AI vs Developer: Can GitHub Copilot or Claude Action Replace My Job?

I challenged two autonomous AI coding agents; GitHub Copilot Code Review and Claude Action to implement the same detailed feature specification without any human help. One of them failed spectacularly, disappointing me with broken promises and poor code quality. The other exceeded my expectations, demonstrating impressive coding skills, clear self-awareness, and effective collaboration. But this experience left me conflicted: if autonomous coding agents can truly handle complex implementations this effectively, are software engineering careers at risk?

In this video, I share the fascinating results of the head-to-head match-up, revealing which AI agent came out on top, why it succeeded, and what this means for the future of programming and our jobs as developers.

AIcoding #GitHubCopilot #ClaudeAI

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▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬ ➡ Transcript and commands: https://devopstoolkit.live/ai/ai-vs-developer-can-github-copilot-or-claude-replace-my-job 🔗 GitHub Copilot Coding Agent: https://github.com/features/copilot

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▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬ 00:00 GitHub Copilot and Claude Action Autonomous Coding Agents 04:32 Copilot's Epic Fail: When AI Gets Overconfident 10:11 Claude's Redemption: The Agent That Changed My Mind 15:30 The Verdict: Should You Trust Autonomous Agents With Your Code? 19:13 The Clear Winner: Why Claude Dominates Autonomous Coding

via YouTube https://www.youtube.com/watch?v=ahTkFqssZxM

Updating an old Ubuntu to a supported version

https://anonymoushash.vmbrasseur.com/2025/07/old-ubuntu-upgrade.html

I host my own Mastodon instance, which generally is pretty easy to maintain. The great team in the Mastodon community does a super job in making it easy to upgrade as they release new versions. I’ve therefore been keeping my Mastodon installation up to date. Go me!

Unfortunately, I haven’t been keeping up on my operating system updates, so my Digital Ocean droplet was still way back on Ubuntu-22.10. The latest LTS release is 24.04. Ooooops.

More unfortunate, the standard do-release-upgrade won’t work between releases that are as far apart as mine is from the latest release. What to do?

The answer is to work my way through the version upgrades manually. This answer on Ask Ubuntu was especially helpful for figuring out how to do this.

Is this a tedious pain in the ass? Yes, yes it is.

Is it entirely my fault for not keeping my OS up-to-date? Also very much yes.

How I did it

Checking the meta-release file for Ubuntu, I see that I need to do two manual upgrades (from kinetic to lunar, then lunar to mantic), then I should be able to use do-release-upgrade from mantic to noble (aka Noble Numbat, aka the current LTS release).

So for each of lunar and mantic, I did these things…

Downloaded the appropriate UpgradeTool from the meta-release file for Ubuntu

Created a directory then unpacked the upgrade tool tar.gz file into it

Solved problems along the way (see below)

Ran the upgrade tool

Then I was able to run do-release-upgrade and, finally after hours of putzing about trying to get the Ubuntu upgrade going, then update Mastodon. Success!

Problems I solved along the way

Irritatingly, the yarn and postgres errors below needed to be fixed before the kinetic to lunar upgrade tool would run successfully.

The yarn gpg key was expired, causing an error during upgrade

The error in question included this line:

The following signatures were invalid: EXPKEYSIG 23E7166788B63E1E Yarn Packaging yarn@dan.cx

According to this issue in the yarn repo, I should’ve just been able to curl the latest GPG key, run apt-key, and all would be well with the world. Except that didn’t work. No, I don’t know why and I don’t much care. I just wanted to get this thing done.

I found an It’s FOSS article about dealing with GPG keys. It’s not the error I was working on, but it was the information I needed to put the correct key in place:

First I backed up the existing key, which was /usr/share/keyrings/yarnkey.gpg.

Then I downloaded and added the latest GPG key:

curl -sS curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | gpg --dearmor | sudo tee /usr/share/keyrings/yarnkey.gpg

And finally, I updated /et/apt/sources.list.d/yarn.list to use that key for decryption:

deb [signed-by=/usr/share/keyrings/yarnkey.gpg] https://dl.yarnpkg.com/debian stable main

That solved the expired key problem. At some point I’ll need to change that back to remove the signed-by bit since I doubt that manually updated key will get any automatic updates.

The postgresql source for apt no longer had a release file for kinetic

The sources for a Postgres update were just as out of date as the ones for Ubuntu. This resulted in this error:

Ign http://apt.postgresql.org/pub/repos/apt kinetic-pgdg InRelease Err http://apt.postgresql.org/pub/repos/apt kinetic-pgdg Release 404 Not Found [IP: 2a04:4e42:2f::820 80]

The answer for this one was pretty easy, once I finally bothered to read the Postgres wiki page for apt. I needed to change /etc/apt/sources.list.d/postgresql.list to point to apt-archive.postgresql.org instead of apt.postgresql.org. The final file contents look like this:

deb [signed-by=/usr/share/postgresql-common/pgdg/apt.postgresql.org.asc] https://apt-archive.postgresql.org/pub/repos/apt kinetic-pgdg main

Fixing that resolved all of the errors and the kinetic to lunar upgrade tool worked without any problems at all.

via {anonymous => 'hash'}; https://anonymoushash.vmbrasseur.com/

July 10, 2025 at 03:00AM

Week Ending July 6, 2025

https://lwkd.info/2025/20250709

Developer News

SIG-Architecture group proposes to form a new Working Group focused on AI Conformance Certification. The WG would define a standardized set of capabilities, APIs, and configurations that Kubernetes clusters must support to reliably and efficiently run AI/ML workloads.

Kubernetes has formed a dedicated Checkpoint/Restore Working Group to integrate native Checkpoint/Restore functionality, enabling container migration and workload pre-emption to improve resource efficiency and support advanced use cases like AI/ML.

Release Schedule

Next Deadline: Code and Test Freeze, July 24/25

Code and Test Freeze starts at 0200 UTC on Friday, July 25. Your PRs should all be merged by then. Vyom Yadav has shared mid-cycle status, including 72 tracked changes. Because this means an extra-long Release Blog, the Comms Team requests that leads submit their release highlights early, if you can.

Cherry-picks for the July Patch Releases are due on July 11.

Featured PRs

131641: DRA kubelet: add dra_resource_claims_in_use gauge vector

This PR introduces a new gauge vector metric, dra_resource_claims_in_use, to the Kubelet; This metric tracks active DRA drivers and informs administrators when a driver is in use, ensuring safe removal of drivers without impacting pod operations; This metric is useful to determine if drivers have active ResourceClaims, preventing issues during the driver removal process.

KEP of the Week

KEP-2831: Kubelet Tracing

This KEP adds support for distributed tracing in the kubelet to help diagnose node-level issues like pod creation latency or container startup delays. It solves the problem of limited visibility into how the kubelet talks to the API server and container runtime by exporting trace data. The implementation uses OpenTelemetry to generate and export spans in the OTLP format. An OpenTelemetry Collector, typically deployed as a DaemonSet, receives and forwards this data to a tracing backend.The feature is enabled through the KubeletTracing feature gate and configured using the TracingConfiguration in the kubelet configuration file.

This KEP is tracked as stable in v1.34.

Other Merges

logger.Error replaced with utilruntime.HandleErrorWithXXX where errors cannot be returned

Fix for validation error when specifying resource requirements at the container level for a resource not supported at the pod level

Declarative Validation enabled for CertificateSigningRequest

Names of new Services are validated with NameIsDNSLabel() relaxing pre-existing validation when RelaxedServiceNameValidation feature gate is enabled

allocationManager’s IsPodResizeInProgress method unexported

New dra_resource_claims_in_use kubelet metrics to inform about active ResourceClaims

Statefulset now respects minReadySeconds

CSIDriverRegistry cleaned up

Function to translate named port to port number cleaned up to avoid duplication

Unit tests for VolumePathHandler

Deprecated

In a major refactoring effort, replaced the deprecated package ‘k8s.io/utils/pointer’ with ‘k8s.io/utils/ptr’ across multiple components

Deprecated gogo protocol definitions removed from k8s.io/externaljwt and k8s.io/cri-api

Subprojects and Dependency Updates

cluster-api v1.11.0-alpha.2: releases alpha version for testing

cluster-api-provider-vspherev1.14.0-alpha.2 : releases alpha version for testing

kustomize [shlex] (https//github.com/google/shlex) has been replaced with carapace-shlex, Bump to viper v1.20.0 and Drop usage of forked copies of goyaml.v2 and goyaml.v3

Shoutouts

No shoutouts this week. Want to thank someone awesome in the community? Tag them in the #shoutouts channel.

via Last Week in Kubernetes Development https://lwkd.info/

July 08, 2025 at 10:56PM

Ep28 - Ask Me Anything About Anything with Scott Rosenberg

There are no restrictions in this AMA session. You can ask anything about DevOps, AI, Cloud, Kubernetes, Platform Engineering, containers, or anything else. Scott Rosenberg, regular guest, will be here to help us out.

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Sponsor: Codefresh 🔗 GitOps Argo CD Certifications: https://learning.codefresh.io (use "viktor" for a 50% discount) ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

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via YouTube https://www.youtube.com/watch?v=-u7zcjeAEh8

Vibe Coding Explained: AI Coding Best Practices

Vibe coding is transforming software development by enabling us to interact with AI through simple, natural language instructions. Instead of manually writing code line by line, we can now direct AI agents to generate code, conduct tests, and manage various software development lifecycle operations. In this video, we'll explore essential best practices for vibe coding, including effective session management, the importance of detailed product requirements, memory and context management strategies, testing guidelines, and tips for making the most of "thinking" AI models.

Discover how to leverage vibe coding efficiently and avoid common pitfalls, such as overtrusting AI or neglecting proper code review. Learn why maintaining a small, manageable codebase, regularly updating AI memory, and consistently seeking second opinions are crucial for success. Whether you're new to vibe coding or already integrating it into your workflow, these practical guidelines will help you collaborate more effectively with AI and elevate your development practices.

VibeCoding #AIProgramming #BestPractices

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▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬ ➡ Transcript and commands: https://devopstoolkit.live/ai/vibe-coding-explained-ai-coding-best-practices 🎬 The Missing Link: How MCP Servers Supercharge Your AI Coding Assistant: https://youtu.be/n0dCFY6wMeI 🎬 From Shame to Fame: How I Fixed My Lazy Vibe Coding Habits with Taskmaster: https://youtu.be/0WtCBbIHoKE 🎬 Outdated AI Responses? Context7 Solves LLMs' Biggest Flaw: https://youtu.be/DeZ-gw_aop0

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▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ BlueSky: https://vfarcic.bsky.social ➡ LinkedIn: https://www.linkedin.com/in/viktorfarcic/

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▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬ 00:00 Intro to Vibe Coding 04:10 UpCloud (sponsor) 05:09 New Session 08:23 Development 11:28 Memory and Context Management 14:38 Miscelaneous 19:27 Vibe Coding Best Practices and Rules

via YouTube https://www.youtube.com/watch?v=W1105cy1D84