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06:00

How an Anthropic seller rebuilt his team's workflows with Claude Code

Anthropic 销售用 Claude Code 从零编程构建内部工具套件

Jared Sires, a former account executive at Anthropic with no coding experience, used Claude Code to build CLAFTS, a Gmail-integrated tool that drafts customer emails in his voice while pulling context from live product documentation. The tool saves 10-15 hours per week. He expanded this into a sales plugin with skills for daily briefs, recaps, and pipeline management, wired into Salesforce, Gong, and other systems via MCP servers. About 80% of Anthropic's sales org now uses the plugin. The piece illustrates how non-technical practitioners can leverage AI coding tools to eliminate technical barriers and deliver workflow-specific software.

claude.com · 9 min · Agent Architecture · AI Engineering · Claude Code · Mcp
06:00

25 Claude Features, Workflows, and Tricks That Most Users Don't Know

Claude Projects 深度指南：25 个被低估的特性、工作流与技巧

A practical guide by @eng_khairallah1 detailing 25 workflows and techniques to fully leverage Claude Projects. The core thesis is treating Projects as evolving, persistent workspaces rather than transient chat sessions. It provides actionable strategies including a structured instruction template, strategic file organization, the Living Instructions pattern, and advanced concepts like voice calibration files and competitive intelligence hubs. The guide emphasizes a compounding knowledge strategy where each interaction refines Claude's contextual understanding, suitable for power users aiming to transform Claude from a generic tool into a domain-specific specialist.

x.com · 16 min · Agent Architecture · AI Engineering · Anthropic · Claude Code · Context Engineering
06:00

How To Build AI Agents in 2026 (That Actually Work)

2026 年如何构建真正可用的 AI Agent：从认知模型到代码实操

This article systematically deconstructs the architecture and engineering practices for building practical AI agents. It clarifies the boundaries between chatbots, AI agents, and agentic AI, emphasizing that a real agent is a system that persistently loops toward a goal rather than delivering a one-shot answer. The author explains the ReAct loop (Reasoning + Acting) and breaks down the five building blocks: the LLM as the brain, tools as hands, short-term and long-term memory, self-correcting loops, and verification. Using a case study of a startup research agent for the fitness niche, the article walks through goal setting, tool integration, loop construction, memory implementation, and the addition of a critic agent, complete with copy-paste system prompts. It highlights six common failure modes and recommends a 2026 tech stack including Claude Code, LangGraph, and MCP. The piece provides a weekend roadmap to build a basic agent from a 50-line Python script and is aimed at developers shifting from prompt engineering to designing agent systems.

x.com · 21 min · Agent Architecture · AI Agents · AI Engineering · LLM · ReAct Pattern