Note for learning
Writing
Tags
About
Tags
keep hungry keep foolish
golang
interface
grpc
grpc-gateway
cron
open_source
GOPATH
UT
mysql
reflect
linux
正则表达式
hive
bigdata
azkaban
clickhouse
kafka
LLM
Looker
AI
agent
职业发展
golang
Golang fallthrough keyword
practice
Golang's fallthrough keyword
practice
Go fallthrough关键字 and label break
Go语法
Golang Mysql数据map到自定义结构体
通用函数
写Golang UT的几种方法
native way
GOROOT，GOPATH，GOVENDOER
区别于使用
golang cron包源码解析
第三方golang代码库源码阅读
GRPC Gateway在response中忽略默认值
solution
Golang Interface{} 与 nil
interface{}的实现
interface
Golang Interface{} 与 nil
interface{}的实现
grpc
GRPC Gateway在response中忽略默认值
solution
grpc-gateway
GRPC Gateway在response中忽略默认值
solution
cron
golang cron包源码解析
第三方golang代码库源码阅读
open_source
推荐一个 149K Star 的 AI Coding 宝藏仓库：Skills
真正值得学习的不是 Prompt，而是 Workflow
A 149K-Star AI Coding Gem: Skills
What matters is not the prompt, but the workflow
golang cron包源码解析
第三方golang代码库源码阅读
GOPATH
GOROOT，GOPATH，GOVENDOER
区别于使用
UT
写Golang UT的几种方法
native way
mysql
Golang Mysql数据map到自定义结构体
通用函数
reflect
Golang Mysql数据map到自定义结构体
通用函数
linux
linux find * 和 *? 的区别
linux find 默认正则语法规则
正则表达式
linux find * 和 *? 的区别
linux find 默认正则语法规则
hive
Hive 查询优化的几种方法（转载）
bloom filter, index and statistics
bigdata
Hive 查询优化的几种方法（转载）
bloom filter, index and statistics
azkaban
在二进制binary的Azkaban job 之前传递参数
Azakaban job paramter 传递
clickhouse
Clickhouse集成kafka流数据的不足
避免踩坑
Clickhouse集成kafka流数据实践
实时数据导入
kafka
Clickhouse集成kafka流数据的不足
避免踩坑
Clickhouse集成kafka流数据实践
实时数据导入
LLM
AI 让代码迁移变快之后，质量保障反而更重要了
读 Anthropic 大规模代码迁移实践：当写代码变便宜，瓶颈转向工程控制
Once AI Makes Code Migration Fast, Quality Assurance Matters More, Not Less
Reading Anthropic's large-scale migration playbook: when writing code gets cheap, the bottleneck moves to engineering control
Coding Agent 的知识管理：不是塞更多上下文，而是构建可复利的工作流
Index → Route → Body 的按需加载纪律
Knowledge Management for Coding Agents: Stop Stuffing Context, Build a Compounding Workflow
The Index → Route → Body discipline for on-demand loading
推荐一个 149K Star 的 AI Coding 宝藏仓库：Skills
真正值得学习的不是 Prompt，而是 Workflow
A 149K-Star AI Coding Gem: Skills
What matters is not the prompt, but the workflow
让 Coding Agent 不再跑偏的 Goal 写法
从一份「写给 AI 读」的 spec 里偷来的消歧纪律
Writing Goals That Keep Coding Agents on Track
Disambiguation discipline stolen from a spec written to be read by AI
Agent Harness 新实践：Claude Code 动态工作流
从 prompt 驱动走向 workflow 驱动
A New Agent-Harness Practice: Claude Code Dynamic Workflows
From prompt-driven to workflow-driven
GPT-5.5、Gemini 3.5 Flash、Claude Fable 5 方案能力对比
模型能扩展思路，但命名约束仍是工程师的事
GPT-5.5 vs Gemini 3.5 Flash vs Claude Fable 5 on Solution Design
Models can broaden your options, but naming the constraints is still the engineer's job
Looker
GPT-5.5、Gemini 3.5 Flash、Claude Fable 5 方案能力对比
模型能扩展思路，但命名约束仍是工程师的事
GPT-5.5 vs Gemini 3.5 Flash vs Claude Fable 5 on Solution Design
Models can broaden your options, but naming the constraints is still the engineer's job
AI
AI 让代码迁移变快之后，质量保障反而更重要了
读 Anthropic 大规模代码迁移实践：当写代码变便宜，瓶颈转向工程控制
Once AI Makes Code Migration Fast, Quality Assurance Matters More, Not Less
Reading Anthropic's large-scale migration playbook: when writing code gets cheap, the bottleneck moves to engineering control
Coding Agent 的知识管理：不是塞更多上下文，而是构建可复利的工作流
Index → Route → Body 的按需加载纪律
Knowledge Management for Coding Agents: Stop Stuffing Context, Build a Compounding Workflow
The Index → Route → Body discipline for on-demand loading
推荐一个 149K Star 的 AI Coding 宝藏仓库：Skills
真正值得学习的不是 Prompt，而是 Workflow
A 149K-Star AI Coding Gem: Skills
What matters is not the prompt, but the workflow
让 Coding Agent 不再跑偏的 Goal 写法
从一份「写给 AI 读」的 spec 里偷来的消歧纪律
Writing Goals That Keep Coding Agents on Track
Disambiguation discipline stolen from a spec written to be read by AI
当 agent 写掉 99% 的代码，工程师的护城河挪去哪了
读 Augment《如何招聘 AI 原生工程师》之后的一点个人思考
When Agents Write 99% of the Code, Where Did the Engineer's Moat Go?
A few personal thoughts after reading Augment's 'How We Hire AI-Native Engineers Now'
Agent Harness 新实践：Claude Code 动态工作流
从 prompt 驱动走向 workflow 驱动
A New Agent-Harness Practice: Claude Code Dynamic Workflows
From prompt-driven to workflow-driven
GPT-5.5、Gemini 3.5 Flash、Claude Fable 5 方案能力对比
模型能扩展思路，但命名约束仍是工程师的事
GPT-5.5 vs Gemini 3.5 Flash vs Claude Fable 5 on Solution Design
Models can broaden your options, but naming the constraints is still the engineer's job
agent
AI 让代码迁移变快之后，质量保障反而更重要了
读 Anthropic 大规模代码迁移实践：当写代码变便宜，瓶颈转向工程控制
Once AI Makes Code Migration Fast, Quality Assurance Matters More, Not Less
Reading Anthropic's large-scale migration playbook: when writing code gets cheap, the bottleneck moves to engineering control
Coding Agent 的知识管理：不是塞更多上下文，而是构建可复利的工作流
Index → Route → Body 的按需加载纪律
Knowledge Management for Coding Agents: Stop Stuffing Context, Build a Compounding Workflow
The Index → Route → Body discipline for on-demand loading
推荐一个 149K Star 的 AI Coding 宝藏仓库：Skills
真正值得学习的不是 Prompt，而是 Workflow
A 149K-Star AI Coding Gem: Skills
What matters is not the prompt, but the workflow
让 Coding Agent 不再跑偏的 Goal 写法
从一份「写给 AI 读」的 spec 里偷来的消歧纪律
Writing Goals That Keep Coding Agents on Track
Disambiguation discipline stolen from a spec written to be read by AI
当 agent 写掉 99% 的代码，工程师的护城河挪去哪了
读 Augment《如何招聘 AI 原生工程师》之后的一点个人思考
When Agents Write 99% of the Code, Where Did the Engineer's Moat Go?
A few personal thoughts after reading Augment's 'How We Hire AI-Native Engineers Now'
Agent Harness 新实践：Claude Code 动态工作流
从 prompt 驱动走向 workflow 驱动
A New Agent-Harness Practice: Claude Code Dynamic Workflows
From prompt-driven to workflow-driven
职业发展
当 agent 写掉 99% 的代码，工程师的护城河挪去哪了
读 Augment《如何招聘 AI 原生工程师》之后的一点个人思考
When Agents Write 99% of the Code, Where Did the Engineer's Moat Go?
A few personal thoughts after reading Augment's 'How We Hire AI-Native Engineers Now'
