XAI Launches Standalone Grok Speech-to-Text and Text-to-Speech APIs, Targeting Enterprise Voice Developers
Elon Musk’s xAI has launched two standalone audio APIs—Grok Speech‑to‑Text (STT) and Grok Text‑to‑Speech (TTS)—built on the same infrastructure that powers Grok Voice in Tesla vehicles and Starlink support. The STT API offers batch and streaming transcription in 25 languages, speaker diarization, word‑level timestamps and costs $0.10 per hour for batch and $0.20 for streaming, while the TTS API provides five expressive voices across 20 languages at $4.20 per million characters. In internal benchmarks, Grok STT achieved a 5.0% error rate on phone‑call entity recognition, substantially lower than ElevenLabs, Deepgram and AssemblyAI. The launch positions xAI directly against established speech‑API providers and targets enterprise voice developers.
A Coding Guide for Property-Based Testing Using Hypothesis with Stateful, Differential, and Metamorphic Test Design
The MarkTechPost tutorial demonstrates how to build a full‑stack property‑based testing suite with Hypothesis, covering invariants, differential, metamorphic, targeted, and stateful testing. It walks through utility functions, custom parsers, statistical checks, and a rule‑based state machine that models a simple...
Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale
Google AI researchers unveiled Auto-Diagnose, an LLM‑powered system that reads integration‑test logs, isolates the root cause, and posts a concise diagnosis to the code review. In a manual study of 71 real‑world failures across 39 teams, it identified the correct...
Top 19 AI Red Teaming Tools (2026): Secure Your ML Models
The article outlines AI red teaming as a systematic approach to probe machine‑learning and generative AI models for hidden vulnerabilities such as prompt injection, data poisoning, and bias exploitation. It lists 19 leading tools for 2026, ranging from open‑source libraries...
A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency...
The tutorial walks readers through building a production‑grade background task system using Huey with a SQLite backend, avoiding external services like Redis. It sets up a threaded consumer in a notebook, defines tasks with priorities, retries, locking, and pipelines, and...
Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities
Alibaba’s Qwen team has open‑sourced Qwen3.6-35B-A3B, a 35‑billion‑parameter vision‑language model that activates only 3 billion parameters per inference thanks to a Sparse Mixture‑of‑Experts design. The architecture uses 256 experts with eight routed per token, linear‑attention Gated DeltaNet blocks and Grouped Query...
RightNow AI Releases AutoKernel: An Open-Source Framework that Applies an Autonomous Agent Loop to GPU Kernel Optimization for Arbitrary PyTorch...
RightNow AI unveiled AutoKernel, an open‑source framework that uses an autonomous LLM‑driven loop to optimize GPU kernels for any PyTorch model. The system iteratively edits kernel code, benchmarks performance, and keeps or reverts changes, completing about 40 experiments per hour...
Meet MaxToki: The AI That Predicts How Your Cells Age — and What to Do About It
MaxToki is a transformer‑decoder foundation model trained on nearly one trillion single‑cell RNA‑seq tokens to predict how individual cells age over time. By encoding transcriptomes as ranked gene lists and extending context length to 16,384 tokens, it can infer the...
How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference
The MarkTechPost tutorial walks readers through building a full‑stack video object removal pipeline using Netflix’s open‑source VOID model combined with the CogVideoX inpainting backbone. It covers environment setup on Google Colab, secure token handling, downloading the 5‑billion‑parameter CogVideoX model and the...
Google DeepMind’s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts
Google DeepMind introduced AlphaEvolve, an evolutionary system that uses a Gemini 2.5 Pro LLM to rewrite the source code of multi‑agent reinforcement‑learning algorithms. Applied to Counterfactual Regret Minimization and Policy Space Response Oracles, the system discovered VAD‑CFR and SHOR‑PSRO, which...
TII Releases Falcon Perception: A 0.6B-Parameter Early-Fusion Transformer for Open-Vocabulary Grounding and Segmentation From Natural Language Prompts
The Technology Innovation Institute unveiled Falcon Perception, a 600‑million‑parameter dense transformer that fuses image patches and text tokens from the first layer, eliminating the traditional encoder‑decoder split. Using hybrid attention, 3D Rotary Positional Embeddings (GGROPE), and a Chain‑of‑Perception sequence, the...
Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use
Arcee AI unveiled Trinity Large Thinking, an open‑weight reasoning model released under the Apache 2.0 license. The 400 billion‑parameter sparse Mixture‑of‑Experts model activates only 13 billion parameters per token and supports a 262,144‑token context window, targeting long‑horizon autonomous agents and multi‑turn tool use....
IBM Releases Granite 4.0 3B Vision: A New Vision Language Model for Enterprise Grade Document Data Extraction
IBM unveiled Granite 4.0 3B Vision, a vision‑language model built as a 0.5 billion‑parameter LoRA adapter for its 3.5 billion‑parameter Granite 4.0 Micro language backbone. The model uses a SIGLIP‑based encoder with 384×384 patch tiling and a DeepStack architecture that injects visual tokens at eight transformer layers....
Z.ai Launches GLM-5V-Turbo: A Native Multimodal Vision Coding Model Optimized for OpenClaw and High-Capacity Agentic Engineering Workflows Everywhere
Z.ai unveiled GLM-5V-Turbo, a vision‑coding model that natively fuses images, video and document layouts into executable code. The model leverages a CogViT vision encoder and a Multi‑Token Prediction architecture to support a 200K context window and up to 128K output...
How to Build a Production-Ready Gemma 3 1B Instruct Generation AI Pipeline with Hugging Face Transformers, Chat Templates, and Colab...
The tutorial walks readers through building a production‑ready inference pipeline for Google DeepMind's Gemma 3 1B Instruct model using Hugging Face Transformers on Google Colab. It covers secure HF token authentication, automatic device and precision selection, loading the tokenizer and model, and creating reusable...