yoshi-taka sre
@ask-yoshi-taka.bsky.social
🔗 https://ask-yoshi-taka.medium.com/
気合と理論で⟹安全工学/Safety II/ICS/Cognitive System/成人教育/Enabling Team/Cloud Finance/言語哲学/因果性
SaaS Tech DD/PMIのお問い合わせ歓迎
気合と理論で⟹安全工学/Safety II/ICS/Cognitive System/成人教育/Enabling Team/Cloud Finance/言語哲学/因果性
SaaS Tech DD/PMIのお問い合わせ歓迎
In Python 3.14, io.DEFAULT_BUFFER_SIZE was bumped to 128 KiB. Love this change for I/O performance.
github.com/python/cpyth...
github.com/python/cpyth...
November 17, 2025 at 4:23 PM
In Python 3.14, io.DEFAULT_BUFFER_SIZE was bumped to 128 KiB. Love this change for I/O performance.
github.com/python/cpyth...
github.com/python/cpyth...
node.js 24 lambda
github.com/aws/aws-cdk/...
github.com/aws/aws-cdk/...
November 15, 2025 at 5:54 AM
node.js 24 lambda
github.com/aws/aws-cdk/...
github.com/aws/aws-cdk/...
python 3.14 lambda
November 11, 2025 at 4:18 PM
python 3.14 lambda
In macOS Tahoe, window resize cursors have become easier to see.
Love this change!
Love this change!
October 4, 2025 at 10:59 AM
In macOS Tahoe, window resize cursors have become easier to see.
Love this change!
Love this change!
August 14, 2025 at 1:45 AM
Zed now shows the publish date of a library version in package.json via tooltip.
August 2, 2025 at 3:33 AM
Zed now shows the publish date of a library version in package.json via tooltip.
Aurora r8g is now supported across all AZs in Tokyo!
July 22, 2025 at 3:15 AM
Aurora r8g is now supported across all AZs in Tokyo!
Here’s a screenshot of the code:
July 8, 2025 at 1:24 PM
Here’s a screenshot of the code:
Hi @quinnypig.com,
We’re running into issues with Aurora r8g in ap-northeast-1 (Tokyo). Some AZs aren’t available, and to make matters worse, it’s inconsistent.
We’re running into issues with Aurora r8g in ap-northeast-1 (Tokyo). Some AZs aren’t available, and to make matters worse, it’s inconsistent.
July 8, 2025 at 1:21 PM
Hi @quinnypig.com,
We’re running into issues with Aurora r8g in ap-northeast-1 (Tokyo). Some AZs aren’t available, and to make matters worse, it’s inconsistent.
We’re running into issues with Aurora r8g in ap-northeast-1 (Tokyo). Some AZs aren’t available, and to make matters worse, it’s inconsistent.
June 12, 2025 at 1:30 PM
May 19, 2025 at 5:13 PM
Twitterの前段にCloudflareがささっている
March 10, 2025 at 4:51 PM
Twitterの前段にCloudflareがささっている
This adorable code can work.
github.com/yoshi-taka/t...
github.com/yoshi-taka/t...
February 15, 2025 at 4:40 PM
This adorable code can work.
github.com/yoshi-taka/t...
github.com/yoshi-taka/t...
CDK JSONNata & variables
github.com/aws/aws-cdk/...
github.com/aws/aws-cdk/...
February 6, 2025 at 5:48 AM
CDK JSONNata & variables
github.com/aws/aws-cdk/...
github.com/aws/aws-cdk/...
Indexing and Sensitive Data Scanner require separate filter settings. If you want to apply exclusions collectively, you’ll need to exclude them from being sent on the client side.
19/
19/
February 5, 2025 at 1:46 PM
Indexing and Sensitive Data Scanner require separate filter settings. If you want to apply exclusions collectively, you’ll need to exclude them from being sent on the client side.
19/
19/
🪵 Identify Unnecessary Log Patterns Using the Patterns Feature
The Patterns feature is often overlooked. Leverage it to pinpoint unnecessary or anomalous log patterns. To optimize costs, prioritize addressing high-volume logs that provide little value.
6/
The Patterns feature is often overlooked. Leverage it to pinpoint unnecessary or anomalous log patterns. To optimize costs, prioritize addressing high-volume logs that provide little value.
6/
February 5, 2025 at 1:46 PM
🪵 Identify Unnecessary Log Patterns Using the Patterns Feature
The Patterns feature is often overlooked. Leverage it to pinpoint unnecessary or anomalous log patterns. To optimize costs, prioritize addressing high-volume logs that provide little value.
6/
The Patterns feature is often overlooked. Leverage it to pinpoint unnecessary or anomalous log patterns. To optimize costs, prioritize addressing high-volume logs that provide little value.
6/
🪵 Explore Log Volume Using Metrics
If you prefer metrics, the datadog.estimated_usage.logs.ingested_bytes metric can provide a clear visualization of log volume. A standout feature is Split Graphs, which allows you to break down graphs by category for better clarity and actionable insights.
5/
If you prefer metrics, the datadog.estimated_usage.logs.ingested_bytes metric can provide a clear visualization of log volume. A standout feature is Split Graphs, which allows you to break down graphs by category for better clarity and actionable insights.
5/
February 5, 2025 at 1:46 PM
🪵 Explore Log Volume Using Metrics
If you prefer metrics, the datadog.estimated_usage.logs.ingested_bytes metric can provide a clear visualization of log volume. A standout feature is Split Graphs, which allows you to break down graphs by category for better clarity and actionable insights.
5/
If you prefer metrics, the datadog.estimated_usage.logs.ingested_bytes metric can provide a clear visualization of log volume. A standout feature is Split Graphs, which allows you to break down graphs by category for better clarity and actionable insights.
5/
🪵 Use Tree Map Visualization to Analyze Log Volume
Leverage the Tree Map feature on the log screen to analyze log volume by category. You can group logs by multiple dimensions simultaneously, such as account, service, or host, for better insights.
4
Leverage the Tree Map feature on the log screen to analyze log volume by category. You can group logs by multiple dimensions simultaneously, such as account, service, or host, for better insights.
4
February 5, 2025 at 1:46 PM
🪵 Use Tree Map Visualization to Analyze Log Volume
Leverage the Tree Map feature on the log screen to analyze log volume by category. You can group logs by multiple dimensions simultaneously, such as account, service, or host, for better insights.
4
Leverage the Tree Map feature on the log screen to analyze log volume by category. You can group logs by multiple dimensions simultaneously, such as account, service, or host, for better insights.
4
That amount does not appear to account for Multi-AZ.
Currently, db.x2iedn.32xlarge is the most expensive option, costing $140,548 per month.
aws.amazon.com/rds/sqlserve...
Currently, db.x2iedn.32xlarge is the most expensive option, costing $140,548 per month.
aws.amazon.com/rds/sqlserve...
January 25, 2025 at 3:06 AM
That amount does not appear to account for Multi-AZ.
Currently, db.x2iedn.32xlarge is the most expensive option, costing $140,548 per month.
aws.amazon.com/rds/sqlserve...
Currently, db.x2iedn.32xlarge is the most expensive option, costing $140,548 per month.
aws.amazon.com/rds/sqlserve...
Develop a Python implementation to analyze the effects of code refactoring using persistent homology, targeting Python code as the subject of analysis. Ensure the solution can evaluate multiple files simultaneously.
January 5, 2025 at 2:31 PM
Develop a Python implementation to analyze the effects of code refactoring using persistent homology, targeting Python code as the subject of analysis. Ensure the solution can evaluate multiple files simultaneously.