Algorithms4Life.com
// Paper. Pencil. Math that still works in 2026.
Before anyone said the word "algorithm," mathematicians were using series equations to predict outcomes. The tools changed. The math didn't. This site exists to prove that anyone — with the right variables and a little patience — can build their own.
The Origin
"I started in engineering school in the late 1970s. Calculators were just coming out. Computers took up whole buildings. And I was solid on the math."
mx + b = future price
or, if you prefer: a² + b² = c²
What Silicon Valley sells today as artificial intelligence, mathematicians were doing by hand fifty years ago. Series equations. Predictable outcomes from defined inputs. The concept hasn't changed — only the speed at which we run it.
In 2008, long before "algorithm" became a household word, these ideas were sketched out on paper: how ordinary people could build their own mathematical models to predict stock prices — or anything else with measurable variables.
Those notes sat in a drawer for fifteen years. Now the world has caught up. Algorithms4Life exists to finally put these tools in your hands — not as a black box, but as something you build and understand yourself.
Because your outcome is only as certain as the input you choose.
What We Believe
01
Series equations, linear regression, geometric prediction — these existed long before computers. AI didn't invent this. It just runs it faster.
02
The best algorithm uses what you already know. Your industry, your local market, your instincts — translated into inputs. Garbage in, garbage out. But wisdom in? That's different.
03
No algorithm removes uncertainty. A good one quantifies it. Understanding your own margin of error is more valuable than false confidence in someone else's black box.
How It Works
What do you know that others don't? Stock price vs. yesterday's close, oil prices, local temperature, commodity costs. Any measurable factor that might influence your outcome belongs in your model.
What are you comparing against? A previous price, a time period, an industry average. Your baseline is your reference point — the anchor everything else is measured from.
Plug in historical data, run the model across multiple points in time, and let a pattern emerge. The more data points you use, the smoother and more reliable the curve becomes.
Once you have the pattern, you can project it forward. Not with certainty — but with your own calculated, informed estimate. That's not a guess. That's a model.