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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 28, 2026 · Artificial Intelligence

Control State vs Data State in AI Agents: From Turing Machines to LangGraph

This article explains the distinction between control state and data state in AI agent frameworks, tracing the concept from Turing machines through operating systems, databases, and compilers, and shows how LangGraph separates these states via a three‑layer architecture, code examples, and design guidelines.

AI AgentsLangGraphcontrol state
0 likes · 13 min read
Control State vs Data State in AI Agents: From Turing Machines to LangGraph
AI Engineer Programming
AI Engineer Programming
Jul 26, 2026 · Artificial Intelligence

Agent Development Lifecycle (ADLC): Vendor‑Neutral Guide to Build, Test, Deploy, Monitor, and Govern AI Agents

This note outlines a vendor‑agnostic Agent Development Lifecycle (ADLC) that extends traditional SDLC with five stages—Build, Test, Deploy, Monitor, and Govern—detailing layer‑wise tooling choices, evaluation strategies, deployment infrastructure, observability practices, and governance concerns for modern AI agents.

AI lifecycleGovernanceLLM
0 likes · 15 min read
Agent Development Lifecycle (ADLC): Vendor‑Neutral Guide to Build, Test, Deploy, Monitor, and Govern AI Agents
AI Engineer Programming
AI Engineer Programming
Jul 26, 2026 · Artificial Intelligence

Analyzing the grill‑me Agent Skills Repository: Making Probabilistic LLMs Deterministic

The article dissects Matt Pocock’s skills repository, explaining how a set of atomic, editable, composable Agent Skills—driven by structured grilling, shared vocabularies, TDD loops, and design checkpoints—turns the inherently probabilistic nature of LLM‑based programming into a repeatable, deterministic workflow while highlighting practical limits and best‑practice patterns.

AgentLLMWorkflow
0 likes · 22 min read
Analyzing the grill‑me Agent Skills Repository: Making Probabilistic LLMs Deterministic
AI Engineer Programming
AI Engineer Programming
Jul 24, 2026 · Industry Insights

Chinese LLMs H1 2024 Sprint: Qwen 3.8, K3, GLM 5.2, Hy3, M3, DeepSeek‑V4, MiMo‑V2.5

The first half of 2024 saw Chinese large‑language‑model providers launch a rapid series of trillion‑parameter and multimodal models—including Kimi K3, Qwen 3.8, DeepSeek‑V4 and others—while climbing to dominate most spots in global leaderboards, yet they now face deeper engineering and commercial challenges.

AI benchmarksChina AILarge Language Models
0 likes · 5 min read
Chinese LLMs H1 2024 Sprint: Qwen 3.8, K3, GLM 5.2, Hy3, M3, DeepSeek‑V4, MiMo‑V2.5
AI Engineer Programming
AI Engineer Programming
Jul 23, 2026 · R&D Management

When Companies Mandate AI-Only Coding, Budgets Override Real Productivity

A junior engineer recounts how his Norwegian tech firm first forced developers to code exclusively with AI, then imposed strict token budgets and bonuses, leading senior staff to abandon AI tools, revealing that financial incentives, not actual productivity gains, dominate corporate AI adoption decisions.

AI codingindustry trendsmanagement
0 likes · 7 min read
When Companies Mandate AI-Only Coding, Budgets Override Real Productivity
AI Engineer Programming
AI Engineer Programming
Jul 22, 2026 · Artificial Intelligence

Is Prompt Engineering Dead? A Deep Dive into Harness, Context Assembly, and Token Generation

The article examines why traditional prompt engineering is no longer sufficient in production AI systems, detailing how harness layers, context reassembly, tool orchestration, token generation methods, training objectives, and architecture choices transform a simple prompt into a complex, multi‑stage workflow that demands robust, system‑level design.

Context ManagementHarnessLLM
0 likes · 16 min read
Is Prompt Engineering Dead? A Deep Dive into Harness, Context Assembly, and Token Generation
AI Engineer Programming
AI Engineer Programming
Jul 21, 2026 · Artificial Intelligence

Understanding EOS, stop_token_ids, and stop_sequences in vLLM

This article dissects how vLLM handles generation termination by comparing token‑level EOS, token‑level stop_token_ids, and string‑level stop/stop_sequences, detailing where each check occurs, how they affect the Scheduler and Detokenizer, and how finish_reason and stop_reason are derived for the API response.

EOSLLMbackend
0 likes · 13 min read
Understanding EOS, stop_token_ids, and stop_sequences in vLLM
AI Engineer Programming
AI Engineer Programming
Jul 19, 2026 · Artificial Intelligence

Why Chat Templates Matter for LLMs: A Deep Dive into Jinja2‑Based Prompt Formatting

Large language models generate text autoregressively from flat token streams, but real‑world conversations require structured roles, system prompts, and multi‑turn history, so Hugging Face’s Jinja2‑driven chat templates serialize these elements, handle BOS/EOT tokens, enforce role alternation, and provide debugging tricks across Meta‑Llama‑3.1, Qwen2.5, and Mistral models.

ChatTemplateJinja2LLM
0 likes · 16 min read
Why Chat Templates Matter for LLMs: A Deep Dive into Jinja2‑Based Prompt Formatting
AI Engineer Programming
AI Engineer Programming
Jul 18, 2026 · Artificial Intelligence

13 Agentic AI Trends to Watch in 2026

The article analyzes thirteen emerging Agentic AI trends for 2026—including CLI agents, the resurgence of MCP, multi‑agent orchestration, agentic commerce, AI governance, personal assistants, context engineering, vertical agents, small language models, recursive LMs, real‑time web access, browser agents, and verifiability—backed by data, case studies, and industry reports.

AI GovernanceAgentic AICLI Agents
0 likes · 29 min read
13 Agentic AI Trends to Watch in 2026