Open Source on KnightLi Blog

Running DeepSeek 4 Locally: Antirez's ds4 Experiment on Apple Silicon Mac

Mon, 11 May 2026 08:51:37 +0800

Antirez has open sourced a new project: ds4. It is not a general-purpose LLM framework, but a local inference engine for DeepSeek V4 Flash, with a focus on Apple Silicon and the Metal backend.

Project URL: https://github.com/antirez/ds4

What is ds4?

ds4 has a clear goal: running DeepSeek V4 Flash locally on a Mac.

It currently provides three ways to use it:

Interactive CLI.
HTTP server.
An experimental Agent mode.

Judging from its positioning, it is more like an inference project deeply optimized for one specific model than a replacement for general-purpose tools such as llama.cpp, Ollama, or vLLM.

Why it is worth watching

There are three main reasons this kind of project is worth following.

First, the author is Antirez, the creator of Redis. He has long focused on low-level systems, performance, and simple tools, and his projects are usually quite direct in style.

Second, DeepSeek V4 Flash points toward efficient inference. If the local running experience is good enough, it could be very attractive for Mac users.

Third, ds4 directly targets Apple Metal. Compared with the route of supporting every platform first and optimizing later, it feels more like a project trying to go deep on one well-defined scenario.

Who should try it

ds4 is better suited for users who:

Use an Apple Silicon Mac.
Want to run DeepSeek V4 Flash locally.
Care about Metal inference performance.
Are willing to try an alpha-stage project.
Want to study lightweight inference engines and model runtime details.

If your goal is stable deployment, cross-platform operation, or OpenAI API-compatible infrastructure, it may not be the first choice at this stage. It is better treated as an experimental tool and a technical project to watch.

How to use it

The basic workflow in the project README is to build it first, then run it.

1
2
3

git clone https://github.com/antirez/ds4.git
cd ds4
make

Run it interactively:

./ds4

Start the HTTP server:

`1`	`./ds4 --server`

Agent mode:

`1`	`./ds4 --agent`

For exact parameters and model file preparation, follow the repository README, because the project is still changing quickly.

Current risks

ds4 is still at an early stage, so set expectations before using it:

Features may be incomplete.
Parameters, model formats, and command-line behavior may change.
Compatibility mainly revolves around Apple Silicon and Metal.
Agent mode is more experimental and is not suitable for direct production use.
When something breaks, you may need to read the README, issues, or source code yourself.

In other words, it is currently more of an open source experiment worth trying than a one-click tool for ordinary users.

How it differs from general inference tools

General-purpose inference tools usually aim for broad compatibility across model formats, platforms, backends, and APIs. ds4 takes a narrower path: local DeepSeek V4 Flash inference on Metal.

That choice has both benefits and trade-offs.

The benefit is that the implementation can stay focused, making performance and user experience easier to optimize around a single target. The trade-off is a limited scope: it is not meant to run every possible model, nor to replace a complete deployment platform.

If you already use llama.cpp or Ollama, ds4 is better treated as a supplementary testing tool, not an immediate replacement for your existing workflow.

Summary

The interesting part of ds4 is not that it is yet another local LLM tool. It is that its scope is intentionally narrow: DeepSeek V4 Flash, Apple Silicon, Metal, and local inference.

If you have a suitable Mac and are willing to tinker with an early-stage project, it is worth watching its performance, model support approach, and server/agent capabilities. For production environments, it is better to keep observing until the interfaces and usage patterns become more stable.

References

GitHub project: https://github.com/antirez/ds4

Pixelle-Video: An Open-Source AI Engine for Generating Short Videos From One Topic

Thu, 07 May 2026 20:25:17 +0800

Pixelle-Video is an open-source fully automated short-video generation engine from AIDC-AI. Its goal is direct: the user enters a topic, and the system automatically writes the script, generates AI images or videos, creates voice narration, adds background music, and renders the final video.

This kind of tool is useful for batch short-video creation, knowledge explainers, talking-head content, novel recaps, history and culture videos, and self-media experiments. It is not a single text-to-video model. It is a production pipeline that connects several AI capabilities.

What It Automates

Pixelle-Video’s default flow can be summarized as:

enter a topic or fixed script;
use an LLM to generate narration;
plan scenes and generate images or video clips;
use TTS to create voice narration;
add background music;
apply a video template and render the final result.

The README describes the flow as “script generation → image planning → frame-by-frame processing → video composition.” The modular design is clear: each step can be replaced, tuned, or connected to a custom workflow.

Key Features

The project covers a fairly complete set of capabilities:

AI script writing: automatically generate narration from a topic;
AI image generation: create illustrations for each line or scene;
AI video generation: connect to video generation models such as WAN 2.1;
TTS voice: support Edge-TTS, Index-TTS, and other options;
background music: use built-in BGM or custom music;
multiple aspect ratios: support vertical, horizontal, and other video sizes;
multiple models: connect to GPT, Qwen, DeepSeek, Ollama, and more;
ComfyUI workflows: use built-in workflows or replace image, TTS, and video generation steps.

Recent updates also mention motion transfer, digital-human talking videos, image-to-video pipelines, multilingual TTS voices, RunningHub support, and a Windows all-in-one package. The project is clearly moving beyond a simple script toward a fuller creation tool.

Installation and Launch

Windows users can first look at the official all-in-one package. It is designed to reduce setup friction: no manual Python, uv, or ffmpeg installation is required. After extracting the package, run start.bat, open the web interface, and configure the required APIs and image generation service.

For source installation, the README gives this basic flow:

1
2
3

git clone https://github.com/AIDC-AI/Pixelle-Video.git
cd Pixelle-Video
uv run streamlit run web/app.py

The source route is suitable for macOS and Linux users, and for anyone who wants to modify templates, workflows, or service configuration. The main prerequisites are uv and ffmpeg.

Configuration Priorities

On first use, the key is not to click “generate” immediately. The important part is connecting the external capabilities properly.

LLM configuration determines script quality. You can choose models such as Qwen, GPT, DeepSeek, or Ollama, then fill in the API Key, Base URL, and model name. If you want to minimize cost, local Ollama is one option. If you want more stable results, a cloud model is usually easier.

Image and video generation configuration determines visual quality. The project supports local ComfyUI and RunningHub. Users who understand ComfyUI can place their own workflows under workflows/ to replace the default image, video, or TTS pipeline.

Template configuration determines the final visual form. The project organizes video templates under templates/, with naming rules for static templates, image templates, and video templates. For creators, this is more practical than generating raw assets only, because the output is a video that can be previewed and downloaded directly.

Who It Is For

Pixelle-Video is especially suitable for three groups:

Short-video creators who want to turn ideas into draft videos quickly.
AIGC tool users who want to connect LLMs, ComfyUI, TTS, and video composition.
Developers and automation users who want to modify templates, workflows, or integrate their own materials and models.

If you only want to make one polished premium video, it may not replace manual editing. But if you want to generate many explainers, talking videos, or science and education videos with a consistent structure, its pipeline approach is valuable.

Things to Note

The ceiling of this kind of tool is determined by multiple links in the chain. A weak script model produces empty content; a weak image model gives scattered visuals; unnatural TTS makes the video feel rough; and a poor template weakens the final result.

So it is better to start with one fixed scenario, such as a “60-second vertical science explainer.” Fix the LLM, visual style, TTS voice, BGM, and template first, then expand to more topics.

The project supports a local free setup, but local setups often require a GPU, ComfyUI configuration, and model files. Users without a local inference environment can reduce setup difficulty by using a cloud LLM plus RunningHub, while keeping an eye on usage cost.

Short Take

Pixelle-Video is interesting not merely because it can “generate a video from one sentence.” Its real value is that it breaks short-video production into replaceable modules: script, visuals, voice, music, templates, and rendering. For ordinary users, it is a low-barrier AI video tool. For developers, it is closer to a hackable short-video automation framework.

If you are studying AI short-video pipelines, or want to connect ComfyUI, TTS, LLMs, and template rendering into a usable product, Pixelle-Video is worth trying and dissecting.

Warp Open Source: From Terminal to Agentic Development Environment

Thu, 07 May 2026 20:15:08 +0800

warpdotdev/warp is the open-source client repository for Warp. Warp now describes itself as an “agentic development environment, born out of the terminal”: it starts from the terminal, but brings AI coding agents, codebase indexing, task management, and development workflows into one environment.

This is not an ordinary open-source terminal emulator repository. It is closer to an answer to a larger question: as agents such as Claude Code, Codex, and Gemini CLI become common, should the terminal itself become a development environment for scheduling, observing, and managing agents?

Warp’s answer is yes.

Current State of the Repository

As of May 7, 2026, warpdotdev/warp is a public repository. GitHub shows roughly 56k stars and 4.1k forks. The README says the Warp client code is now open source and welcomes community contributions.

The main language is Rust. GitHub’s language breakdown shows Rust at over 98%, which matches Warp’s positioning: it is not a web wrapper, but a cross-platform native development tool.

Several README details matter:

Warp is an agentic development environment, born out of the terminal.
It can use its built-in coding agent and can also connect to external CLI agents such as Claude Code, Codex, and Gemini CLI.
OpenAI is the founding sponsor of the newly open-sourced Warp repository.
The agentic management workflows in the repository are powered by GPT models.
Warp UI framework crates use the MIT license, while the rest of the code uses AGPL v3.

This shows that Warp’s open source move is not merely publishing a terminal. It is operating the project as an experiment ground for agent workflows.

Warp Is More Than a Terminal

Traditional terminals mainly do three things:

start a shell;
run commands;
display output.

Warp’s earlier differentiation was making the terminal feel more modern: command blocks, completion, history, collaboration, UI-style interactions, and cross-platform polish. Now the focus has moved further toward organizing development around AI agents.

From the README, Warp no longer only emphasizes “a better terminal.” It emphasizes:

built-in coding agents;
external CLI agent support;
issue triage;
spec writing;
PR review;
contributor coordination;
observable agent sessions.

In other words, Warp wants to turn the terminal from “where you type commands” into “where you work with multiple agents.”

Oz and Open-Source Project Management

The README mentions Oz several times.

Warp’s contribution overview shows thousands of Oz agents working on issue triage, specs, implementation, and PR review. This is interesting because it extends AI agents from “helping one person write code” to “helping manage open-source collaboration.”

The hardest part of many open-source projects is not writing code, but maintenance:

too many issues, not enough classification;
bugs and feature requests mixed together;
new contributors unsure which tasks are approachable;
PR review pressure;
maintainers struggling to follow every community thread.

Warp’s idea is to let agents take on part of the project management and collaboration work first. The README also mentions Oz for OSS, a maintainer-facing program for bringing similar agentic open-source management workflows to other repositories.

This suggests that Warp’s ambition is not only the terminal product itself, but also a new model of open-source maintenance in the AI era.

Repository Structure and Tech Stack

From the repository structure, Warp is a large Rust project.

The root contains:

app/: main application code.
crates/: core Rust crates.
assets/: resource files.
command-signatures-v2/: command signature related content.
docker/, script/, resources/, specs/, and other engineering directories.
.claude/, .warp/, .agents/skills, and other agent-related configuration.

WARP.md gives more engineering detail. It describes Warp as a Rust-based terminal emulator using an in-house UI framework called WarpUI.

The major modules can be roughly understood as:

app/: terminal emulation, shell management, AI integration, Drive, authentication, settings, workspace, and sessions.
crates/warp_core/: core utilities and platform abstraction.
crates/editor/: text editing functionality.
crates/warpui/ and crates/warpui_core/: the in-house UI framework.
crates/ipc/: inter-process communication.
crates/graphql/: GraphQL client and schema.

WARP.md also mentions architectural features such as:

an Entity-Handle system;
a modular workspace structure;
macOS, Windows, Linux, and WASM targets;
AI integration, including Agent Mode, context awareness, and codebase indexing;
Warp Drive cloud sync.

This complexity is closer to a full IDE than a lightweight traditional terminal.

Local Build Commands

The README gives a concise local build flow:

1
2
3

./script/bootstrap
./script/run
./script/presubmit

Where:

./script/bootstrap performs platform-specific initialization.
./script/run builds and runs Warp.
./script/presubmit runs formatting, clippy, tests, and other pre-submit checks.

WARP.md also lists more detailed commands:

cargo run
cargo bundle --bin warp
cargo nextest run --no-fail-fast --workspace --exclude command-signatures-v2
cargo fmt
cargo clippy --workspace --all-targets --all-features --tests -- -D warnings

If you want to contribute to Warp, ./script/presubmit is effectively required.

Contribution Flow

Warp’s contribution flow is not simply “open a PR.”

The README describes a lightweight process from issue to PR:

Search existing issues first.
If there is no duplicate, file a bug or feature request.
Maintainers review the issue and may add readiness labels.
ready-to-spec means the design can be expanded into a spec.
ready-to-implement means the design is clear enough to start an implementation PR.
Contributors can pick up labeled issues.

This process fits a large open-source project. It separates ideas, design, and implementation, reducing the risk that contributors spend time building in the wrong direction.

It also fits AI agents well. An agent can organize issues, draft specs, add tests, and then move into implementation. Warp itself uses this pattern to demonstrate agentic project management.

License: MIT + AGPL v3

Warp uses a dual license structure.

The README says:

the Warp UI framework, namely the warpui_core and warpui crates, uses the MIT license;
the rest of the repository uses AGPL v3.

This matters. AGPL v3 has stronger open-source requirements for network services and distribution. If you are learning, researching, or contributing, it is usually straightforward. But if you want to use Warp code in a commercial product or closed-source derivative, you need to read the license carefully and consult legal advice if necessary.

In short, Warp is open source, but not “take it and close-source it freely” open source.

Why It Is Worth Watching

First, Warp brings the terminal, agents, and project management together.

Many AI coding tools are still CLI tools or editor plugins. Warp starts from the terminal entry point and tries to unify agent tasks, code execution, command output, PR workflows, and team collaboration.

Second, Warp’s open-source approach is a good place to observe agent workflows.

It does not only publish code. It also exposes contribution overviews, agent sessions, issue triage, and spec workflows. For anyone studying how AI can participate in open-source collaboration, the repository itself is a sample.

Third, Warp is a complex Rust desktop application.

If you want to study Rust GUI, terminal emulation, cross-platform apps, GraphQL clients, cloud sync, and AI integration, the repository has a lot to read. But it is not a small project, so new contributors should read the docs and issue process first.

Fourth, Warp supports both a built-in agent and a “bring your own CLI agent” approach.

This is realistic. Developers will not use only one agent. Claude Code, Codex, Gemini CLI, OpenCode, OpenClaw, and similar tools are likely to coexist. If Warp can become a workbench for them, it becomes more valuable than a single-purpose terminal.

Who Should Care

If you are a normal terminal user, Warp matters because the terminal may be changing from a command-line tool into an AI workbench.

If you are a heavy AI coding agent user, Warp is worth watching because it tries to manage multiple agents rather than act as another chat entry point.

If you maintain open-source projects, the Oz for OSS direction is worth attention. It explores agent-based issue triage, PR review, community collaboration, and contributor onboarding.

If you are a Rust developer, Warp is a real large-scale desktop application worth studying for UI organization, terminal internals, cloud sync, AI integration, and cross-platform code.

If you only want a terminal that can replace your current one immediately, it is better to download the stable release first, then decide whether to study the source. Building from source is more suitable for contributors and deep users.

Short Take

The point of Warp going open source is not merely “a modern terminal became open source.”

More precisely, Warp is trying to upgrade the terminal into an agentic development environment: the terminal connects the shell, codebase, command execution, agents, issues, PRs, and collaboration flow.

As AI coding agents keep growing, the entry point of the development environment may change. In the past, the IDE dominated the developer experience while the terminal ran commands. Now the terminal may become the center of agent collaboration. The Warp repository is exploring that possibility.

GitHub repository: https://github.com/warpdotdev/warp
Warp website: https://www.warp.dev
Warp documentation: https://docs.warp.dev
Warp build overview: https://build.warp.dev
WARP.md: https://github.com/warpdotdev/warp/blob/master/WARP.md
CONTRIBUTING.md: https://github.com/warpdotdev/warp/blob/master/CONTRIBUTING.md

Open Source on KnightLi Blog

Running DeepSeek 4 Locally: Antirez's ds4 Experiment on Apple Silicon Mac

What is ds4?

Why it is worth watching

Who should try it

How to use it

Current risks

How it differs from general inference tools

Summary

References

Pixelle-Video: An Open-Source AI Engine for Generating Short Videos From One Topic

What It Automates

Key Features

Installation and Launch

Configuration Priorities

Who It Is For

Things to Note

Short Take

Warp Open Source: From Terminal to Agentic Development Environment

Current State of the Repository

Warp Is More Than a Terminal

Oz and Open-Source Project Management

Repository Structure and Tech Stack

Local Build Commands

Contribution Flow

License: MIT + AGPL v3

Why It Is Worth Watching

Who Should Care

Short Take

Related Links