The reason enterprises have been slow to connect AI agents to internal APIs and databases isn't the models — it's the credentials. In most production deployments, the agent carries authentication tokens with it as it […]
Although visitors to an event like TechEx North America will always want to see the cutting edge front and centre stage, the nuance and detail brought to the show by the speakers and exhibitors mean […]
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Pretraining frontier-scale LLMs in FP8 is now standard practice, but moving to 4-bit floating point has remained an open research problem because narrower formats compress dynamic range and amplify quantization error at long token horizons. […]
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via […]
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import subprocess, sys def pip(*pkgs): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *pkgs]) pip(“llmcompressor”, “compressed-tensors”, “transformers>=4.45”, “accelerate”, “datasets”) import os, gc, time, json, math from pathlib import Path import torch from transformers import AutoModelForCausalLM, AutoTokenizer from datasets […]
For AI systems to keep improving in knowledge work, they need either a reliable mechanism for autonomous self-improvement or human evaluators capable of catching errors and generating high-quality feedback. The industry has invested enormously in […]
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Transitioning from controlled testing environments to live enterprise deployment is a very different proposition. A small-scale test might perform perfectly using carefully selected data sets, but deploying that ability in thousands of employees and interconnected […]
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