calesthio

OpenMontage (calesthio/OpenMontage) is free and open-source (AGPL-3.0). Clone the repo, run make setup, then open it in Claude Code, Cursor, Codex, or similar and just prompt your video idea. It handles the rest. Core path uses free local tools + stock footage with zero cost. Optional paid APIs improve quality (with built-in spend limits).

OpenMontage is trending on GitHub, with 70k+ stars. It is a free, open-source tool that turns your AI coding assistant into a video maker. You describe the video, and it plans, edits and builds it. Works with Claude Code and Cursor 🎬 calesthio/OpenMontage #OpenSource #AI

Keep OpenMontage's Local LLM From Competing With Your Video Renderer Keep OpenMontage's Local LLM From Competing With Your Video Renderer You deploy a local LLM for workflow orchestration. You run inference in isolation, validate the server, ship it. Two weeks later, production rendering jobs start failing with cryptic CUDA OOM errors at 3 AM. The inference server is still running. GPU memory looks fine in nvidia-smi. The blame lands on "the AI integration," and suddenly your careful architecture is fighting for its reputation. This is the predictable failure mode when inference and rendering share GPU resources without explicit boundaries. I'm evaluating OpenMontage as an external integrator. The repository advertises 12 production pipelines, 100+ tools, and over 700 skill and production-knowledge files. Local inference is an attractive integration point: planning, tool selection, and revision loops stay on the workstation. But making it coexist with GPU-heavy video work requires deliberate resource isolation, not just a memory utilization flag. This architecture is a deployment boundary around OpenMontage's coding-assistant workflow, not a description of verified built-in inference adapters. Repository-specific configuration still needs checking against the version you deploy. Separate the Director From the Machinery The end-to-end path should be explicit: User brief → assistant and skill selection → bounded inference request → validated tool invocation → production worker → artifact store. The inference server generates decisions. Production workers execute media operations. Durable job state and artifacts belong outside both processes. Put a bounded queue before inference. Keep tool output, transcripts, and production knowledge outside the prompt until needed; retrieve relevant sections instead of concatenating the entire project history. Apply the token budget after assembling the complete request, including tool schemas. Most importantly, a GPU-memory utilization flag is not a hardware partition. If inference and rendering share one GPU, serialize their GPU-heavy phases or enforce isolation outside the model server. The configuration below assumes a dedicated 24 GiB inference GPU. Why the Toy Setup Fails The 70B OOM incident worth diagnosing is not necessarily a bad model load. Partial layer offloading can make weights fit while leaving insufficient room for the configured context and concurrent requests. KV-cache capacity depends on architecture, cache precision, sequence length, and concurrency. Weight quantization does not automatically quantize that cache. Increasing context while preserving concurrency can consume the remaining headroom. Long-running throughput degradation introduces another trap: retained allocator memory and an intentionally preallocated KV-cache pool can resemble a leak. A rising nvidia-smi number alone cannot establish one. Compare equivalent workloads, active-request counts, and cache occupancy after requests drain. For an initial integration, I would use a smaller model with bounded context rather than force a partially offloaded 70B model onto the production workstation. One Bounded Server, One Streaming Probe This configuration uses Qwen2.5-7B, an 8,192-token context, four active sequences, and an 80% GPU-memory budget. These are conservative starting limits, not universally optimal settings. Run in a CUDA-compatible Linux environment with Python 3.11 and nvidia-smi. Select the inference GPU through CUDA_VISIBLE_DEVICES; GPU_INDEX must identify that same physical GPU for measurement. python -m pip install 'vllm==0.10.2' aiohttp export CUDA_VISIBLE_DEVICES=0 export GPU_INDEX=0 export MODEL=Qwen/Qwen2.5-7B-Instruct export BASE_URL=http://127.0.0.1:8000/v1 vllm serve "$MODEL" \ --host 127.0.0.1 --port 8000 \ --dtype half \ --kv-cache-dtype auto \ --gpu-memory-utilization 0.80 \ --max-model-len 8192 \ --max-num-seqs 4 \ --max-num-batched-tokens 4096 \ --enable-chunked-prefill & SERVER_PID=$! trap 'kill "$SERVER_PID" 2>/dev/null || true' EXIT python - <<'PY' import asyncio, json, os, statistics, time import aiohttp BASE = os.environ["BASE_URL"].rstrip("/") MODEL = os.environ["MODEL"] GPU = os.environ.get("GPU_INDEX", "0") HEADERS = {} if os.environ.get("API_KEY"): HEADERS["Authorization"] = "Bearer " + os.environ["API_KEY"] async def memory(): p = await asyncio.create_subprocess_exec( "nvidia-smi", "-i", GPU, "--query-gpu=memory.used", "--format=csv,noheader,nounits", stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) out, err = await p.communicate() if p.returncode: raise RuntimeError(err.decode()) return int(out.decode().strip()) async def request(session): start, first = time.perf_counter(), None payload = { "model": MODEL, "messages": [{"role": "user", "content": "Write a concise six-shot production plan for a coffee advert."}], "max_tokens": 128, "temperature": 0, "stream": True, } async with session.post( BASE + "/chat/completions", json=payload ) as response: response.raise_for_status() async for raw in response.content: line = raw.decode().strip() if not line.startswith("data:"): continue data = line[5:].strip() if data == "[DONE]": break event = json.loads(data) if "error" in event: raise RuntimeError(event["error"]) for choice in event.get("choices", []): if choice.get("delta", {}).get("content") and first is None: first = time.perf_counter() - start if first is None: raise RuntimeError("Stream returned no content") return first, time.perf_counter() - start async def main(): timeout = aiohttp.ClientTimeout(total=180, sock_read=60) async with aiohttp.ClientSession( headers=HEADERS, timeout=timeout ) as session: for attempt in range(180): try: async with session.get(BASE + "/models") as r: if r.status == 200: break except aiohttp.ClientError: pass await asyncio.sleep(2) else: raise RuntimeError("Server readiness timed out") await request(session) # Warm up before measuring. baseline = await memory() samples = [baseline] async def sample(): while True: samples.append(await memory()) await asyncio.sleep(0.25) semaphore = asyncio.Semaphore(2) async def bounded(): async with semaphore: return await request(session) monitor = asyncio.create_task(sample()) try: results = await asyncio.gather(*(bounded() for _ in range(8))) finally: monitor.cancel() try: await monitor except asyncio.CancelledError: pass print(json.dumps({ "requests": len(results), "median_ttft_s": statistics.median(x[0] for x in results), "median_latency_s": statistics.median(x[1] for x in results), "gpu_baseline_mib": baseline, "gpu_peak_mib": max(samples), "gpu_peak_delta_mib": max(samples) - baseline, "gpu_after_mib": await memory(), }, indent=2)) asyncio.run(main()) PY Interpret the Boundary, Not Just the Numbers TTFT starts when the request enters the HTTP client; latency ends when streaming completes. Both exclude time waiting for the benchmark's semaphore. Production queue delay needs its own measurement. GPU measurements cover the entire device, not just vLLM. Sampling can miss brief peaks, and a preallocated cache can produce almost no measured delta. This is a smoke benchmark, not a capacity certification. For an optional remote comparison, the same harness can target B-Lost's relay endpoint, https://b-lost.com/v1, using an authorized API_KEY and an available model identifier. I have not measured that endpoint here. Local GPU readings would not describe its server-side memory. The trade-off is deliberate: bounded context and concurrency sacrifice flexibility to protect production work. Expand either only after replaying representative tool-heavy requests alongside the actual rendering schedule. --- The hardest operational question is not whether to bound inference resources, but when to stop defending the boundary and admit that inference and rendering need separate hardware. If you are running 70B models with 32k context windows while rendering 4K video timelines, no amount of semaphore discipline will save you. At what utilization threshold do you declare victory for clever resource sharing and buy another GPU? I have drawn that line differently on every project. What does your architecture assume, and where has it broken?

OpenFusion: Efficient sub task routing preview.redd.it The main agent routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Would also really appreciate if someone could benchmark using some other harnesses.

不會拍也不會剪？GitHub 3 個開源神器直接出片：ArtCraft・OpenMontage・hypit 不會拍、不會剪，也能做影片？這支 60 秒把 GitHub 上三個「AI 做影片」開源專案一次講清楚，repo 全名都在畫面上。 ① storytold/artcraft｜37,549★｜Rust｜Apache-2.0 storytold/artcraft 「給創作者的 IDE」：2D 畫布排構圖 → 3D 舞台擺姿勢、自己定機位 → 再生成。先搭好場景才出圖，不是一句話盲抽。桌面版在 getartcraft.com 下載。 ② calesthio/OpenMontage｜72,883★｜Python｜AGPL-3.0 calesthio/OpenMontage 自稱第一個「agentic」影片製作系統：12 條產線、100+ 工具、700+ 技能檔。裝法就是 clone 下來 make setup（要 Python 3.10+、FFmpeg、Node 18+，再配一個 AI 編程助手），之後用白話講需求，查資料、寫腳本、配音、配樂、字幕、出片交給它跑。 ③ hypit-ai/hypit｜26,408★｜TypeScript｜Apache-2.0 附條件 hypit-ai/hypit 丟一支爆紅影片進去，它先拆解「為什麼會紅」，再換臉、換詞、換 B-roll，一條指令出一百個版本。安裝：npx skills add hypit-ai/hypit -g（Node 22.15+）。 三個要提醒你的實話 ・軟體免費，不等於全免費：模型 API、生成積分、編程助手訂閱都要另外付（hypit README 也直接寫「free to use，但模型服務自己算」）。 ・復刻的是結構，不是素材：別人的畫面、音樂、臉不能直接搬。 ・YouTube 合作夥伴計畫對搬運、批量重複的內容不給變現。 數字怎麼來的 星數／授權／語言是 2026-10-29 直接打 GitHub API 實測（不是轉述別人的截圖），片中的示意畫面都是自己畫的，沒有使用這三個專案的示範影片。 影像素材（Wikimedia Commons） ・Photo: Incase from San Francisco, USA · CC BY 2.0 ・Photo: Mattbr · CC BY 2.0 編製：螯蝦Pi(小蝦) #GitHub #開源 #AI影片 #短影音 #Shorts #ArtCraft #OpenMontage #hypit

OpenFusion for sub task routing Using this your main agent routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Open Sourcing this. You can use it if it helps you with token and time saving. I am seeing about 25% cost and 25-40% time saving for complex multi-step tasks within Codex Would also really appreciate if someone could benchmark using some other harnesses. calesthio/OpenFusion reddit.com

Introducing OpenFusion for appropriate model routing It routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Open Sourcing this. You can use it if it helps you with token and time saving. I am seeing about 30% token and 25-40% time saving for complex multi-step tasks within Claude Code. Would also really appreciate if someone could benchmark using some other harnesses. calesthio/OpenFusion

30 Best Claude Plugins on GitHub (Sorted by Your Job) 30 Claude plugins with 3.9 million GitHub stars between them, sorted into 5 rows so you only need yours: coding, research, saving tokens, design, and writing + video. Don't add all 30. Pick the row that fits your week, add one plugin, and open its repo first to check what it can touch on your machine. 💬 Comment ANYLANCER and I'll send you all 30 links, or grab them here: 1) You write code • Superpowers: obra/superpowers • Matt Pocock's Skills: mattpocock/skills • ECC: affaan-m/ECC • Karpathy Skills: multica-ai/andrej-karpathy-skills • Ponytail: DietrichGebert/ponytail • Agent Skills: addyosmani/agent-skills 2) You research big things • Graphify: Graphify-Labs/graphify • Agent Reach: Panniantong/Agent-Reach • Understand Anything: Egonex-AI/Understand-Anything • Last 30 Days: mvanhorn/last30days-skill • Career Ops: career-ops-hq/career-ops • REA: morluto/rea 3) Huge projects, few tokens • gstack: garrytan/gstack • Caveman: JuliusBrussee/caveman • Claude-Mem: thedotmack/claude-mem • Headroom: headroomlabs-ai/headroom • GSD: gsd-build/get-shit-done • BMAD Method: bmad-code-org/BMAD-METHOD 4) Design & visuals • UI UX Pro Max: nextlevelbuilder/ui-ux-pro-max-skill • Taste: Leonxlnx/taste-skill • Archify: tt-a1i/archify • Impeccable: pbakaus/impeccable • PPT Master: hugohe3/ppt-master • Diagram Design: cathrynlavery/diagram-design 5) Writing & video • Anthropic Skills: anthropics/skills • OpenMontage: calesthio/OpenMontage • HyperFrames: heygen-com/hyperframes • Humanizer: blader/humanizer • I Have ADHD: ayghri/i-have-adhd • Marketing Skills: coreyhaines31/marketingskills Built a skill people would pay for? Sell it on Anylancer: anylancer.com Which row are you? 👇 Star counts from GitHub on Oct 29, 2026. This video was made with HyperFrames. #ClaudeCode #AIAgents #GitHub #Shorts

一人影視班底！OpenMontage 開源專案 60 秒｜AI 助手變影片工作室 一個人做影片，現在有整個製作團隊！小蝦主播 60 秒帶你看開源專案 OpenMontage。 Making videos alone? Now you have a whole production crew! Shrimp the anchor covers the open-source project OpenMontage in 60 seconds. 📌 重點 / Key points ・OpenMontage（calesthio/OpenMontage）：開源的影片製作系統，把 AI 編程助手變成影片製作工作室；本片拍攝時 GitHub 約 7.2 萬顆星、Python、AGPL-3.0。 OpenMontage (calesthio/OpenMontage) is an open-source video production system that turns your AI coding assistant into a video studio; about 72.5k GitHub stars at the time of writing, Python, AGPL-3.0. ・可搭配 Claude Code、Codex、Cursor、Copilot、Windsurf 等 AI 編程助手使用。 Works with AI coding assistants such as Claude Code, Codex, Cursor, Copilot and Windsurf. ・製作流程：研究 › 提案 › 腳本 › 分鏡 › 素材 › 剪輯 › 合成，每個階段都有專屬的導演技能檔。 Production flow: research › proposal › script › scene plan › assets › edit › compose, each stage guided by a director skill file. ・內建解說、動畫、數字人、電影感預告、長片切短片、紀錄片剪輯、翻譯配音、螢幕示範、真人講話等十多種流程。 Ships ten-plus pipelines: animated explainer, animation, avatar spokesperson, cinematic, clip factory, documentary montage, localization and dub, screen demo, talking head and more. ・可從 YouTube、Short、Reel、TikTok 或本機影片出發：分析逐字稿、節奏、場景、關鍵畫格與風格，再提出 2–3 個不同的新方案與成本預估。 Start from a YouTube video, Short, Reel, TikTok or local clip: it analyzes transcript, pacing, scenes, keyframes and style, then proposes 2-3 differentiated concepts with cost estimates. ・Backlot 分鏡看板：生成素材前先逐場景核准，不對就提前攔下。 Backlot storyboard board: approve scene by scene before generation, and catch problems early. ・也能用 Archive.org、NASA、Wikimedia Commons、Pexels、Unsplash 等開放素材剪成片；內建免費 Piper 旁白、Remotion、FFmpeg 與逐字字幕。 Can also cut real footage from Archive.org, NASA, Wikimedia Commons, Pexels and Unsplash; free Piper narration, Remotion, FFmpeg and word-level subtitles are built in. ・提醒：授權為 AGPL-3.0，商用前請先閱讀條款。 Note: the license is AGPL-3.0, so read the terms before commercial use. 📚 資料來源 / Source OpenMontage（GitHub）：calesthio/OpenMontage 星數與功能為整理當下的 GitHub 公開資料，會隨時間變動。 Figures and features come from the public GitHub page at the time of writing and may change. 字幕：提供中文與英文字幕（可在播放器開啟）。 Captions: Chinese and English available. 編製：螯蝦Pi（小蝦） #OpenMontage #ClaudeCode #Codex #開源 #AI影片 #GitHub #科技主播 #shorts

Automatiza toda la producción de tus videos 🎬 ¡Puedes automatizar gran parte de la producción de vídeos con inteligencia artificial! 🤯 📌 OpenMontage es un proyecto de código abierto que permite conectar tareas como la escritura del guion, la generación de imágenes, la narración y el montaje mediante asistentes como Claude Code o Cursor. 🎬 • Una alternativa interesante para explorar nuevos flujos de creación de contenido y reducir el trabajo manual. 🤩 👉🏻 Si quieres conocerlo, ingresa al link openmontage.video 🔗 calesthio/OpenMontage 📲 Visita el enlace de mi perfil para unirte a mi comunidad exclusiva y aprender más sobre IA y sus aplicaciones prácticas. • #InteligenciaArtificial #OpenMontage #HerramientasIA #CreacionDeContenido #ClaudeCode #AITools #EdicionDeVideo #ProductividadIA [ar] أتمتة عملية إنتاج الفيديو بالكامل 🎬 يمكنك أتمتة جزء كبير من إنتاج الفيديو باستخدام الذكاء الاصطناعي! 🤯 📌 OpenMontage مشروع مفتوح المصدر يربط مهامًا مثل كتابة السيناريو، وإنشاء الصور، والتعليق الصوتي، والتحرير باستخدام مساعدين مثل Claude Code أو Cursor. 🎬 • بديل مثير للاهتمام لاستكشاف أساليب عمل جديدة لإنشاء المحتوى وتقليل العمل اليدوي. 🤩 👉🏻 لمعرفة المزيد، تفضل بزيارة openmontage.video 🔗 calesthio/OpenMontage 📲 انضم إلى مجتمعي الحصري عبر الرابط الموجود في ملفي الشخصي، وتعرّف أكثر على الذكاء الاصطناعي وتطبيقاته العملية. • #الذكاء_الاصطناعي #OpenMontage #أدوات_الذكاء_الاصطناعي #إنشاء_المحتوى #ClaudeCode #أدوات_الذكاء_الاصطناعي #تحرير_الفيديو #إنتاجية_الذكاء_الاصطناعي [en-US] Automate your entire video production process 🎬 You can automate a large part of video production with artificial intelligence! 🤯 📌 OpenMontage is an open-source project that connects tasks like scriptwriting, image generation, narration, and editing using assistants like Claude Code or Cursor. 🎬 • An interesting alternative for exploring new content creation workflows and reducing manual work. 🤩 👉🏻 If you want to learn more, visit openmontage.video 🔗 calesthio/OpenMontage 📲 Visit the link in my profile to join my exclusive community and learn more about AI and its practical applications. • #ArtificialIntelligence #OpenMontage #AITools #ContentCreation #ClaudeCode #AITools #VideoEditing #AIProductivity [pt-BR] Automatize toda a produção dos seus vídeos 🎬 Você pode automatizar grande parte da produção de vídeo com inteligência artificial! 🤯 📌 O OpenMontage é um projeto de código aberto que conecta tarefas como roteirização, geração de imagens, narração e edição usando assistentes como Claude Code ou Cursor. 🎬 • Uma alternativa interessante para explorar novos fluxos de trabalho de criação de conteúdo e reduzir o trabalho manual. 🤩 👉🏻 Se você quiser saber mais, visite openmontage.video 🔗 calesthio/OpenMontage 📲 Visite o link no meu perfil para participar da minha comunidade exclusiva e aprender mais sobre IA e suas aplicações práticas. • #InteligênciaArtificial #OpenMontage #FerramentasIA #CriaçãoDeConteúdo #ClaudeCode #FerramentasIA #EdiçãoDeVídeo #ProdutividadeIA