Author: Ricardo

Artificial Intelligence Computer Vision

A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
ByRicardo August 2, 2026August 2, 2026

In this tutorial, we design a whole GeoAI workflow for extracting constructing footprints from high-resolution NAIP aerial imagery. We start by configuring the geospatial deep studying setting, downloading raster imagery and vector labels, and inspecting their spatial properties earlier than producing georeferenced picture chips and segmentation masks. We then prepare a U-Net mannequin with a…

Read More A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
Uncategorized

Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model
ByRicardo August 2, 2026

Thinking Machines Lab has launched Inkling-Small, an open weights Mixture-of-Experts mannequin with 276B complete parameters and 12B lively. That is a couple of quarter the dimensions of Inkling, which carries 975B complete and 41B lively parameters. The mannequin was educated on NVIDIA GB300 NVL72 methods. It causes natively over textual content, photos and audio. The…

Read More Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model
Uncategorized

NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework
ByRicardo August 2, 2026

Agentic reinforcement studying analysis is fixed algorithm modification. New estimators, new pipeline phases, new rollout schemes. In mainstream frameworks every change threads via layers of coach, distributed backend, and rollout glue. That price lands on the researcher at each iteration. Molt, from NVIDIA’s NeMo staff, targets that price straight. Its a PyTorch-native agentic RL framework…

Read More NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework
Artificial Intelligence Editors Pick

End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
ByRicardo August 2, 2026August 2, 2026

In this tutorial, we construct a sophisticated end-to-end time-series forecasting workflow with TimesFM 2.5. We start by configuring the runtime, putting in the required dependencies, detecting out there {hardware}, and producing a sensible multi-store retail dataset with pattern, seasonality, pricing, promotions, holidays, temperature results, and random variation. We then load and compile the TimesFM 2.5…

Read More End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
Agentic AI AI Infrastructure

AMD Releases Instella-MoE-16B-A3B: A Fully Open Mixture-of-Experts LLM With 2.8B Active Parameters Trained On Instinct GPUs
ByRicardo August 1, 2026

AMD launched Instella-MoE-16B-A3B, a completely open Mixture-of-Experts language mannequin skilled from scratch on Instinct MI300X and MI325X GPUs. The mannequin holds 16B complete parameters however prompts solely 2.8B per token. AMD is publishing weights from each coaching stage, together with information mixtures, coaching configs, and inference code. Two systems-level decisions carry the discharge: Gated Multi-head…

Read More AMD Releases Instella-MoE-16B-A3B: A Fully Open Mixture-of-Experts LLM With 2.8B Active Parameters Trained On Instinct GPUs
AI Infrastructure Artificial Intelligence

Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking
ByRicardo August 1, 2026August 1, 2026

In this tutorial, we discover how NVIDIA Transformer Engine accelerates transformer workloads by combining fused GPU kernels, BF16 computation, and hardware-aware FP8 execution. We start by putting in Transformer Engine and detecting the lively GPU structure in order that we are able to decide whether or not the runtime helps TE kernels, FP8 tensor cores,…

Read More Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking
Agentic AI AI Agents

Supabase Releases Evals: an Open Source Benchmark That Scores Claude Code, Codex and OpenCode on Real Supabase Tasks
ByRicardo August 1, 2026August 1, 2026

Supabase has open sourced Supabase Evals, its benchmark and framework for testing how nicely AI brokers construct utilizing Supabase. It runs coding brokers together with Claude Code, Codex, and OpenCode in opposition to actual duties, comparable to constructing a schema, debugging a failed Edge Function, or fixing a damaged RLS coverage, then scores the end…

Read More Supabase Releases Evals: an Open Source Benchmark That Scores Claude Code, Codex and OpenCode on Real Supabase Tasks
AI Shorts Applications

MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio
ByRicardo August 1, 2026

MiniMax releases MiniMax H3, a general-purpose multimodal technology mannequin. MiniMax H3 shouldn’t be a text-to-video mannequin with add-ons. MiniMax describes it as a general-purpose multimodal technology mannequin that reads textual content, pictures, video, and audio as one unified context and returns video with native stereo sound. The mains specs embody: 2K output, 4–15 seconds, integer…

Read More MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio
Agentic AI AI Infrastructure

DeepSeek Upgrades DeepSeek-V4-Flash-0731 with Major Agentic and Coding Gains
ByRicardo July 31, 2026

DeepSeek revealed DeepSeek-V4-Flash-0731 on Hugging Face and moved the official V4-Flash API into public beta on July 31, 2026. The mannequin card is express that that is the official launch superseding the preview, and that the structure and measurement are unchanged. The positive aspects come from re-post-training, not a brand new design. The checkpoint ships…

Read More DeepSeek Upgrades DeepSeek-V4-Flash-0731 with Major Agentic and Coding Gains
Applications Artificial Intelligence

LingBot-Map Tutorial: GPU-Aware Inference and Point Cloud Export
ByRicardo July 31, 2026

In this tutorial, we implement an end-to-end streaming 3D reconstruction pipeline with LingBot-Map. We start by configuring the enter supply, reconstruction settings, checkpoint choice, and output controls, then probe the obtainable GPU and routinely tune body limits, digicam iterations, scale frames, and KV-cache parameters in accordance with the detected VRAM. We set up the repository…

Read More LingBot-Map Tutorial: GPU-Aware Inference and Point Cloud Export

Author: Ricardo

A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN

Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model

NVIDIA AI Releases Molt: A PyTorch-Native Agentic Reinforcement Learning Framework

End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment

AMD Releases Instella-MoE-16B-A3B: A Fully Open Mixture-of-Experts LLM With 2.8B Active Parameters Trained On Instinct GPUs

Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking

Supabase Releases Evals: an Open Source Benchmark That Scores Claude Code, Codex and OpenCode on Real Supabase Tasks

MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio

DeepSeek Upgrades DeepSeek-V4-Flash-0731 with Major Agentic and Coding Gains

LingBot-Map Tutorial: GPU-Aware Inference and Point Cloud Export

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