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News
Morning Routine 2024-11-19 (Tue): CJ restructuring (Only if you are interested in domestic stocks) / Increasing number of middle-aged and elderly people in cryptocurrency (Sharp increase in those over 60) / 30s selling houses, 50s buying houses (Statistics Korea) / Record high inflow into US ETFs (Worth considering Warren Buffett's increase in cash) / Rumor that Lotte has liquidity problems
Learning Practical Data Analysis with Python in https://elice.io/ko
03 Data Type Conversion using Netflix Viewing Data ~ 04 Organizing complex data from TED talks
TDD: It should become a habit, but it's difficult TT
- "나는 왜 10년이나 늦게 TDD를 시작했을까?" - 현직 시니어 개발자의 후회록
- 당신도 TDD를 시작하고 싶었지만, 막상 시작하지 못했던 이유
TDD (Test-Driven Development)One of the three principles of "stops writing production code once the test passes" has the following meaning.
Test-first development: In TDD, you first write a test case, then write the minimum amount of production code to pass that test. This principle encourages developers to avoid writing unnecessary code.
Maintaining code quality: The moment the test passes, it means that the function meets the requirements. Therefore, by not writing any more production code, the complexity of the code can be reduced and code quality can be maintained.
Opportunity for refactoring: After the test passes, it provides an opportunity to improve the existing code by refactoring. At this time, the stability of the code can be ensured by checking whether the test still passes.
In conclusion, this principle helps prevent developers from writing unnecessary code and improves code quality and stability.
- Production Code: Code that implements the actual functionality of the application. It is the part that users directly experience and includes the software's business logic and functions.
- Test Code: Code written to verify the behavior of the production code. It is mainly written to check whether a specific function is working correctly. In TDD, test cases are written first, and then production code is written to pass those tests.
Future Outlook of AI: Three Predictions
Original Article
Prediction 1: AI-native generations will lead AI adoption
According to data, 68% of office workers responded that college graduates currently graduating are more proficient in AI technology than the average employee in the company.
Meaning: Leaders can utilize young employees proficient in AI as mentors to promote peer learning.
Prediction 2: AI-native generations are at risk of decreased social connection
81% of AI users seek help from AI instead of friends or colleagues on important projects.
Meaning: Leaders need to utilize AI not simply as an efficiency tool, but as a catalyst for reconstructing human collaboration.
Prediction 3: Job seekers will prefer companies proficient in AI
Three out of four office workers want to work for a company that provides and utilizes AI tools.
Meaning: Companies that are slow to adopt or lack support for AI may have difficulty recruiting and retaining top talent.
- GN⁺: AI Hype가 식어가고 있다 | GeekNews
- 기업 임원진의 99%가 향후 AI 투자 계획이 있음. 그러나 생성형 AI 도입 이후 처음으로 글로벌 노동자들 사이에서 AI 도입률이 둔화되고 AI에 대한 열기가 식고 있음Slack의 노동력 지수(Workforce Index) 조사 (17,000명 이상의 사무직 근로자 대상)에 따르면, 직원들이 AI 사용을 숨기는 이유, AI 도구가 업무량을 증가시킬 수 있
AI Long Tail
Original Article: Companies are categorized by AI layer.
- AI의 롱테일 | GeekNews
- 2022년 11월 ChatGPT 출시 이후, AI 스타트업은 투자자들의 집중적인 관심을 받았으며, 2024년 2분기 전체 벤처 자금의 49%가 AI 및 머신러닝 스타트업에 투자됨.OpenAI와 같은 모델 제공업체, Perplexity나 Jasper와 같은 AI 네이티브 회사들이 주목받고 있지만, 실제로 AI의 영향을 받는 "비AI" 회사들의 수가 더 많음.
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