All Posts

AI

What Are Tokens in LLMs? Understanding How AI Language Models Actually Work

Updated: September 17, 2026

I learned what a "token" was by watching an LLM ignore a simple persona and promote my $15k/year janitor to "CEO." That bug sent me down the rabbit hole of tokenization: why models don't read words the way we do, how BPE/WordPiece/SentencePiece slice text into chunks, and why "next-token prediction" explains both the magic and the lies. If you've ever hit a context limit or watched a model forget what you just told it, this is the real reason. read on »

Startups

Don't Disrupt Payroll: Why Startups Should Outsource Boring Infrastructure and Focus on Their Core Product

Updated: September 17, 2026

Stop "disrupting" the boring stuff. Payroll, compliance, accounting, and infrastructure aren’t your product - they’re the floor you stand on. Buy the boring vendor, pay people on time, follow the checklist, and save your team’s brainpower for the thing customers actually pay for. read on »

Startups

How to Hire When AI Makes Every Resume Look Perfect | Screening Real Engineers in 2026

Updated: September 17, 2026

AI resumes are getting too good at sounding right-and too bad at sounding human. This post breaks down the new red flags (perfect grammar, keyword mirroring, zero scar tissue) and the screens that still work: informal writing, concrete artifacts, and a short paired task that forces real thinking. If your hiring process still treats the resume as the truth layer, you're selecting for costume quality, not engineering ability. read on »

AI

Vector Spaces Explained: Why They're Crucial for Modern AI, ML & NLP

Updated: September 17, 2026

Vector spaces: the unsung heroes of modern machine learning. From natural language processing to image recognition, these mathematical constructs form the backbone of AI. But what exactly are they, and why are they so crucial? This post demystifies vector spaces, exploring their history, basic concepts, and real-world applications. We'll dive into multi-dimensional visualization, compare traditional embeddings with LLM approaches, and even create a simple fruit-based vector space. Whether you're a beginner or a seasoned pro, understanding vector spaces is key to navigating the complex world of artificial intelligence. read on »

AI

Orchestrating Specialized Systems Trumps AGI

Updated: September 17, 2026

The pursuit of Artificial General Intelligence (AGI) may be misguided. Instead of creating a singular, omniscient AI entity, we should focus on orchestrating a symphony of specialized AI systems. This approach leverages current AI capabilities, allows for faster development and deployment, and mitigates ethical concerns associated with AGI. By embracing an AI orchestra rather than a single all-knowing entity, we can create practical, powerful AI tools that tackle complex real-world challenges while complementing human capabilities. read on »

Programming

Excel Serial Date Converter and the 1900 Leap Year Bug

Updated: September 17, 2026

Excel dates are a quiet mess: a fake 1900 leap day that shifts serial numbers, "helpful" auto-conversions that turn gene names into dates, two‑digit year guesses, and locale traps like 03/04/2023. This post breaks down the weird history behind it, why it still breaks real pipelines, and the boring Python approach I trust instead: force types, pick a format, and fail loudly. read on »

Startups

6 Signs a Startup Is Failing: The Death Flailing Warning Signs

Updated: September 17, 2026

Startups don't usually fail out of nowhere. They telegraph it for months-silence, frozen hiring, fake "transparency," desperate cost cuts, exec exits, and a rotating cast of "saviors." This post breaks down the six smoke signals, the numbers you must track before the runway disappears, and how to shut down with integrity if it's truly over. read on »

MySQL

10 MySQL Performance Tuning Tips for Faster Queries

Updated: December 22, 2025

Discover 10 essential MySQL performance tuning tips to optimize your database queries. Learn about proper indexing, query optimization, table structure, caching, server configuration, and more to significantly improve your database efficiency and speed. read on »

Startups

MVP Meaning: Build a Learning Machine, Not a Tiny Version of Your Product

Updated: March 18, 2026

Your MVP isn't a tiny "platform." It's a learning machine built to test one scary assumption fast. Cut features until only the core pain remains, validate with real behavior (not opinions), and use "smoke and mirrors" ethically if it helps you learn sooner. Ego and feature creep burn runway; a good MVP measures reality and ships fixes daily. read on »

Startups

The Lean Startup Method: A Guide for Tech Entrepreneurs

Updated: December 22, 2025

Explore the Lean Startup methodology for tech entrepreneurs, covering its core principles, build-measure-learn loop, MVP creation, pivoting strategies, and customer development techniques. Learn how to apply these concepts to build successful, innovative startups through real-world examples and practical tips. read on »

Programming

How to Refactor Legacy Python Code Without Breaking Everything | Python Refactoring Guide

Updated: March 18, 2026

If you can read the manual, you can refactor legacy code without turning it into a crime scene. This post lays out how I assess a codebase, why tests come first, and six refactors that actually pay rent: modularize the blob, remove duplication, modernize syntax, add type hints, and fix logging so failures stop hiding. Small changes. One sentence per PR. Ship, repeat. read on »

Programming

Python Asyncio Tutorial: Async/Await, Tasks & Concurrency Guide

Updated: March 18, 2026

Asyncio is what you reach for when your code is "working" but mostly just waiting: network calls, disk reads, slow database queries. This post shows the core mental model (coroutines, tasks, event loop), the common `await` vs `create_task()` trap, and a few practical patterns-from `gather()` fan-out to an async chat server and `async with` cleanup. If you expected three seconds and got one, you're already learning. read on »

Programming

Functional Programming in Python: Lambda, Map, Filter, and Reduce Explained

Updated: March 18, 2026

Functional Python is a nice middle ground: you get pure-ish functions, less mutation, and cleaner data pipelines without signing a Haskell vow. This post walks through lambdas, map/filter/reduce (and why they can get ugly), plus two tools worth keeping: functools.partial and simple function composition. read on »

Programming

Design Patterns in Python: Singleton, Factory, and Observer Explained with Examples

Updated: March 18, 2026

Design patterns show up right after your codebase grows a second "event system" and a third "config loader." They aren't magic. They're just names for common shapes that help you manage state, creation, and notifications. This post walks through three patterns that actually ship in Python-Singleton, Factory, and Observer-plus a small example that combines all three (and a few warnings so you don't pattern-collect your way into a mess). read on »

Programming

Python vs JavaScript in 2025: Which Language Should You Actually Use?

Updated: March 18, 2026

Stop arguing Python vs JavaScript like it's a personality test. Pick the language that gets you to "working" fastest, then add guardrails so you can change your mind later. Python shines for data, ML, automation, and quick backends. JavaScript wins anywhere the browser (and UI chaos) is the real risk. read on »

Python

Python Type Hints Tutorial: Catch Bugs Before Production with mypy

Updated: March 18, 2026

Type hints won't make Python "typed," but they will stop a lot of dumb bugs before they hit prod. This post shows how hints act like inline docs, why the payoff is in messy glue code, and how tools like mypy catch problems (like passing a string where an int belongs) before runtime. It also covers Optional, Union, Callable, generics, and a sane "start where it hurts" approach-without turning your repo into a type museum. read on »

Python

Functional Programming in Python: map, filter, reduce, and Beyond | Python Guide

Updated: March 18, 2026

List comprehensions got you hooked. Now open the rest of Python's functional toolbox: first-class functions, `map()`/`filter()` for lazy pipelines, `reduce()` (carefully), `partial()` for clean specialization, and `lru_cache` for instant speedups. The payoff is simple: fewer side effects, more predictable code, and transformations you can test without drama. read on »

Python

12 Python Built-ins and Standard Library Tools for Cleaner, More Maintainable Code

Updated: March 18, 2026

Smart code that nobody can maintain isn't smart. This post walks through 12 boring Python built-ins and stdlib tools that make intent obvious: `enumerate()`, `zip(strict=True)`, `any()`/`all()`, `partial()`, `iter(..., sentinel)`, `filter()`/`map()`, `chain()`, `defaultdict`, `groupby()`, and `lru_cache()`. Each one cuts glue code, prevents quiet bugs, and makes the next dev's life easier-especially after the "wizard" leaves. read on »

Python

Mastering Python's Context Managers: Beyond the Basic 'with' Statement

Updated: December 22, 2025

Explore Python's context managers beyond basic 'with' statements. Learn to create custom managers, handle multiple resources, and use them for timing and logging. Discover how mastering context managers can enhance your Python skills and even relate to startup management. read on »

Python

Python Walrus Operator (:=) Explained: Use Cases, Examples & When to Avoid It

Updated: March 18, 2026

The walrus operator (`:=`) lets you assign a value inside an expression. Used well, it cuts repetition in `while` loops, `if` checks, and comprehensions. Used badly, it turns clean code into a puzzle. Here's how to spot the difference, with real examples and a few foot-guns to avoid. read on »