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A Beginner’s Reading List for Large Language Models for 2026
The large language models (LLMs) hype wave shows no sign of fading anytime soon: after all, LLMs keep reinventing themselves at a rapid pace and transforming the industry as a whole. Source link
7 Advanced Feature Engineering Tricks Using LLM Embeddings
You have mastered model. Source link

How to Build Advanced Quantum Algorithms Using Qrisp with Grover Search, Quantum Phase Estimation, and QAOA
In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum data, construct entangled states, and then progressively implement Grover’s search with automatic uncomputation, Quantum Phase Estimation, and a full QAOA workflow for the MaxCut…

SMART launches new Wearable Imaging for Transforming Elderly Care research group | MIT News
What if ultrasound imaging is no longer confined to hospitals? Patients with chronic conditions, such as hypertension and heart failure, could be monitored continuously in real-time at home or on the move, giving health care practitioners ongoing clinical insights instead of the occasional snapshots — a scan here and a check-up there. This shift…

How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks
In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style…

Google Releases Conductor: a context driven Gemini CLI extension that stores knowledge as Markdown and orchestrates agentic workflows
Google has introduced Conductor, an open source preview extension for Gemini CLI that turns AI code generation into a structured, context driven workflow. Conductor stores product knowledge, technical decisions, and work plans as versioned Markdown inside the repository, then drives Gemini agents from those files instead of ad hoc chat prompts. From chat based…
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