Post Syndicated from Jeronimo De Leon original https://www.backblaze.com/blog/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 the playlist rewritten after every segment, which makes B2 a live broadcast target rather than an archive that fills up after the fact. Reads go out as path-style presigned URLs, working around a known issue where virtual-host-style presigning fails on a private B2 bucket. B2’s own Event Notifications, five signed webhook rules, keep the review room in sync in real time, with a fallback to plain polling if an account’s Event Notifications API isn’t enabled. The app also watches B2’s own transaction cap and backs off to local disk instead of crashing when a call gets rejected for exceeding it.

On Genblaze, generation runs through an AgentLoop scored by a ThresholdEvaluator. The judge is a real vision model, an NVIDIA NIM llama-3.2-90b-vision-instruct instance grading the actual rendered keyframes, not a fixed retry count. Two pipeline branches, audio and video, fan into a single compositor node instead of running as a straight chain, a fallback_models failover is wired in and confirmed to trigger only on a genuine model error rather than a timeout, and every run carries two layers of lineage: a shared run id across scenes and loop iterations, plus a second, custom chain id layered on top of that. The team even embedded the manifest directly inside the delivered MP4, so a separate verification command can re-download and re-hash every asset the file claims exists. Along the way they filed three pull requests and an issue against Genblaze itself.

firstframe puts B2’s feature set to work in front of the reviewer. Object Lock in Governance mode, scoped application keys, lifecycle rules, Event Notifications, and presigned URLs are all load-bearing parts of the review workflow, doing visible work on every scene that moves through it. That’s architecture built to be trusted, not just to work.

2nd place · beavous

https://devpost.com/software/beavous

github.com/AmirmLotfy/beavous

A campaign generator built to double-check its own storage, re-verifying every asset the moment it’s read back.

beavous is built for the marketers and small commerce teams who need a full paid-social campaign out of a single product photo: not one hero image, but four creative concepts, sixteen cropped aspect ratios for every placement, on-label ad copy, and a portrait video reel, packaged as a verified ZIP pulled straight from B2.

A public API hands off to a private worker behind a task queue, and every campaign is namespaced to an organization so tenants stay isolated from each other. When a generation gets rejected, the app doesn’t start over. It chains a correction onto the original attempt, which is closer to how a real creative review actually works than a one-shot retry.

On B2, keys are organized hierarchically by organization and campaign rather than by content hash, because the product is multi-tenant by design. Every upload and download goes through a presigned URL, and the database never stores a raw link, only an object key and a hash. Every time an asset is read back, beavous re-downloads it and re-hashes the bytes independently, rather than trusting a manifest check alone. B2 is treated as the single, sole system of record for every generated asset: a clean, one-source-of-truth design with nothing else to keep in sync.

On Genblaze, three custom providers handle Gemini image generation, Gemini video, and Veo image-to-video, each with its own tiered pricing registered on the model. The more interesting move is a correction chain: when a generation gets rejected, its manifest becomes the parent of the next attempt, an explicit correction lineage most one-shot generation pipelines skip entirely. Prompts are marked private so the text never lands in the public manifest, only a hashed reference to it does, and before any manifest is trusted as a correction parent it gets independently re-verified, not assumed correct just because it was the app’s own write.

beavous’s answer to “do you trust your own storage” is simple: no, never. Check it again, every time. That’s a slower design than trusting your own write, and a more honest one.

3rd place · takegraph

https://devpost.com/software/takegraph

github.com/Enoch208/takegraph

A build system for generative media that can prove its own reuse, recovery, and release integrity, live, against B2.

takegraph is built for teams running a production, not a single generation: the kind of project where a script tweak halfway through shouldn’t mean re-rendering everything from scratch. It treats the whole thing like a software build: a content-addressed dependency graph that, when a spec changes, recomputes fingerprints, rebuilds only what’s actually invalidated, and reuses everything else.

A team can also re-download and re-hash the actual bytes behind any reuse, recovery, or release straight from B2, live, checking integrity themselves instead of trusting a log. That kind of self-auditing separates a pipeline meant to run unattended for months from one built to survive a single demo.

On B2, content-addressed keys use a two-level hash split so directory listings stay fast at scale, B2’s own Event Notifications (HMAC-SHA256-signed webhooks) feed a background process, and a separate reconciler periodically re-checks everything by hand in case a webhook is ever missed, coordinated across workers with a database lock so only one reconciler runs at a time. Unvalidated uploads land in a quarantine prefix backed by a real lifecycle rule that expires it automatically, and a bad key gets rejected outright rather than silently rewritten. Two least-privilege application keys, one for day-to-day work and one for releases, are each scoped to a single bucket, and CORS rules on the work bucket exist specifically to support presigned browser uploads. Verification (re-download, re-hash, prove it) is a feature of the product, not an internal tool.

On Genblaze, takegraph builds a real pipeline around the idea: a dedicated run builder, a content-addressable storage sink, manifests, and observability events tied to every step. The team kept its media-generation side lean, calling straight through the GMI Cloud connector for image and video (one of the hackathon’s partner platforms), and pointed all of its custom engineering at the layer that makes the whole pitch work: the storage and consistency system underneath.

takegraph turns storage verification into the product itself: reuse, recovery, and release integrity are things a user can ask the system to prove, live, rather than take on faith. That’s B2 treated as a system of record in the fullest sense: self-healing, event-driven, and built to survive a missed webhook without anyone noticing.

Special mentions

Ninth

https://devpost.com/software/ninth-mkcgtv

A comic studio with a provider for every model it needed, and a manifest for every frame it drew.

Ninth is built for indie comic authors and motion-comic creators working on a small budget, who revise a scene a dozen times before it’s right and can’t afford to pay for a fresh generation on every pass. Describe a story, and it writes, draws, casts, and stages it panel by panel on an editable timeline, then bakes the result to an MP4.

An edit doesn’t force a re-generation: assets are pulled from a semantic library whenever something close enough already exists, and everything the AI produces (timing, layer position, camera, bubble placement, even the artwork itself) stays editable in place afterward. That reuse-first design is backed by 126 regression tests and a render path that shares its engine with the live editor, so what a creator previews is exactly what gets exported.

Its B2 layer favors verified durability: flat content-addressed keys and a manifest per run, plus real, working endpoints for audit, restore, and backfill that report exactly what’s on B2 versus what’s only on local disk, and rebuild the difference with a SHA-256 check on every recovered file.

On Genblaze, Ninth wrote seven or eight custom providers, covering nearly every model it reached for: Azure’s image model, Gemini’s aspect ratios, Veo keyframes, Gemini’s video interpolation model, ElevenLabs music, and two separate structured-text providers. Each one exists for the same reason: to keep that artifact inside a pipeline and give it a manifest, so a generated frame always carries a record of the run and the prompt that made it.

Ninth’s mention is for discipline: the project that took “every generated frame should be reproducible and recoverable” most literally, and built the tooling to prove it on demand.

Spatialize

https://devpost.com/software/spatialize

A spatial twin that gives a person’s spoken correction the same provenance as a model’s output.

Spatialize is built for venue and accessibility teams who need to rehearse a step-free route through a space before anyone sets foot in it: it turns a flat floor plan into a voice-navigable spatial twin, extracting validated 3D geometry from the plan image, then answering spoken questions with route guidance grounded in that geometry.

Because the underlying use case is safety-adjacent, nothing the model proposes is trusted outright. Every extracted floor plan, and every voice-driven edit to it, has to pass the same deterministic geometry validator before it’s accepted, so a plausible-looking but wrong route never quietly makes it into a rehearsal. When that validator rejects an attempt, it hands back the exact error, and the next attempt corrects specifically that, not a blind retry.

On B2, Spatialize favors resilience: every read goes through a short-lived, presigned link, and if B2 isn’t configured at all, the app quietly falls back to a local, path-safe store instead of failing to boot. It runs two storage paths side by side (its own hierarchical object store for run and scene state, and a separate Genblaze storage sink dedicated to generated-media provenance), keeping application state and generation history cleanly apart.

Under the hood, an agentic loop drives the floor-plan extraction, evaluated at each attempt by that same deterministic validator rather than an LLM’s opinion. Three custom text-to-speech providers form a genuine fallback ladder: a cloud model first, a lightweight third-party API next, and a self-hosted, zero-credential model as the last resort, so a narration request degrades gracefully rather than failing outright. The standout idea: a voice-driven edit to the scene creates a new version tagged as a human change, with the actual transcript kept as evidence, so every point in the final scene traces back to either a model’s manifest or a person’s own words.

Spatialize’s real achievement is conceptual: it gives a spoken correction the same seriousness as a model’s output, tracing every point in the final scene back to either a manifest or a transcript. That’s provenance thinking applied somewhere most generative pipelines never point it.

Same brief, different rigor

Every winning project found its own discipline. What they share is how many different, equally valid kinds of rigor a small team can bring to the same two primitives in six weeks. firstframe made B2’s write-once guarantee visible to a reviewer in real time. beavous made re-verification a habit instead of an afterthought. takegraph made its storage layer heal itself. Ninth made every one of its seven custom providers answerable to the same manifest. Spatialize made a human correction as provable as a model’s.

None of that shows up until a team stops treating storage and orchestration as plumbing and starts treating them as part of the design. firstframe’s win sits at the intersection of both: the same rigor that scores a generated frame with a vision model also locks the finished one against deletion.

What you can build on

Strip the five projects back and the same handful of moves keep appearing, none of which need a hackathon, a large team, or a novel model:

• Object Lock in Governance mode turns “approved” from a database flag into a storage-level fact, and firstframe proves the hold is real by attempting the delete by version ID and catching the rejection B2 returns.
• Bucket prefixes (incoming, running, approved, rejected) give you queue semantics without a queue, with a separate lifecycle rule per prefix so stalled multipart uploads and dead rejects expire on their own timers.
• Re-download and re-hash on read, the way beavous does, because a non-deterministic pipeline produces a lot of near-identical artifacts and hashing is how you know which one you’re holding.
• Event Notifications as the fast path, a periodic reconciler as the source of truth, so a dropped webhook costs you latency instead of correctness.
• Content-addressed keys with a two-level hash split keep directory listings fast at scale and make reuse cheap: same fingerprint, same bytes, no second generation to pay for.
• Application keys scoped one per role, like a reviewer key limited to the readFiles capability with a name prefix restricting it to the approved folder.
• Correction lineage instead of retries: the rejected run’s manifest becomes the parent of the next attempt and carries the specific error forward. beavous and Spatialize arrived at this independently, which suggests it’s the general shape rather than a niche trick.

All of it is available today. B2 starts free with 10GB, and Object Lock, lifecycle rules, Event Notifications, scoped application keys, and presigned URLs are in the API from day one. Genblaze gives you one Pipeline API across OpenAI, Google, Runway, Luma, ElevenLabs, and Stability Audio, plus models served through GMI Cloud and NVIDIA NIM, and a canonical provenance manifest out of every run that embeds straight into the .mp4, .png, or .mp3 you ship.

A good first project is smaller than any of these five. Take a pipeline you already have, write a manifest for every run, persist it to B2, and add one endpoint that re-downloads and re-hashes an asset to prove the manifest is honest. That’s a weekend of work, and everything above is a variation on it. Genblaze is open source, and three of the pull requests behind firstframe are already in it.

For more ideas, the full project gallery has every entry from the hackathon, and there’s a lot in there beyond the five covered here.

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