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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.
DEV.to 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
As a mini-blog, it is a nice alternative for Medium to publish and share information about programming.
However, the community and the organization are biased toward social justice (and they are open to it). You can read its Code of Conduct, it is so vague and politically leads (I prefer a term of service because it defines fair rules for everybody). So it alienates developers that we don't care about politics in pro of people that want to talk about any other topic such as sexuality, how women are unprivileged, and such. It even mandates to use inclusive language. Good grief.
My main complaint is the quality of the community. It is not StackOverflow (so we don't want to ask for an answer here), and most of the top topics are clickbait, such as "how to become a rockstar developer in ... days", "100 tips to become a better programmer" (and it doesn't even talk about programming).
Technically this "mini blog" site allows us to use markdown, and it is okay. However, the whole experience is really basic. Even the template is ugly.
Based on our record, DEV.to seems to be more popular. It has been mentiond 667 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.
Def _walk_comments(comment): """Yield this comment and every comment in its subtree, at any depth.""" yield comment for child in comment["children"]: yield from _walk_comments(child) Def audit(): ... for a in my_articles(): if not a["comments_count"]: continue for root_comment in api(f"/comments?a_id={a['id']}"): for c in... - Source: dev.to / 1 day ago
So I came across the Frontend Challenge: Comfort Food Edition on dev.to, and I thought: "Hey, what if I build a Rust crate that lets you render SVG sushi directly in your WASM frontend? How hard can it be?". - Source: dev.to / 2 days ago
This is a tiny, silly personality quiz built for the DEV.to Weekend Doggos challenge. Answer questions about your Saturday mornings, your feelings on doorbells, and your ideal compliment โ and find out which dog breed you truly are today. (Not forever. Today. Check back tomorrow, you might be a different dog.). - Source: dev.to / 4 days ago
Verifiable only with effort. Anything touching the filesystem, package installs, CI config, database changes. Wrongness here is quiet and expensive. These get the strongest model I have and a sandboxed dry run before anything executes. I wrote a separate piece on dry-running assistant-written shell commands because this category keeps hurting people. - Source: dev.to / 6 days ago
Def _pending_leaves(comment): if not comment["children"]: if comment["user"]["username"] != ME: yield comment return for child in comment["children"]: yield from _pending_leaves(child) Def _pending_entries(comment, drafted_codes): for leaf in _pending_leaves(comment): if leaf["id_code"] in drafted_codes: continue yield { ... - Source: dev.to / 6 days ago
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