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How Haskell confused me, Rust prepared me, F# made the idea click, and Python made me build the missing piece for data and ML workflows. A nightly feature pipeline fails while we are sleeping. Imagine a common failure case. A source table arrived late. One column has a new nullable value. A model metadata file is missing the training window. The pipeline is supposed to handle these cases because they are ordinary failures in data work. They are not developer mistakes in the same category as an index error or a broken invariant. ...

The practical question is where your hot path lives: in Python code, native libraries, or custom stateful logic. The Rewrite Question Usually Starts Too Late Suppose a data pipeline is slower than you want. It reads a few million rows, computes some derived columns, joins a dimension table, aggregates by user segment, and then runs a small amount of business logic over events. The code is in Python. Someone on the team has been learning Rust. The suggestion comes up quickly: maybe this should be rewritten. ...

A working hypothesis from repeated coding agents use: FP-style structure seems to make non-FP codebases easier for coding agents to understand, change, and verify. The Missing Variable In Agentic Coding Picture a Monday-morning task in a Python or TypeScript service. The request looks ordinary: add a validation rule, thread one more field through a pipeline, keep the old API stable, update the tests, and do not break the nightly job. You hand the task to a coding agent. In one repository the run feels clumsy. The agent reads too much, edits the wrong layer first, and produces a patch that looks plausible until you notice the missed edge case. In another repository the same class of task goes much better. The agent locks onto the relevant types, updates a few focused functions, adjusts the tests, and lands in the right area with much less wandering. ...

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. ...