Fable 5 is the rare launch where the capability claims and the caution flags are both true. We read the launch-week reviews — named engineers, independent labs, the HN threads — and sorted them for one reader: the person deciding whether their team switches. Short version: move your hardest, longest, best-specified work onto it; leave your defaults alone; and never take its word for what it tested.
A 13 GB GPT-OSS-20B inference container: 571s → 40.65s. A 2.4 GB Llama-3.2 container: 91s → 7.4s. Same hardware, same app, only the image format and snapshotter config changed.
The obvious first move — lazy loading — made things 66% slower. The biggest single win came from a stack of hidden config knobs we had to read the snapshotter's Go source to find. The last bottleneck wasn't in containerd at all; it was the AWS EBS default write ceiling.
If you ship LLM images and your cold start is over a minute, this is for you.
If you ship a multi-gigabyte ML container — Hugging Face weights baked in, PyTorch + transformers, a FastAPI server — and you've watched the deploy take five-plus minutes from docker run to first inference, this post is the playbook we wish we had.
Every AI media provider has its own SDK, its own parameter names, its own polling logic. Tarash Gateway gives you one interface for all of them. Switch providers with a config change, chain them together, and never rewrite an integration again.
The AI media generation space moves fast. Every few weeks there's a new model worth trying: better motion, better prompt adherence, better pricing. So we tried them all.
Every switch meant the same work: different SDK, different parameter names, different polling logic, different error format. duration_seconds on one provider, duration on another, an enum string on a third. Each integration took a day. Each one was slightly different from the last in ways that didn't matter but had to be handled anyway.
And when a provider went down, the pipeline went with it. No fallback, no graceful degradation. Just errors until someone manually switched.
We weren't solving a new problem each time. So we built Tarash Gateway. We're Vertexcover, and we build tooling for teams working with AI-generated media.
SceneFlow isn't just a video cutter, it's an intelligent editor. By combining signal processing with Multimodal AI, it finds the perfect cut points, ensuring your videos never end with awkward freezes, mid-sentence chops, or blinking eyes.
If you've ever worked with AI-generated videos, you know the problem. The content is great, but the endings are... weird. The avatar freezes, stares blankly into the soul of the viewer, or cuts off mid-breath.
Notice the awkward movement of the subject at the end of this AI-generated video
To make these videos production-ready, you have to manually trim that awkward tail. For one video, it's fine. For 1,000 personalized outreach videos? It's a nightmare.
We wanted to automate this. But simple tools like ffmpeg or basic silence detectors aren't enough. They don't "see" the video. They don't know if an avatar's face looks unnatural.
So we built SceneFlow.
Linux provides a native Asynchronous I/O (AIO) API that should theoretically allow true background file operations. Yet popular frameworks like Node.js and Python's asyncio avoid it entirely, relying instead on thread pools with blocking I/O. Why? The answer reveals important lessons about API design, cross-platform compatibility, and the gap between theoretical elegance and practical engineering. The future may lie in io_uring, a modern interface that addresses many of AIO's shortcomings.
We recently built a LinkedIn scraping tool that can extract profiles, company data, and connections without getting flagged.
Sounds simple, right? Well, three weeks and countless "unusual activity detected" warnings later, We learned that scraping LinkedIn is less about parsing HTML and more about outsmarting their anti-bot systems.
Full source code: github.com/vertexcover-io/linkedin-spider
LLM agentic systems fail in subtle ways. At Vertexcover Labs, we use a 5-part evaluation approach—powered by a structured logging foundation:
Foundation: a structured logging layer that makes all of the above trivial to build and maintain.
| Scenario | Avg. wall-clock |
|---|---|
| No caching | 1 h 10 m |
| Layer-cache hit | 6 m |
| Layer-cache miss (deps change) | 52 m |
| Cache-mount + Cache-Dance | 8 m |
Stop rebuilding the world on every pull-request—turn on these flags and ship faster.
Replacing dual React-ffmpeg architecture with unified browser-based solution, achieving 100,000 daily video renders with consistent preview-output matching and halved development effort.
When designing for scale, the limiting factor is the GPU availability. So all rate limits / queueing must be designed around GPU availability.