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Generative AI
Google DeepMind Releases Gemma 4 QAT Checkpoints: Q4_0 and Mobile Format Cut On-Device Memory
Google DeepMind has released Quantization-Aware Training (QAT) test environments for the Gemma 4 family. The release targets local use on…
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Machine Learning
My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server
. The functions had grown too long and the variable names made no sense anymore. Every time I wanted feedback…
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Machine Learning
The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy
is often introduced through a long list of algorithms. SARSA, Q-learning, PPO, DQN, SAC etc. Each name seems to point…
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Machine Learning
Google AI announcements from May 2026
Find out what's possible with Gemini 3.5. We have introduced our latest family of models that combine frontier intelligence with…
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AGI
Building AI Agents and Workflows for Every Role Without Coding Through Smart Learning
Many experts are short on ideas. They are short on time. Much of the work day is lost in tasks…
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Machine Learning
Automate Writing Your LLM Prompts
Image created by Serj Smorodinsky, co-author of Building LLM Applications with DSPy we’ve probably all had the experience of getting…
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Machine Learning
How to Fine-tune SLM for Emotion Recognition
Introduction models (SLMs) are fine-tuned to categorize emotions as single outputs, capturing the entire emotional tone of a text. In…
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ANI
3 SpaCy Strategies for Effective Word Processing and Business Recognition
# Introduction Thanks especially to the current major language models, natural language processing (NLP) is the cornerstone of modern AI…
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Generative AI
NVIDIA AI Releases Dynamo Snapshot: A CRIU-Based Fast Startup System for AI Inference on Kubernetes
In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. Cold-starting inference workloads on Kubernetes can…
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Generative AI
Building a Semantic Search Engine and Open State Classifier on ResearchMath-14k Dataset
from sentence_transformers import util def search(query, k=5): q = model.encode([query], normalize_embeddings=True) sims = util.cos_sim(q, emb)[0].cpu().numpy() idx = sims.argsort()[::-1][:k] print(f'n=== Query:…
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