Can an agency fit its entire production line onto a single panel? The speaker answers with Hermes Agent OS, an agentic operating system : a setup where every AI tool that produces video, images, and voice is managed from one control panel . The claim is bold: a studio built from no-code commands. This article gathers what is shown together with its background.
The starting point is a development question from Shayan, a member of the AI Profit Boardroom community: how can Agent OS be used with Hermes for image, video, speech synthesis , and ebook work? The speaker generalizes the answer beyond Shayan to anyone automating workflows. There is no measurable test or comparison table; the format is a promotional demo. Even so, the setup pattern described is concrete and repeatable.
No-code production tools: video, images, and voice
The first stop is free video generation. The speaker demonstrates OpenMontage, an open-source project embedded in the panel: an agentic video generation approach where a typed prompt plus a chosen style produces the clip. Two options are described: a free cinematic model and a movie-style version said to run on the engine the speaker calls VEO. The OpenMontage project describes itself as open-source agentic video production (openmontage.video); the VEO match is the speaker's claim alone and is unverified. The pattern is still clear: one prompt, no video editor.
Next is free image generation: ERNIE-Image from Baidu. The video renders the name as Ernie; the correct name is Baidu's open-source text-to-image model ERNIE-Image, with code on GitHub, weights on Hugging Face, and a technical report on arXiv. The speaker says he runs the model locally and that it slowed his Mac Studio, an honest reminder that local generation carries a hardware cost . For those with a strong setup, free local generation is a genuine option.
The installation story is the most repeatable part: take a GitHub repository address, hand it to the embedded assistant, and order it to add an image-generation section to the panel. The assistant takes over the setup, and the same pattern applies to video and voice tools. The example scenario is concrete: a workflow producing short promo clips and cover graphics for the Boardroom. The critical step is checking the repository's trustworthiness first.
The preferred alternative and the voice layer
On video, the speaker states a clear personal preference: Remotion. The free project hosted on GitHub teaches AI assistants to code corporate-style animation; the Remotion team positions the product as programmatic video infrastructure (remotion.dev). The speaker claims it runs in one click from the panel's video tab and that all samples were produced that way. The reasoning is functional, not aesthetic: corporate animation output suited to business content. With no measurements offered, this reads as a statement of preference.
The voice layer follows the same pattern: search GitHub for TTS, pick a repository with strong stars and support, and let the assistant install it. There is a concrete data point: Coqui TTS, a deep-learning text-to-speech toolkit, has collected roughly 46 thousand stars on GitHub, matching the video's most-popular-repository claim. The install sentence is one line again: give the repository to Hermes and have it installed inside the panel. Whether it runs in the panel or straight through Hermes is left to the user.
Custom tabs and the three-step method
The speaker says the Hermes assistant can take on every capability discussed: OpenMontage, ERNIE, promo work, and speech synthesis, adopting the setup once given the repository address. He points to Hermes Oracle, a custom workflow, and a dedicated video-generation tab as examples. The concrete case of the week is an ad studio: the team asked the Codex assistant for a section monitoring campaign data, had it tested, and added it as a panel tab. The Hermes Agent product from Nous Research (nousresearch.com) already fits this profile: MIT-licensed open source with a messaging gateway, scheduled tasks, and parallel subagents.
The method of the video condenses into three steps: pick the task to automate, build it with an AI assistant, and test until it works. The speaker stresses that several attempts are normal. The ebook workflow follows the same pattern: request the custom flow, review the outputs, and move to production. The essence is no-code setup : you write the description and the assistant builds it. The thesis repeats for every new panel tab.
Put together, the picture is this: prompt-driven video (OpenMontage), local or panel images (Baidu ERNIE-Image), repository-based voice (Coqui TTS via GitHub), programmatic corporate clips (Remotion), and a Hermes-based panel gathering them all (the NousResearch ecosystem). The strength is reducing every step to one-sentence commands; the weakness is the absence of speed, cost, or quality measurements at any step. The promotional load deserves its note too: the membership promising a 30-day roadmap, four live coaching calls a week, and a ready-to-install package is mentioned in one sentence, and the 3,400-member figure is unverified.
Key moments
- Opening thesis: the AI studio on a single panel
- Shayan question: images, video, voice, and ebooks with Hermes
- Free prompt-to-video with OpenMontage
- Local image generation with Baidu ERNIE-Image
- Remotion preference: corporate-style animation
- Voice layer with Coqui TTS
- Hermes Oracle and the Codex ad-studio example
- The three-step method and the no-code thesis
AI commentary
"The narrator watched the demo end to end and checked each claim against independent sources. The setup pattern is elegantly simple; the promises are unmeasured and the promotional load is heavy. Every paragraph keeps the speaker's claims visibly separate from verified information."
AI assessment
The strongest objection is lock-in: once the whole production line lives on one panel and a handful of open-source repositories, maintenance becomes invisible. If a repo goes stale, an interface changes, or a model license is updated, the workflow can break silently. The set-and-forget promise reads as optimistic without version tracking and a backup plan. Scattered but independent tools are sometimes more resilient.
The video offers no measurements: production time, unit cost, output quality, and failure rates are never discussed. Commercial-use licensing stays murky; open-source code is not the same thing as the usage terms of model weights. Privacy is another open question: panels handling client data never clarify the local-versus-cloud split. These gaps make additional research mandatory before any business decision.
The speaker presents himself as the digital twin of Julian Goldie, and a large part of the video is devoted to the AI Profit Boardroom membership. That turns the piece from pure tutorial into a customer-acquisition funnel. Figures such as 3,400 members and four coaching calls a week could not be verified from independent sources. Readers should keep claims and promotion apart.
The practical takeaway is sequential: pick one task, start with a cleanly licensed tool, and test output before it touches client work. Budget the hardware before moving to local models; take the slowdown warning from the Baidu ERNIE-Image example seriously. Before copying a panel setup, verify star counts, maintenance frequency, and licensing on GitHub.
Sources
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hermes agent os · openmontage · remotion · ernie-image · coqui tts · no-code automation