#KV Cache optimization
378 related articles
·3 min
Building a DeepSeek Agent from Scratch: Principles, Token Optimization, and Bootstrapped Development
Learn how to build a DeepSeek V3 AI Agent from scratch with zero dependencies, covering Agent loop mechanics, token optimization, cache hit strategies, and bootstrapped development.
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·3 min
Firebase AI Logic Deep Dive: Hybrid Inference, Prompt Templates & Multi-Layer Security Deployment
Deep dive into Firebase AI Logic: server-side prompt templates to prevent leakage, Cloud Function triggers, four-layer security defense, AI monitoring with context caching for cost control, and cross-platform hybrid inference.
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·2 min
MiniMax M3 Launches on Fireworks: 512K Context and MSA Sparse Attention Explained
MiniMax M3 launches on Fireworks with 512K context and multimodal input. MSA sparse attention delivers 9x prefill and 15x decode speedups. Deep dive into architecture, pricing, and open-model competition.
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·3 min
Fireworks AI Launches Qwen 3.7 Plus: Zero Data Retention and 99.9% SLA for Enterprise Deployment
Fireworks AI launches Qwen 3.7 Plus with latency/throughput optimization, zero data retention, and 99.9% SLA enterprise guarantees. Explore the full-stack deployment solution for commercial open-source model inference.
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·2 min
Qwen3-Coder Deep Dive: A Coding Model Built for Long-Horizon Agent Loops
Deep dive into Qwen3-Coder: 11 hours continuous operation, 10K+ lines of code, 1000+ calls. Explore its long-horizon agent loop architecture, reasoning persistence, thinking mode switching, and deployment on Fireworks.
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·4 min
AI + Java Backend Learning Roadmap: Four Stages from CRUD to Senior AI Engineer
A complete AI + Java backend learning roadmap based on Spring AI Alibaba: from prompt engineering and LLM API integration to RAG knowledge bases and Agent systems across four stages.
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·2 min
The 4-Stage Roadmap for AI Application Development: A Career Transition Guide from Beginner to Senior Engineer
A 4-stage roadmap for AI application development: from Python and RAG basics to Agent cluster architecture, covering the core skills needed for career growth.
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·3 min
Fed Up with Every AI Coding Assistant, a Veteran Game Developer Built His Own Coding Agent
Veteran game dev Mario tried every AI coding tool including Claude Code, found them all lacking, and built Pi — a minimalist, extensible coding agent framework centered on developer control.
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·3 min
5 Ways to Deploy LLMs Locally: From Getting Started to Production
A complete guide to 5 local LLM deployment methods: LlamaCPP, Ollama, LM Studio, vLLM/SGLang, and MLX-LM — from personal dev to production environments.
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·4 min
5 Models Coding Showdown: Claude, GPT, DeepSeek, M3 — Who's the Best Engineering Tool?
Hands-on comparison of Claude Opus 4.8, GPT 5.5, MiniMax M3, DeepSeek V4 Pro, and Mimo 2.5 Pro across SVG drawing, 3D game generation, elevator scheduling, and real bug fixing.
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·2 min
DiffusionGemma: Google's Open-Source Diffusion Language Model Exceeding 500 Tokens/s
Google releases DiffusionGemma, an open-source diffusion language model with Apache 2.0 license. The 26B-parameter MoE model achieves over 500 tokens/s in real-world tests.
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·3 min
Claude Code with Local LLMs: Token-Free Deployment Guide & Configuration
Learn how to connect Claude Code to local LLMs for token-free AI coding. Covers three-layer architecture, Ollama/LM Studio/vLLM setup, protocol translation, and hardware selection.
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·2 min
A Complete Guide to LLM Infrastructure: Core Challenges from GPU Clusters to Inference Optimization
A deep dive into core challenges and key technologies for LLM infrastructure, covering GPU cluster management, inference optimization, distributed training, cost control, and observability.
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·3 min
LFM2.5 Local Deployment Hands-On: An 8B Parameter Model That Outperforms GPT-o3s in Tool Calling
Hands-on test of Liquid AI's LFM2.5 local deployment: architecture breakdown, 16GB VRAM troubleshooting, and GraphRAG tool-calling benchmarks vs GPT-o3s.
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·4 min
AI Engineer Job Search: The Capability Leap from Demo to Production System
AI job demand is surging but companies can't find qualified candidates. Learn the 3 core skills—advanced RAG, local model deployment, and full-stack monitoring—to leap from demo builder to production engineer.
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·3 min
Open-Source Models Keep Throwing Errors in Your Code? The Problem Might Be the Framework
DeepSeek and Kimi keep failing at coding? The problem may not be the model but the framework. Learn how Commander Code fixes this with cache routing, tool call repair, and continuous learning.
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·3 min
Hermes Agent Deep Dive: A Self-Evolving Agent Architecture with Practical Implementation Guide
Deep dive into Hermes Agent's core architecture: four-layer memory system, Skill self-evolution mechanism, Harness Engineering methodology, OpenCloud comparison, and Feishu integration tutorial.
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·2 min
Model Router Prism Integrates Fable 5: 30% Cost Reduction Without Quality Loss
AI model router Prism announces Fable 5 integration, achieving up to 30% cost reduction per task without quality loss through per-turn intelligent routing and cache-aware technology.
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·4 min
7 Context Management Tips to Save Tokens in AI Conversations
AI conversations getting worse over time? Master these 7 context management tips—including manual compression, cache rules, and streamlined instructions—to save tokens and boost Claude and GPT output quality.
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·3 min
Multi-Model Unified Orchestration Framework: Runtime Dynamic Switching Across Eight AI Services in Practice
Deep dive into a runtime AI chatbot integrator architecture covering unified orchestration of OpenAI, Claude, DeepSeek text models and 11Labs, Azure TTS services with latency testing and streaming synthesis.
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