Search AI News
Find articles from 100+ AI sources
Found 42 results for "The AI Edge"
How LLM Distillation Trains Smaller Models
LLM distillation trains one language model using the behavior of another. The teacher supplies answers or…
How Quantization Compresses LLMs
LLM quantization represents a model’s numbers with fewer bits. Post-training quantization does this after…
How CUDA Graphs Speed Up LLM Inference
CUDA Graphs can speed up repeated GPU work by preparing its execution plan once and replaying it with new…
How LLM Rerankers Order Retrieved Passages
A reranker takes a question and a shortlist of retrieved passages, reads them again, and changes their order…
How Looped Transformers Reuse Their Weights
A looped Transformer reuses a group of neural-network layers several times before predicting the next token.…
How the KV Cache Shapes AI Agent Costs
An AI agent can spend most of a model call reading a history it has already processed. The KV cache preserves…
How Hybrid LLMs Combine Memory, Attention, and Experts
A hybrid LLM processes a sequence with more than one kind of memory. Some layers update a compact running…
How Persistent Agents Keep Working
A persistent agent keeps a task moving after the model's current context, the worker process, or the…
How Self-Improving Agents Learn from Experience
A self-improving agent uses evidence from earlier tasks to change something retained for later tasks, then…
How DeepSeek V4.1 Flash Separates Reading and Generation
DeepSeek V4.1 Flash generates text one token at a time. A token is a piece of text, often a word or part of…
Build a Jev-Supervised AI Agent
An AI agent does more than write its final answer. It chooses a tool, reads the result, decides whether to…
How Jev Turns Questions Into Decisions
Jev is a decision model whose interface asks bounded questions about a supplied state and returns…
The Complete Guide to Evaluation Pipelines for AI Agents
An agent can say that a refund is complete while the customer's account shows no refund. For an agent that…
How AI Agent Memory Updates a Changed Preference
An AI coding assistant can remember that you use Python for deployment scripts and still get the next…
How LLM Routers Balance Cache Reuse and Queue Time
An LLM request router sits in front of several replicas of the same model and chooses which replica should…
How Test-Time Scaling Allocates LLM Reasoning Across Depth, Width, and Feedback
Test-time compute is the work an already-trained model performs for one request while producing, checking, or…
How Constrained Decoding Makes LLM Outputs Follow a Schema
An LLM can return JSON that parses perfectly, matches every required field, and is still dangerous to…
How Sparse Attention Makes Long-Context LLMs Cheaper, and What It Misses
An LLM can accept a very long prompt without treating every earlier piece the same way. The model represents…
Four Retrieval Techniques for Video Search
Video looks like one input, but an AI cannot inspect it as one indivisible object. A two-hour recording is a…
Deep Dive: How AI Text Watermarks Work
An AI text watermark leaves detectable evidence in the words a model chooses. During generation, a secret…
Deep Dive: How Speculative Decoding Makes LLMs Faster
Speculative decoding can make a coding assistant respond faster by having a small model propose several…
AI Digest: Model Validation, Agents, and Generative AI
This edition explores how a model can pass validation for the wrong reason, how an agent can earn a reward…
AdalFlow: A PyTorch-Like Framework to Auto-Optimizing Prompt for your LLM agent
AI Agent frameworks are becoming just as important as model training itself! I am excited to introduce you to…
Last Week to Register: Build Production-Ready Agentic-RAG Applications From Scratch Course!
This is the last week to register for the Build Production-Ready Agentic-RAG Applications From Scratch…