Browsing: LLM

  # Introduction  JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead…

Inference efficiency has quietly become one of the most consequential bottlenecks in AI deployment. As agentic coding systems such as Claude Code, Codex, and Cursor scale…

The fundamental tension in conversational AI has always been a binary choice: respond fast or respond smart. Real-time speech-to-speech (S2S) models — the kind that power…

import subprocess, sys subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, “-U”, “torchao>=0.16”, “trl>=0.20”, “transformers>=4.45”, “datasets”, “peft>=0.13”, “accelerate”, “bitsandbytes”, ]) import sys as _sys for _m in [m for…

Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe…

As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in…

(WORK_DIR / “judge.prompty”).write_text(“””— name: Judge model: api: chat configuration: type: openai connection: open_ai_connection model: gpt-4o-mini parameters: temperature: 0 max_tokens: 150 response_format: {type: json_object} inputs: question: {type:…

Technology that’s meant to simplify our lives can lead us to give up all privacy at home. Most smart speakers rely on the cloud, where every…

@dataclass class MemoryItem: memory_id: int topic: str entity: str slot: str value: str text: str def build_memory_bank() -> List[MemoryItem]: entities = [ { “entity”: “Astra”, “topic”:…

Image by Author   # Introduction  Building large language model (LLM) applications is very different from using consumer-facing tools like Claude Code, ChatGPT, or Codex. Those products…