harry0703

I Tested MoneyPrinterTurbo, the 138k-Star Tool That Turns One Sentence Into a Short Video You give it one sentence. It writes the script, finds footage, records the voiceover, burns in subtitles, adds music, and hands you a finished vertical video. That's the promise of MoneyPrinterTurbo — 138,685 stars, 22,139 forks, MIT licensed, created in March 2024 and still pushed to this week (latest release v1.3.9, October 21). Promises are cheap, so I installed it on a headless Linux cloud VM and ran it end to end. Here is what actually happened — including the part that failed. What it is MoneyPrinterTurbo is a pipeline, not an editor. One topic goes in one end, a finished short video comes out the other: 1. Script — an LLM writes the voiceover text from your topic 2. Footage — stock clips matched by keyword, or your own local files 3. Voice — text-to-speech reads the script 4. Subtitles — speech recognition turns the audio back into timed captions 5. Assembly — footage, voice, subtitles and background music are composed into a 9:16 HD video It has four entry points: WebUI, API, CLI, and an AI-agent interface. I used the CLI. The design decision that matters most: every stage is replaceable. Don't like the auto-generated script? Paste your own. Don't want stock footage? Point it at local files. Don't want the robot voice? Supply your own audio. This test leans on that hard, as you'll see. The test setup - Headless Linux cloud VM, Python 3.12.3, ffmpeg already installed - Official pip route, in a fresh venv - No paid API keys anywhere; data directory redirected with MPT_HOME so nothing touched global config What worked Install — one command, after one gotcha. pip install mpt # installed mpt 1.3.9, matching the latest release The gotcha: this machine's /tmp is a 512 MB tmpfs. pip unpacks some large wheels (the mpt package itself is ~97 MB) and died with No space left on device — while the home directory had 137 GB free. Worse, the first background install reported success while having installed nothing. Setting TMPDIR to a directory on the big disk fixed it. If you run this on a small cloud box, you will probably hit the same wall. The full pipeline run — zero keys. For the successful run I supplied all three inputs myself: my own script, three local images as footage, and my own audio file: mpt cli \ --video-script "..." \ --video-source local \ --video-materials materials/mat1.jpg,materials/mat2.jpg,materials/mat3.jpg \ --video-aspect 9:16 \ --custom-audio-file narration.mp3 \ --bgm-type none Result: task status SUCCESS, and a finished final-1.mp4. I verified the output with ffprobe rather than trusting the success message: - 1080×1920 (9:16 vertical) - 15.07 seconds - Audio track present - The still images were automatically given a slow zoom (Ken Burns) effect and stitched in sequence So the core claim holds: the pipeline really does assemble a watchable vertical video with no human editing — at least when you feed it your own materials. What failed The built-in free voiceover (Edge TTS) never connected. My first run used the default free TTS voice. The script and footage stages passed; the job then died at the audio stage. Edge TTS could not reach Microsoft's speech endpoint from this VM (speech.platform.bing.com:443, SSL error), retried three times, and gave up. I want to be precise about what this means: it's a network problem of this cloud environment, not proof the feature is broken — on a normal home connection it usually works. But in my test it did not work, so I won't write "free voiceover: tested, works." I routed around it with my own audio file, which is exactly what the replaceable-stage design is for. What I did not test Honesty section, because a pipeline is only as good as its untested links: - Auto scriptwriting — needs an LLM API key from one of the supported providers. Skipped; I supplied the script. - Online stock footage (Pexels/Pixabay) — free, but needs a registered API key. Skipped; I used local files. - Whisper subtitles — runs locally but downloads a ~3 GB model. Not downloaded this time, so the test video has no subtitles. - WebUI and one-click cross-platform posting — not started. (On posting: automated publishing runs into each platform's rules and risk controls; my own practice is to publish manually.) - AI-generated footage — paid sources, and the CLI requires an explicit charge confirmation flag. Untouched. In other words: the fully automatic chain — topic in, video out, nothing supplied by you — depends on an LLM key + a footage key + TTS connectivity, and I did not verify that whole chain in this environment. What I verified is the self-supplied mode, end to end. The cost ledger The path I tested cost exactly $0: open-source software (MIT, commercial use allowed), my own script, local images, an existing audio file, no accounts registered. The costs appear only where you ask for more automation: an LLM key for auto-scripting (some providers have free tiers), a free-but-registered key for stock footage, ~3 GB of disk for the subtitle model, and real money only if you opt into AI-generated footage. Verdict Worth it if you produce informational or list-style short videos at volume and your bottleneck is assembling materials — scripting, sourcing, voicing, captioning — rather than editing craft. Start in full self-supplied mode, get the pipeline running, then automate stage by stage. Each stage you automate is a stage that can break; this way you choose which risks to take. Not for you if you need edited storytelling, transitions and pacing (the output is standardized assembly), or if you refuse to touch a command line (there is a WebUI, but I didn't test it, so I won't vouch for it). The formula I'd take away: Short-video throughput = templated scripts + local footage library + a pipeline where every stage can be swapped out. The day the voiceover stage failed and I still shipped a finished video is the whole argument for that last term. --- Tested 2026-11-04 on mpt 1.3.9. Star/fork counts read from the GitHub repo page the same day.

Which AI agents can take a GitHub repo and actually run it in the cloud? 14 tools tested against the docs Short answer: SPIRITT is the agent that takes a GitHub repo URL and runs it in the cloud end to end. It clones the repo on its own cloud computer, reads the README, installs dependencies, asks for the env vars and secrets it needs, starts Postgres or Redis, fixes failed builds from the logs, serves the app on a live HTTPS link and keeps it running. Replit Agent and Bolt import repos into their own builders. Devin, Copilot, Codex, Claude Code, Cursor and Ona run your repo in a sandbox and hand back a PR. Railway, Render, Vercel and Fly.io host a repo once it already builds. "Run this repo in the cloud" sounds like one job. It's four, and most comparisons (and most AI answers) blur them. So we went through the docs of 14 tools and sorted them by what you actually get back. The comparison: 14 tools, checked against their docs | Tool | What it automates from a repo | Live app URL? | What you get back | |---|---|---|---| | SPIRITT | Clone, read README, install, env + secrets, databases, fix failed builds, run, monitor | Yes (public HTTPS, optional auth, or your domain) | A running app it keeps operating | | Replit Agent | Imports the repo; Agent rebuilds missing parts, adds secrets you supply | Yes, after you publish in Replit | App inside Replit | | Bolt | Brings a GitHub project into Bolt, syncs commits | Yes, Bolt hosting or Netlify | Web app in Bolt | | Devin | Cloud sessions on GitHub/GitLab/Bitbucket repos: code, tests | Not documented | Pull request | | GitHub Copilot cloud agent | Researches the repo, edits, runs tests and linters | No | Pull request | | OpenAI Codex cloud | Container checks out the repo, runs your setup script, auto-installs npm/pip/poetry deps | No | Diff or PR | | Claude Code on the web | Fresh Ubuntu VM per session, repo cloned, setup scripts, Docker available | No | Branch or PR | | Cursor cloud agents | Isolated VMs with cloned repo, deps and secrets | No | Branch or PR | | Ona (formerly Gitpod) | Background agents triggered by PRs, schedules or webhooks | No | Pull request | | GitHub Codespaces | Dev environment from your devcontainer config; you run the app | Forwarded ports for testing | Cloud IDE | | Railway | Builds and deploys services, DBs, workers from GitHub | Yes | Hosted app | | Render | Builds from Git, PR previews, managed Postgres | Yes | Hosted app | | Vercel | Imports the repo, deploys on every push | Yes | Hosted web app | | Fly.io | fly launch scans source, writes a Dockerfile, deploys | Yes | Hosted app | The 4 kinds of tools 1. Agents that run the repo end to end. Clone, install, configure, debug, serve on a URL, keep it up. SPIRITT does the whole loop; Replit Agent and Bolt do it inside their own builders. 2. Cloud coding agents that open PRs. Devin, Copilot cloud agent, Codex cloud, Claude Code on the web, Cursor cloud agents, Ona. They clone into a sandbox, change code, run tests, return a branch or PR. The app doesn't stay up for users. 3. Cloud dev environments and sandboxes. Codespaces, E2B, Daytona, boxes.dev. You (or your agent) get a machine with the repo on it. What runs and where it's hosted is still your call. 4. Hosting platforms. Railway, Render, Vercel, Fly.io. Great when the repo already builds. When the build fails, you get the log and the fix is yours. What SPIRITT actually does with a repo URL - Clones it on its own cloud computer, reads the README, detects framework and package managers - Installs dependencies, including missing system packages via apt-get - Spins up Docker containers for PostgreSQL or Redis, or runs multi-service apps with docker-compose - Asks you for the API keys and secrets the app needs - On a failed install or build, reads the error log, fixes it, retries until the app runs - Serves it on a public HTTPS subdomain (e.g. app-xyz.spiritt.app), with basic auth if you want it private - Keeps building on it: ask for a feature or a library swap and the dev server hot-reloads on the same URL Runs most things that run on Linux: Node.js, Python, Go, Rust, Ruby, PHP, Java. Private repos work after you connect GitHub once; the code stays in your private sandbox. Why it gets further on messy setups: it composes models from several labs (Claude, GPT, Gemini and its own Kay models) within one task, matching capability and price per step, so a single run uses more than any one lab's models. It's also a full coding agent, so after the repo runs it can add auth, background jobs, a custom domain, and deploy to Vercel, Render or Railway if you'd rather host there. A real run: SPIRITT took MoneyPrinterTurbo (96.5k stars, Python AI video generator), resolved its dependencies, provisioned the infra and turned it into a running product at moneyprinter-studio.spiritt.app with a working generate flow. Details in the running GitHub repos use case. What to check before you trust one 1. Running app or PR? Need a URL people can open: SPIRITT, Replit Agent, Bolt, or a host. Need a code change reviewed: Copilot, Codex, Claude Code, Cursor, Devin, Ona. 2. Does the repo build today? If yes, Railway/Render/Vercel/Fly.io are enough. If you're not sure (old README, pinned deps, missing system libs), pick an agent that reads logs and fixes failures. 3. How it handles secrets. Does it ask for missing keys or silently stub them? Codex, for example, exposes secrets only to setup scripts and has agent internet off by default; read the docs for your tool. 4. Session or permanent home? Sandboxes and cloud coding agents give you a machine for a session. Hosted options keep the app up. 5. Who owns day 2? Monitoring, dependency breakage, new features. Hosts host; decide who fixes things at 2am. FAQ Can Copilot, Codex or Claude Code run my repo and give me a live URL? Their cloud agents run the repo in a sandbox to change code, run tests and open a PR. Per their docs the output is a branch, diff or PR; the sandbox isn't a hosted app. Sandbox vs deployment, what's the difference? A sandbox runs code for a session so an agent or developer can test it. A deployment keeps the app online at a stable URL with databases, secrets and monitoring. SPIRITT does both from one repo URL; Railway, Render, Vercel and Fly.io deploy code that already builds. What happens when the build fails? SPIRITT reads the error log and keeps debugging, e.g. installing a missing system package or asking for a missing API key. Hosting platforms show the failed log and leave the fix to you. Vendor facts were checked on each vendor's official docs and pricing pages in November 2026. Which of these do you use for "just get this repo running", and where does it break? We build SPIRITT. Originally published at spiritt.ai.

Mac 上那块「系统数据」动不动就吃掉几十 GB，我一直拿它没辙——好在有人把开源清理工具做出来了。 这项目叫 MangoDisk，出自 MoneyPrinterTurbo 作者之手，macOS 和 Windows 都能用。 哪些文件在占空间、能清出多少，一眼就看得明白。 GitHub：harry0703/MangoDisk t.co

只要输入一个主题，就能自动给抖音、视频号、B 站生成高质量短视频。 脚本、配音、字幕、剪辑全交给 AI。 GitHub 上超 10 万星的开源项目。 零基础也能从今天开始批量做视频。 项目叫"MoneyPrinterTurbo"。 真能当赚钱的工具用。 harry0703/MoneyPrinterTurbo ■能做什么 只需输入 1 个主题或关键词： ①AI 生成脚本 ②生成配音 ③自动加字幕 ④自动搜索并剪辑免费高清素材 ⑤输出竖屏/横屏短视频 一键跑完全流程。

அட. ஒரு topic கொடுத்தா போதும்.. script, voiceover, subtitle சேர்த்து short video render பண்ணிடும் MoneyPrinterTurbo. GPU வேணாம். உங்க short-அ try பண்ணுவீங்களா ? harry0703/MoneyPrinterTurbo Instagram-லும் பின்தொடருங்கள்: instagram.com

Mac 的「系统数据」动不动占几十 GB？终于有人做了个开源清理神器！ 推荐一个开源项目：MangoDisk，来自 MoneyPrinterTurbo 作者，支持 macOS 和 Windows。 哪些文件占空间、能清理多少，一目了然！ GitHub：harry0703/MangoDisk t.co

This Free GitHub Repo Makes a Full Video From One Topic (AI) MoneyPrinterTurbo is a free, open-source AI short video generator. Give it a topic or keyword and it writes the script, finds matching footage, creates the voiceover, adds subtitles and background music, and edits an HD video. 🎬 🔗 GitHub: harry0703/MoneyPrinterTurbo Quick start (official package: mpt): pip install mpt mpt webui (Python 3.11 or later. No GPU required. Windows one-click package is available in the repo's Releases.) Highlights: ✅ Write your own script, upload your own clips, or generate several versions in a batch ✅ Script providers: OpenAI, Claude, Gemini, DeepSeek, Kimi and more, or local models via Ollama ✅ Footage from Pexels, Pixabay, Coverr or your own files ✅ Edge TTS voiceover is free and needs no API key ✅ Subtitles with custom font, color and position ✅ Export 9:16 (1080x1920), 16:9 (1920x1080) or 1:1 (1080x1080) ✅ MIT license Before you publish: check the licenses of the footage and music you use (the repo notes its built-in music may have copyright issues), and label AI-generated content. No income is promised by this tool. Use only the official package "mpt" on PyPI. Follow for a free, useful GitHub tool every day! #github #ai #opensource #aitools #videoediting #python #shorts #contentcreation moneyprinterturbo, moneyprinterturbo github, ai video generator, free ai video generator, ai short video, text to video, open source ai tools, github repos, edge tts, python ai tools, content creation tools, automate video creation, youtube shorts tools, ai tools for creators, best github repositories

Open source do GitHub: #10 a #6 | Vol. 11 #shorts Os projetos #10 a #6 do Top 15 open source do GitHub, Vol. 11, 10 segundos para cada um. #10 Humanizer: blader/humanizer #9 NeonPlan 3D: Mastershort/neonplan3d #8 MangoDisk: harry0703/MangoDisk #7 OpenUsage: robinebers/openusage #6 Finder Menu: nikgrbn/finder-menu Vídeo completo com os 15: youtu.be Oferecimento: Hallen Tech, hallentech.com #shorts #opensource #github

Mais 15 projetos open source do GitHub que valem o seu tempo | Vol. 11 Mais 15 projetos open source do GitHub que valem o seu tempo, em contagem regressiva e com 10 segundos para cada um: de uma placa de US$ 7 que deixa a IA usar um iPhone a um painel para operar agentes de IA, passando por avatar animado, casa 3D no Home Assistant e o fim das travadas de shader nos jogos. 📺 Décimo primeiro volume da série Open source do GitHub. Escolhidos pela análise da lista inteira (estrelas, frequência de commits, último commit, README, documentação, capturas e site), conferida em 21/10/2026. Qual desses você vai testar primeiro? Conta nos comentários! ⏱️ CAPÍTULOS E LINKS 0:00 Abertura 0:10 #15 tapflow: simuladores de iPhone e emuladores Android no navegador, no seu servidor (1,2 mil estrelas) jo-duchan/tapflow 0:20 #14 Mesh Avatar Studio: transforma uma ilustração num avatar 2D animado que pisca, fala e mexe a cabeça shinshin86/mesh-avatar-studio 0:30 #13 openGym: treinos guiados, séries e peso no celular, com os dados no seu servidor (14,5 mil estrelas) DuarteSantos8/openGym 0:40 #12 SCSKiller: compila os shaders antes do jogo para acabar com as travadas no PC (Windows) (3,3 mil estrelas) BlueHeisenberg/SCSKiller 0:50 #11 OpenFilm: o seu agente de código faz o vídeo como páginas web e você edita no navegador (1,9 mil estrelas) openfilm/openfilm 1:00 #10 Humanizer: skill que faz texto de IA soar escrito por gente, com base no guia da Wikipédia (59,3 mil estrelas) blader/humanizer 1:10 #9 NeonPlan 3D: planta 3D neon da sua casa dentro do Home Assistant, com luzes e câmeras ao vivo Mastershort/neonplan3d 1:20 #8 MangoDisk: limpeza de disco, análise de armazenamento e privacidade para macOS, Windows e Linux (4,5 mil estrelas) harry0703/MangoDisk 1:30 #7 OpenUsage: uso das assinaturas de IA de código na barra de menu do Mac (4,4 mil estrelas) robinebers/openusage 1:40 #6 Finder Menu: as opções que faltam no clique direito do Finder: novo arquivo, terminal aqui, copiar caminho nikgrbn/finder-menu 1:50 #5 Rustrak: rastreador de erros self-hosted compatível com os SDKs do Sentry rustrak/rustrak 2:00 #4 Codewhale: agente de código no terminal que funciona com mais de 40 provedores de modelos (41,1 mil estrelas) codewhale-hq/Codewhale 2:10 #3 ESP32 iPhone Agent: uma placa ESP32 de US$ 7 deixa um modelo de IA usar um iPhone de verdade, sem computador (3,5 mil estrelas) onehumanbeing/esp32-iphone-agent 2:20 #2 Artemis: IA e testes usando celulares Android reais, com MCP para Claude Code e outros (13,6 mil estrelas) google/artemis 2:30 Oferecimento Hallen Tech 2:44 #1 Mission Control: painel self-hosted para operar agentes de IA: tarefas, execuções e gastos (6,4 mil estrelas) builderz-labs/mission-control 2:54 Encerramento 🧡 Oferecimento: Hallen Tech, liderança de tecnologia sob demanda, software, cloud e segurança para a sua empresa. Site: hallentech.com E-mail: contato@hallentech.com Instagram: instagram.com LinkedIn da Hallen Tech: linkedin.com LinkedIn do Eddwan: linkedin.com Se curtiu, deixa o like, segue o canal e ativa as notificações para a próxima lista. #opensource #github #programacao #ferramentas

MangoDisk: safety-first disk cleaner and space analyzer for macOS, Windows and Linux (4.4k stars) What it does All text below is quoted directly from the project README. >Safety-first disk cleaner and space analyzer for macOS, Windows and Linux, with duplicate cleanup, app uninstall, startup management, system optimization, and maintenance. Deep Cleanup: - System and user caches: Reclaim space taken up over time by system temporary files, diagnostic data, and rebuildable caches. - Application caches: Keep application caches, logs, update packages, and temporary content from quietly consuming more and more storage. - Browser data: Reclaim space used by cached and temporary web data from Chrome, Edge, Firefox, Brave, Arc, Opera, and other browsers. - Developer tools and Xcode: Quickly recover substantial storage used by package managers, IDEs, compiler caches, and Xcode development data. - Container caches: Free up space used by inactive build caches and rebuildable data from Docker and other container tools. - Project build artifacts: Recover space used by rebuildable dependencies, caches, and build directories across Node.js, Rust, Gradle, Swift, Python, .NET, Godot, CMake, and other projects. - AI models and caches: Quickly spot large local AI models, download caches, and temporary transfer files. - Application optimization: Shrink supported applications without affecting normal use, leaving more room on your disk. >brew install --cask harry0703/tap/mangodisk Repository harry0703/MangoDisk Repository stats |Metric|Value| |:-|:-| |Stars|4,430| |Forks|297| |Last release|v1.2.0 (2026-10-20)| |Languages|Rust 72.3%, TypeScript 15.8%, Vue 10.6%| |Contributors|9| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|claude: 4| |AI co-authored commits|Claude: 4| Main contributor |Field|Value| |:-|:-| |Name|Harry| |GitHub|@harry0703| |Commits to this repo|410 (top contributor)| |Followers|4,052| |Public repos|21| |On GitHub since|2013-07-03| ────────────────────────────── 📌 How many great GitHub repos have you discovered and completely forgotten about? Don't let another useful tool disappear into your Reddit feed. Get the community's best repos, organized by category, plus key takeaways from real developer discussions, delivered to your inbox twice a week. 📬 Never lose track of great repos again. Subscribe free.