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Machine Learning Pills

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substack AI Media Kit

Growth

~ Estimated

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Newsletter subscribers

17K

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1.7K paid

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Engagement: < 0.1%1.0 posts/wk #93 in category

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Vik's Newsletter

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Engagement

~ Estimated

Avg Likes

6.7

Engagement Rate

0.04%· ~7 likes + comments/post

(comments + likes) / subscribers

Quality Score

Trending in AI this week

AI Action Letter

14K subs+4.5%/wk

Vik's Newsletter

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vs 151 newsletters in AI

Subscriber Distribution

This newsletter

40K

Bottom 25%MedianTop 25%Top 10%

Bottom 25%: 1KMedian: 2KTop 25%: 9KTop 10%: 40K

17K places you in the top 25% of newsletters in this category.

vs Category Medians

Engagement Rate

0.04%

Above median

median: 0.00%

Posts per Week

1.0

At median

median: 1.0

Publishing Consistency

75/100

At median

median: 66/100

Subscribers

17K

Above median

median: 2K

Publishing

~ Estimated

Publishing Frequency

Weekly

Last published Apr 12, 2026

1.0 posts/week avg

Consistency:75Consistent

AI Pricing

$6/mo median

At average

Monetization Readiness

Already monetizing

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The Neural Maze

substack13K subscribers

AI Adopters Club

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Recent Posts

Title	Date	Engagement	Access
Issue #127 - Beyond the Vanilla RNN: Why We Needed LSTM and GRULast time, we introduced Recurrent Neural Networks, the first architecture that gave neural networks a sense of time. Unlike feedforward networks that treat every input independently, RNNs process sequences step by step, passing a hidden state forward at each time step so that what the network saw before can influence what it does now. It was a genuine leap. Language, audio, time series, sensor data — all of these are fundamentally sequential, and RNNs were the first deep learning architecture that could actually reason about order and context.Apr 12, 2026	Apr 12, 2026	—512.1k words❤5	Subscriber-only
DIY #21 - Step-by-Step Guide to Time Series Forecasting with RNNIn the last article, we introduced the theory behind Recurrent Neural Networks. This time, we will use a hands-on example to illustrate the process of training a Vanilla RNN to forecast time series data.Mar 21, 2026	Mar 21, 2026	—611.5k words
Extra #5 - Real-World Scenarios Where RNNs Still Beat TransformersWelcome to the premium deep dive. In our main issue, we broke down the mechanics of Recurrent Neural Networks (RNNs). We also acknowledged the elephant in the room: in the realm of Natural Language Processing (NLP) and Generative AI, Transformers have completely stolen the spotlight. If you look at modern models like GPT-5 or Gemini, they are all built on the Transformer architecture. Transformers process entire sequences simultaneously rather than sequentially, which completely bypasses the vanishing gradient problem and allows for massive parallelization on GPUs.Mar 18, 2026	Mar 18, 2026	—71
Issue #124 - Introduction to RNNsAre you looking to master Time Series forecasting, Natural Language Processing (NLP), or speech recognition? If so, your journey must begin with understanding Recurrent Neural Networks (RNNs). Theoretical Introduction RNNs are a specialized family of neural network architectures explicitly engineered for sequence-based tasks. Unlike traditional feed-forward neural networks, which treat every input as an isolated and independent event, RNNs are designed to recognize patterns across time. They excel in situations where the sequence, order, and context of the data are just as crucial as the data itself.Mar 15, 2026	Mar 15, 2026	—61
Extra #3 - The Prompt Injection Defense PlaybookSecurity is usually an afterthought in software development until a critical vulnerability breaks your trust, your system, or your budget. In the world of Large Language Models (LLMs) and autonomous AI agents, that vulnerability is often Prompt Injection. Unlike traditional SQL injection, which relies on rigid syntactic exploits, prompt injection is semantic. It manipulates the model’s natural language reasoning, weaponizing the AI’s core feature—its ability to understand and follow instructions—against itself. If you are running a premium service, relying on a basic, polite system prompt is no longer enough. You are dealing with adversarial inputs, and you need a multi-layered, defense-in-depth strategy.Mar 4, 2026	Mar 4, 2026	—52
Issue #122 - The 12-Step Blueprint for Building an AI Agent. Part IBuilding an AI agent requires shifting your mindset from prompt engineering to systems engineering. An agent is not just a language model; it is an orchestrated system where a language model acts as the reasoning engine to drive actions, utilize memory, and interact with the outside world. Here you have a step-by-step breakdown of the core architecture required to build a production-ready AI agent. This issue will cover the first 6 steps, covering the remaining 6 the next week.Mar 1, 2026	Mar 1, 2026	—84
Issue #121 - How to Deploy your AI Agent?So You Built an AI Agent… Now What? There is a magical moment when your AI agent works for the very first time. You type a message, it thinks, and it responds. It feels alive. And then someone says: “Great. Now deploy it.” Suddenly, you are hit with a storm of jargon—servers, REST APIs, load balancers, Docker, Redis, vector databases—and it feels like you accidentally enrolled in a distributed systems PhD program. It is completely normal to feel overwhelmed at this stage. Let’s slow everything down. This article breaks down exactly what happens when you turn your local AI agent into a robust application that real people can use. No jargon storms, and no assuming you already know how the internet works. Just a clear, straightforward mental model of the full picture.Feb 22, 2026	Feb 22, 2026	—10
Issue #120 - Turn-based voice AI agentsTalk to a good voice agent and it feels effortless: you speak, it understands, it answers, and the pacing feels almost human. Underneath that experience, though, every system is making an architectural choice that shapes everything else—latency, reliability, cost, and how “natural” the conversation can feel. In practice, most voice agents fall into two families.Feb 8, 2026	Feb 8, 2026	—111
Issue #119 - AI “Iron Triangle”: balance Speed, Cost & AccuracyIf you have ever managed a project, you know the classic rule: “Fast, Cheap, Good—pick two.” In the world of Generative AI, the variables have shifted slightly, but the constraint remains. The three corners are now Speed, Accuracy, and Cost. Understanding how to navigate these trade-offs is the difference between a profitable tool and a financial black hole. Here is your guide to choosing the right mix for your AI agent.Feb 1, 2026	Feb 1, 2026	—71
DIY #19 - Evaluator-Optimiser LLM Agent with LangChainImagine you are writing a critical novel. You don’t just type “The End” after the first draft and send it to the printer. Instead, you write a draft, hand it to a ruthless editor who covers it in red ink (”This character is weak,” “The pacing is slow here”), and then you rewrite it. You repeat this cycle until the manuscript is polished.Jan 23, 2026	Jan 23, 2026	—22

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