40余款新车密集上市，史上最挤“金九”放榜！
作为汽车销售旺季，今年“金九”放榜受到了各界关注。据记者不完全统计，9月共有超过40款新车上市发布。仅9月26日一天，就有至少10款新车同步上市、发布或预售。
随着车企密集发布推新，价格战加速洗牌，消费热情也被逐渐释放。近日，多家车企公布了9月销量及交付量成绩单。
同时，港股新能源汽车股应声大涨。10月2日港股收盘，蔚来-SW、小鹏汽车-W、理想汽车-W、比亚迪股份、吉利汽车、长城汽车等集体飘红，其中理想汽车涨幅居前，报收121.5港元/股，涨12.40%；长城汽车报收15.84港元/股，涨9.24%；比亚迪股份报收299.40港元/股，涨5.50%。

“涨”声一片
作为率先官宣停售传统燃油车的车企，比亚迪抓住新能源汽车走强的机遇期，持续领跑市场。
10月1日晚间，比亚迪股份（01211.HK）在港交所发布9月产销快报。据了解，9月比亚迪新能源汽车销量为41.94万辆，去年同期为28.75万辆；9月新能源汽车动力电池及储能电池装机总量约为19.8GWh，2024年累计装机总量约为127.72GWh。今年1—9月，比亚迪新能源汽车销量为274.79万辆，同比增长32.13%。
10月1日，小米汽车官宣，小米SU79月交付量超1万辆，目前已连续4个月达成破万交付目标。同时，小米汽车称，10月，小米汽车工厂将持续提产，单月生产目标冲刺2万辆，预计11月提前完成全年10万辆交付目标。
同日，“鸿蒙智行”官方微博发文称，9月鸿蒙智行全系交付新车39931辆。其中，AITO问界系列车型继续领跑市场，9月共交付新车35560辆。
此外，“蔚小理”9月交付汽车数量均同比大幅增长。‍‍‍‍‍‍‍‍‍

蔚来汽车发布的公告显示，9月交付汽车约2.12万辆，同比增长35.4%。
理想汽车9月交付新车53709辆，创单月交付量历史新高，同比增长48.9%。截至9月30日，理想汽车2024年共交付341812辆，历史累计交付量为975176辆。
得益于小鹏MONA M03交付破万辆，小鹏汽车9月共交付新车21352辆，创单月交付历史新高，同比增长39%，环比增长52%。

零跑汽车9月交付量继续增长，达到33767辆，同比增长113.7%，前三季度累计交付172861辆，同比增长94.6%。
传统车企孵化的新品牌方面，极氪宣布9月交付21333辆，创历史新高，同比增长77%，环比增长18%。极氪今年1—9月共交付142873辆，同比增长81%。截至9月底，极氪累计交付近34万辆。
岚图汽车10月1日宣布，9月交付量为10001辆，平均售价为33.6万元。另外，全新岚图梦想家上市12天交付超5000辆，岚图旗下另一款新车知音将于10月13日正式上市。

从销量的绝对值上来看，9月广汽埃安的销量表现也比较突出，当月销售新车30016辆，同比增长121%，今年1-9月累计销量182321辆，同比增长132%。
“以旧换新”政策拉动“金九银十”
全国乘用车市场信息联席会秘书长崔东树表示，除了新车密集上市潮，国家报废更新政策及各地方以旧换新置换政策稳定发力，共同拉动车市增长。
“叠加中秋和‘十一’的节日效应，9月车市呈高速增长态势，‘金九’效果显著。”他告诉证券时报·e公司记者，随着国家对汽车报废更新补贴力度的加强，市场回暖对车市起到明显的拉动效果，价格战对车企的压力相对减缓，年末车市将进入持续走强的良好态势。

事实上，从8月第二轮汽车以旧换新政策细则正式出台以来，各省、直辖市关于汽车置换更新补贴的政策逐步落地，截至9月29日已有31个地区出台了具体政策。
信达证券的研究报告显示，在原有国家层面关于报废更新补贴的基础上，各省、直辖市对于转让旧车并购买新能源乘用车或燃油乘用车，均出台了对应的置换补贴政策。大多数地区以价格带划分补贴等级，且并未限制新购车型价格上限。对于新购新能源乘用车的消费者，补贴金额普遍更高，如云南、海南、青海最高补贴金额可达2万元。除置换补贴之外，青海、西藏等地还对新车购置出台补贴政策，最高补贴金额可达1.8万元。
该报告认为，随着各地区置换政策逐步落地，叠加新车供给旺盛，潜在的换购需求有望进一步被挖掘，且大多数地区要求的新车购买时间是置换补贴发布之日开始到年末，有望进一步提升“金九银十”传统旺季车市景气度。

汽车销售顾问招聘启事
本公司（福特汽车）因业务发展需求，现面向社会公开招聘女性华人汽车销售顾问。
我们期待你的加入与公司共创辉煌！
一、招聘岗位：汽车销售顾问
二、招聘人数：5名女性华人
三、岗位职责：
1.负责公司汽车产品的销售及推广，完成销售任务。
2.维护现有客户关系，扩展新客户资源。
3.了解客户需要，提供专业的购车建议，为客户带来优质的购车体验。
4.协助客户办理购车手续，提供售后服务。
5.参加公司的各类培训，提升自身的业务能力。
四、任职要求：
1.入职要求：有市场营销，汽车销售等相关经验者优先。
2.年龄要求：能够长期稳定工作的28岁以上女性。
3.具备良好的沟通能力，善于与人交往。
4.有团队协作精神、团结互助、积极上进、责任心强
五、薪资待遇：
1.底薪+提成，待遇丰厚，上不封顶。
2.享有年终奖，节日福利等。
3.提供完善的培训体系和职业发展通道。
4.舒适的工作环境，和谐的工作氛围。
5.每周工作5天，总共40小时。
六、应聘方式：
1.联系电话：6474968612    联系人：杨先生
2.了解应聘者实际情况后，负责人将尽快安排面试以及工作，请保持电话畅通。
3.工作地址：福特汽车销售

JS 网站上的招聘信息，
SWE Base 250K
Quant Base 300K
这都是NG 的职位
https://www.janestreet.com/join-jane-str...n=new-york

这个消息对波音公司是好是坏。如何影响股价
https://l.smartnews.com/p-Q5mCF/ZVQhYR
Boeing will lay off 10% of its employees as a strike by factory workers cripples airplane production

我最近给谷歌做个项目
和几个组开会。二十几口人。开二小时。最后我整明白了，在会的除了一个是谷歌正式员工，其他都是外包，contractors.
为省钱，解雇美国人，去印度外包。长此以往，死翘翘地啦。I heard Ackman sold all his google stake.
Meta, 疫情期间抢了不少AI人才，现在风生水起。这就是差别

工作市场竞争加剧是未来的常态，这种状态下的选校的思维
角度1：最强的孩子去哪儿都一样。这个可能对但未必全对。选校的目的不是证明自己多强，而是学校能否带来额外收益。一个不善于发挥软性资源的孩子，长期能走多远？所以答案恰恰是，越是强的孩子，越要考虑如何有选择性的领域，最好不要趋同。
角度2：名校是否有光环。肯定是有的，特别是对于中间部分的，名校加持的作用更明显。
角度3：行业退潮对什么部分影响最大。CS 的繁荣期（是个毕业生都有工作，而且收入大大超过其他行业）不可能永远这样。看看法学院，商学院的几十年的情况就清楚了，一线的学院还会很好，但后面的就业前景可能就非常一般了。所以竞争优势会向头部的集中。

### **Behavioral Question:**
**"Tell me about a time when you had to handle conflicting priorities on a project. How did you manage it, and what was the result?"**

### **Good Example Answer:**
"While leading a project to develop a new feature for our product, I encountered conflicting priorities from two different teams. The sales team wanted the feature to be delivered quickly to meet a client’s urgent request, while the engineering team was concerned about ensuring the feature was fully tested and met quality standards before release. Both priorities were valid, but they created tension regarding the timeline.

To address this, I first held meetings with both teams separately to fully understand their concerns and requirements. I realized that the sales team’s urgency was tied to a specific client demo, while the engineering team needed extra time for testing to ensure we wouldn’t introduce bugs. I brought both teams together for a joint meeting, where we discussed the constraints and came to a compromise.

We decided to prioritize a minimal viable version (MVP) of the feature for the client demo that would meet their core needs without overloading the development timeline. At the same time, we allocated time after the demo to complete additional testing and implement the full version of the feature. By clearly defining the scope and communicating openly with both teams, we were able to balance the conflicting priorities effectively.

The demo went well, and the client was happy with the MVP. The full feature was delivered two weeks later with all the necessary testing, and it was released without any major issues. The experience taught me the importance of facilitating collaboration, setting clear expectations, and finding a balanced solution when managing competing demands."

---

### **What You Should Not Say:**

1. **"I ignored the concerns of one team and focused solely on the more vocal team’s priorities."**
  - Demonstrates an inability to manage competing interests and risks alienating key stakeholders.

2. **"I tried to meet both demands without compromising, which led to an overstressed team and subpar results."**
  - Shows poor prioritization and lack of negotiation skills, leading to potential burnout and quality issues.

3. **"I didn’t consult with the teams and made a unilateral decision on my own."**
  - Reflects poor collaboration and decision-making, which could lead to friction and lack of team buy-in.

4. **"I delayed the project indefinitely because I couldn’t decide how to handle the conflicting priorities."**
  - Indicates indecisiveness and an inability to take action in a timely manner.

5. **"I compromised quality to meet the deadline, which caused issues after the feature was released."**
  - Suggests a short-term focus without considering the long-term impact of sacrificing quality.

### **Behavioral Question:**
**"Tell me about a time when you encountered a major roadblock in a project. How did you handle it, and what was the outcome?"**

### **Good Example Answer:**
"During a project to build a new feature for our platform, we hit a significant roadblock when a critical third-party API we were relying on became unreliable, causing data inconsistencies. The feature’s functionality depended heavily on this API, and without stable data, we couldn’t move forward. The project was already halfway through development, and deadlines were approaching.

I immediately convened a meeting with the team to assess the situation and explore potential solutions. Rather than halting the entire project, we decided to isolate the feature components that didn’t rely on the external API, allowing the rest of the development to continue while we figured out a way to handle the unreliable data source.

Next, I worked with the engineering team to create a caching mechanism to mitigate the impact of the API’s downtime. This allowed us to serve users previously fetched data while the API was unavailable, ensuring minimal disruption to the user experience. We also added more robust error handling to gracefully manage any API failures, giving the application a fallback system.

I made sure to keep stakeholders informed throughout the process, explaining the issue, the steps we were taking to resolve it, and how this affected the timeline. Once the caching system was implemented and tested, we were able to continue development and meet the original deadline with only a slight adjustment to our feature scope. In the end, the project was delivered on time, and the fallback system we implemented even improved the platform’s resilience against future third-party API issues. This experience taught me the importance of adaptability, creative problem-solving, and transparent communication when dealing with unexpected challenges."

---

### **What You Should Not Say:**

1. **"I waited for the third-party API to stabilize instead of trying to find a workaround."**
  - Suggests passivity and a lack of initiative in finding alternative solutions.

2. **"I didn’t communicate with the team or stakeholders and just tried to fix it on my own."**
  - Indicates poor collaboration and failure to keep key stakeholders informed, which can lead to confusion and frustration.

3. **"I focused solely on fixing the API issue and let the rest of the project stall."**
  - Reflects an inability to manage parallel tasks or find ways to keep the project moving forward despite roadblocks.

4. **"I blamed the third-party service for the delay and didn’t take ownership of the solution."**
  - Shows a lack of accountability and problem ownership, which is critical when facing external challenges.

5. **"I made changes to the system without fully testing the fallback, which caused more issues later."**
  - Highlights poor attention to detail and testing, which can lead to greater problems down the line.

### **Behavioral Question:**
**"Tell me about a time when you had to refactor or redesign a front-end component due to performance issues. How did you approach the problem, and what was the outcome?"**

### **Good Example Answer:**
"While working on a web application, we started receiving complaints from users that a key dashboard component was loading slowly, especially when handling large datasets. This component was crucial for users to analyze data, and the performance issues were affecting the overall user experience.

I began by profiling the application to identify performance bottlenecks. The main issue turned out to be inefficient rendering due to the way we were handling data updates—every time the data changed, the entire component re-rendered, even for minor updates. Additionally, the component wasn’t optimized for virtual scrolling, which led to heavy memory usage when large datasets were loaded.

To resolve this, I first refactored the component to implement React's `shouldComponentUpdate` and memoization techniques, ensuring that only the necessary parts of the UI re-rendered when data changed. I also introduced virtualized lists to handle large data more efficiently, allowing the dashboard to render only the visible rows instead of the entire dataset at once.

Next, I optimized the data fetching logic by implementing lazy loading, so that the dashboard only retrieved data when the user needed it, reducing initial load times. Throughout the process, I kept close communication with the design and backend teams to ensure that the changes didn’t break any existing features or APIs.

After these optimizations, we saw a 40% improvement in load times and smoother interaction with large datasets. User feedback was overwhelmingly positive, and the application became much more responsive, particularly for our power users who dealt with large data daily. The experience reinforced the importance of profiling performance issues early and leveraging techniques like memoization and virtualization to improve front-end efficiency."

---

### **What You Should Not Say:**

1. **"I decided to rebuild the entire component from scratch without analyzing the root cause of the issue."**
  - Shows a lack of problem-solving focus, opting for a complete rebuild instead of targeting the specific performance bottlenecks.

2. **"I didn’t bother profiling the application and just tried random fixes until something worked."**
  - Reflects a trial-and-error approach rather than a structured, data-driven approach to problem-solving.

3. **"I focused only on fixing the performance issue but didn’t test for any potential breakages or regressions."**
  - Suggests a lack of thoroughness, which could lead to new bugs or issues in other parts of the application.

4. **"I didn’t communicate with the backend or design teams, so I implemented changes that caused inconsistencies with other components."**
  - Demonstrates poor collaboration, which can result in misalignment and unexpected issues when integrating with other parts of the system.

5. **"I didn’t measure the performance improvements after making the changes, so I don’t know how effective they were."**
  - Indicates a lack of validation, which is crucial when solving performance issues to ensure the solution is impactful.

### **Behavioral Question:**
**"Tell me about a time when your machine learning model didn’t perform as expected after deployment. How did you handle the situation, and what changes did you make?"**

### **Good Example Answer:**
"While working on a fraud detection model for a financial services client, our model performed exceptionally well during development and testing, with high precision and recall metrics. However, after deploying the model into production, we began receiving feedback from the operations team that the model was generating a high number of false positives, flagging legitimate transactions as fraud. This caused frustration for both customers and internal teams.

I immediately gathered detailed logs from the production environment and compared them to our test data. One of the first things I noticed was that the distribution of transactions in the live data differed significantly from the data we used to train the model. This was likely due to changes in customer behavior and newer fraud patterns that weren’t present in the training data.

To address this, I collaborated with the data team to acquire more recent and representative data to retrain the model. I also introduced a more dynamic data pipeline that would refresh the training data periodically to keep it up-to-date with current transaction patterns. Additionally, I implemented an ensemble method combining the original model with a more interpretable rules-based system, which allowed us to catch complex fraud cases while giving the business team control over some decision-making thresholds.

Once these changes were implemented, we redeployed the model and closely monitored its performance. The false positive rate dropped by 30%, and customer complaints decreased significantly. This experience reinforced the importance of continuously monitoring and retraining models in production environments and ensuring that your training data is reflective of real-world conditions."

---

### **What You Should Not Say:**

1. **"I ignored the issue, thinking it was an anomaly and would resolve itself over time."**
  - Indicates a lack of ownership and failure to address production issues promptly.

2. **"I blamed the data team for providing outdated data without taking steps to resolve it myself."**
  - Reflects a lack of collaboration and problem ownership, which are critical for cross-functional projects.

3. **"I made quick tweaks to the model without gathering enough data or understanding the root cause."**
  - Demonstrates a short-sighted approach and failure to investigate the issue deeply, which can lead to recurring problems.

4. **"I didn’t update stakeholders or the operations team until after the issue was resolved."**
  - Shows poor communication and could lead to stakeholder frustration due to lack of transparency.

5. **"I replaced the model entirely instead of trying to fix it, which delayed the project significantly."**
  - Indicates a lack of problem-solving and troubleshooting skills, opting for drastic solutions instead of incremental fixes.