Results from the Backblaze Generative AI Media Hackathon

Building generative media applications on object storage: What the strongest projects have in common

Between June 22 and August 3, 2026, 1,314 people entered hundreds of projects into a single brief: build a generative media app on Backblaze B2 object storage, orchestrated through the open-source Genblaze SDK, for a share of a $10,000 prize pool.

Almost every entry could generate an image, a video, or a soundtrack, and generate it well. What separated the strongest was the layer underneath: what gets written down, what can be proved later, and what happens when a file has to be checked, corrected, or locked against deletion.

Here’s a closer look at the five that went furthest, and the parts worth reusing. Every entry is browsable in the hackathon project gallery.

The brief, and why it was narrow on purpose

The brief was narrower than most hackathons get. Submissions couldn’t just be a working demo and a repo. Devpost required teams to explain, in writing, how their app actually used both Backblaze B2 and Genblaze, and to list every provider and model behind it. Judging ran against four criteria: real-world utility, production readiness, meaningful use of B2, and meaningful use of Genblaze.

That last pair is the constraint that did the work. "Meaningful use" rules out the default architecture, where a team generates an asset, drops the bytes in a bucket, and calls that storage. It forces a decision about what the storage layer is actually for. What follows is written the way a technical and creative director would walk five finished pipelines against exactly that rubric: who each app is actually for, whether it holds up past the demo, and how deep the storage and orchestration choices go.

The two primitives

Backblaze B2 is the ground every one of these apps had to build on: S3-compatible object storage, sized for the sheer volume of generated assets, thumbnails, metadata, and provenance records a media pipeline throws off, free to start with 10GB included.

Genblaze, Backblaze’s open-source orchestration SDK, is what feeds it: a unified Pipeline API spanning providers like OpenAI, Google, Runway, Luma, ElevenLabs, and Stability Audio, plus models served through platforms such as GMI Cloud and NVIDIA NIM, so a team can swap providers without rewriting its orchestration. Every run produces a canonical provenance manifest that can be embedded directly into the media file itself (an .mp4, a .png, an .mp3) and persisted to B2 or any S3-compatible store.

The hackathon also partnered with GMI Cloud, giving teams easy access to open-source generative models for image, video, audio, chat, reasoning, and multimodal work, which is why it turns up as a provider more than once below.

Two primitives, six weeks, hundreds of teams, and five very different examples of rigor.

Winners

1st place · firstframe

https://devpost.com/software/firstframe

github.com/migarci2/firstframe

A review room for AI video ads that doesn’t make you wait for the whole render.

firstframe builds a review room for the marketing and creative teams who commission AI-generated video ads: instead of waiting on a full multi-scene render before anyone can react, it streams the first finished scene as a live HLS playlist the moment it’s ready, appending segments as later scenes land. A reviewer starts giving notes while the ad is still being made, not after.

Every generated scene is scored by an actual vision model before a human ever sees it, so obviously broken output gets caught and retried automatically rather than shipped to a reviewer’s inbox. A failover step swaps in a backup model only on a genuine provider error, never on an ordinary slow response, and once a scene clears review its master file and manifest are locked against deletion for thirty days: a guarantee the code proves by trying to delete a locked file and catching the rejection. Automated QA paired with a tamper-evident record of what was approved is what turns a generation pipeline into something a brand could actually sign off on.

On B2, the bucket’s folder structure doubles as a workflow: a scene moves through incoming, running, provenance, approved, and rejected prefixes as it clears review. Once a scene is approved, its master file and manifest get a real thirty-day write-once hold using B2’s Object Lock in Governance mode, and the code proves that isn’t just decorative by trying to delete a locked object by its version ID and catching the rejection B2 throws back. A reviewer’s application key is scoped to the readFiles capability with a name prefix restricting it to the approved folder alone. Four separate lifecycle rules cover the bucket’s different prefixes: stalled uploads in incoming have their multipart parts cancelled after 24 hours, while rejected, in-progress, and approved objects each age out on their own separate timers. The video segments themselves land in B2 as ffmpeg finishes each one, with...