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Building a Code Dataset Pipeline from NVIDIA Nemotron-Pretraining-Code-v3 Metadata with Streaming, Pandas, and tiktoken

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we work with NVIDIA’s Nemotron-Pretraining-Code-v3 dataset as a large-scale metadata index for code pretraining research. Instead of downloading the full multi-gigabyte dataset, we stream it, inspect its schema, and build a manageable sample for analysis. We then explore the dataset by studying languages, file extensions, repository frequency, and directory depth, which helps […]

Building a Code Dataset Pipeline from NVIDIA Nemotron-Pretraining-Code-v3 Metadata with Streaming, Pandas, and tiktoken Read More »

Google Releases Gemini 3.5 Live Translate, a Streaming Speech-to-Speech Audio Model Covering 70+ Languages Across Meet, Translate, and the Live API

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Audio Language Model, Editors Pick, Language Model, New Releases, Software engineering, Staff, Technology, Voice AI

Google just announced Gemini 3.5 Live Translate. It is their latest audio model for live speech-to-speech translation. Speech-to-speech means spoken audio goes in, and translated spoken audio comes out. The model detects over 70 languages automatically and generates translated speech. It preserves the speaker’s intonation, pacing, and pitch in the output. Turn-by-turn systems wait for

Google Releases Gemini 3.5 Live Translate, a Streaming Speech-to-Speech Audio Model Covering 70+ Languages Across Meet, Translate, and the Live API Read More »

NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab

ai, AI (Artificial Intelligence), AI Infrastructure, Applications, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we implement an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. We start by preparing a Colab-friendly environment, checking the available GPU, driver, CUDA, and cuTile installations before running any kernel code. We then build tiled examples for vector addition,

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A New Study from Harvard and Perplexity Finds AI Agents Perform 26 Minutes of Autonomous Work per Session vs 33 Seconds for Search

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Software engineering, Staff, Tech News, Technology, Uncategorized

A new working research from Perplexity and Harvard offers field evidence on what AI agents do to knowledge work. It draws on production data from two Perplexity products: Search and Computer. The setup is a natural comparison. Search is a conversational answer engine. Computer is an agent that plans and executes tasks end to end.

A New Study from Harvard and Perplexity Finds AI Agents Perform 26 Minutes of Autonomous Work per Session vs 33 Seconds for Search Read More »

ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Security, Staff, Tutorials

In this tutorial, we use the ClawHub Security Signals dataset to examine how different security scanners assess AI skills and related files. We load the dataset directly from the Hugging Face Parquet conversion to avoid compatibility issues with newer dataset metadata, then inspect the main columns, verdict distribution, scanner outputs, and severity labels. After exploring

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Xiaomi MiMo and TileRT Push a 1-Trillion-Parameter Model Past 1000 Tokens Per Second on Commodity GPUs

ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, New Releases, Software engineering, Staff, Tech News, Technology, Uncategorized

Inference speed is becoming a competitive metric for large language models. Xiaomi’s MiMo team just released MiMo-V2.5-Pro-UltraSpeed, built in collaboration with the TileRT systems group. It decodes faster than 1000 tokens per second on a 1-trillion-parameter model. Xiaomi team describes this as a first at trillion-parameter scale. Demos show generation peaks near 1200 tokens per

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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, ai, AI (Artificial Intelligence), Artificial Intelligence, Audio Language Model, Editors Pick, Language Model, New Releases, Software engineering, Staff, Technology, Voice AI

Last week Microsoft AI has announced MAI-Transcribe-1.5. It is the second iteration of the company’s in-house speech-to-text family. The model targets accuracy across 43 languages, accents, and noisy environments. The Microsoft team positions it for production transcription workloads. What is MAI-Transcribe-1.5 MAI-Transcribe-1.5 is an automatic speech recognition (ASR) model. It takes audio as input and

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 Read More »

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

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, New Releases, RAG, Technology

Google Research team has introduced a new agentic RAG framework. It is built into the Gemini Enterprise Agent Platform. It powers a feature called Cross-Corpus Retrieval, now in public preview. The target is a known failure mode in enterprise search. Standard single-step RAG was not built for multi-source, multi-hop queries. Ask “What are the specs

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

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve the way a language model solves arithmetic word problems. We begin with a weak seed prompt, create a small deterministic benchmark, define a structured evaluator, and pass actionable feedback to GEPA so it can understand why a candidate prompt fails. We also

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

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

agentic ai, ai, AI (Artificial Intelligence), AI Tool, AI Tools Club, Applications, Artificial Intelligence, Editors Pick, Software engineering, Staff, Tech News, Technology, Top, Uncategorized

Low-code and no-code platforms have moved from simple drag-and-drop builders to AI-native development environments. In 2026, most of them ship a built-in assistant that turns a text prompt into a working app, agent, or automation. This list covers 21 tools that AI practitioners use today, grouped by what they do best. Each tool name links

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