Browsing: LLM

A humorous-but-real tour of SwarmKV — KV-snapshot fan-out, copy-on-fork host buffers, and how to make a two-agent analytical pipeline ~1.95× faster (and the second branch’s activation…

print(“\n########## 5. ANALYSIS ##########”) import numpy as np, pandas as pd def find_latest_report(): cands = [] for base in [os.path.expanduser(“~/.local/share/garak/garak_runs”), os.path.expanduser(“~/.cache/garak”), “.”]: cands += glob.glob(os.path.join(base, “**”,…

Image created by Serj Smorodinsky, co-author of Building LLM Applications with DSPy we’ve probably all had the experience of getting responses that weren’t quite what we…

locally sounds straightforward. Download the weights, start the server, and send requests. That works for a chatbot, but it doesn’t automatically work for an agent. In…

Speculative decoding is a technique for speeding up large language model inference. A small, fast draft model proposes several tokens. The large target model verifies them…

Large language models become static after pretraining. Their knowledge does not update as the world changes. Retraining a full LLM is too expensive at modern scales.…

Long-context inference makes the KV cache one of the main costs of serving LLMs. During autoregressive decoding, the cache grows with context length, batch size, and…

Introduction an agentic AI network for my company that advises manufacturing plants on how to mature their operations. The system was designed to be data-driven, allowing…

themes from a call corpus to the customer table. Customers without transcripts get NULL. NULL gets filled with zero, or with “no issue mentioned,” or quietly…

TL;DR a full working implementation in pure Python, with real benchmark numbers. Most teams evaluate LLM responses by reading them and guessing. That breaks the moment…