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A Coding Implementation to Build Bulletproof Agentic Workflows with PydanticAI Using Strict Schemas, Tool Injection, and Model-Agnostic Execution
In this tutorial, we build a production-ready agentic workflow that prioritizes reliability over best-effort generation by enforcing strict, typed outputs at every step. We use PydanticAI to define clear response schemas, wire in tools via dependency injection, and ensure the agent can safely interact with external systems, such as a database, without breaking execution.…

Exposing biases, moods, personalities, and abstract concepts hidden in large language models | MIT News
By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they’re far from simple answer-generators; they can also express abstract concepts, such as certain tones, personalities, biases, and moods. However, it’s not obvious exactly how these models represent abstract concepts to begin with from the knowledge they contain.Now…

Zyphra Releases ZUNA: A 380M-Parameter BCI Foundation Model for EEG Data, Advancing Noninvasive Thought-to-Text Development
Brain-computer interfaces (BCIs) are finally having their ‘foundation model’ moment. Zyphra, a research lab focused on large-scale models, recently released ZUNA, a 380M-parameter foundation model specifically for EEG signals. ZUNA is a masked diffusion auto-encoder designed to perform channel infilling and super-resolution for any electrode layout. This release includes weights under an Apache-2.0 license…

Parking-aware navigation system could prevent frustration and emissions | MIT News
It happens every day — a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.Most popular…

[Tutorial] Building a Visual Document Retrieval Pipeline with ColPali and Late Interaction Scoring
import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call([sys.executable, “-m”, “pip”] + cmd) pip([“uninstall”, “-y”, “pillow”, “PIL”, “torchaudio”, “colpali-engine”]) pip([“install”, “-q”, “–upgrade”, “pip”]) pip([“install”, “-q”, “pillow<12”, “torchaudio==2.8.0”]) pip([“install”, “-q”, “colpali-engine”, “pypdfium2”, “matplotlib”, “tqdm”, “requests”]) Source link

Tavus Launches Phoenix-4: A Gaussian-Diffusion Model Bringing Real-Time Emotional Intelligence And Sub-600ms Latency To Generative Video AI
The ‘uncanny valley’ is the final frontier for generative video. We have seen AI avatars that can talk, but they often lack the soul of human interaction. They suffer from stiff movements and a lack of emotional context. Tavus aims to fix this with the launch of Phoenix-4, a new generative AI model designed…
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