“Model inference”

Cloud platform for running, fine-tuning and serving open-source AI models behind an OpenAI-compatible API.

Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment.

Generate AI videos using ByteDance Seedance models via Volcengine Ark API.

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed…

Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 3 maximum-likelihood inference…

Build and run production workloads on Amazon Bedrock with current model availability, Converse API, agents, guardrails, AgentCore, and IAM controls.

Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch…

Check if a task runs locally vs cloud.

The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse…

The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse…

BridgeNode MCP

bridgenode-ai

BridgeNode — x402 pay-per-request AI inference.

Use when writing Python code for DSGE models, HANK models, numerical economic computation, causal inference, or quantitative economic data analysis

Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer.

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse…

The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse…

The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse…

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.

AGENTS.md from NVIDIA-NeMo/Speech: 147 lines covering Project Overview, Build & Install, Code Style & Pre-commit, Testing and more.

Post-training 4-bit quantization for LLMs with minimal accuracy loss.

OpenAI-compatible inference on open-weight models, served on hardware Linkrra owns and operates.

Provides AI and machine learning techniques for CTF challenges.

RNN+Transformer hybrid with O(n) inference.

Hosted model APIs, GPU cloud and agent sandboxes on one platform, with token-based billing for models.

Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation.