I've been interested in AI since undergrad.  Hofstatder's GED was one of my favorites.  In the 90s though, AI felt like it was still purely academic.  Around 2015 I was lucky enough to work at a start up whose value proposition was focused on displacing an industry standard with AI. I was impressed by how far things had come and decided to try to get back up to speed.  I took it upon myself to learn the math necessary for understanding modern Machine Learning and finished several math courses at a local college.  The canonical coursework calls for Calculus (through multivariable), Linear Algebra, and Statistics/Probability Theory.  I went a bit further and got into some math ended up being surprisingly helpful to everyday Software Engineering. My goal here is to share some of them in case they help anyone else. Information Theory  There are so many immediately, practically useful ideas here for a computer scientist.  The fundament...

English is efficient for human communication because of the shared context we have.  We can mostly understand each other without having to spell out every detail.  Step into a foreign culture and even if you're fluent in the language, you'll find you still struggle with communication until you've built up the shared cultural experience. This is true even for regional moves within a given macro-culture like the USA.  I grew up moving frequently with a military family.  As a boy, I'd dedicate time after each move to learn all the local sports teams and their players' names.  This was the language the local boys spoke.  It was the minimal necessary to follow conversations at school.  Code has to be unambiguous because the computer requires precise instruction.  However, there's necessarily ambiguity in the spoken word.  This is the result of cultural evolution.  A culture that spelled everything out verbally the way code does w...

  In the movie  Get On Up , there’s a moment where James Brown stops rehearsal and walks through the band, calling out each musician one by one. He reminds them that no matter what they play—guitar, horns, bass, keys—their instrument is a drum . He’s drilling his core philosophy into them: every instrument in his band drives rhythm. He wants the horns to punch like percussion, the guitar to play tight, clipped rhythmic patterns, and the bass to hit with strict precision. There’s no room for loose, melodic wandering. Brown treats the entire band like one giant rhythmic machine . In Fintech, every system is a subledger.  Your lead engineer may not have designed it with accounting in mind.  They may have intentionally, avoided accounting concepts.  Nonetheless, if financial data flows through it, it's a subledger.  With rhythmic precision, at the end of each month its data will get aggregated and flow in the General Ledger. James Brown says, "...God made yo...

  The combination of where the economy is at the moment and the introduction of new generative AI tools is making it difficult for RCGs (recent college graduates) to find work.  What employers are looking for is Senior Engineers with enough experience under their belt to be able to provide guidance and guard rails  to the tools.  The relatively low-value-add coding that a Junior Engineer would ordinarily do is largely being automated. This isn't a new phenomenon, it's just been exacerbated by recent developments.  The hiring sweet spot for businesses has long been the Senior Engineer.  They're experienced enough to consistently deliver.  They're not experienced enough to command higher compensation or title.  In other words, they're low risk high reward employees. Recruiters know this and cater to it.  They profit by moving inventory.  They get a large number of reqs for Senior Engineers and are incentivized to fit a candidate into the o...

  In 2025, many are wondering, "Will AI take my job?"  If you want to answer this question, there's an easy test, "Is it okay for me to frequently be wrong?"  If the answer is yes, AI may take your job. If your job requires you to be right most of the time, AI isn't ready to replace you.  Ironically, what we've grown comfortable expecting of computer software may not be true with AI.  The software we've known is logical to a fault.  AI is capable of logical contradiction, even hypocrisy.  It's a statistical engine that confidently offers the most likely answer given what it's aware of in the moment. Unlike technology we've used to date, it's not deterministic.  It's probabilistic.  It can't be relied upon to provide the same, correct answer reach time.  When it's wrong, it's not a "bug."  It's an expected behavior.  It's an acceptable tradeoff given the utility we get in return.  Consequently, when...

After spending some time coding with LLMs, I feel like I've caught my footing and have the following takeaways: Cursor is Worth it For much of my career I'd often have my dev environment yanked away entirely and have to relocate to a temporary F.O.B.  This was usually done to solve some time critical emergency.  Since I didn't know where I'd be when disaster struck I had to be ready for any environment.  I learned to code out of VI because I knew I could count on its availability on whatever piece of hardware I ended up on.  I learned NOT to rely on IDEs. When it came time to try out Cursor as the tool of choice for AI development, I was hesitant.  I stuck to CLI prompts, copying and pasting.  Cursor simplifies the task switching though.  Prompt, inspect, repeat.  Its ability to learn context in-place reduces the friction and allows me to maintain a flow state.  The Bots are Bold If you were speaking with a teammate and something was ambiguou...

 Motivation If you're a young person trying to decide a career path but don't know much about the world and haven't established a particular passion in any field, it stands to reason that an objective measure may provide better guidance than following media hype or a random experiment based on anecdotal evidence. To that end, I asked an LLM for an analogous measure to the Sharpe Ratio used for choosing stocks by risk rated return.    The Sharpe Ratio: Sharpe Ratio = R p − R f σ p \text{Sharpe Ratio} = \frac{R_p - R_f}{\sigma_p} where: R p R_p ​ = average return of the portfolio (or investment) R f R_f ​ = risk-free rate of return (e.g., yield on Treasury bills) σ p \sigma_p ​ = standard deviation of the portfolio’s returns (a measure of volatility)    The Career Efficiency Score (CES): A Risk-Adjusted Metric for Career Choice Inspired by the Sharpe ratio in finance, the Career Efficiency Score (CES) measures how much career reward you gain per un...

  As a kid, I had a friend who spoke with a New York accent.  He didn't pronounce the "H" in his "Hu"s.  We can hear this when Trump speaks, "Huge" become "Yuge."  Among our friends was a boy named "Hugh."  At one point my friend was reminiscing and asked me,  "You remember when Hugh XYZ'ed? That was so crazy." To which I responded, "I never did that." "No Hugh did." "No I don't remember ever doing that.  Seriously." "No Hugh did, Hugh." "Not me man, you're crazy."   At last, he added Hugh's last name, I caught on, and we had a good laugh.   I see this same pattern frequently in software and it goes like this: Observing the data, an engineer notes that two concepts behave uniformly Not being a subject matter expert, they make the decision to represent both concepts as one and the same.  After all, the parsimonious solution reduces space and complexity Some ...

 We're in a period of collective imagination.  The phenomenon is periodic in nature and at the heart of our economy.  A new idea comes in fashion, promising to stimulate innovation.  Capital chases it.  Markets expand in anticipation.  The idea may be little more than a fantasy at this point, "let's put people on the moon." Where there's a will there's a way.  Engineers race to realize the vision and capture the business for their owners before the competition does.  Some time later, some black and white version of the dream is realized.  Maybe the vision is still colorful to the layman, but the engineer's broken it down and rendered it mundane. It takes time to realize a vision.  Necessity is the mother of invention.  Innovation is problem solving.  Problem solving is a matter of fully exploring a problem space and understanding it.  This is primarily what engineers do, explore and define the boundaries of a problem space...

The story of Hansel and Gretel can supposedly be explained by famine.  The witch was an old woman, abandoned in the forest by her family once they were no longer able to feed her.  To survive she ate anything she could.  She relied on her appearance as a sweet old granny to lure in other humans as prey.  Tales of the elderly being abandoned during such famines are widespread.  They're also apparently the back story to Japan's Yamanba , a cannibalistic hag living in the mountains. When I lived in the Japanese countryside , I once saw a real live Yamanba.  Leaving my house, I noticed a woman, emaciated, covered in dirt, with leaves in her knee-long hair, stark naked.  She was digging through the trash.  She looked right at me and made eye contact.  I was terrified.  Probably the only thing scarier than being surprised by a wild animal is being surprised by a wild human! I jumped in my car and headed to work, watching her in the rear...

  You know how kids start asking "WHY?" to everything to try to annoy their parents? Pro-tip: Find a series of answers that loop and just keep repeating them until the kid gets annoyed. ... Why? So the tree can grow. Why? So they can get bigger. Why? So they can get more sun. Why? So they can get bigger. Why? So they can get more sun. etc... The kids get bored and give up.

Rhetoric can be split into three pillars, Logos, Pathos, and Ethos.  When communicating, it's useful to know what your audience responds to and customize the delivery to them. As the audience, we can feel the emotion in the Braveheart "Freedom" speech and can relate to how it may have helped inspire warriors into battle.  Imagine a speech of similar delivery given to a team of software engineers.  They'd probably have to hold back the laughter and/or yawns. Each profession or specialty generally carries with it a certain disposition.  Some respond better to logic.  Others to heart.  Others still to a more subtle combination.  When framing your communication, it's worth considering this to more effectively reach your audience.      

On the heels of the dot.com bubble, the term "labor arbitrage," appeared as a technical euphemism for what the general US labor force referred to with great anxiety as, "Outsourcing."   Thanks to changes in Clinton era policy and the emergence of the internet, it was easier than ever to employ abroad at lower rates.  In doing so, the hope was that this quid-pro-quo-ish arrangement would lead millions of latent foreign consumers to buy American products.  This would in turn lead to a global economic interdependence and subsequent great peace and prosperity for all. Things didn't play out that way exactly.  China, India, Singapore, and others certainly did grow richer.  For various reasons, American businesses didn't swoop in and recapture that wealth though.  Moreover, the labor trade goes both ways.  Despite not adding any intrinsic value itself, arbitrage provides the general economic utility of helping market prices reach equilibrium.  If l...

  In The Simpson's episode Homer's Enemy ,  Frank Grimes is a unlucky man who's worked hard his life but failed to reap any of the benefit.  When confronted with the inequity of his circumstances compared to Homer's he breaks down into a self destructive fit of jealousy that eventually ends his life prematurely.   The episode is a 1990s comedic expression of the general observation that life is not fair.  Several decades later, as income inequality has increased it's become a popular talking point that, "Hard work alone doesn't make you successful."  In Gen A slang a, "Try Hard" is someone like Frank Grimes who does himself discredit by giving it too much effort. For us conscientious types, this is not only counter intuitive but also elicits an emotional reaction.  Folk heroes like Rocky show us that while God may not have blessed us with any particularly useful talent, we can make up for it for it with heart.  Hercules taught us that even wi...