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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

53 recent articles

AI Engineer Programming

Apr 29, 2026 · Fundamentals

Balancing Core Stability and Extensibility: Design and Implementation of pi Agent’s Extension System

The article explains how the pi agent’s extension system resolves the tension between core stability and capability extensibility by using inversion of control, dependency injection, adapter and event‑driven patterns, two‑phase initialization, and concrete Python implementations, while comparing it with other plugin architectures.

AI AgentPythondependency injection

0 likes · 26 min read

Balancing Core Stability and Extensibility: Design and Implementation of pi Agent’s Extension System

AI Engineer Programming

Apr 28, 2026 · Artificial Intelligence

Image & Video Showdown: GPT Image 2 vs Nano Banana 2, Seedance 2.0 vs HappyHorse 1.0

The article compares Google’s Nano Banana 2 and OpenAI’s GPT Image 2 on the image track, and ByteDance’s Seedance 2.0 versus Alibaba’s HappyHorse 1.0 on the video track, detailing release dates, underlying technologies, resolution, text rendering accuracy, multilingual support, and platform access points.

AI image generationAI video generationGPT Image 2

0 likes · 5 min read

Image & Video Showdown: GPT Image 2 vs Nano Banana 2, Seedance 2.0 vs HappyHorse 1.0

AI Engineer Programming

Apr 26, 2026 · Artificial Intelligence

From Bag‑of‑Words to Semantics: How Embeddings Turn Meaning into Numbers (Part 2)

The article explains how embedding techniques encode semantic information into numeric vectors, covering Word2Vec and GloVe fundamentals, BERT anisotropy, SimCSE contrastive learning, alignment and uniformity metrics, ANN index structures such as HNSW, IVF and PQ, Matryoshka representation learning, practical deployment challenges, and evaluation best practices.

ANNBERTEmbedding

0 likes · 23 min read

From Bag‑of‑Words to Semantics: How Embeddings Turn Meaning into Numbers (Part 2)

AI Engineer Programming

Apr 26, 2026 · Artificial Intelligence

2026 AI Model API Prices – DeepSeek V4 Flash Costs Only 1% of GPT‑5.5

The article provides a detailed April 2026 comparison of API pricing for six major AI model families—including DeepSeek, GLM‑5.1, Kimi, Claude, GPT‑5.5, and Gemini—covering official and proxy channels, context limits, discount periods, peak‑time surcharges, and practical selection recommendations for developers.

AI Model PricingClaudeDeepSeek

0 likes · 11 min read

2026 AI Model API Prices – DeepSeek V4 Flash Costs Only 1% of GPT‑5.5

AI Engineer Programming

Apr 25, 2026 · Artificial Intelligence

Quantization Across Signal Processing, AI Inference, and RAG Vector Search

This article explains how quantization—originating from signal processing—reduces precision to save resources, details its application to neural network weights and activations via PTQ, QAT, GPTQ, AWQ, and SmoothQuant, and shows how vector quantization enables fast, memory‑efficient retrieval in large‑scale RAG systems.

AWQGPTQLLM

0 likes · 19 min read

Quantization Across Signal Processing, AI Inference, and RAG Vector Search

AI Engineer Programming

Apr 24, 2026 · Artificial Intelligence

From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design

The article traces the shift from Prompt Engineering to Context Engineering and now Harness Engineering, analyzing their origins, methods, limitations, and future directions such as Coordination, Intent, Ecosystem, and Cognition engineering, while emphasizing the decreasing human involvement and increasing system autonomy.

AI agentsAgent SystemsHarness Engineering

0 likes · 24 min read

From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design

AI Engineer Programming

Apr 23, 2026 · Artificial Intelligence

From Zero to One: A Roadmap for Building Trustworthy AI Agent Evaluations

The article outlines why rigorous, automated evaluation is essential for AI agents, defines core concepts such as tasks, trials, graders, and frameworks, compares code‑based, model‑based and human graders, and presents an eight‑step roadmap—from early testing to open‑source maintenance—to create reliable, scalable agent assessments.

AI agentsLLM gradingagent development

0 likes · 22 min read

From Zero to One: A Roadmap for Building Trustworthy AI Agent Evaluations

AI Engineer Programming

Apr 22, 2026 · Artificial Intelligence

Free LLM API Tokens: Complete Provider List, Limits, and Usage Tips

This guide compiles free large‑language‑model APIs from official vendors and third‑party platforms, detailing each service's token quotas, rate limits, base URLs, usage restrictions, and available models, while offering practical advice on token optimization, multi‑platform rotation, rate‑limit handling, and key security.

AIFree APILLM

0 likes · 15 min read

Free LLM API Tokens: Complete Provider List, Limits, and Usage Tips

AI Engineer Programming

Apr 21, 2026 · Artificial Intelligence

From Bag‑of‑Words to Semantic Vectors: Understanding Embeddings and Similarity Search (Part 1)

The article explains how diverse data can be represented as high‑dimensional vectors, describes exact and approximate nearest‑neighbor search, explores vector quantization, product quantization, locality‑sensitive hashing, and HNSW graphs, and analyzes their speed, accuracy, and memory trade‑offs for large‑scale similarity retrieval.

HNSWLSHVector Search

0 likes · 16 min read

From Bag‑of‑Words to Semantic Vectors: Understanding Embeddings and Similarity Search (Part 1)

AI Engineer Programming

Apr 20, 2026 · Artificial Intelligence

Evaluating Retriever Quality in RAG: Essential Metrics for Production Reliability

The article explains why retrieval quality dominates RAG performance and outlines a rigorous evaluation framework—including prompt, ranked results, and ground‑truth annotations—and detailed metrics such as Precision, Recall, MAP@K, NDCG@K, MRR, and F‑scores, while discussing chunking strategies, embedding choices, hybrid retrieval, and CI/CD‑driven monitoring to ensure production reliability.

LLMMAPNDCG

0 likes · 12 min read

Evaluating Retriever Quality in RAG: Essential Metrics for Production Reliability