Salma Mayorquin PRO

salma-remyx

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https://remyx.ai

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reacted to NatalieY's post with 🔥 3 days ago

Spent a while chasing a genuinely strange iOS bug while building Aiden (a physical agent that drives phones over USB HID): modifier-key shortcuts like Cmd+V would silently fail while plain keystrokes worked fine every time. Turned out iOS was routing the command to the wrong process (SpringBoard, not the actual foreground app) whenever a keyboard and mouse were both present at the same time as AssistiveTouch. Confirmed it wasn't specific to our hardware, reproduced it on a completely unrelated gaming keyboard and a Bluetooth keyboard too. Full writeup with the actual experiment table and log output: https://huggingface.co/blog/NatalieY/debugging-aiden Curious if anyone here has hit this same failure mode building on iOS accessibility APIs. Repo: https://github.com/AidenAI-IO/aiden-firmware

reacted to DedeProGames's post with 🔥 3 days ago

🚀 Introducing the GRM-3.2 Family The GRM-3.2 family is a new generation of reasoning-focused models from OrionLLM, purpose-built for long-horizon agentic tasks, extremely difficult reasoning problems, advanced coding, and local AI workflows across a wide range of hardware constraints. GRM-3.2-Sky is the flagship model in the family: a 35B-A3B Mixture-of-Experts model built on the Ornith-1.0-35B architecture, designed for elite structured reasoning, complex multi-file coding, advanced mathematics, and sustained coherence across extended agentic workflows. It represents a substantial leap in long-horizon task capability over its predecessor, GRM-2.6-Plus. GRM-3.2-Cliff is the mid-sized workhorse: a 9B-parameter model optimized for long-horizon agentic tasks and difficult reasoning in low-to-mid GPU environments. It delivers strong multi-step planning, debugging, and terminal-agent performance without demanding flagship-level hardware. GRM-3.2-Turf is the lightweight edge model: a 1.2B-parameter model based on the LiquidAI/LFM2.5-1.2B-Thinking architecture, engineered for efficient on-device execution, high-fidelity instruction following, and robust tool use on mobile, embedded, and other resource-constrained hardware. All three models are designed for users who need dependable reasoning engines that can maintain goal-directed behavior, planning quality, and task fidelity across many steps—whether on a server, a local workstation, or an edge device. Models: GRM-3.2-Sky: https://huggingface.co/OrionLLM/GRM-3.2-Sky GRM-3.2-Cliff: https://huggingface.co/OrionLLM/GRM-3.2-Cliff GRM-3.2-Turf: https://huggingface.co/OrionLLM/GRM-3.2-Turf Organization: https://huggingface.co/OrionLLM

posted an update 3 days ago

The evidence that gets a method published usually isn't the evidence you need to decide whether to ship it in your project. We've been building Validate in Remyx to surface the evidence devs & maintainers need to make that call. Within GitHub and our studio you can: * define your project's validation configuration * implement & run it against a proposed implementation * review the results alongside the diffs We're looking for a small group of engineers actively integrating new AI methods into production to help shape Validate. If that sounds like your team, reach out! LI: https://www.linkedin.com/in/smayorquin/ email me: salma@remyx.ai

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1811

The evidence that gets a method published usually isn't the evidence you need to decide whether to ship it in your project.

We've been building Validate in Remyx to surface the evidence devs & maintainers need to make that call.

Within GitHub and our studio you can:
* define your project's validation configuration
* implement & run it against a proposed implementation
* review the results alongside the diffs

We're looking for a small group of engineers actively integrating new AI methods into production to help shape Validate. If that sounds like your team, reach out!

LI: https://www.linkedin.com/in/smayorquin/
email me: salma@remyx.ai

Post

3039

VQASynth adds a code-as-action annotation agent

Data synthesis frameworks may distill a pipeline of expert models through templates or LLM re-phrasing, but templates limit the variety of the training data.

Wanting more flexibility for VQASynth, we've added an experimental SpatialAnnotator agent with scene understanding tools-as-methods, built on NVIDIA-labs OO Agents (NOOA): https://github.com/NVIDIA-NeMo/labs-OO-Agents

In NOOA an agent is a Python class. Fields hold state, docstrings are prompts, type annotations are contracts, and a method body of ... becomes an LLM-driven loop. Scene understanding tools stay ordinary deterministic methods.

from nooa import Agent

    class SpatialAnnotator(Agent, llm=llm):
        """Answer spatial questions about an image using scene understanding tools."""

        scene: SceneContext          # masks, depth, intrinsics, canonical transform

        # Deterministic Python. Bodies omitted here.
        def measure(self, a, b=None, kind="object_to_object") -> Measurement
        def count(self, target: str) -> Count

        # Agentic. The runtime hands this to the model.
        async def annotate(self, image: Image, question: str) -> SpatialAnswer:
            """Answer the spatial question. This is offline labeling, so there is
            no human available for clarification. Prefer a canonical
            interpretation, and decline if the question cannot be grounded."""
            ...