Thulasiraj Komminar

Industry 4.0 | Smart Manufacturing | IIoT | Data Platform Engineer | AWS Community Builder | Terraform | MLOps | 👨‍💻@SchubergPhilis

[Thulasiraj Komminar @tkomminar]

Mind-blowing and refreshingly different.
#obsession

[Thulasiraj Komminar @tkomminar]

Fuck you, Bradley 😀 #SendHelp

[Thulasiraj Komminar @tkomminar]

#OverYourDeadBody

[Thulasiraj Komminar @tkomminar]

#InTheGrey

[Thulasiraj Komminar @tkomminar]

#MortalKombat

[Mitchell Hashimoto @mitchellh]

I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem.

As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)!

I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work.

It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results?

88ms => 1.5ms
150K allocs => ~500 allocs

Incredible right? Nope.

My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path.

This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput.

The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity.

Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.

[Thulasiraj Komminar @tkomminar]

@ATRightMovies Lawrence of Arabia & Dune Movies

[Thulasiraj Komminar @tkomminar]

@HouseOfHorrorCo TRASH

[Jarred Sumner @jarredsumner]

@rustaceans_rs blog post

[Thulasiraj Komminar @tkomminar]

Renewed for my 3rd year as an AWS Community Builder! 🎉

Year after year, this community keeps me learning and growing. Excited for what’s ahead!

#AWSCommunityBuilder #AWS

[Thulasiraj Komminar @tkomminar]

From Zero to Fargate: Running Containerized Apps with Aurora on AWS
https://t.co/BRUx8hvIt1

[Thulasiraj Komminar @tkomminar]

Terraform 1.15 Finally Lets You Use Variables in Module Source — And It's About Time

https://t.co/E9EUcQ5hFU

[Thulasiraj Komminar @tkomminar]

What a fun movie! Really enjoyed it.
#ProjectHailMary

[Thulasiraj Komminar @tkomminar]

Try it out and start logging ✈️
#flightlog #flighttracker #selfhosting
https://t.co/81HaxdfVe8

[Thulasiraj Komminar @tkomminar]

https://t.co/UTdvEGWWe4

[Adi Oltean @AdiOltean]

We have just used the @Nvidia H100 onboard Starcloud-1 to train the first LLM in space!

We trained the nano-GPT model from Andrej @Karpathy on the complete works of Shakespeare and successfully ran inference on it.

We have also run inference on a preloaded Gemma model, and we plan to try more exciting models in the future.

Getting the first H100 to work in space required a lot of innovation and hard work from the incredible Starcloud team to make this breakthrough.

This is a significant first step toward moving almost all computing off Earth to reduce the burden on our energy supplies and take advantage of abundant solar energy in space! 🚀

[Thulasiraj Komminar @tkomminar]

#Pluribus pulls you right in. It’s such a refreshing take on sci-fi.

[Thulasiraj Komminar @tkomminar]

Explore AWS Capabilities by Region #AWSBuilderCenter
https://t.co/8YhFVVOilT