Scheduled Power Automate - Check Outlook Shared Calendar and Get your employee's leave status

 

When you want to know who is on leave today, and this automation is to collect the leave summary and send an email to you by every day morning

1. Get calendar (V2)

2. Compose Start

Inputs: formatDateTime(utcNow(),'yyyy-MM-ddT00:00:00Z')

Remote Desktop to Server - how to fix it (Encryption Oracle Remediation)

 

Via Group Policy (Recommended for Windows Pro/Enterprise)

This is the safest and most standard method.

1. Press Win + R, type gpedit.msc, and press Enter to open the Local Group Policy Editor.

2. Navigate to:
   Computer Configuration → Administrative Templates → System → Credentials Delegation

3. In the right panel, find and double-click "Encryption Oracle Remediation".

4. Select Enabled. Then, in the "Protection Level" dropdown below, choose Vulnerable.

5. Click OK to save.

6. Restart your computer (or run gpupdate /force in Command Prompt as Administrator).

7. 
   

CMD : Find all Wi-Fi passwords with only 1 command | Windows 10 / 11

 CMD:

#: netsh wlan show profile

#: netsh wlan export profile folder=c:\ key=clear

RMAC 「沙田10K」河畔賽 2026

 

1. 配速策略完美

• 第1公里 4:45：你壓住了！這是整場比賽最關鍵的決定。沒有被腎上腺素帶快，為後程儲存了能量。

• 第2-3公里 4:36→4:32：平穩過渡到目標區間，心率從158上升到171，完全可控。

• 第4-6公里 4:38-4:35：在最艱難的中段，你穩住了節奏，心率維持在175-176。

• 第7-9公里 4:41-4:39：這是意志力的考驗，你沒有崩掉，只是輕微掉速，然後迅速找回。

• 第10公里 4:31：完美的收官！ 最後1公里提速，把所有剩餘能量用完。

2. 心率證明一切

• 平均心率170 bpm，最高184 bpm

• 對跑者來說，這是在Zone 4-5邊緣的精準控制

• 你沒有過早燒盡，也沒有留有餘力——完美配速

3. 意志力的勝利

• 第4公里心率已達176，但你堅持到了第10公里

• 第7-8公里輕微掉速時，你沒有放棄，而是穩住

• 最後1公里還能提速到4:31，證明你還有儲備

46分鐘

Using a vector index

 For example, imagine you have two documents with the following contents:

"The children played joyfully in the park."

"Kids happily ran around the playground."

These two documents contain texts that are semantically related, even though different words are used. By creating vector embeddings for the text in the documents, the relation between the words in the text can be mathematically calculated.

Imagine the keywords being extracted from the document and plotted as a vector in a multidimensional space:

The distance between vectors can be calculated by measuring the cosine of the angle between two vectors, also known as the cosine similarity. In other words, the cosine similarity computes the semantic similarity between documents and a query.

By representing words and their meanings with vectors, you can extract relevant context from your data source even when your data is stored in different formats (text or image) and languages.