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MiniMax-H3, explained with your favourite TV shows

If you've been watching the open text-to-video space, MiniMax-H3 is one of the more interesting drops of the year. It generates short cinematic clips with a synced soundtrack from a text prompt, and you can drive it end-to-end without ever touching a GPU yourself. The easiest way to explain what that actually looks like is to point at the results people have been posting. My feed has been full of H3 recreations of famous TV moments — Breaking Bad lab scenes, Friends coffee-shop bits, mockumentary moments from The Office . // Detect dark theme var iframe = document.getElementById('tweet-2084562933162602866-755'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084562933162602866&theme=dark" } In this post I'll cover: What MiniMax-H3 actually is How you can run it yourself What is MiniMax-H3? MiniMax-H3 is a text-to-video model that produces short clips at cinematic resolutions. Two things make it stand out compared to earlier open video models: Sound comes out of the same model. Most open text-to-video pipelines output silent frames and you bolt on a separate audio model afterwards. H3 emits a soundtrack aligned with the visual content in one pass. // Detect dark theme var iframe = document.getElementById('tweet-2084353489061499021-723'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084353489061499021&theme=dark" } Keyframe conditioning. You can pass an optional first frame and/or last frame image and the model will interpolate a motion path between them. This turns it from a pure "vibe generator" into something you can actually direct. // Detect dark theme var iframe = document.getElementById('tweet-2084378446122319973-582'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2084378446122319973&theme=dark" } The knobs are the ones you'd expect: Prompt — free f

2026-08-13 原文 →
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Running the same SQL checks in a browser, CLI and pull request

I wanted one set of SQL checks to work in three places: while exploring a query, from a terminal and during code review. That became SQL Atlas. It is a local, deterministic SQL analyzer with a browser interface, a CLI and a GitHub Action. This article covers the interfaces, the CI contract and the limits of static SQL analysis. One analyzer, three interfaces The analyzer returns structured data instead of printing messages directly. Each interface decides how to present the same result: The browser explains findings and links them to learning material. The CLI returns text, JSON or Markdown and uses stable exit codes. The GitHub Action converts findings into file annotations and a job summary. Keeping presentation outside the analyzer prevents the CLI and Action from becoming separate implementations with different behavior. A CLI needs a contract The CLI accepts one or more files, or SQL through standard input: npx --yes sql-atlas@0.5.1 analyze query.sql echo "SELECT * FROM customers;" | npx --yes sql-atlas@0.5.1 analyze - It supports PostgreSQL, MySQL, Oracle, SQLite, SQL Server and a generic mode. Output can be text for a person, JSON for another program or Markdown for an issue or report. Exit codes are part of the interface: 0 means analysis completed and the configured policy passed. 1 means analysis completed but a severity or score threshold failed. 2 means the command or input was invalid. This distinction matters in CI. A policy failure is not the same as a broken invocation. Turning findings into pull request feedback The Action runs as a bundled Node 24 program and does not download dependencies at runtime. A minimal workflow looks like this: name : SQL review on : pull_request : paths : - " **/*.sql" permissions : contents : read jobs : sql-atlas : runs-on : ubuntu-latest steps : - uses : actions/checkout@v7 - uses : milekv/sql-atlas@v0.5.1 with : paths : | migrations/**/*.sql schema/**/*.sql dialect : postgresql fail-on : critical min-score : 60 Findin

2026-08-13 原文 →
AI 资讯

I Was Tired of Losing Disk Space to node_modules - So I Built ArtifactSweep

Being a developer, we all create many projects for learning, work, and experiments. Over time my machine started filling up — not with source code, but with generated junk : node_modules target dist / build framework caches like .next , .angular , .nuxt and more of the same across every cloned repo Every few months I would hunt folders manually, delete something, free a few GB, then the same problem would come back. Only learning about “clean your disk” tips doesn’t help much. Building something for the problem does. So I ended up building ArtifactSweep — a small open-source tool for this everyday developer issue. The real problem As developers we regenerate these folders all the time: npm install cargo build ng build They are not our source of truth. But they sit on the SSD for months. The painful part is not only size. It is: Finding them across many project roots Knowing how big they are before delete Not deleting the wrong folder by mistake I wanted something that could: Scan a folder tree Show sizes Let me clean with more control Work on my day-to-day machines (Windows, Linux, Mac) Step 1: Start with a CLI I started with the command line first. Why CLI? Fast to build and test Fits terminal-first workflow Easy to script and share The CLI is called sweep . Basic usage: # Safe: only list junk under a path sweep scan . # Preview deletes sweep clean . --dry-run # Delete sweep clean . On one of my project folders alone, it reclaimed nearly 5 GB . That was enough validation: this is not a fake problem. Every active developer hits it. Step 2: Then came the desktop app CLI is great when you already know the path and trust dry-run. But sometimes I wanted to: See a list of folders and sizes Filter by type Confirm before delete Click through without remembering flags So I added a desktop app on top of the same idea (same cleanup job, different UI). Flow is simple: Choose folder Scan Review results (and filters if needed) Clean with confirmation If you like GUIs for this ki

2026-08-13 原文 →
AI 资讯

I Built HackForPinas to Make Philippine Hackathons Easier to Discover

In my previous article, I talked about Train Track, the transit app I built around Metro Manila's railway systems. This project started with a completely different problem. I kept thinking about how difficult it can be to discover hackathons and coding competitions. Not because they don't exist. They do. The problem is that they're scattered everywhere. A university might announce one. A government agency might host another. A private company might run one. A developer community might post another. And suddenly you're checking multiple websites just to figure out: What can I actually join? So I built HackForPinas. What is HackForPinas? HackForPinas is a free, public, and open-source directory for Philippine: Hackathons Coding challenges Technology competitions The idea is pretty straightforward: Make opportunities easier to discover. Events can be filtered by: Region Format Organizer type Status Organizers are categorized as: Government University Private Instead of browsing through unrelated websites, users can explore opportunities in one place. But the more I worked on it, the more I realized that the directory itself wasn't the hardest part. The data was. The Data Problem Imagine trying to collect hackathons from different websites. One might have an RSS feed. Another might use WordPress. Another might expose an API. Another might have an ordinary HTML page. And another might not have anything structured at all. So HackForPinas uses multiple scraping strategies: WordPress REST API RSS GDG Community Eventbrite HTML + Cheerio The scraper runs through a background endpoint and collects events from different Philippine technology sources. The interesting part wasn't: "Can I scrape a website?" It was: Can I turn information from completely different sources into one consistent dataset? That became a much more interesting engineering problem. I Didn't Want Anyone to Publish Directly There's another problem with a public directory. If anyone can submit an event, what s

2026-08-13 原文 →
AI 资讯

A Remote Coding Agent Can Deadlock on a Local Permission Dialog

The nastiest failure mode in a remote coding agent is not a bad patch. It is a permission prompt that nobody can see. You start a long-running job on a workstation, leave the desk, and check it from a phone later. The agent reaches a command that needs approval. If that request only exists as a modal in the desktop UI, the job has not technically failed. It has just stopped forever. That is worse. A failed job is observable. A hidden wait looks healthy until someone notices no work has moved. The permission prompt is protocol state The fix starts with a small change in how you model approval. A permission request is not UI state. It is durable state owned by the job that is doing the work. The lifecycle should look more like this: asked → persisted → surfaced → answered → applied → resolved The desktop dialog, phone screen, CLI, or web controller is only one view over that state. Closing a window must not erase it. Reconnecting must not create a second request. Two controllers must not be able to resolve different requests because a stale button happened to be on screen. This also changes what a remote-control protocol needs. A controller should be able to fetch job status with pending approvals, submit an answer for one request ID, and observe the resulting event. It should not become a filesystem or runtime proxy just to click “allow.” What needs to survive a disconnect At minimum, the pending request needs a stable request ID, its owning job/session, the requested action and resources, and enough ordering information to render concurrent requests deterministically. The answer also needs an identity. If request abc is pending, an answer for xyz must fail. Replaying the same answer for abc should be harmless. Replaying a different answer under the same ID should not quietly overwrite the first decision. That sounds fussy until a phone reconnects on a flaky network and retries the last command. Then it is the difference between idempotence and “the agent ran it twic

2026-08-13 原文 →
AI 资讯

# 🚀 I Built a Jenkins CI/CD Pipeline From Scratch — Here's Every Bug I Hit (and How I Fixed Them)

A learning-in-public story about Flask, Jenkins, AWS EC2, systemd, and finally shipping a live demo on Vercel. 🎯 TL;DR I built PyPulse, a tiny Flask app, and wired it up to a full CI/CD pipeline: push to GitHub → Jenkins builds → tests → deploys to AWS EC2 → auto-triggered via webhook → managed by systemd. Along the way I broke almost every piece of it at least once, and fixed each one. I also deployed a permanent live demo on Vercel, since my EC2 instance is running on the AWS free trial and won't live forever. 🔗 Live demo: pypulse-pi.vercel.app 🔗 Live demo (health check): pypulse-pi.vercel.app/health If you're learning DevOps and want to see what the real, messy version of "just set up a CI/CD pipeline" looks like — not the polished tutorial version — this is that. 🧰 The Stack Piece Tool Job App Flask + pytest + gunicorn The actual web app and its tests CI/CD Jenkins (on EC2, Ubuntu 22.04) Build → Test → Deploy automation Source control GitHub Single source of truth Trigger GitHub Webhook Auto-runs the pipeline on every push Process management systemd Keeps the app alive on reboot/crash Permanent demo Vercel Live URL that survives EC2 termination 🏗️ The App: PyPulse Nothing fancy on purpose — the whole point of this project was the pipeline, not the app. python app.py from flask import Flask, jsonify from datetime import datetime, timezone app = Flask( name ) @app.route("/") def home(): return jsonify({ "message": "Hello from PyPulse", "time": datetime.now(timezone.utc).isoformat() }) @app.route("/health") def health(): return jsonify({"status": "ok"}), 200 if name == " main ": app.run(host="0.0.0.0", port=5000) Two routes. Two tests. That's it. Small enough that when something broke, I knew it wasn't the app — it was the plumbing around it. That turned out to be the right call, because the plumbing broke a lot. 😅 ⚙️ The Pipeline: Build → Test → Deploy Here's the mental model I ended up with for a Jenkinsfile: Each stage is a gate. If Build fails, Test never runs.

2026-08-13 原文 →
AI 资讯

It lasted one day: a developer has already released a 'watermark-remover' for all AI-generated text

Following Anthropic's confirmation that all text generated by its new Claude models will carry an invisible watermark in order to identify that the text has been generated by AI. Read more about this measure at: https://support.claude.com/en/articles/16266773-how-claude-marks-ai-generated-content Today, developer Guillaume Meyer published "watermarks-remover" on GitHub: an open-source project that cleans those signals generated by LLMs, such as Claude, Gemini, OpenAI and others, removing invisible Unicode characters, C2PA metadata and more. 🔗 Repository link: https://github.com/guillaumemeyer/watermarks-remover

2026-08-13 原文 →
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