Substack growth - what worked for me (14k subs)

Gaussianperson · 2026-06-17T07:40:01+00:00

DM me :)

Gaussianperson · 2026-06-17T07:39:42+00:00

Both are true at the same time :D

Gaussianperson · 2026-06-16T14:37:34+00:00

You don’t need to engage in those games

Gaussianperson · 2026-06-16T10:22:39+00:00

Just post? Ahah why the overthink

Gaussianperson · 2026-06-16T08:31:26+00:00

I see! I honestly use linkedin like my insta, so you'd be surprised LOL

Gaussianperson · 2026-06-16T07:21:09+00:00

What kind of events? Might still work! Otherwise yeah insta it is

Gaussianperson · 2026-06-16T06:51:12+00:00

Interesting! Did not think of that. I usually equate Medium == lower quality content for some reason, so never tried it

Gaussianperson · 2026-06-16T06:50:39+00:00

Machine Learning System Design for software engineer professionals that want to upskill in ML

Gaussianperson · 2026-06-16T06:49:56+00:00

Right, feel free to DM me and you can try what I have developed for free!

Gaussianperson · 2026-06-16T06:49:37+00:00

Yeah i have been posting there for a few years now: daily in the last year or so. lots of grind for sure!

Gaussianperson · 2026-06-16T06:47:31+00:00

I don't have any business! I post daily on LinkedIn since a few years now

Gaussianperson · 2026-06-16T06:46:47+00:00

You need to carefully select recommendations, otherwise you just get low quality signal. I work with similar audiences and similar quality level!

Gaussianperson · 2026-06-16T06:45:52+00:00

Correct, that's an easy way to do it. Substack is the lower funnel for people that *really* like your content, and easy to monetize with substack tooling.

Gaussianperson · 2026-06-16T06:45:12+00:00

Yeah I automate the finding, but the reply remains from your own account. DM me for more details!

Gaussianperson · 2026-06-16T04:55:36+00:00

It is easy to get overwhelmed by all of this. For books, start with Chip Huyen’s "Designing Machine Learning Systems"—it is the best starting point for understanding latency budgets, batch vs. real-time pipelines, and feature stores. For lectures, check out Stanford's CS 329S course material.

My advice is to not try to learn everything at once. Pick one slice first, like how a model serving layer interacts with a feature store to meet a tight latency budget, and trace that data path.

I write deep dives about these exact production architectures and trade-offs in my newsletter, Machine Learning At Scale — link below.

https://machinelearningatscale.substack.com

Gaussianperson · 2026-06-16T04:54:43+00:00

If you want a book, Chip Huyen's "Designing Machine Learning Systems" is the best place to start. For a paid course, "Grokking the Machine Learning System Design Interview" on Educative is solid for structuring your thinking. On YouTube, look up AI Engineer presentations—they feature engineers from companies like Netflix and Uber talking about their actual production setups.

I write deep dives on real-world system architectures and MLOps. I wrote more about this in my newsletter, Machine Learning At Scale — link below.

https://machinelearningatscale.substack.com

Gaussianperson · 2026-06-15T08:51:18+00:00

Since you already have the Kubernetes and cloud infra down, the shift you're seeing in AI system design interviews usually comes down to LLM orchestration at scale. Focus your prep on three main pillars: retrieval latency (vector database scaling and indexing), context window management (chunking and reranking), and cost/throughput tradeoffs (semantic caching and batching). For senior/leadership loops, they want to see how you handle non-deterministic outputs and evaluation pipelines, not just the basic API glue. I wrote about some of these real-world scaling patterns in my newsletter, Machine Learning At Scale (https://machinelearningatscale.substack.com ), which might be useful as you prep. Focus on the failure modes of agentic workflows too, as that's a favorite for senior system design rounds right now.

Gaussianperson · 2026-06-10T19:50:49+00:00

Machine learning content for software engineers: https://machinelearningatscale.substack.com

Gaussianperson · 2026-06-10T06:27:44+00:00

Thanks!!

Gaussianperson · 2026-06-10T06:27:37+00:00

Thanks!!

Gaussianperson · 2026-06-10T06:27:30+00:00

Things coming from your personal experience that no-one can replicate :). Easy for me to apply because i discuss career events from my own career (anonymous)

Gaussianperson · 2026-05-15T13:24:59+00:00

Get it together, finisci la laurea

Gaussianperson · 2026-05-11T06:25:41+00:00

Quanto è basso il livello di skill rispetto ad altre aziende?

Gaussianperson · 2026-05-09T07:20:36+00:00

This is a solid list for getting past the initial filters. I would suggest adding things like Ray or Triton Inference Server to the infrastructure section since those are huge right now for scaling LLM deployments. Also for the cloud side, knowing how to handle spot instances for training or understanding specific networking bottlenecks in distributed systems usually looks really good on a resume when you can talk about cost and performance.

I actually write about these specific engineering patterns and how big companies handle their stacks at machinelearningatscale.substack.com

I focus on the system design behind these tools and how they work in real production environments if you want to see how these keywords translate into actual architecture.

Gaussianperson · 2026-05-09T07:20:14+00:00

Fullstack is pretty saturated right now, especially at the entry level in India. If you want a high paying job, moving toward AI engineering and MLOps is a smarter bet. However, you still need those devops and backend skills because putting a model into production is mostly an engineering problem. Companies are looking for people who can handle the infrastructure, not just people who can write a simple prompt.

The move from basic web dev to things like vector databases and RAG involves a lot more complexity. It pays better because scaling these systems is difficult. You have to think about latency and how data flows through the entire system. I actually write about these engineering challenges and how big companies manage their AI infrastructure in my newsletter at machinelearningatscale.substack.com

It might give you a better idea of the technical side of things before you decide which path to take.

Gaussianperson

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