Science lessons & courses

Most microbes on Earth have never been grown in a laboratory, so for a century microbiology studied the small fraction that cooperated. This lesson covers the great plate count anomaly, how 16S sequencing and metagenomics read organisms directly, and what those methods still cannot tell you.

An antibiotic has to break something a bacterium has and you do not. This lesson covers the four target families, how MIC is measured, the four ways bacteria defeat a drug, and how a resistance gene moves between cells that are not even related.

Bacterial growth is exponential, which makes the arithmetic simple and the intuition terrible. This lesson covers doubling time, the four phases of a culture, and the gap between the cells you can see down a microscope and the ones that will actually grow on a plate.

A bacterium and one of your cells are both alive and share almost no structure beyond that. This lesson walks the bacterial cell part by part, the wall, the loop of DNA, the smaller ribosome, and shows why each difference is exactly what an antibiotic aims at.

There are tens of thousands of engineering materials and no way to compare them by intuition. This lesson builds the material index, the single combined number that ranks candidates for a stated job, shows why steel, aluminium and titanium have almost identical stiffness per kilogram, and covers the environment and processing constraints that overrule the ranking.

Most catastrophic structural failures happen far below yield strength, because a crack was already there. This lesson builds the fracture mechanics that predicts the largest crack a part can carry, shows why raising strength shrinks that number, and works through the two disasters that turned fatigue from a curiosity into a design discipline.

A dislocation is a line of atomic misfit that lets a crystal slip a few bonds at a time, and its mobility decides whether a material bends or breaks. This lesson explains how it moves, why some crystal structures permit it and others do not, and the four standard ways engineers obstruct it to buy strength at the price of ductility.

A perfect copper crystal should yield at about 7 GPa. Real annealed copper gives way at 10 MPa, hundreds of times lower, and the gap took thirty years to explain. This lesson separates stiffness from strength from toughness, works the theoretical calculation, and shows why the answer turned out to be defects rather than arithmetic.

QKD has an unconditional security proof and almost no deployment. This lesson covers the authentication bootstrap it cannot solve, distance limits and the trusted node compromise, attacks on real hardware that the proof does not cover, why NSA and NCSC recommend against it, and where quantum genuinely delivers.

Quantum cryptography uses physics rather than computational hardness. This lesson covers the no-cloning theorem, the BB84 protocol step by step, why measurement in the wrong basis leaves a detectable trace, the error rate threshold, and the entanglement-based alternative.

What the mRNA platform can actually treat. This lesson covers preventive vaccines, personalized cancer vaccines built from a patient's own tumor mutations (with the Moderna-Merck mRNA-4157 melanoma results), and the protein-replacement and cell-therapy frontiers, plus the honest limits that still constrain the field.

A therapeutic mRNA is destroyed in the body within minutes and cannot cross a cell membrane on its own. This lesson explains the lipid nanoparticle that solves it: its four components, the ionizable-lipid trick that releases mRNA inside the cell, where the particles travel after injection, and the cold-chain limitation that follows.

Instead of manufacturing a protein, an mRNA medicine delivers the instructions and lets your own cells build it. This lesson covers the anatomy of a therapeutic mRNA, the immune problem that stalled the field for decades, and the modified-nucleoside breakthrough by Kariko and Weissman that made it work, the reason mRNA is a programmable platform.

Using AI in clinical care responsibly means protecting patients above all. Learn the human-in-the-loop imperative and why the physician stays accountable, how to handle protected health information and privacy, why bias and equity are safety issues, what transparency patients are owed, and how regulation and oversight keep AI-assisted medicine safe. A safety-first framework for clinical AI.

A practical guide to the clinical workflows where AI helps physicians most. Learn how ambient AI scribes reduce documentation burden and burnout, how decision support surfaces guidelines and drug interactions to inform judgment, how imaging AI acts as a second read, and how AI drafts patient communication, all with the physician verification that keeps care safe.

AI is entering medicine through documentation, decision support, and imaging, but patient safety sets it apart from every other field. Learn the categories of clinical AI tools, the difference between regulated diagnostic AI and general assistants, why AI supports rather than replaces physician judgment, and the caveats, hallucination, bias, and accountability, that make oversight non-negotiable.

AI touches nearly every stage of research, from reading the literature to analyzing data to writing the paper. Learn the categories of AI tools scientists use, the crucial difference between general assistants and specialized scientific AI like protein-structure predictors, and the caveats, hallucinated citations and reproducibility, that make rigor essential.

Using AI in research responsibly means protecting the standards that make science trustworthy. Learn the integrity rules, disclosure, authorship, and the line between assistance and fabrication, why reproducibility and transparency constrain AI use, what AI genuinely cannot do in the scientific method, and how peer review and honest reporting keep AI-accelerated science reliable.

A practical guide to the research workflows where AI helps most. Learn how to use AI to search and synthesize the literature (and verify it), write and debug analysis code even without being a programmer, run and interpret statistics, and draft scientific writing, all with the verification and reproducibility discipline that keeps the work rigorous.

The frontier of longevity is the search for a pill that slows aging. Learn what the science actually shows for the leading candidates, rapamycin, metformin, senolytics, NAD boosters, and GLP-1 drugs, how each targets a hallmark of aging, why results in mice so often fail in humans, and how to tell genuine research from supplement marketing. Evidence-first, not medical advice.

Two people can be the same age in years but decades apart in how their bodies have aged. Learn how scientists measure biological age with epigenetic clocks that read your DNA, what these tests can and cannot tell you, whether aging can really be reversed, and why even our data on the world's longest-lived people, the Blue Zones, turns out to be shakier than the headlines suggest.

Longevity advice is a sea of hype, but a few levers have genuinely strong evidence behind them. Learn to rank interventions by evidence quality, then meet the biggest proven ones: cardiorespiratory fitness (the single strongest predictor of lifespan), muscle strength, diet, sleep, and the avoidables. See the real numbers from large studies and why these boring basics act on the hallmarks of aging.

Aging is not vague wear and tear, it is a set of specific, measurable biological processes. Learn the crucial difference between lifespan and healthspan, then the twelve hallmarks of aging, the scientific framework of what actually goes wrong in the body over time, from telomere shortening and zombie cells to chronic inflammation, and why they form a connected web rather than a single cause.

What Shor, Grover, and Hamiltonian simulation really promise, how big each speedup is once error-correction overhead is paid, why post-quantum cryptography exists regardless of timelines, and how to evaluate any claimed quantum application.