:
claude mcp add --transport http openshorts http://localhost:8000/mcp
How OpenShorts picks and frames the clips, from its README:
Gemini reads the transcript and scene cuts and picks 3 to 15 moments. Two people on camera get stacked, captions on the seam. One speaker gets a face-tracked crop. Subtitles come from faster-whisper.
🚨 ESTO ES UNA LOCURA
Un proyecto de GitHub que convierte vídeos largos en clips cortos listos para publicar en redes.
Se llama OpenShorts y es open source.
¿Lo mejor? 👇
→ Encuentra los mejores momentos del vídeo
→ Genera clips verticales
→ Añade subtítulos automáticamente
→ Prepara contenido para Shorts, TikTok y Reels
Todo en una misma herramienta.
Ideal para reutilizar podcasts, entrevistas o vídeos largos sin tener que editar cada clip desde cero.
Y sí, puedes ver el código en GitHub.
Enlace abajo 👇
Self-hosted is free under MIT, with no watermark and no upload limits. It needs Docker and 8GB+ RAM, and takes 5 to 8 min per 8-min video on CPU.
No Google key? Point LLM_BASE_URL at Ollama or LM Studio and the moment picker runs on your own model.
Scheduled tasks run Claude on a timer: a code review every morning, a weekly dependency audit.
It needs a Pro, Max, Team or Enterprise plan. On Linux it installs with apt.
What else the Claude Code desktop app does, from its quickstart docs:
After an edit, click the +12 -1 counter to open the diff. Comment on any line and Claude revises. Or type /code-review and it reviews its own changes.
We've been working hard on making the Claude Code desktop app great for running lots of sessions side by side.
It's now much quicker to start sessions, easier to split work across a few, and you can always see what each of them is doing. Here’s what's new:
Once a PR is open, it watches the CI checks and can fix failures, or merge once every check passes.
Long jobs can go to the cloud, so they keep running after you close the app.
A reply adds the part after listing: directories re-scan servers on a schedule, so keep the endpoint, TLS and health check up or the listing drops.
The registry is still in preview, so breaking changes or data resets can happen.
How to get an MCP server into the official registry, from its own quickstart:
The registry stores metadata only. Ship the server as a package first (npm, PyPI, NuGet, OCI, MCPB), or list a public remote endpoint with no package at all.
Then four commands:
brew install mcp-publisher
mcp-publisher init
mcp-publisher login github
mcp-publisher publish
init writes server.json, and its name must match mcpName. login prints a code you enter on GitHub.
Best use, per the model card: agents that reason and call tools. Pair it with retrieval or web search for knowledge-heavy work.
16 languages, Hindi included. 131K context.
A 24B model built to run agents on a laptop. Only 2B parameters are active per token, so it decodes 184 tokens/s on an Apple M5 Max.
It was trained with reinforcement learning inside OpenClaw, Hermes Agent, Pi and OpenCode, so it already knows their tools and prompts.
Today we release 𝙻𝙵𝙼𝟸.𝟻-𝟸𝟺𝙱-𝙰𝟸𝙱, the largest 𝙻𝙵𝙼𝟸.𝟻 family member. It is an efficient local model for laptops and PCs to work through multi-step tasks inside popular agent harnesses.
> 24B total parameters, 2B active per token
> Context expanded to 131K tokens
> A 128K-token vocabulary
> 33T pretraining tokens
> It is a reasoning model
> Very easy to fine-tune and customize
> Performs competitively with Qwen3.6-35B-A3B, a model with ~50% more total and active parameters, on agentic and tool-use tasks.
> Open base and post-trained weights on @huggingface 🤗
1/
To point Hermes Agent at it, serve it locally on port 8080, then:
hermes config set model.provider custom
hermes config set model.base_url http://localhost:8080/v1
hermes config set model.default LFM2.5-24B-A2B
How to set it up:
1. Sign up at https://t.co/3q6NZJJX6E, no card needed
2. Import your repo from GitHub, or upload a ZIP
3. Set the start command and deploy
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