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 […]
banner(“§12 CLAUDE.md”) sh(“repowise generate-claude-md”) md = TARGET / “CLAUDE.md” if md.exists(): print(md.read_text()[:4000]) banner(“§13 MCP tools via CLI”) base = [ (“get_dead_code”, “repowise dead-code –safe-only”), (“search_codebase”, ‘repowise search “timestamp expiry validation”‘), ] llm_only = [ (“get_overview”, […]
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New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run. For the last two […]
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