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Agentic AI Artificial Intelligence

Microsoft AI Introduces MAI-Transcribe-1.5: 2.4% WER on Artificial Analysis, Best-in-Class FLEURS Accuracy, and Up to 5x Faster Long-Audio Transcription
ByRicardo June 8, 2026

Last week Microsoft AI has introduced MAI-Transcribe-1.5. It is the second iteration of the corporate’s in-house speech-to-text household. The mannequin targets accuracy throughout 43 languages, accents, and noisy environments. The Microsoft workforce positions it for manufacturing transcription workloads. What is MAI-Transcribe-1.5 MAI-Transcribe-1.5 is an automated speech recognition (ASR) mannequin. It takes audio as enter and…

Read More Microsoft AI Introduces MAI-Transcribe-1.5: 2.4% WER on Artificial Analysis, Best-in-Class FLEURS Accuracy, and Up to 5x Faster Long-Audio Transcription
Agentic AI Artificial Intelligence

Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries
ByRicardo June 8, 2026

Google Research workforce has launched a new agentic RAG framework. It is constructed into the Gemini Enterprise Agent Platform. It powers a characteristic known as Cross-Corpus Retrieval, now in public preview. The goal is a identified failure mode in enterprise search. Standard single-step RAG was not constructed for multi-source, multi-hop queries. Ask “What are the…

Read More Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries
Applications Artificial Intelligence

Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
ByRicardo June 7, 2026

In this tutorial, we use GEPA as a reflective prompt-evolution framework to enhance the way in which a language mannequin solves arithmetic phrase issues. We start with a weak seed immediate, create a small deterministic benchmark, outline a structured evaluator, and move actionable suggestions to GEPA so it could possibly perceive why a candidate immediate…

Read More Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
Agentic AI AI Tool

Best 21 Low-Code and No-Code AI Tools in 2026
ByRicardo June 7, 2026June 7, 2026

Low-code and no-code platforms have moved from easy drag-and-drop builders to AI-native growth environments. In 2026, most of them ship a built-in assistant that turns a textual content immediate right into a working app, agent, or automation. This record covers 21 instruments that AI practitioners use right this moment, grouped by what they do greatest….

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Agentic AI AI Agents

Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b
ByRicardo June 7, 2026

Most search brokers are educated as insurance policies over a rising transcript. The mannequin decides search. It should additionally bear in mind what it noticed, which proof issues, and which claims it checked. A staff of researchers from University of Illinois Urbana-Champaign, UC Berkeley, and Chroma argues this asks an excessive amount of. Reinforcement studying…

Read More Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b
Agentic AI AI Infrastructure

NVIDIA garak Tutorial: Build a Complete Defensive LLM Red-Teaming Workflow with Custom Probes and Detectors
ByRicardo June 7, 2026June 7, 2026

In this tutorial, we analyze NVIDIA garak as a sensible framework for defensive LLM red-teaming. We begin by organising Garak, then transfer by means of plugin discovery, dry runs, real-model scans, multi-probe evaluations, report evaluation, customized probe creation, customized detector creation, and AVID export. Instead of operating solely a single scan, we use Garak end-to-end…

Read More NVIDIA garak Tutorial: Build a Complete Defensive LLM Red-Teaming Workflow with Custom Probes and Detectors
Agentic AI AI Agents

Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal
ByRicardo June 6, 2026June 6, 2026

This week, Google AI crew launched the Colab CLI. The software connects your native terminal to distant Colab runtimes. It lets builders and AI brokers run code on cloud GPUs and TPUs. You keep in your terminal the complete time. The CLI is open supply below the Apache 2.0 license. What is Google Colab CLI…

Read More Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal
Agentic AI AI Agents

Moonshot AI Releases Kimi Code CLI: A Terminal AI Coding Agent Built in TypeScript for Next-Gen Agents
ByRicardo June 6, 2026

Moonshot AI has launched Kimi Code CLI, an open-source coding agent that runs in the terminal. The software reads and edits code, runs shell instructions, searches recordsdata, and fetches net pages. It then chooses its subsequent step primarily based on the suggestions it receives. The undertaking is MIT-licensed and lives on GitHub.. Kimi Code CLI…

Read More Moonshot AI Releases Kimi Code CLI: A Terminal AI Coding Agent Built in TypeScript for Next-Gen Agents
Agentic AI Artificial Intelligence

NVIDIA Releases Nemotron 3.5 ASR: A 600M-Parameter Cache-Aware Streaming Model Transcribing 40 Language-Locales in Real Time
ByRicardo June 6, 2026June 6, 2026

NVIDIA’s Nemotron Speech workforce has launched Nemotron 3.5 ASR. It is a 600M-parameter streaming Automatic Speech Recognition (ASR) mannequin. A single checkpoint transcribes 40 language-locales in actual time. Punctuation and capitalization are constructed in natively. The mannequin ships as open weights on Hugging Face. The license is OpenMDW-1.1. The structure is a Cache-Aware QuickConformer-RNNT. What…

Read More NVIDIA Releases Nemotron 3.5 ASR: A 600M-Parameter Cache-Aware Streaming Model Transcribing 40 Language-Locales in Real Time
Artificial Intelligence Computer Vision

A Hands-On Coding Tutorial on Qualcomm AI Hub Models for Classification, Object Detection, and Hardware-Aware Deployment
ByRicardo June 5, 2026

In this tutorial, we work by way of an end-to-end workflow for Qualcomm AI Hub Models. We begin by organising the required package deal, discovering the out there mannequin assortment, and loading MobileNet-V2 for native PyTorch inference. We additionally deal with an essential input-shape problem by changing NHWC picture tensors into the NCHW format anticipated…

Read More A Hands-On Coding Tutorial on Qualcomm AI Hub Models for Classification, Object Detection, and Hardware-Aware Deployment

Posts

Microsoft AI Introduces MAI-Transcribe-1.5: 2.4% WER on Artificial Analysis, Best-in-Class FLEURS Accuracy, and Up to 5x Faster Long-Audio Transcription

Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries

Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation

Best 21 Low-Code and No-Code AI Tools in 2026

Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b

NVIDIA garak Tutorial: Build a Complete Defensive LLM Red-Teaming Workflow with Custom Probes and Detectors

Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal

Moonshot AI Releases Kimi Code CLI: A Terminal AI Coding Agent Built in TypeScript for Next-Gen Agents

NVIDIA Releases Nemotron 3.5 ASR: A 600M-Parameter Cache-Aware Streaming Model Transcribing 40 Language-Locales in Real Time

A Hands-On Coding Tutorial on Qualcomm AI Hub Models for Classification, Object Detection, and Hardware-Aware Deployment

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