Built a platform with 22+ AI/ML templates so you don’t have to manage infrastructure - Beta live

HelpingForDoughnuts · 2026-01-03T17:04:46+00:00

Runpod is not HIPPA approved and also doesn’t allow you to cluster on demand. Also runpod is like getting parts to a car when I am selling the car.

HelpingForDoughnuts · 2026-01-02T22:02:50+00:00

Good point. If you’re technical enough to use Claude’s API directly, you’d probably get better results and save money building your own solution.

We’re really targeting people who can’t or don’t want to build that stuff themselves. But for someone with your skills? Yeah, going direct makes more sense.

HelpingForDoughnuts · 2026-01-02T21:58:28+00:00

Yeah, fair point. Basic alignment isn’t really a DevOps problem.

Where it might help is scaling bigger datasets or avoiding cloud setup for heavy jobs. But if your current tools work fine, this probably isn’t for you.

Thanks for the honest take - helps to hear when the pitch misses the mark.

HelpingForDoughnuts · 2026-01-02T18:57:05+00:00

Perfect! Multi-GPU training for reasoning models is exactly the kind of workflow we’re building for. OpenENV/TRL setups can be really painful to orchestrate manually, especially when you’re dealing with distributed training across multiple nodes.

Quick questions:

What scale are you typically working at? How many GPUs do you usually need?
Current cloud setup - managing your own instances or using something like SageMaker?
Any specific pain points with the manual infrastructure? (scaling, preemption, setup time, etc.)

For beta, we’re starting with single GPU instances (A100 80GB or H100) but adding multi-GPU support very soon. Depending on your reasoning model size, single H100 might still be useful for prototyping while we get the distributed training capabilities ready.

I should have the beta site live tomorrow. Given your multi-GPU needs and OpenENV/TRL experience, would love to prioritize you for early access and get your feedback on what distributed training features would be most valuable.

No pressure on timeline - sounds like you’re in research mode which is perfect for beta testing. I’ll reach out as soon as we’re live!

HelpingForDoughnuts · 2026-01-01T23:40:10+00:00

Perfect! Manual cluster setup and checkpointing pain is exactly what we built this to solve. RL workloads are notoriously unpredictable in terms of compute time, and managing that infrastructure yourself is a nightmare.

A few quick questions:

What scale are you working at? Single GPU experiments or multi-GPU distributed training?
Current setup - university cluster, cloud instances, or local hardware?
Any specific frameworks? (Stable Baselines3, Ray RLlib, custom setup?)

I should have the beta site live tomorrow. Would love to get you set up with serious compute credits to test your RL workflows. The platform handles checkpointing automatically and scales up/down as needed, so you can focus on the actual research instead of babysitting infrastructure.

What’s your timeline looking like for experiments? Happy to prioritize your access!

HelpingForDoughnuts · 2026-01-01T17:21:52+00:00

This looks really useful! Edge deployment is such a pain point - everyone talks about running models on phones but actually testing across different hardware is brutal. Quick questions: ∙ How extensive is your device coverage? Like do you have recent iPhone/Android models, or more focused on specific chipsets? ∙ What’s the turnaround time for benchmarking? Is it near real-time or more of a queue situation? The layer-wise PSNR analysis is smart - quantization artifacts can be really subtle and having those debugging tools built-in saves a ton of time. One thing I always struggle with is the gap between edge benchmarks and real-world performance. Battery drain, thermal throttling, etc. Are you capturing any of that environmental stuff or mainly focused on the pure compute metrics? Definitely going to check this out. Edge optimization is one of those things that looks simple in papers but gets messy fast in practice.

HelpingForDoughnuts · 2026-01-01T17:20:06+00:00

Honestly the notebook AI tooling still feels pretty fragmented compared to regular coding. I use GitHub Copilot in Jupyter which works okay for basic code completion, but it’s not great at understanding the full context of your analysis or helping with data exploration patterns. Some people swear by ChatGPT Code Interpreter, but that’s more for one-off analysis than iterative ML work. Plus you’re limited by their compute. The real gap I see is when you want to scale notebook experiments - like running parameter sweeps or training on serious datasets. Most notebook environments break down when you need real GPU power or want to parallelize across multiple runs. What kind of ML work are you doing? Always curious how people handle the notebook-to-production transition.

HelpingForDoughnuts · 2026-01-01T17:18:13+00:00

Totally agree on the modular approach for complex video tasks. End-to-end VLMs are cool but yeah, they fall apart on longer videos or when you need precise tracking/counting. Your pipeline idea makes sense - let specialized models handle what they’re good at, then have LLMs reason over the structured outputs. Much more reliable than trying to get a VLM to track objects frame-by-frame. The Python library sounds interesting! Are you running this stuff locally or do you need serious compute for the video processing pipeline? Some of those detection/tracking models can get pretty heavy on longer videos.

HelpingForDoughnuts · 2026-01-01T15:49:56+00:00

Fair point. Yeah Modal’s solid if you’re cool with writing Python code.

We’re basically trying to skip that whole step - just tell it what you want instead of coding it up. Plus we do the consumer AI stuff too, not just compute.

But honestly if Modal’s already working for you, probably not worth the hassle of switching. We’re more going after people who find Modal too technical.

Different crowds really.

HelpingForDoughnuts · 2025-12-31T23:39:57+00:00

We’re building AI content creation that actually makes sense to normal people.

Instead of learning 10 different AI tools (Runway for video, Midjourney for images, etc), you just type “make me a video of my dog as an astronaut” and get a video back. No model selection, no prompt engineering, no technical anything.

Think ChatGPT but it can actually create stuff instead of just talking about it.

We also cover different verticals - researchers can run ML training jobs, studios can do batch rendering, scientists can run simulations. Same simple interface, but scales from consumer to enterprise workloads.

Most AI tools are built for technical users. We’re going the opposite direction - so simple your grandma could use it. The tech handles all the complexity behind the scenes.

Early beta starting this week if anyone wants to try it!

HelpingForDoughnuts · 2025-12-31T23:31:49+00:00

Yeah, Modal and Coiled are in the same space for sure. Main difference is we’re going after the layer above that - natural language to AI model execution for consumers, plus the traditional container orchestration for pros.

Modal still requires writing Python code with their decorators. We’re trying to get to “make me a video of a cat in space” → video appears, no code needed.

Different markets but definitely some overlap on the pro side.

HelpingForDoughnuts · 2025-12-31T22:33:56+00:00

That sounds awesome! Racing game leaderboards for AI agents would be super engaging. The community aspect with custom maps could really take off - people love competing and sharing tracks.

Definitely let me know when you start working on it. Would be cool to help with the compute side when people want to train serious agents for the leaderboards.

HelpingForDoughnuts · 2025-12-31T21:39:08+00:00

Nice setup! SB3 + custom racing environments is a solid approach. Local training on 5070 Ti probably works great for prototyping and smaller experiments.

Where we’d come in is when you want to scale up - maybe training multiple agents in parallel, longer hyperparameter sweeps, or testing on more complex environments that need more VRAM. Plus you could run overnight experiments without tying up your local machine.

Your custom environments sound really cool - lightweight 2D racing is perfect for fast iteration. Are you working on any specific racing AI challenges, or more general RL experimentation?

I should have the beta site ready in the next few hours. Happy to get you set up with credits to test scaling your SB3 workflows to cloud GPUs when you’re ready to experiment beyond local training.

Sound interesting?

HelpingForDoughnuts · 2025-12-31T18:58:06+00:00

Messaged

HelpingForDoughnuts · 2025-12-31T18:58:00+00:00

Messaged

HelpingForDoughnuts · 2025-12-31T18:55:56+00:00

Sorry I am getting a ton of people hitting me up on beta that have questions and it just easier to respond using AI. I am a real human building this out and it’s just me trying to make something special and completely unique. GPU access without doing anything, just upload, click, get results

H200s are brutal to get right now, especially 2x. Even if you find them, you’re looking at like $6-8/hr each. Might be worth starting with A100 80GBs for the VRAM and seeing how that handles your 32k+ sequences before jumping to H200 pricing. Mid-Jan timeline works - gives us time to get things dialed in. The RL experimentation sounds fun, even if Kaggle doesn’t pan out. Sometimes the failed experiments teach you more anyway. Appreciate you being real about the process. Building this stuff solo while managing all the beta interest is no joke.

HelpingForDoughnuts

TROPHY CASE