Alternatives
Products that do what Kineforge AI/ML model for robotics does
Train robot brains without demos on one GPU
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RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.
Jul 2026 · github.com
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- 4IB
We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
2025 · tinytpu.com
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NeuroBlock▲120No-code AI Lab: Train models, access datasets, run inference
Feb 2026 · neuro-block.com
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2016 · github.com
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2015 · qlearning.4ck5.com
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Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com
- 11FO
This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's pretty easy to run, and can do so efficiently if you have access to a recent gaming GPU. Although the diffusion model only take 2GB of VRAM (of course you can decide to scale it up), the compute needed to train them isn't negligible. For context, running the example until…
2025 · github.com
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The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.
Mar 2026 · github.com
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The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
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Hey folks, Check out a project I built over the past few days .. a lego mindstorms robot that autonomously drives around a track using vision/neural networks. It was coded entirely in Python. I did a short write-up and would love to get feedback. link: http://slowping.com/2012/self-driving-lego-mindstorms-robot/
2012
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2019 · scale.ai
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Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
7d ago · github.com
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Hi HN, I’m Ben, founder of K-Scale Labs (YC W24). Last year, I wanted to buy a humanoid robot that I could hack on, but the few options for sale were either too expensive, proprietary, or had a limited SDK. We set out to build an affordable humanoid robot using off-the-shelf components that can be built and shipped today, capable of running modern machine learning models, and make it completely open-source for developers like me. Today, we’re releasing our reinforcement learning library and sim2real pipeline for people who want to train policies for humanoid robots. If you have a computer,…
2025 · github.com
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We are exploring how teams can automatically audit data quality, detect problematic demonstrations, and build smaller, high-quality training sets before spending GPU time. We are looking for design partners to help shape the Calibra.
12d ago · calibrarobotics.com
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Jan 2026 · github.com
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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
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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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