Alternatives
Products that do what WorkloadTruth does
Verify if a GPU job is training or idling
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We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell:…
2018
- 16CA
A recent HackerNews comment - “For one programmer's hourly cost, you could run 4000 CPU cores continuously. Can there really be no practical way to apply thousands of cores to boosting the programmer's productivity?” https://news.ycombinator.com/item?id=19339467 This is what we have come up with. The current productivity tools - Slack, Asana, Trello, Facebook Workplace, etc. - are great, but lack direct access to your code. Building a tool directly around the code makes it more powerful for software developers: CoDiff. https://codiff.com The foundation of CoDiff is a…
2019
- 17BG
2018 · github.com
- 18RS
Hi HN! I've been hacking on this side project for the last month or two with the goal of making it dead simple to use cloud GPUs. I ran into this problem personally during the phd, and built my own tooling around it. I always thought it'd be fun to try to turn that tooling into a more general product... and bitbop.io is the result! All you have to do is run `ssh bitbop.io`, and you get your own personal dev GPU workstation in the cloud. Looking forward to hearing your thoughts!
2024 · twitter.com
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Artifex is a machine-first, headless CLI runtime built for autonomous coding agents to author, validate, and render media node graphs locally. The agent talks to Artifex through a structured CLI interface. Workflows are DAGs, and each node is a plugin that can implement its own execution logic.. Each node has capability to inject logic into graph processing, WebGPU rendering, audio processing and their own SKILL.md file. Nodes can also inject their react components (not available with CLI) - which will be available with the desktop app. Execution is topological and supports checkpoint…
23d ago · gatewai.studio
- 20GR
I'm continuing to improve my RunsOn tool for launching self-hosted runners for GitHub Action on AWS, this time with support for any GPU-enabled instance type from EC2, and using the official Deep Learning AMIs as the runner image. Much cheaper than the official GitHub Actions runners, and accessible on any GitHub plan.
2024 · runs-on.com
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2020 · github.com
- 22DI
Hi HN community, Shen and I created a service for anyone to easily train deep learning model on GPU power harnessed from the crowd. We have completed the first version DeepCluster.io (http://deepcluster.io) and welcome ML researchers to try it out for free! We are enthusiastic of deep learning, but often found training models with GPU instances on AWS very expensive. Meanwhile, some of our friends have idle GPUs that are used to mine cryptos. So we decided to borrow their GPUs for training deep learning model ourselves, and believe this could be a service that benefits other ML…
2019
- 23GA
Sep 2025 · github.com
- 24AG
We built, saga[1] a layer that lets smaller teams access enterprise GPU discounts through collective buying power. How it works: 1. Aggregate GPU spend across hundreds of ML teams 2. Get enterprise rates through combined volume 3. Pass savings to users, monetize via provider partnerships Technical notes: - Works at billing layer only (no access to code/data) - Supports existing cloud setups or managed GPUs - Private beta running since January, opening more spots for March - Currently seeing ~50% savings on H100s/A100s [1] https://trysaga.ai
2025 · trysaga.ai
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