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AI Engineer Programming
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AI Engineer Programming

In the AI era, defining problems is often more important than solving them; here we explore AI's contradictions, boundaries, and possibilities.

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Latest from AI Engineer Programming

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AI Engineer Programming
AI Engineer Programming
Jul 15, 2026 · Backend Development

Choosing the Right Protocol for AI Streaming: SSE, Chunked, or WebSocket (Part 1)

This article analyzes three HTTP‑based streaming approaches—Server‑Sent Events (SSE), HTTP Chunked Transfer Encoding, and WebSocket—detailing their underlying mechanisms, implementation steps, pros and cons, and ideal use cases to help developers select the most suitable protocol for AI‑driven real‑time output.

AI streamingHTTP ChunkedSSE
0 likes · 17 min read
Choosing the Right Protocol for AI Streaming: SSE, Chunked, or WebSocket (Part 1)
AI Engineer Programming
AI Engineer Programming
Jul 14, 2026 · Backend Development

How vLLM Generates Tokens: A Deep Dive into the Source Code

This article walks through vLLM’s token‑generation pipeline by dissecting its source files, process roles, ZMQ communication, scheduling, and deployment considerations, revealing how prompts become streamed token IDs and highlighting the key factors that limit concurrency and performance.

GPU memoryLLM inferenceZMQ
0 likes · 17 min read
How vLLM Generates Tokens: A Deep Dive into the Source Code
AI Engineer Programming
AI Engineer Programming
Jul 13, 2026 · Artificial Intelligence

Top AI Agent Frameworks for 2026: Which One Fits Your Stack?

This guide evaluates seven AI agent frameworks—LangChain, CrewAI, Microsoft Agent Framework, LlamaIndex Workflows, Google ADK, OpenAI Agents SDK, and Mastra—across prototype speed, production reliability, observability, ecosystem integration, and pricing to help developers choose the best fit for their tech stack.

AI AgentsCrewAIFramework Comparison
0 likes · 38 min read
Top AI Agent Frameworks for 2026: Which One Fits Your Stack?
AI Engineer Programming
AI Engineer Programming
Jul 12, 2026 · Artificial Intelligence

Building a Full-Agent Observability and Quality Evaluation System: From Data Collection to the Data Flywheel

This article presents a comprehensive, engineering‑focused practice for observing and evaluating large‑model agents, covering new data‑collection challenges, a three‑layer observability architecture, offline and online testing pipelines, quality‑gate mechanisms, and a self‑reinforcing data flywheel that continuously improves performance, cost, and safety.

AIOpsAgentData Flywheel
0 likes · 18 min read
Building a Full-Agent Observability and Quality Evaluation System: From Data Collection to the Data Flywheel
AI Engineer Programming
AI Engineer Programming
Jul 11, 2026 · Operations

Building an Observability Platform for LLM Agents with OpenTelemetry

This article explains why LLM agents need a dedicated observability platform, introduces OpenTelemetry’s core concepts and architecture, shows how to manually instrument Python code, enable automatic instrumentation, configure the Collector, handle common distributed‑system pitfalls, and extend OTel with agent‑specific semantics and evaluation loops.

CollectorLLM agentObservability
0 likes · 20 min read
Building an Observability Platform for LLM Agents with OpenTelemetry
AI Engineer Programming
AI Engineer Programming
Jul 9, 2026 · Industry Insights

What Is an FDE? A Deep Dive into the Role Bridging Tech, Business & Ops

The article provides a comprehensive analysis of the emerging FDE role, detailing its definition as a translation officer that combines technical implementation, business translation, and on‑site delivery to bridge the four‑layer gap between mature AI technology and real‑world enterprise adoption.

AI DeploymentAI ImplementationBusiness translation
0 likes · 18 min read
What Is an FDE? A Deep Dive into the Role Bridging Tech, Business & Ops
AI Engineer Programming
AI Engineer Programming
Jul 7, 2026 · Artificial Intelligence

LlamaIndex RAG Retrieval: A Three‑Stage Pipeline (Pre‑, During‑, and Post‑Retrieval)

The article breaks down LlamaIndex RAG into three production stages—pre‑retrieval (metadata filtering, document constraints, query transformation), during retrieval (vector‑store ANN/hybrid/MMR, query modes, embedding consistency, extended retrievers) and post‑retrieval (filtering, reranking, context expansion, layout and recency handling)—and provides concrete code snippets, component choices, and practical recommendations for each stage.

LlamaIndexRAGhybrid search
0 likes · 15 min read
LlamaIndex RAG Retrieval: A Three‑Stage Pipeline (Pre‑, During‑, and Post‑Retrieval)
AI Engineer Programming
AI Engineer Programming
Jul 7, 2026 · Industry Insights

Why Do Tech Giants Still Dominate Even When AI Makes Code Almost Free?

Although AI has driven the marginal cost of writing code close to zero, the dominant expenses of distribution, trust, support and liability remain unchanged, leading to a hollowed‑out middle layer where independent developers thrive on action independence while giants retain advantage through scale and integration.

AIOpen Sourcedeveloper ecosystem
0 likes · 10 min read
Why Do Tech Giants Still Dominate Even When AI Makes Code Almost Free?
AI Engineer Programming
AI Engineer Programming
Jul 5, 2026 · Artificial Intelligence

Will Stronger Models Render Harnesses Obsolete? (Part 2)

The article analyzes how advancing model capabilities are displacing traditional Harness components such as Context Reset and Sprint Contract, outlines which Harness functions remain essential, and offers engineering practices for co‑evolving Harnesses with ever‑more capable AI agents.

AI EngineeringAgent FrameworkHarness
0 likes · 14 min read
Will Stronger Models Render Harnesses Obsolete? (Part 2)