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A structured playbook for the 2026 campus hiring season in China. Covers timeline, big tech intern vs full-time differences, resume strategy, algorithm prep, and how AI is changing the screening process.
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Understanding the memory systems that make AI coding agents effective—from context windows to persistent memory, from automatic consolidation to retrieval-augmented recall.
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An in-depth analysis of how Claude Code implements AI control, behavioral guardrails, and safe autonomy. Learn the engineering patterns behind production-grade coding agents from leaked source code insights.
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Why how you structure, prune, and manage conversation context determines whether your AI interview assistant helps or hurts your performance—and the engineering principles behind it.
Read articlearrow_forwardWhy Claude Code uses regex for sentiment detection, XML as internal protocol, and structured outputs as tools—lessons on why deterministic engineering beats probabilistic AI for control problems.
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From 2024's prompt engineering hype to 2026's harness discipline—why AI control, guardrail design, and behavioral steering have become the highest-leverage skills for engineers working with production LLMs.
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A practical guide to harness engineering—the discipline of controlling and guiding AI systems through guardrails, constraints, and behavioral steering. Learn what real teams build, what fails, and how to demonstrate this skill in interviews.
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A curated collection of Harness Engineering interview questions from Google, Meta, Anthropic, OpenAI, and leading AI startups. Includes behavioral questions, system design challenges, and deep-dive discussions on guardrails, evaluation, and AI safety.
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A deep technical analysis of OpenClaw's memory system—how it separates durable files, searchable transcripts, and runtime recall mechanisms. Essential reading for understanding production AI agent memory design.
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