Alternatives
Products that do what RoboActions does
Infrastructure for Physical Intelligence
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General Compute▲315AI models that run on an inference cloud optimized for speed
May 2026 · generalcompute.com
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Tighter instruction adherence in speech agents
Feb 2026 · developers.openai.com
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Powers faster, efficient reasoning for long-running agents
Jun 2026 · developer.nvidia.com
- 10PI
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com
- 11DO
Demo of agent based model on GPU with CUDA and OpenGL (Windows/Linux) Agent instances on GPU memory Uses SSBO for instanced objects (with GLSL 450 shaders) CUDA OpenGL interops Renders with GLFW3 window manager Dynamic camera views in OpenGL (pan,zoom with mouse) Libraries installed using vcpkg (https://github.com/KienTTran/ABMGPU)
2023 · github.com
- 12TR
2018 · actcast.io
- 13VI
Most inference UIs that I've come across pretty much just give us a chat-like interface to toy around with models in a single visual conversation thread. Given the fact that we are limited to seeing only one output at a time, it's kind of hard to compare outputs from different models, adjustments made to the prompting, and sampler settings. But even when keeping the generation parameters the same (e.g., to test for reliability in the output) and just going for multiple passes, there is no easy way to have a side-by-side comparison to keep track of the outputs from the multiple "rounds". I…
2024 · github.com
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I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
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- 16PR
I built this because I couldn't find honest numbers on how well VLA models [1] actually work on commercial tasks. I come from search ranking at Google where you measure everything, and in robotics nobody seemed to know. PhAIL runs four models (OpenPI/pi0.5, GR00T, ACT, SmolVLA) on bin-to-bin order picking – one of the most common warehouse operations. Same robot (Franka FR3), same objects, hundreds of blind runs. The operator doesn't know which model is running. Best model: 64 UPH. Human teleoperating the same robot: 330. Human by hand: 1,300+. Everything is public – every run with…
Mar 2026 · phail.ai
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Train robot brains without demos on one GPU
Jun 2026 · app.notion.com
- 18CW
Hey HN! We’re excited to share Orion [1] — our new visual agent that sees, reasons, and acts across images, videos, and documents. Frontier VLMs (GPT, Claude, Gemini) can describe what they see, but they can’t reliably act on visual inputs. Ask them to detect objects, segment images, or chain visual steps — they’ll fail in surprisingly inconsistent ways. High-res images collapse to ~1024px. And the visual AI ecosystem is fragmented across separate APIs for image understanding, OCR, image-gen, video-gen, etc. We built Orion to fix this. Orion combines VLM reasoning with reliable…
Nov 2025 · chat.vlm.run
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- 20QS
2020 · picsellia.com
- 21AH
This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…
2025
- 22G4
When new Large Multimodal Models (LMMs) are released, there is excitement as we explore new capabilities. What can a model do? What can't a model do? What strange behaviors does the model exhibit? With that said, such analyses are frozen in time. At a hackathon toward the end of last year, the Roboflow team made a tool that runs the same set of tests with the GPT-4 with Vision API every day. This allows people to see how the model performs over time as updates are made. The last seven days of results are displayed on a web page; the rest of the data is archived in GitHub. We started the site…
2024 · gptcheckup.com
- 23RA
Hi there, looking for feedback on my new project "Featherless.AI" The idea is to allow users to run all the models on hugging face instantly. Via the OpenAI API compatible endpoint. Why? Because its a real chore to download models and spin up GPUs, especially if you want to test multiple models. Not to mention GPUs cost multiple dollars an hour to rent. And if we want more people to use open source AI, we got to make it easier for them to try and play with all of them. So what if instead of spinning up dedicated GPUs per model (which is what every provider is doing) We can startup a LLM…
2024 · featherless.ai
- 24EB
2019 · github.com
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