Haawke Research Overview

Mission

Haawke Neural Technology builds applied AI tools for creators in audio and video, while conducting research at the intersection of machine learning, expressive media, and cross‑cultural communication. We focus on practical systems that reduce editing friction, elevate creative quality, and enable artists to share work across languages and cultural contexts.

Organization home: haawke.com [0]

Core Capabilities

Adoption & reliability: 10,000+ professional users, ~85% editing time saved, 24/7 support, ~99.9% uptime. [0]

Research Focus Areas

• Cross‑language media accessibility: high‑quality transcription/translation; prosody‑aware voice conversion that preserves artistic intent across languages. [0]
• Generative music & adaptive scoring: controllable models that respond to narrative structure, tempo maps, and emotional arcs. [0][1]
• Assistive video editing: content‑aware editing suggestions; semantic trim tools; color normalization for mixed‑camera productions. [0]
• Multimodal creativity: image‑to‑image and texture/style transformation for visual storytelling pipelines. [1]
• Cultural bridge tooling: creator‑centric pipelines that bundle translation, captioning, and guided localization to reduce friction in cross‑cultural publishing. [0]

Open Models & Code

Hugging Face — inoculatemedia

• seesharp — image‑to‑image; updated recently. [1]
• techno-music-melodik — music‑focused work. [1]
• comic_olsskool — stylization experiments. [1]
• renegade_tardigrade — creative model. [1]
• diffuse_tardigrade — diffusion experiments. [1]

Profile: huggingface.co/inoculatemedia [1]

GitHub — inoculate23

• Frontend with MCP server for Praxis‑Live. [2]
• Custom boilerplate with embedded animation player workflows. [2]

Profile: github.com/inoculate23 [2]

GitHub — Haawke org

Organization presence for collaboration and distribution. [3] — github.com/Haawke

Selected Demos

Proposed Work (12–18 months)

• WP1 — Cross‑language audio pipeline: train/evaluate prosody‑aware voice conversion and multilingual captioning; open‑source tooling for artists and educators.
• WP2 — Assistive video editor: prototype semantic‑trim and filler‑word removal integrated with caption alignment; evaluate editing time saved vs. baseline NLE workflows.
• WP3 — Generative scoring: controllable music generation conditioned on scene descriptors; user studies with filmmakers and musicians on fit and creative control.
• WP4 — Culture‑bridge toolkit: end‑to‑end localization (transcription, translation, captions, audio style transfer) with creator‑friendly UX and documentation.

Deliverables: open models/tooling, evaluation reports, datasets (where ethically permissible), exemplar media projects, and practical guides.

Impact & Evaluation

• Efficiency: percent editing‑time reduction, automation acceptance, and re‑edit rates compared to control groups.
• Quality: audio clarity (objective metrics), color consistency, caption accuracy, and user satisfaction surveys.
• Accessibility: multilingual reach, caption uptake, and localization turnaround times.
• Cultural exchange: creator adoption across languages; qualitative feedback from cross‑cultural collaborators.
• Reproducibility: open protocols, datasets, and code to support independent replication.

Ethics, Inclusion, and Safety

• Consent‑forward pipelines; clear attribution and opt‑out mechanisms for model training and localization.
• Bias assessment for language coverage; attention to dialects, indigenous languages, and accessibility needs.
• Artist agency preserved via controls, previews, and non‑destructive edits.

Funding Request & Utilization

• Personnel: ML research, audio engineering, video post, and localization specialists.
• Compute & tooling: training/inference infrastructure, dataset curation, evaluation platforms, and donations or grants of time on cloud GPUs.
• Community pilots: collaborations with creators across languages; user studies and impact reporting.
• Open resources: documentation, tutorials, and exemplar projects to accelerate adoption.

Goal: accelerate practical AI that reduces creative friction and expands cultural reach, with measurable benefits for artists, educators, and audiences.

About & Team

Haawke operates across Las Vegas and Vancouver, BC, integrating AI research with hands-on audio/visual production. [0]

• Leadership: Craig Ellenwood — founder and principal engineer; open-source contributor and AI/creative technologist. Profile: github.com/inoculate23 [2]
• Track record: Trusted by 10,000+ professionals; production experience spanning podcasting, broadcast, and film. [0]
• Resume: See organizational background and Craig’s resume via the About page. [4]
• Org: Haawke GitHub organization for collaboration. github.com/Haawke [3]

More details: haawke.com/about [4]

CV Highlights

• Founder & principal engineer, Haawke Neural Technology — applied AI for audio/visual production and cross‑cultural media. [0][4]
• Open‑source contributions: Praxis‑Live MCP frontend; animation player boilerplate for embedded models. [2]
• Model author: seesharp (image‑to‑image; updated recently), techno-music-melodik (Oct 28, 2023), comic_olsskool (Sep 16, 2023), renegade_tardigrade (Mar 6, 2023), diffuse_tardigrade (Mar 3, 2023). [1]
• Production practice: podcasting, broadcast engineering, and film/video editing; locations in Las Vegas and Vancouver, BC. [0]
• Community impact: 10,000+ professional users; ~85% editing time saved; ~99.9% uptime reliability. [0]

References

1. [0] Haawke Neural Technology — https://www.haawke.com/
2. [1] Hugging Face — inoculatemedia (Haawke) — https://huggingface.co/inoculatemedia
3. [2] GitHub — inoculate23 (Craig Ellenwood) — https://github.com/inoculate23
4. [3] GitHub — Haawke org — https://github.com/Haawke/
5. [4] Haawke — About — https://www.haawke.com/about
6. [5] Motion Transfer — YouTube — https://youtu.be/zNoUxpHZoTc
7. [6] WebGPU Texture Mapping — YouTube — https://youtu.be/pSq5tcyOZv4

Contact: Craig Ellenwood — Haawke Neural Technology