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AI video generation
Higgsfield's Supercomputer is an agentic AI tool that plans and executes video/content-creation tasks as staged background subtasks rather than one-shot chatbot replies, and its advanced layer — skills, a three-part memory system, connectors, scheduled tasks, goal mode, and a virality predictor — lets a creator go from brief to a pre-tested, on-brand ad with minimal manual prompting.
Supercomputer differs from a normal chatbot (Claude, ChatGPT) by breaking a request into a step-by-step plan of subtasks and running each one in the background rather than generating directly from the prompt.
Two run modes trade off control against speed/credits: auto run lets it act without asking about every small detail (but still confirms before actually generating images/video), while confirm before running checks in constantly and can burn credits faster on unimportant confirmations.
A model picker lets users choose the underlying model (Opus, Sonnet, Gemini, GPT); Opus 4.7 gives the best quality for complex tasks but costs more credits, while Sonnet 4.6 is described as near-equivalent for most tasks at lower cost.
Soul ID creates a custom character asset from 5 reference images and keeps that character visually consistent across an entire video.
Skills are reusable, slash-command-invoked instruction sets (browsable under My Skills/Community) that collapse multi-step manual prompting into one call — e.g. product photo shoot, cinematic flow, brand analyzer, trend picker, storyboard generation, and TV ad skills.
The brand analyzer skill extracts a full brand kit (logo, hex-coded palette, typography) from a reference in about 30 seconds.
Storyboard generation produces shot descriptions, lighting setups, and camera angles before actual generation, specifically to avoid wasting credits on unwanted results.
Users can author their own custom skills directly in chat ('create in chat') instead of relying only on marketplace skills.
Memory operates in three layers: short-term (immediate context), long-term (manually shaped via a memory graph and permanently retained), and episodic (automatically learned from successful patterns) — together removing the need to re-brief the AI each session.
30+ connectors give Supercomputer bidirectional read/write access to external tools (Google Drive, Slack, Telegram, Google Docs) without manual copy-paste.
Scheduled tasks run autonomously at fixed intervals, saving an estimated 30 minutes per week per task.
Goal mode lets a user state a measurable condition (e.g., '10 approved ad variants using my Soul ID character, each scoring above 70 on the virality predictor'), and Supercomputer then generates, tests, and reviews its own output in a loop — visualized on a Kanban board — regenerating failures and keeping passes until the condition is met.
The virality predictor scores finished content 0–100 by modeling brain responses across vision, sound, and memory, analyzing hook, pacing, and estimated retention curve, and giving actionable fixes — letting a creator pre-test performance before posting instead of waiting about a week for real results.
Supercomputer — Higgsfield's agentic tool that breaks a request into a step-by-step plan of subtasks and runs each in the background, unlike a normal chatbot that answers directly from the prompt. Apply: Use it as the primary interface for multi-step AI ad/video production instead of prompting a standard chatbot one shot at a time.
Auto run — A run-mode setting that lets Supercomputer proceed without asking permission for every small detail, while still confirming before it actually generates an image or video. Apply: Enable it to get faster results and avoid credits being drained by unnecessary confirmation prompts on minor steps.
Confirm before running — The alternate run-mode setting that checks in with the user before doing anything, giving tighter control over the AI agent's actions. Apply: Switch to it when maximum oversight of every AI action matters more than speed or credit efficiency.
Model picker — A setting for choosing which underlying AI model (Sonnet, Opus, Google Gemini, OpenAI GPT) powers Supercomputer's reasoning. Apply: Pick the model per task based on the quality-versus-credit tradeoff before running a job.
Opus 4.7 — A Claude model option in the model picker described as the best in quality and well suited to complex tasks, but heavier on credit use. Apply: Select it for complex or high-stakes generation tasks where output quality matters most.
Sonnet 4.6 — A Claude model option described as extremely good for most tasks while using fewer credits than Opus, and the option most people use. Apply: Default to it for routine tasks to conserve credits while keeping near-equivalent performance.
Soul ID — A custom character asset creation tool that uses 5 reference images to keep a character visually consistent across an entire video. Apply: Upload 5 reference images to Soul ID to lock a consistent character/avatar for use across every shot in a given ad or video.
UGC ad flow — A workflow inside Supercomputer for producing user-generated-content-style ads, demonstrated with a 15-second protein-bar ad. Apply: Use it to generate short, social-native-looking product ads from a brief and reference product/character details.
Skills (marketplace) — Reusable instruction sets invoked via slash commands, browsable under My Skills and Community tabs, that replace repeated manual prompting for a given task type. Apply: Search the skills marketplace for an existing skill matching your task and invoke it by slash command instead of writing a fresh prompt from scratch.
Product photo shoot skill — A skill that generates multiple product shots from a single prompt, cutting manual prompting time from over 30 minutes to under 2 minutes. Apply: Run it against a product to auto-produce around 5 varied shots instead of manually iterating prompts.
Cinematic flow skill — A named skill in the marketplace oriented toward producing cinematic-style shots. Apply: Invoke it when a project calls for cinematic camera/lighting treatment rather than straightforward product photography.
Brand analyzer skill — A skill that extracts a complete brand identity kit — logo, hex-coded color palette, typography — from a reference in about 30 seconds. Apply: Point it at a brand's existing materials (e.g., a website) to auto-populate brand assets instead of manually collecting them for each project.
Trend picker skill — A skill referenced in the context of platforms like TikTok and Instagram for surfacing current content trends. Apply: Use it to identify trending formats/styles to base new AI-generated content on.
Storyboard generation skill — A skill that produces shot descriptions, lighting setups, and camera angles before the actual video is generated, specifically to avoid wasting credits on unwanted output. Apply: Generate and review the storyboard first, adjust as needed, and only then commit credits to full video generation.
TV ad skill — A skill for producing ad-format video content, demonstrated on a 15-second Obsidian dark-chocolate TV ad. Apply: Invoke it to produce a structured, polished ad rather than manually building the ad's shot sequence.
Create in chat (skill authoring) — A feature for authoring a custom skill directly within the chat interface. Apply: Use it to build a reusable custom skill for a recurring workflow not already covered by marketplace skills.
Memory graph (memory settings) — A panel, accessed from the left side panel, that visualizes the AI's memory as a graph and lets users add or edit memory nodes such as brand identity. Apply: Open Memory settings to inspect and manually edit what Supercomputer permanently remembers about your brand or preferences.
Three-layer memory system (short-term, long-term, episodic) — A memory architecture with short-term memory for immediate context, long-term memory that is manually shaped and permanently retained, and episodic memory that auto-learns from successful patterns. Apply: Manually set long-term memory items (e.g., brand identity, a 9x16 aspect-ratio preference) once so they persist across sessions without re-briefing, and let episodic memory adapt on its own from what has worked.
Connectors — 30+ integrations that give Supercomputer bidirectional read/write access to external tools like Google Drive, Slack, and Telegram, removing the need for manual copy-paste. Apply: Connect a tool such as Google Drive so Supercomputer can read source files and write outputs there directly as part of a workflow.
Scheduled tasks — Tasks configured to run autonomously at fixed intervals, saving an estimated 30 minutes per week per task. Apply: Set up a recurring scheduled task for routine, repeated content needs so it runs without manual re-triggering.
Goal mode — A mode where the user states a measurable condition and Supercomputer repeatedly generates, tests, and reviews its own results until that condition is met. Apply: Define a concrete pass condition (e.g., '10 approved ad variants scoring above 70 on the virality predictor, using my Soul ID character') and let goal mode run unattended, such as overnight, to batch-produce approved content.
Kanban board (goal mode tracker) — A visual board that appears during a goal mode run, showing each generation move through stages in real time. Apply: Watch it to monitor the live progress of a goal mode batch job as items are generated, scored, kept, or regenerated.
Virality predictor (virality score) — A feature (not a skill) that scores content 0-100 by modeling brain responses across vision, sound, and memory, letting creators test performance before posting instead of waiting about a week for real results. Apply: Run a finished ad or video through it before publishing and use the numeric score as the pass/fail condition in goal mode or as a standalone pre-publish check.
Hook analysis — A component of the virality predictor's evaluation that assesses the content's opening/engagement element. Apply: Review the hook-analysis feedback and revise the opening seconds of a video to better capture attention before re-testing.
Pacing analysis — A component of the virality predictor's evaluation that measures the video's temporal rhythm. Apply: Use the pacing feedback to adjust cut timing/rhythm in the edit before re-scoring.
Retention curve estimation — A component of the virality predictor that predicts the viewer drop-off pattern across the video. Apply: Use the estimated retention curve to identify where viewers are predicted to drop off and revise that section before publishing.
The video frames auto run as the more credit-efficient choice specifically because confirm before running 'keeps asking you unimportant stuff before actually going to do a simple task,' inverting the usual assumption that more confirmation checkpoints save resources or add safety.
Goal mode turns ad production into a closed-loop optimization problem: define a numeric pass condition tied to the virality predictor, then let the system self-generate/self-test/self-select unattended overnight, functioning as a batch content factory rather than a single-shot generator.
Long-term memory is split into a user-editable, permanent layer (edited via a visual memory graph, e.g. setting brand identity or a 9x16 aspect-ratio preference once) and a separate episodic layer that the system updates on its own from what worked — a distinction that gives users direct control over only part of what the AI 'remembers.'
The virality predictor is positioned as compressing the content feedback loop from roughly a week of real-world posting down to an instant pre-publish score plus specific fixes, and the video explicitly flags it as underused ('a feature I don't see many people talk about') despite applying across formats (UGC ads, TV ads, cinematic shots, cartoons).
«Higgsfield supercomputer is here, and most people have no idea on how they're going to use it. That's why today, I'm going to show you everything inside, from how to set it up to the most advanced features, such as AI skills, the memory system, and even scheduled tasks.»
— 00:00
«supercomputer isn't just creating a video from the prompt we gave it. Instead, it starts to build a step-by-step plan and breaks the entire task into subtasks. And then it will run each one in the background. So that's the actual difference between a normal chatbot and Supercomputer inside Higgsfield.»
— 02:22
«it would take me at least 30 minutes to get them. And now, with supercomputer, I can actually get them in less than 2 minutes, which is a complete game changer for somebody who doesn't have hours to spend on prompting and iterating AI images.»
— 07:55
«keeps generating, testing, and reviewing its own results until that condition is met.»
— 16:29
«I'll set the goal to produce 10 approved ad variants for our new shoe launch. Each one must use my Soul ID character and score above 70 on the virality predictor. Keep generating and testing until 10 pass.»
— 16:37
«This is a feature I don't see many people talk about, even though it's extremely useful for creating AI content, no matter if it's cinematic shots or just a cartoon.»
— 17:15
«Instead of uploading a video and waiting a week to see if it actually performs, you can drop it inside Supercomputer first and see its virality score.»
— 17:34
«It'll take a full analysis of it from the hook, the pacing, and the estimated retention curve. Plus, it tells you exactly what to fix for the best results.»
— 17:42
Reception
Strong positive reception with viewers praising clarity and value, while actively engaging with follow-up questions.
This is a dense product tutorial rather than an argumentative piece: its value is in mapping out Supercomputer's full feature set (run modes, model picker, Soul ID, skills, memory layers, connectors, scheduled tasks, goal mode, virality predictor) with concrete settings and worked examples rather than making an original claim beyond 'this tool is more capable than people realize.'
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