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AI Model Optimization

5 articles · Page 1 of 1
IT Services Circle
IT Services Circle
Aug 9, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code System Prompts Without Dropping Performance

Anthropic removed more than 80% of the system prompts for Claude Code (Claude 5), yet benchmark scores stayed stable, prompting a deep dive into why overly strict rules hurt the model, how progressive disclosure and context‑engineered prompts improve efficiency, and what this means for prompt design and skill usage.

AI Model OptimizationClaudeContext Management
0 likes · 16 min read
Why Anthropic Cut 80% of Claude Code System Prompts Without Dropping Performance
DeWu Technology
DeWu Technology
May 8, 2026 · Interview Experience

From Rookie to Tech Pro in One Year: How Two New Engineers Mastered Real-World Projects

The article interviews two engineers who joined DeWu a year ago, revealing how structured mentorship, hands‑on project ownership, AI model optimization, and a supportive learning ecosystem accelerated their growth from beginners to contributors capable of leading complex technical initiatives.

AI Model OptimizationSoftware Engineeringcareer growth
0 likes · 13 min read
From Rookie to Tech Pro in One Year: How Two New Engineers Mastered Real-World Projects
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 16, 2025 · Artificial Intelligence

How CosyVoice 2.0 Cuts First‑Chunk Latency for High‑Fidelity Voice Cloning

CosyVoice 2.0, Alibaba DAMO Academy's next‑gen high‑fidelity speech synthesis model, introduces architecture decoupling, streaming generation, reference‑audio caching and dynamic load balancing to dramatically reduce first‑packet latency and improve real‑time factor while supporting multi‑language voice cloning.

AI Model Optimizationlow latencyspeech cloning
0 likes · 9 min read
How CosyVoice 2.0 Cuts First‑Chunk Latency for High‑Fidelity Voice Cloning
DataFunSummit
DataFunSummit
Sep 1, 2025 · Artificial Intelligence

How We Cut ERNIE Model Resource Use by 75% with Pruning, Structured Slimming, and ONNX Runtime

In this detailed engineering guide we diagnose a heavyweight ERNIE‑Base text‑classification service consuming 128 CPU cores and 96 GB RAM, then apply a three‑step optimization—model selection, structured pruning with PaddleSlim, and engine migration to ONNX Runtime—achieving a 75% reduction in resource usage while keeping recall above 99.5% and boosting inference speed by over 20%.

AI Model OptimizationModel PruningONNX Runtime
0 likes · 11 min read
How We Cut ERNIE Model Resource Use by 75% with Pruning, Structured Slimming, and ONNX Runtime
Data Thinking Notes
Data Thinking Notes
Feb 20, 2025 · Artificial Intelligence

How to Deploy DeepSeek R1 671B Model Locally with Ollama: A Step‑by‑Step Guide

This article provides a comprehensive tutorial on locally deploying the 671‑billion‑parameter DeepSeek R1 model using Ollama, covering model selection, hardware requirements, dynamic quantization, detailed installation steps, performance observations, and practical recommendations for consumer‑grade hardware.

AI Model OptimizationDeepSeekDynamic Quantization
0 likes · 14 min read
How to Deploy DeepSeek R1 671B Model Locally with Ollama: A Step‑by‑Step Guide