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neural frames
Framesail is the AI video platform for long-form YouTube: one pipeline that takes a script to a finished video, with your characters and style locked across every shot.
Making long-form AI video today means 8+ tabs stitched by hand โ an LLM for the script, a voice model, an image model, a video model โ copying assets and prompts between them, with characters drifting between tools and style resetting at every export. Templated tools have the opposite problem: preset styles, generic stock characters, short clips instead of episodes. Framesail replaces both: one pipeline, end to end, in your style.
The pipeline has six stages. Style: paste images, videos, or YouTube links and Framesail reverse-engineers the look, voice, and direction, then reuses it across every video. Script: describe your idea and let AI write in your narrative style, or write it yourself. Reference images: Framesail reads your script and generates a reference image for every character, place, and prop, so visuals stay consistent scene to scene. Voiceover: one narrator or multiple characters, each with their own voice, timed word by word. Storyboard: planned scene by scene using your style, characters, and voiceover. Editor: add captions, music, and sound effects โ or just tell the director what to change โ then export.
Framesail is also fully agent-controllable: a REST API plus an MCP server with 65+ tools, so an agent like Claude Code can run the whole pipeline while you make the key decisions. And a BYOK plan runs covered jobs on your own provider keys โ zero credits, no token markup.
No black box: you control every prompt, asset, model, and setting at every step.
GitHub FramesailFramesail's answer:
One pipeline end to end, versus the patchwork stack (8+ tabs stitched by hand) and templated tools (preset-locked output). Your characters and style are locked across every shot, and you control every prompt, model, and setting. Agent-controllable via REST API + MCP server (65+ tools), and a BYOK plan with no token markup โ none of our competitors offer this. Refined storyboard creation and image prompts across thousands of tests.
Framesail's answer:
Compared to competitors it's the lowest cost of entry, and best value per token. It also allows for complete customization of prompts, models, and model settings.
Framesail's answer:
Faceless YouTube creators and long-form video makers using AI; agencies and high-volume creators who want control and cost efficiency; developers and agent users automating video via API/MCP.
Framesail's answer:
We are two founders that started as Youtube faceless creators. When AI started to take the community by storm, we saw creators struggling with all the different tools that were involved, and so we decided to create a single platform that contains the entire pipeline
Framesail's answer:
10+ AI providers
Based on our record, GitHub seems to be more popular. It has been mentiond 2476 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
"""Clones a single branch of a GitHub repository into a temporary directory for local scanning.""" Import logging Import shutil Import subprocess Import tempfile Logger = logging.getLogger(__name__) Class RepoFetchError(RuntimeError): """Raised when the target repository/branch cannot be cloned.""" Class RepoFetcher: """Shallow-clones a single branch of a repository so its tests can be scanned... - Source: dev.to / 3 days ago
# video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / 4 days ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 9 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 16 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 17 days ago
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BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
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VS Code - Build and debug modern web and cloud applications, by Microsoft
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