Niklas Heringer
Niklas Heringer
Cybersecurity & Math.
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Niklas Heringer

Research

My latest findings and long-form analytical articles in IT security.
System One AI for Cybersecurity: Not Every Decision Needs a Generative Model
research

System One AI for Cybersecurity: Not Every Decision Needs a Generative Model

Security pipelines ask frontier LLMs millions of (basically) yes/no questions. I tested whether cheap decision models like Jev can triage HTTP responses instead: four models, three LLMs, one open harness. The answer is a qualified yes, plus a warning about what the gate throws away.
A Hacker's Guide to Post-Quantum Cryptography: ML-KEM & FIPS 203
research

A Hacker's Guide to Post-Quantum Cryptography: ML-KEM & FIPS 203

Everything you need to understand the algorithm that's replacing RSA (in terms of Key Exchange), from polynomial rings to key encapsulation, with zero hand-waving.
Curing University Math Trauma: A Hacker's Guide to Cryptography
research

Curing University Math Trauma: A Hacker's Guide to Cryptography

Remember sitting in university linear algebra, staring at the ceiling, begging the universe to tell you when you would EVER use this in the real world? Welcome. In this series, we take the seemingly useless math you slept through and show you how they secretly power the entire internet.
npts

Chaos Threat Actors: Why Corporations, Parents and Legal Systems Fail against Teenage Hackers

A discussion on Joe Tidy's "Ctrl + Alt + Chaos" and my takeaways. From the release of Julius Kivimäki to the rise of "Chaos-Evil" groups, I analyze company reactions to hacks, parents navigating the digital minefields and why the legal system might need a "cartel-style" overhaul.
Statistical Dreams: The Intimate History of AI
artificial-intelligence

Statistical Dreams: The Intimate History of AI

From ancient automatons to the Transformer age, this is the story of how we taught machines to think. We'll dive into breakthroughs like the Perceptron, Backpropagation, and Attention, taking a critical look at AI's origins, its dangers, and where it's headed next.
More to Go: Clean Code & Core Concepts
go

More to Go: Clean Code & Core Concepts

A hands-on walkthrough of Go’s core building blocks. Arrays, slices, loops, functions, structs, and maps - explained with performance in mind. No fluff, just clarity.
Why Go? A Hacker’s First Dive into Golang
go

Why Go? A Hacker’s First Dive into Golang

Ever wondered why Go keeps popping up in modern toolchains and cloud-native stacks? Here’s a hands-on dive into the syntax, philosophy, and quirks of Golang; written from a hacker’s point of view.
Data Engineering With Dagster Part Eight: Metadata
dagster

Data Engineering With Dagster Part Eight: Metadata

Dig into materialization metadata, inline visualizations, and best practices for asset observability using Dagster.
Data Engineering With Dagster Part Seven – Event-Driven Pipelines with Sensors
dagster

Data Engineering With Dagster Part Seven – Event-Driven Pipelines with Sensors

From Passive Pipelines to Reactive Workflows So far, we’ve scheduled jobs based on time: “Run this every Monday” or
Data Engineering With Dagster Part Six – Partitioning & Backfills
dagster

Data Engineering With Dagster Part Six – Partitioning & Backfills

Learn how to make your pipelines smarter by slicing them into manageable, date-based partitions and handling backfills like a pro.
Data Engineering With Dagster Part Five – Automating With Schedules
dagster

Data Engineering With Dagster Part Five – Automating With Schedules

Dagster finally earns its “orchestrator” title - this part dives into jobs, asset selection, cron expressions, and how to wire everything into automated schedules.
Data Engineering With Dagster – Part Four: Resources, DRY Pipelines, and ETL in Practice
dagster

Data Engineering With Dagster – Part Four: Resources, DRY Pipelines, and ETL in Practice

A deeper look at how Dagster handles reusable components like API clients and cloud connectors through resources - with best practices and cookie metaphors baked in.
Data Engineering With Dagster - Part Three: Definitions and Code Locations
dagster

Data Engineering With Dagster - Part Three: Definitions and Code Locations

How Dagster uses Definitions and Code Locations to scale cleanly across teams, assets, and environments.
Data Engineering With Dagster - Part Two: Dependencies, DuckDB, and Geo Heatmaps
dagster

Data Engineering With Dagster - Part Two: Dependencies, DuckDB, and Geo Heatmaps

Learning to wire assets together, load data into DuckDB, and build heatmaps with real NYC taxi data.
Data Engineering With Dagster - Part One: A Fresh Take on Orchestration
dagster

Data Engineering With Dagster - Part One: A Fresh Take on Orchestration

A student-hacker’s perspective on learning data engineering with Dagster: asset-centric thinking, orchestrator basics, and setting up the mental model.