Amith Hegde

Developer (Data, Voice and Web), Yoga, Books, Movies

[aFuzzyBear 🇺🇦 @aFuzzyBear2]

@system_monarch if you got a thundering herd running around yer severs you got bigger problems than a key missing a lock,

next time don't headshot the guy with a brain in-between his ears...
just not a nice thing to do, I mean silence is about as good as it gets at that point

[Akhilesh Mishra @livingdevops]

Kubernetes world is moving away from sidecar patterns.
Let me explain why.

A sidecar is a helper container. It sits next to your app container. Same pod. Same network. Same lifecycle.

People used sidecars for a lot of things.

Service mesh was the big one. Envoy would sit next to your app.
- It handled traffic. It handled retries and timeouts. It handled security between services(MTLS). Your app never knew it was there.

- Logging was another one. A small agent would sit next to your app. It would read the logs. It would ship them somewhere else.
- Metrics scraping worked the same way.
- Secrets injection too. Vault agent would run as a sidecar. It would fetch secrets and write them to a file.

This pattern felt smart. One app. Many helpers. No code changes needed.

But then problems showed up.

Every sidecar is another container.
That means more CPU, more memory.
One sidecar on ten pods is fine.
One sidecar on ten thousand pods is not fine.

Every sidecar can crash.
Every sidecar needs patching.
Every sidecar needs upgrading.
Multiply that by hundreds and thousands.

Startup order became a pain too.
- Sometimes the sidecar was not ready before the app started.
- Sometimes the sidecar died before the app finished its work. Small bugs. Big headaches.

Debugging got harder.
- Now you have two containers to check instead of one.

Cost added up quickly at scale.

So the industry asked a new question.

What if we do not need the sidecar at all?

Istio built something called an ambient mesh that required no sidecar injection.
The proxy moves completely outside the pod.

Cilium went even further by using eBPF (a kernel-level technology).
That means the networking logic lives in the kernel, so no sidecar container, and lightning-fast networking.

OpenTelemetry solved this for metrics and logs.
Instead of one sidecar per pod, you get one collector for many pods(runs as a DaemonSet and deployment).

Even Kubernetes noticed the pain.
In version 1.29, it gave sidecars a proper place in the pod lifecycle, fixing a lot of old bugs and startup issues.
It did not remove the sidecar. It just made it better.

Today we have two paths.
Some teams are removing sidecars completely.
Some teams are keeping sidecars but doing it the right way.

Where do you stand?

[Vlad F @Vlad_GitHub]

On August 17, GitHub experienced a significant outage that disrupted developers and organizations around the world. If you were trying to ship software that day, we let you down.

I posted in March and April about the steps we’re taking to make GitHub more reliable. The work is still underway. Here is an update:

https://t.co/9KaQy7O4CG

[Neo Kim @systemdesignone]

If you want to become GOAT-level with system design, learn these 21 concepts (save now):

[Andrew Ng @AndrewYNg]

New: A map of the most important skills in AI Engineering. https://t.co/VVkn1Dqp1N

[Jeff Dean @JeffDean]

Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen <at>NAE Grand Challenge problems. We think doing this well requires strong expertise in machine learning as well as large-scale systems.

[Nikesh Arora @nikesharora]

In the presence of legends! This is not an AI post :). But for those who know, they know.

[Vaibhav Sisinty @VaibhavSisinty]

Satya Nadella just warned every company using AI: you are paying twice. Once with money. Again with something far more valuable.

He published an article introducing something called the Reverse Information Paradox.

And it changes how you think about every AI tool your company uses.

Nobel laureate Kenneth Arrow described the original paradox: a seller risks giving away knowledge just to sell it. Nadella says AI flips this completely.

In the AI age, the buyer gives away knowledge just to use what they bought.

Every time your team uses Claude or GPT at work, every prompt reveals what you are building. Every correction teaches the model what good looks like inside your company. Every eval shows what you value. Every trace exposes your workflow.

The model provider learns more about you with every interaction. You learn almost nothing about what they are learning in return. Your corrections are distilled institutional know-how. The kind a competitor could never buy.

And it leaks trace by trace, correction by correction, without you noticing.
His line: "You can offload a task. You can offload a job. But you can never offload your learning."

If the model provider disappears tomorrow, do you still own the intelligence your team built on top of it? Your evals. Your memory. Your traces. Your workflows. Or did all of that compound inside someone else's infrastructure?

In the cloud era, companies accumulated data. In the AI era, they accumulate learning. Right now, most of that learning is compounding inside the model provider. Not inside the company paying for it.

The CEO pushing AI harder than anyone just told you to protect your knowledge from the very tools he is selling you. That should tell you everything.

[Amith Hegde @mythcard]

@Pallavi_345 Interesting you don’t give Oracle as an option

[Amith Hegde @mythcard]

@Criticskeeda @englandcricket In the kids defense he did execute 2 sixes, so 40 mts defense does not hold

[The Exploited TaxPayer @IndiaNewGen]

US has meritocratic country.
India is a bureaucratic country.

We celebrate people who has no real world experience, no tech, sales understanding, preparing for govt exams for 5 years &amp; then become a baby selected by the babus than the wealth creators.

[Physics In History @PhysInHistory]

Every kid starts out as a natural-born scientist, and then we beat it out of them. A few trickle through the system with their wonder and enthusiasm for science intact.

-- Carl Sagan

[Andrej Karpathy @karpathy]

@Siddhos sometimes money and "genuine, positive, non-zero sum value creation" are not highly correlated, and sometimes they *really* are. well-earned!

[Amith Hegde @mythcard]

@manuginobili Too painful. #GoSpursGo

[Thom van Lieshout @thomvlieshout]

guys be honest, are you working less because of AI

because I'm working more than ever

[Ramesh RB @Rameshchess]

For the serious learners in chess, I am restarting my YouTube channel from today. Have been planning this for some months and recently managed to put together a small team to help with recording and editing content. Small baby steps. Will try to post regularly instructive contents in the channel.

[Hans Niemann @HansMokeNiemann]

India's chess rise is one of the great sports stories of our era, backed by Adani, celebrated by PM Modi, embraced by cricket stars and the whole culture. That's what national belief looks like. American chess has the talent to compete at that level. What we need now is that same support behind our next generation. 🇺🇸

[Manu Ginobili @manuginobili]

Let's say it again... "The Spurs are the Western Conference Champions!". My first one as a fan!

[@jason @Jason]

spurs in six

[Barack Obama @BarackObama]

On Memorial Day, we pay tribute to the brave men and women in uniform who gave their lives for this country that we love. It is a debt we can never fully repay, but we must never stop trying. I’ll always be grateful to our fallen heroes and their families, whose sacrifice reminds us of what it means to live for something greater than ourselves.