Lakshan Sivagnanasothy

Senior Software Engineer @wso2 @OpenChoreo | 🎓 MSc

[Lakshan Sivagnanasothy @SLakshan23]

OpenChoreo 1.0 is here, officially a @CloudNativeFdn Sandbox project! 🏆 https://t.co/dHqHGrVWkn This 1.0 release is a testament to the hard work of a brilliant team and a growing community. Thank you to everyone who has contributed, tested, and cheered us on!

[Dan Kornas @DanKornas]

Your Kubernetes platform doesn’t need another pile of glue code

OpenChoreo is an open-source developer platform for Kubernetes for teams that want to give developers self-service paths without assembling every layer themselves.

It helps platform engineers translate developer intent into Kubernetes resources through higher-level abstractions, while exposing the platform through a Backstage-powered portal, CLI, GitOps, or AI agents.

Key features:

• Developer portal – a Backstage-powered portal gives developers a dedicated platform interface
• Higher-level abstractions – model components, endpoints, dependencies, environments, pipelines, and namespaces instead of working only with low-level primitives
• Intent translation – the control plane turns developer and platform intent into underlying Kubernetes resources
• Built-in delivery options – the optional CI plane uses Buildpacks and Argo Workflows by default, alongside GitOps support
• Configurable guardrails – Kubernetes CRDs, ComponentTypes, Traits, and Workflows let platform teams define standards without custom controllers

It’s open-source (Apache License 2.0).

Link in the reply 👇

[OpenChoreo @openchoreo]

1 hour to go.

Kelsey Hightower joins the OpenChoreo maintainers for a practical conversation on building an internal developer platform around Backstage, from architecture and platform capabilities to developer experience.

Join us: https://t.co/ceaTG9WNEn

[Dr Uday S Kulkarni @MulaMutha]

Our neighbour. Traffic discipline in Colombo.

[OpenChoreo @openchoreo]

Backstage has become the de facto developer portal.

OpenChoreo builds on it with the control plane needed for deployments, environments, policies, observability, and runtime operations.

OpenChoreo maintainer @sameerajayasoma explains how to move from portal to platform:
https://t.co/CEro5lLgz5

[OpenChoreo @openchoreo]

The next OpenChoreo Community Call is happening on August 20!

Join us for project updates, community discussions and live Q&A.

Have a topic you'd like us to cover? Comment it below.

4 PM CEST | 7 AM PDT | 7:30 PM IST

Register: https://t.co/ICY0zQDZ98

[OpenChoreo @openchoreo]

Most IDPs are tightly coupled to their gateways and observability backends. Now they also need to work with AI agents.

@SLakshan23 explains how OpenChoreo addresses both through its multi-plane architecture, built-in agents, and MCP servers.

Read more:
https://t.co/9ONYRVTVfm

[OpenChoreo @openchoreo]

OpenChoreo is coming to KubeCon Japan 🇯🇵

📍 Yokohama
Wed, July 29
Project Pavilion, Table T-3
10:45 AM–2:45 PM

Maintainer @tishan89 will share live demos and answer questions about Kubernetes-native platform engineering.

Stop by and say hi ➡️ https://t.co/7ImpdVXz0s

[OpenChoreo @openchoreo]

Join us on July 23 for project release updates, including Cilium, scale to zero, and external observability modules, plus a live MCP code walkthrough and community Q&A.

Register:
https://t.co/xkXHrRx5qr

[OpenChoreo @openchoreo]

A new demo environment is now available for OpenChoreo.

It lets you try:
• Developer self-service
• Application deployment workflows
• Environment-based delivery
• Platform topology views
• Kubernetes-native platform capabilities
• MCP-powered platform interaction

Try the browser-based demo environment here: https://t.co/Oz7fNQ4EBN

[Gergely Orosz @GergelyOrosz]

This is exactly why experienced software engineers are valuable and will be valuable.

If you don’t know what good code looks like you will have no idea if what the models generate are any good

Of course “AI reviews the code” etc etc… it doesn’t work as reliably. Via @mitchellh

[OpenChoreo @openchoreo]

Our first OpenChoreo community call since joining @CloudNativeFdn as a Sandbox project is happening this Thursday, June 25th!

Here's what's on the agenda:
• Project update - where we are and where we're headed
• Sneak peek - upcoming features in the works
• Technical deep dive - multi-plane architecture of OpenChoreo
• Q&A

🕓 Time: 4:00 PM CEST | 7:00 AM PDT | 7:30 PM IST

Register now → https://t.co/6JHvgod0OZ

[OpenChoreo @openchoreo]

We just posted the OpenChoreo Quick Start Guide on YouTube.

Join Binura as he takes us through a step-by-step setup designed to get you from zero to "running" in less than 10 minutes. If you’ve been looking for a streamlined way to handle your Kubernetes environment, this is it.

Check out the full video: https://t.co/bS27W7znNz

[Anuj @anujcodes_21]

🚨Anthropic just showed a 24-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

this is one of the best workflows I've seen in a long time

in this video he breaks down exactly how most people are using Claude:

- the 14% you lose to CLAUDE.md before typing a word
- the plugins that 95% of users have never installed
- the caching setup that keeps it at 95% hit rate and almost free
- why starting every chat from zero is the slowest way to use Claude

if you've been using Claude for more than a month and never left the chat window, you've been using one project when you could be running a team of them

instead of another show tonight, watch this

make sure to bookmark it before it gets lost in your feed

full guide in the article below

[Sanjiva Weerawarana @sanjiva]

Blog: Thank you message to WSO2

This is the final message I sent to the team on Sunday nite. I hope it inspires more people to do what WSO2 has shown can be done.

YOU can be they.

https://t.co/MaTZthCMG5

[Justine Moore @venturetwins]

Me using Claude Opus 4.8 to rename a file

[Elon Musk @elonmusk]

😂

[OpenChoreo @openchoreo]

Kubernetes gives teams powerful primitives, but building a developer platform on top of it is still hard.

Join @Chaam_M at Cloud Native London to explore how OpenChoreo uses the right abstractions to simplify developer workflows on Kubernetes: https://t.co/KrtMYzKMui

[Rimsha Bhardwaj @heyrimsha]

A Wharton economist ran a randomized controlled trial on almost a thousand high school students in Turkey.

The result was so brutal for the AI-in-education narrative that it had to be peer-reviewed by PNAS before people would believe it.

Her name is Hamsa Bastani. She teaches operations and information at the Wharton School at the University of Pennsylvania, and the study she published in 2025 alongside her co-authors is one of the cleanest experiments anyone has run on what AI actually does to learning when you remove it from the equation and check what is left.

The setup was a randomized controlled trial, the same methodology used in clinical drug trials. Nearly a thousand high school math students in Turkey were split into three groups and put through four sessions of ninety minutes each. One group practiced with GPT Base, a standard ChatGPT-4 interface that could answer any question directly. One group practiced with GPT Tutor, a version of the same model that had been prompted to guide students with hints rather than hand them the answer. One group practiced with nothing but their textbook and their own head.

During the practice sessions, the AI groups looked like a miracle. The GPT Base group solved 48% more problems than the students working alone. The GPT Tutor group solved 127% more. Every administrator looking at those numbers would have written a press release about the transformative power of AI in education and moved on.

Then the actual exam came, and AI was not allowed.

The students who had practiced with GPT Base scored 17% worse than the students who had practiced alone. Seventeen percent worse, despite having solved nearly half again as many problems in the sessions leading up to it. The students who had struggled the most, who had sat with the confusion and worked through it without a tool to rescue them, were now the only ones who could actually do the math when it counted.

Bastani's team read through the chat logs to understand what had actually been happening during the practice sessions, and the answer was exactly what the exam results had already implied. The GPT Base group had not been learning. They had been extracting answers and moving on, and every moment that felt like understanding was actually the model doing the cognitive work while the student's brain waited for the next problem to arrive. The paper describes it precisely: without guardrails, students attempt to use GPT-4 as a crutch during practice, and subsequently perform worse on their own.

The detail that should follow every conversation about AI in education is the one buried in the post-test survey results. The students who had relied on AI the most during practice were also the most confident they had understood the material. The tool had not just failed to teach them. It had convinced them they had learned something they had not, which is a different kind of failure entirely and a much harder one to correct because the student has no idea it is happening.

The crutch had made them confident and weak at the same time.

[Suryansh Tiwari @Suryanshti777]

Andrej Karpathy just explained the future of software engineering without directly saying it.

The best AI engineers are no longer “prompting.”

They’re building systems around the agents.

Karpathy’s biggest insight wasn’t:
“Claude can code.”

It was:
LLMs become dramatically better when you force them into disciplined workflows.

That’s why "CLAUDE.md" files are suddenly everywhere.

Not because they’re prompts.

Because they behave like an operating system for the agent.

Karpathy called out the exact problems with AI coding:

- models assume instead of asking
- they overengineer simple tasks
- they hide confusion
- they rewrite unrelated code
- they optimize for completion, not correctness

So developers started encoding rules directly into the workflow:

→ Think before coding
→ Simplicity first
→ Surgical edits only
→ Goal-driven execution

And the results are wild.

People are now running multiple Claude Code agents in parallel like engineering teams:

• one agent researching
• one debugging
• one writing tests
• one optimizing code
• one validating outputs

Not “AI assistance.”

Actual orchestration.

And this part from Karpathy changes everything:

“Don’t tell the model what to do. Give it success criteria and let it loop.”

That is the shift.

From:
“write this function”

To:
“here’s the goal, constraints, tests, and verification system — now iterate until correct.”

The craziest part?

This already feels like a phase shift in engineering.

A lot of developers quietly went from:
80% manual coding → to 80% agent-driven coding in just months.

Not because AI became perfect.

Because the leverage became impossible to ignore.

We’re entering an era where the highest leverage engineers won’t necessarily be the best coders.

They’ll be the people who build the best systems around AI agents.