A TF-IDF vignette for designing around data flow, types, and contracts before code. In my November 9, 2025 post, Functional Programming in Python, I argued that FP is less about adopting a pure functional language and more about adopting a different engineering discipline. The core habits were simple: think in expressions, prefer immutability, isolate side effects, and model your domain explicitly. That line of argument tends to invite a practical question. If a team gives you a vaguely defined task on Monday morning, where does the work begin, and what does “FP-style” look like before the code exists? ...

Coding agents can write code, but reliable multi-step work depends on the harness around the model. Source: r/ClaudeCode The agent looked competent until the workflow got longer Picture a small infrastructure team trying to standardize how it uses a coding agent across a growing repository. At first the experience is encouraging. Someone asks the agent to add a CLI flag, wire a metrics endpoint, update a test, and explain the change. The answer is fast, the patch is plausible, and the team starts to feel that a large part of everyday engineering has become compressible. ...

The leak showed how quickly code can be copied once AI turns appropriation into ordinary workflow. A proprietary codebase became public infrastructure before breakfast At 4 AM on March 31, 2026, a developer in Seoul woke up to a phone full of notifications. Claude Code’s source had leaked. Within hours the code was mirrored across GitHub, screenshotted across social feeds, mocked as “vibe-coded garbage,” and ported into new repositories by people who had not worked at Anthropic the day before. By the time many engineers on the U.S. East Coast opened Slack, the incident had already passed through several distinct phases: exposure, ridicule, cloning, and legal containment. ...

Good NLP starts with reading the text: define the task, keep meaning-bearing words, then build simple, inspectable baselines. Some Text Problems Punish Shallow Reading Suppose you need to flag notes that describe active chest pain right now. The task sounds easy until the notes arrive. Reports chest pain x2d, worse on exertion. Denies chest pain; shortness of breath improved. Family history of CAD. No active chest pain today. Chest pain improved after nitroglycerin. Cannot rule out infection. No known drug allergies. SOB with exertion. Family hx diabetes. These fragments share vocabulary, but not meaning. Some describe active symptoms. Some are negated, historical, or about family history. Some express uncertainty. Some hide ordinary clinical meaning inside abbreviations. A model that treats them as ordinary word overlap will make confident mistakes fast. ...

Spec-driven development is older than AI, but agent workflows make the boundary between design artifact and executable logic newly important. (Source: “A very comprehensive and precise spec” from CommitStrip) An Older Engineering Habit Under New Pressure Picture a platform team reviewing an internal agent workflow for customer support. They are debating a scheduler that fans out work across multiple agents, retries failed steps, persists thread state, and hands off certain actions to a ticketing extension. One engineer wants to write the logic directly. Another wants a design document first. A third wants an RFC with interface contracts and failure semantics before anyone touches the service loop. ...

While the headlines scream of mass layoffs, the data suggests a more subtle and structural shift: the ladder is being pulled up from the bottom. Source: https://www.anthropic.com/research/labor-market-impacts (Figure 2) The Ghost in the Unemployment Statistics “AI is coming for your job.” It’s the refrain of every tech influencer and doom-scrolling headline. Two days ago, a report dropped that seemed to confirm the worst. Anthropic, one of the leading labs behind the very tech in question, released Labor Market Impacts of AI: A New Measure and Early Evidence. ...

We disentangle population risk from model-specific performance when subgroups enter with different risk profile. (Core idea of the adjusted TPR; some image components generated by NanoBanana 2) In part 1, we saw that a raw true-positive-rate gap can be a mirror of baseline risk differences, not just model behavior. In part 2, we make that distinction operational and show how to measure fairness once those risk differences are held constant. ...

We dive into whether and how forcing equal outcomes in healthcare algorithms can overlook the reality of baseline risk and demographic diversity. Clipart generated by Google NanoBanana 2 A Familiar Evaluation Result — and an Uncomfortable Question Let’s begin with a short vignette. A healthcare analytics team is reviewing a mortality risk prediction model used to trigger palliative care consultations. The model has been deployed for several months. As part of routine governance, the team evaluates fairness across demographic groups. ...

A language-first view of modern AI systems (2026) (source: Companies Bring AI Agents to Healthcare (WSJ)) A system that sounds simple—until it isn’t Imagine a health system designing a conversational AI service for telemedicine. Patients describe symptoms, concerns, and fragments of medical history in free text. The system responds conversationally, drawing on prior encounters, internal documentation, and clinical guidelines. It answers routine questions, summarizes relevant context, and—when appropriate—routes cases to clinicians. ...

Opening Happy new year! In my previous post, My Winter NLP Journey, I wrote about motivation: why I wanted to build a old model like GPT-2 and why implementation feels like the fastest path to understanding. I am glad to report I completed the journey as planned. This post is a reflection on what actually changed in my head after working through the course on my own. The biggest gain was simple to name but hard to achieve: several ideas that were vague for years finally became clear. ...