Aviva deploys AI to stop £230M in sophisticated insurance fraud
Aviva has uncovered a record £230 million in insurance fraud claims and is using AI tools to counter the growing problem. The battleground has changed, and the culprits are also coming armed with a new generation of tools. We’re now in an environment where AI is being used not just to defend against fraud, but […]
Weis Markets adds Instacart AI-powered shopping carts to stores
Weis Markets is adding Instacart’s AI-powered shopping carts, Caper Carts, to select stores in Pennsylvania, bringing digital coupons, loyalty features, and repeat-purchase recommendations into the grocery aisle. The Pennsylvania-based grocery chain is working with Instacart to deploy the smart carts, which include cameras, certified scales, location systems, and a touchscreen. According to Instacart, Caper Carts […]
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
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 […]
Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries
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 […]
Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
def make_problems(n, seed=0): rng = random.Random(seed) out = [] for _ in range(n): t = rng.choice([“discount”, “travel”, “wallet”, “chain”]) if t == “discount”: unit = rng.choice([40, 60, 80, 120]) qty = rng.choice([5, 6, 8, 10]) disc = rng.choice([10, 20, 25, 50]) total = unit * qty gold = total – total * disc // 100 […]
We Should Train AI to Betray Its Users
The dilemma employee at an engineering company but have uncovered a deadly secret. Your company is performing ill-advised engineering activities that have already killed six contractors in a landslide. Despite this the company is pressing ahead, creating risks of further landslides, a catastrophic dam breach and/ or groundwater contamination. Instead of dealing with the problem, […]
Building a Multi-Agent System in Python
are the talk of the town. We see them everywhere, even being used for the simplest of tasks on our phones. They are convenient, fast, and pretty much reliable, and help us navigate day-to-day life. If you want an easy explanation of a scientific concept, you ask ChatGPT. You want a guide for your picky […]
Best 21 Low-Code and No-Code AI Tools in 2026
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 […]
Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b
Most search agents are trained as policies over a growing transcript. The model decides how to search. It must also remember what it saw, which evidence matters, and which claims it checked. A team of researchers from University of Illinois Urbana-Champaign, UC Berkeley, and Chroma argues this asks too much. Reinforcement learning ends up optimizing […]
NVIDIA garak Tutorial: Build a Complete Defensive LLM Red-Teaming Workflow with Custom Probes and Detectors
print(“\n########## 5. ANALYSIS ##########”) import numpy as np, pandas as pd def find_latest_report(): cands = [] for base in [os.path.expanduser(“~/.local/share/garak/garak_runs”), os.path.expanduser(“~/.cache/garak”), “.”]: cands += glob.glob(os.path.join(base, “**”, “*report.jsonl”), recursive=True) cands = [c for c in cands if os.path.getsize(c) > 0] return max(cands, key=os.path.getmtime) if cands else None report_path = report_path or find_latest_report() print(“Analysing:”, report_path) evaluations = […]