Alternatives
Products that do what Run `SSH bitbop.io`, get a personal GPU dev machine does
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!
- 1
- 2DG
2016 · github.com
- 3GB
2016 · paperspace.com
- 4IR
Democratisation of local AI is key. I've been working on pushing the limits of commercial hardware, squeezing any extra bit possible. My Scientific Agentic AI hareness helped me to reallocate every single bit of it. I rewrote the Kernel, I went down the CUDA rabbit hole until I have been able to explain any bit and any ms of computational power involved in the process pushing the Qwen 30B-A3B from 8 tok7s to 19 tok/s with llama.cpp up to 22.2 tok/s with my project and 109 tok/s on not novel content and speeding up the prefill by 5-9X
Jul 2026 · github.com
- 5IB
You know that old TI calculator you used in high school, then put in a box and forgot about? Have you ever wished you had an operating system for your calculator with preemptive multitasking, dynamic memory management, a tree filesystem conforming to the FHS, and all the comforts of Unix? Well, good news: that's totally a thing that exists. I've been working on my kernel for about three and a half years now and I'm looking for new contributors to help out. It's written entirely in z80 assembly, and it's both challenging and fun to work on. There's an IRC channel for contributors or people…
2014
- 6VG
2015 · vanitykeys.io
- 7SS
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
- 8IM
Hey HN! I made a completely open sourced alternative to Weights and Biases with (insert cringe) blazingly fast performance (yes we use rust and clickhouse) Weights and Biases is super unperformant, their logger blocks user code... logging should not be blocking, yet they got away with it. We do the right thing by being non blocking. Would love any thoughts / feedbacks / roasts etc
2025 · github.com
- 9IM
I spent the past few weeks making an open source cloud code editing environment with an AI copilot and multiplayer collaboration! It's fully self-hostable in 5-10 minutes. There's a lot of minor improvements to be made, and some are already listed in the Github issues. Let me know what you think and feel free to try it out.
2024 · github.com
- 10MF
I know everyone is a bit burned out on blockchain and cryptocurrency related stuff around these parts, but I've been working on as a side project for the last few months and thought you guys might be interested: https://cryptoempire.io/ It's still a bit buggy and incomplete (the mobile ux needs a lot of work, the leaderboard isn't functional yet, etc.), but I've reached a point where I'm not totally ashamed of sharing it :) Any feedback would be most welcome!
2018
- 11WR
Hi HN, I previously wore SWE/SRE hats on GCP. Later, I was on the other side, managing thousands of machines. The friction points of onboarding and operating cloud systems are personal problems to me. I’ve found it hard to keep track of all the random cloud resources floating around, especially as my team’s assets proliferated. Occasionally, there is a resource affecting an outage but no one remembers where it is. I am constantly frustrated by existing tooling. APIs can work, after you’ve navigated the byzantine documentation, but I often find myself doing ad-hoc tasks which are best…
2022 · listvms.com
- 12AE
2016 · github.com
- 13IM
Hi HN, I’m one of the devs behind StarDesk, a P2P-first remote desktop we’ve been building for ~1 year. We started this partly out of frustration: some popular RDP tools haven’t seen meaningful updates in a long time, and long-standing issues are often left unaddressed. As devs, we really dislike software that stagnates once it “works well enough”. StarDesk is our attempt to do it differently. What we focus on: -P2P by default (relay as fallback) -Low RTT + low jitter > chasing max bitrate -Encoder / input path tuned for real-time interaction -Modern E2EE (TLS/DTLS) UX-wise, setup…
Jan 2026 · stardesk.net
- 14IW
Jul 2026 · aidekin.com
- 15DI
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
- 16
- 17WF
It's annoying having so many ML and GS training tools rely exclusively on cuda/nvidia . So for our open-source command-line gaussian splat converter/compressor, we decided to try WebGPU instead. It's working well so far and a single codebase runs on Linux, MacOS and Windows without too much fuss. This is mostly thanks to Google's dawn project <3. Eventually some of this could also run directly in the browser.
Sep 2025 · github.com
- 18IB
Definitely one of those "20 minute adventure gone wrong" projects where all I wanted initially was a quick wireless rubber ducky for bitlocker keys and the like and then I kept adding stuff like AES-256..... Currently working on adding WebAuthn/FIDO support because the hardware is already there and scope creep is a lifestyle at this point. Would love feedback, especially on the security side. Repo and PCB files are fully open source.
Jul 2026 · github.com
- 19SA
Hey folks, I figured this might be useful for a few people who want to play with hadoop and spark locally. Feedback hugely appreciated. I built this from the pain of wanting to setup a cluster for demos. I have gpu bits in there due to the use case but figured maybe some could draw inspiration from this. Happy to answer questions if there's any interest. Link here: https://github.com/deeplearning4j/docker
2016
- 20IM
This is a personal project I've been working on and off for the past few years. It's a set of tools that have allowed me to prototype quickly small (and increasingly bigger) kernels, and create userspace programs to interact with them. Supports riscv64, amd64 and i386. The intel port has been tested and used on real hardware, from big dual-socket Xeon machines to an old X220. It is all C, I have plan to make rust bindings for the kernel library. But again, they're plans at this stage. Porting to new architectures is relatively simple, a basic port to riscv took me a couple of weeks of…
2024 · github.com
- 21GR
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
- 22WC
Hi we created the Spaces Protocol: https://spacesprotocol.org We also created Akron wallet to support it. Akron is also open source: https://github.com/imperviousinc/akron
2025 · akron.io
- 23RR
runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop…
Jul 2026 · github.com
- 24TC
the cloud is a walled garden built for profit [ not efficiency ]. you’re paying for idle, autoscaling is too slow, and serverless is a scam. locked in, overcharged, and out of options. lttle.cloud is different. decentralized, open-source, and built for real performance [ sub-10ms cold start ], true pay-for-what-you-use pricing, and zero vendor lock-in. deploy anything as "serverless" from any docker image, no rewrites, no bullshit. powered by the blockchain and built in public, it’s transparent, efficient, and free from vendor lock-in. run your own private cloud or join the decentralized…
2025
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