NVIDIA Researchers released PersonaPlex-7B-v1, a full duplex speech to speech conversational model that targets natural voice interactions with precise persona control. From ASR→LLM→TTS to a single full duplex model Conventional voice assistants usually run a cascade. Automatic Speech Recognition (ASR) converts speech to text, a language model generates a text answer, and Text to Speech (TTS) converts back to audio. Each stage adds latency, and the pipeline cannot handle overlapping speech, natural interruptions, or dense backchannels. PersonaPlex replaces this stack with a single Transformer model that performs streaming speech understanding and speech generation in one network. The model operates on…

In this tutorial, we build an advanced agentic AI workflow using LlamaIndex and OpenAI models. We focus on designing a reliable retrieval-augmented generation (RAG) agent that can reason over evidence, use tools deliberately, and evaluate its own outputs for quality. By structuring the system around retrieval, answer synthesis, and self-evaluation, we demonstrate how agentic patterns go beyond simple chatbots and move toward more trustworthy, controllable AI systems suitable for research and analytical use cases. !pip -q install -U llama-index llama-index-llms-openai llama-index-embeddings-openai nest_asyncio import os import asyncio import nest_asyncio nest_asyncio.apply() from getpass import getpass if not os.environ.get("OPENAI_API_KEY"): os.environ["OPENAI_API_KEY"] = getpass("Enter OPENAI_API_KEY:…

Black Forest Labs releases FLUX.2 [klein], a compact image model family that targets interactive visual intelligence on consumer hardware. FLUX.2 [klein] extends the FLUX.2 line with sub second generation and editing, a unified architecture for text to image and image to image, and deployment options that range from local GPUs to cloud APIs, while keeping state of the art image quality. From FLUX.2 [dev] to interactive visual intelligence FLUX.2 [dev] is a 32 billion parameter rectified flow transformer for text conditioned image generation and editing, including composition with multiple reference images, and runs mainly on data center class accelerators. It…

def _now_iso() -> str: return datetime.utcnow().replace(microsecond=0).isoformat() + "Z" def _stable_id(prefix: str, seed: str) -> str: h = hashlib.sha256(seed.encode("utf-8")).hexdigest()[:10] return f"{prefix}_{h}" class MockEHR: def __init__(self): self.orders_queue: List[SurgeryOrder] = [] self.patient_docs: Dict[str, List[ClinicalDocument]] = {} def seed_data(self, n_orders: int = 5): random.seed(7) def make_patient(i: int) -> Patient: pid = f"PT{i:04d}" plan = random.choice(list(InsurancePlan)) return Patient( patient_id=pid, name=f"Patient {i}", dob="1980-01-01", member_id=f"M{i:08d}", plan=plan, ) def docs_for_order(patient: Patient, surgery: SurgeryType) -> List[ClinicalDocument]: base = [ ClinicalDocument( doc_id=_stable_id("DOC", patient.patient_id + "H&P"), doc_type=DocType.H_AND_P, created_at=_now_iso(), content="H&P: Relevant history, exam findings, and surgical indication.", source="EHR", ), ClinicalDocument( doc_id=_stable_id("DOC", patient.patient_id + "NOTE"), doc_type=DocType.CLINICAL_NOTE, created_at=_now_iso(), content="Clinical note: Symptoms, conservative management attempted,…

Google AI has released TranslateGemma, a suite of open machine translation models built on Gemma 3 and targeted at 55 languages. The family comes in 4B, 12B and 27B parameter sizes. It is designed to run across devices from mobile and edge hardware to laptops and a single H100 GPU or TPU instance in the cloud. TranslateGemma is not a separate architecture. It is Gemma 3 specialized for translation through a two stage post training pipeline. (1) supervised fine tuning on large parallel corpora. (2) Reinforcement learning that optimizes translation quality with a multi signal reward ensemble. The goal is…

As context lengths move into tens and hundreds of thousands of tokens, the key value cache in transformer decoders becomes a primary deployment bottleneck. The cache stores keys and values for every layer and head with shape (2, L, H, T, D). For a vanilla transformer such as Llama1-65B, the cache reaches about 335 GB at 128k tokens in bfloat16, which directly limits batch size and increases time to first token. https://arxiv.org/pdf/2601.07891 Architectural compression leaves the sequence axis untouched Production models already compress the cache along several axes. Grouped Query Attention shares keys and values across multiple queries and yields…

Transformers use attention and Mixture-of-Experts to scale computation, but they still lack a native way to perform knowledge lookup. They re-compute the same local patterns again and again, which wastes depth and FLOPs. DeepSeek’s new Engram module targets exactly this gap by adding a conditional memory axis that works alongside MoE rather than replacing it. At a high level, Engram modernizes classic N gram embeddings and turns them into a scalable, O(1) lookup memory that plugs directly into the Transformer backbone. The result is a parametric memory that stores static patterns such as common phrases and entities, while the backbone…

In this tutorial, we build a clean, advanced demonstration of modern MCP design by focusing on three core ideas: stateless communication, strict SDK-level validation, and asynchronous, long-running operations. We implement a minimal MCP-like protocol using structured envelopes, signed requests, and Pydantic-validated tools to show how agents and services can interact safely without relying on persistent sessions. Check out the FULL CODES here. import asyncio, time, json, uuid, hmac, hashlib from dataclasses import dataclass from typing import Any, Dict, Optional, Literal, List from pydantic import BaseModel, Field, ValidationError, ConfigDict def _now_ms(): return int(time.time() * 1000) def _uuid(): return str(uuid.uuid4()) def _canonical_json(obj): return…

Google Research has expanded its Health AI Developer Foundations program (HAI-DEF) with the release of MedGemma-1.5. The model is released as open starting points for developers who want to build medical imaging, text and speech systems and then adapt them to local workflows and regulations. https://research.google/blog/next-generation-medical-image-interpretation-with-medgemma-15-and-medical-speech-to-text-with-medasr/ MedGemma 1.5, small multimodal model for real clinical data MedGemma is a family of medical generative models built on Gemma. The new release, MedGemma-1.5-4B, targets developers who need a compact model that can still handle real clinical data. The previous MedGemma-1-27B model remains available for more demanding text heavy use cases. MedGemma-1.5-4B is multimodal.…

In this tutorial, we build an advanced, multi-turn crescendo-style red-teaming harness using Garak to evaluate how large language models behave under gradual conversational pressure. We implement a custom iterative probe and a lightweight detector to simulate realistic escalation patterns in which benign prompts slowly pivot toward sensitive requests, and we assess whether the model maintains its safety boundaries across turns. Also, we focus on practical, reproducible evaluation of multi-turn robustness rather than single-prompt failures. Check out the FULL CODES here. import os, sys, subprocess, json, glob, re from pathlib import Path from datetime import datetime, timezone subprocess.run( [sys.executable, "-m", "pip", "install",…