In the workplace, have you ever encountered a situation like this?

Your boss casually throws out an ambiguous request, such as: "Help me hire a reinforcement learning engineer."

At this point, a common reaction from beginners is:

• They start by looking at candidates' resumes, checking QS rankings, education, work experience...

• Then they begin to struggle with what criteria to use for ranking, getting more confused and directionless...

• Finally, after spending a lot of time on a report, the boss glances at it and says, "You missed the point."

😨 A lot of effort wasted, and efficiency is poor!

The expert approach is to use logical thinking to break down the task and turn a vague problem into a structured analysis.

Step 1: Break Down the Task and Identify the Real "Key Criteria"

When faced with a task, you shouldn't make decisions based on intuition. Instead, you need to stand in the boss's shoes and understand what they truly want.

✅ The boss wants the candidate's real reinforcement learning ability, not QS rankings or high education.

So, we need to first define key criteria, such as:

1. Project Experience — Has this person actually worked on reinforcement learning projects?

2. Code Quality — Is the code open source? Are there any real contributions? Can the code run?

3. Past Contributions — Do they have papers, competition awards, or industry recognition in reinforcement learning?

These criteria allow decisions to be more data-driven rather than based on subjective judgment.

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Step 2: Data-Driven — Use Clear Data Analysis to Make Reliable Conclusions

With the criteria set, the next step is to quantify the data and put the analysis into practice.

📊 How to avoid "feeling good"? Use data scoring!

This way, you can clearly tell the boss:
"We scored candidates based on reinforcement learning project experience, code quality, and contributions. The highest score is C, so C is more suitable."

✅ This way, the boss only needs to glance at the conclusion to make a quick decision without being overwhelmed by a lot of scattered information.

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Step 3: Efficient Workplace Habits — Record Everything to Make Tasks Trackable

Here, the "Todo List" approach in Manus Demo is very insightful.

Many workers are accustomed to using Excel to record what they do every day, including time, tasks, and progress...

This not only helps them review their work but also creates a clear task trail for easy review and optimization.

🎯 Three functions of a work log:

1. Prevent forgetting task details

2. Show your progress to the boss, increasing trust

3. Enhance a sense of accomplishment by seeing how much you've completed!

Have you ever had such an experience? Worked all day but can't recall what you actually did...
At this time, if you have a clear Todo List, your daily efforts can be quantified, and growth becomes visible!

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Step 4: How to Write Emails/Reports That the Boss Can Understand at a Glance?

💡 The boss doesn't have time for long essays, so reports should have these three points:

1. Summary at the top — Let the boss see the most critical insights at a glance.

2. Logically break down the vague problem — Tell the boss how you derived the conclusion.

3. Data supports decision-making — Use data, not "I think," to persuade the boss.

Example 1 (Wrong Demonstration):

🚫 Report the boss can't understand:

These 10 candidates graduated from Stanford, MIT, CMU... Their QS rankings are 1, 3, 5... We ranked them based on their educational background as follows...

👎 Problem: The boss doesn't care about QS rankings; they want to know who has stronger reinforcement learning skills!

Example 2 (Correct Demonstration):

✅ Report the boss prefers:

Summary:
We scored 10 candidates based on reinforcement learning project experience, code quality, and paper contributions. The highest score is C (total score 13), recommended for priority consideration.

Analysis Process:

• We focused on candidates' practical experience in reinforcement learning, not education.

• C participated in 2 reinforcement learning projects, has excellent code quality (with open source contributions), and published papers, thus ranking first.

• A and B have advantages in different areas, with the same total score, but slightly weaker code ability than C.

Data Analysis: (Attached Excel sheet)

👑 Such a report allows the boss to understand your thought process at a glance, making decisions more efficient!

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Step 5: Critical Thinking — How to Demonstrate Thinking Ability in Daily Work?

In Manus's second Demo, there is an example about Redfin vs. Trulia.

In the U.S. real estate market, some know Redfin, and some know Trulia, but their user groups and market positioning are slightly different.

This actually involves a very important question:
🤔 When faced with multiple choices, how do you make decisions?

🎯 Excellent professionals actively think about the logic behind information rather than passively accepting information.

Just like YZ said, "The new team member looked at his report and asked what to do."
True experts won't wait for others to tell them what to do but will actively think about "why do it?" and "how to do it better?".

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Summary: How to Turn Vague Problems into High-Quality Analysis?

💡 Summary of "Expert Thinking" in the Workplace:
✅ Break down tasks and grasp the real key points (Logical Thinking) — Don't get stuck in vague problems, but break them down into structured analysis.
✅ Speak with data, not intuition (Data Analysis) — Make decisions more evidence-based, not just gut feelings.
✅ Efficient recording, enhance a sense of accomplishment (Todo List habit) — Make work organized, easy to review and optimize.
✅ Write reports for the boss, starting with conclusions, then logic — Let the boss see key insights at a glance, improving work efficiency.
✅ Cultivate Critical Thinking and actively think about the logic behind it — Enhance your analytical skills, not just passive execution.

If you can master these ways of thinking, whether in the workplace or in future entrepreneurship, you can solve problems more efficiently!

如何从模糊问题到高质量分析？——从 Manus 里学到的职场必备思维

在职场中，你有没有遇到过这样的情况？

老板随口抛出一句模棱两可的问题，比如：“帮我招个强化学习工程师。”

这时候，新手常见的反应是：

• 先翻开候选人的简历，看QS排名、学历、工作经验……

• 然后开始纠结到底该按照什么标准排序，越想越乱，越做越没头绪……

• 最后，花了大把时间做出来的报告，老板看了一眼，说：“你重点抓错了。”

😨 白忙活一场，效率拉胯！

而高手的做法是—— 用逻辑思维拆解任务，把模糊问题变成结构化分析。

第一步：拆解任务，找出真正的“关键标准”

面对任务，你不能凭感觉做决定，而是要 站在老板的角度，搞清楚TA真正想要的是什么。

✅ 老板想要的是候选人真实的强化学习的能力，而不是QS排名和高学历。

所以，我们要先定义关键标准，比如：

1. 项目经历 —— 这个人有没有真正做过强化学习的相关项目？

2. 代码质量 —— 代码是否开源？有无实际贡献？写的代码能不能跑起来？

3. 过往贡献 —— 是否在强化学习领域有论文、比赛获奖或业内认可的经历？

这些标准可以让决策更有数据支持，而不是靠主观判断。

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第二步：数据导向——用清晰的 Data Analysis 做出靠谱结论

有了标准，下一步就是 量化数据，把分析落到实处。

📊 如何避免“感觉上的好”？用数据评分！

这样，你就能清晰地告诉老板：
“我们按照强化学习项目经历、代码质量和贡献这三个维度打分，得分最高的是 C，所以 C 更合适。”

✅ 这样，老板只需要一眼看结论，就能快速决策，而不会被一堆杂乱信息淹没。

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第三步：职场高效习惯——随手记录，让任务变得可追踪

这里，Manus Demo 里的 “Todo List” 思路很有借鉴意义。

很多打工人都习惯随手用 Excel 记录自己每天做了什么，时间、任务、进展……
不仅能帮自己回顾工作，还能 形成一个清晰的任务轨迹，方便复盘和优化。

🎯 工作日志的三个作用：

1. 防止自己忘记任务细节

2. 让老板看到你的进展，提高信任感

3. 增强成就感，看看自己完成了多少！

你是不是也有过这样的经历？工作了一整天，却回想不起来自己到底做了什么……
这时候，如果你有一份清晰的 Todo List，每天的努力都能量化，成长也就变得可见了！

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第四步：如何写出让老板一眼看懂的邮件/报告？

💡 老板没有时间看长篇大论，所以写报告要有这三点：

1. Summary 放最上面 —— 让老板一眼就能看到最关键的 insights。

2. 逻辑清晰地拆解模糊问题 —— 告诉老板你是怎么推导出结论的。

3. 数据支持决策 —— 用数据，而不是“我觉得”来说服老板。

示例 1（错误示范）：

🚫 老板看不懂的报告：

这 10 位候选人分别毕业于 Stanford、MIT、CMU……他们的 QS 排名分别是 1、3、5……我们根据他们的学历背景排序如下……

👎 问题：老板并不关心 QS 排名，他想知道谁的强化学习能力更强！

示例 2（正确示范）：

✅ 老板更愿意看的报告：

Summary:
我们基于强化学习项目经验、代码质量、论文贡献三个维度，对 10 名候选人进行了评分，得分最高的是 C（总分 13 分），建议优先考虑。

分析过程:

• 我们重点关注候选人在强化学习领域的实战经验，而不是学历。

• C 参与过 2 个强化学习项目，代码质量优异（有开源贡献），并发表过论文，因此排名第一。

• A 和 B 分别在不同方面有优势，总分相同，但在代码能力上略逊色于 C。

数据分析：（附 Excel 表格）

👑 这样的报告，老板一看就明白你的思考过程，决策更高效！

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第五步：Critical Thinking——如何在日常工作中体现思考力？

在 Manus 的第二个 Demo 里，有一个关于 Redfin vs. Trulia 的例子。

对于美国房地产市场，有人知道 Redfin，有人知道 Trulia，但它们的用户群体和市场定位略有不同。

这背后其实涉及到一个很重要的问题：
🤔 当你面临多个选择时，你是如何做决策的？

🎯 优秀的职场人，会主动思考背后的逻辑，而不是被动接受信息。

就像 YC 说的，“新来的组员看了他的 report，还来问他要做点什么。”
真正的高手，不会等着别人告诉你要做什么，而是会主动思考“为什么做？”、“怎么做得更好？”。

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总结：如何把模糊问题变成高质量分析？

💡 职场中的“高手思维”总结如下：
✅ 拆解任务，抓住真正关键点（Logical Thinking） —— 不被模糊问题困住，而是拆解成结构化的分析。
✅ 用数据说话，而不是靠直觉（Data Analysis） —— 让决策更有依据，而不是拍脑袋选人。
✅ 高效记录，增强成就感（Todo List 习惯） —— 让工作变得有条理，方便回顾和优化。
✅ 写给老板的报告，先讲结论，再讲逻辑 —— 让老板一眼就看到关键 insights，提高工作效率。
✅ 培养 Critical Thinking，主动思考背后的逻辑 —— 提升你的分析力，而不是被动执行。

如果你能掌握这些思维方式，不管是在职场，还是未来创业，都能更高效地解决问题！