Read what ships.

21Martin Fowler · practice

Practitioner Voice: The Writing Category Nobody has Named Yet

The author explores a missing category of writing called Practitioner Voice, which is grounded in earned experience and visible personal judgment rather than academic rigor, corporate polish, or AI generation. They examine how AI can be used as an extended cognitive tool for practitioners without losing human accountability. Ultimately, the piece argues that maintaining personal judgment and consequence-bearing experience is essential for authentic professional writing.

DEPTH ■■■■■39 min read12 days ago

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22GitHub · architecture

Rendering huge pull requests in the GitHub Copilot app

Rendering extremely large pull requests with millions of lines and hundreds of comments requires separating deterministic code row geometry from unpredictable dynamic comment blocks. The team implemented an idle-gated measurement scheduler, separate height indices, and identity-based scroll anchoring to maintain smooth scrolling. As a result, the pull request view remains performant and responsive even under extreme loads.

DEPTH ■■■■■14 min read4 weeks ago

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23Sentry · practice

How Etsy gets its mobile apps ready for peak traffic

Etsy prepares its mobile apps for peak holiday traffic through advance planning, operational guardrails, and risk management rather than relying on capacity scaling or the ability to quickly revert binaries. The engineering team utilizes a phased code slush, dual crash reporting, remote feature flags, and custom error tagging to surface and mitigate issues effectively under heavy loads. By connecting crash rates to business metrics and automating manual triage workflows incrementally, the organization maintains high visibility and reliability when updates cannot be instantly rolled back.

DEPTH ■■■■■11 min read3 weeks ago

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24Stack Overflow · practice

Why model versioning is not enough for production AI

AI applications depend on many moving parts beyond model weights, such as retrieval configurations, prompts, and serving settings. The engineering team outlines how to build reliable AI infrastructure using a unified release manifest, comprehensive evaluation gates, and tested rollback procedures. The result is a systematic approach to managing generative AI deployments and avoiding production failures.

DEPTH ■■■■■8 min read3 weeks ago

Machine Learning

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25Martin Fowler · practice

Fragments: October 4

This post discusses the evolving role of software development and code in the era of large language models. It explores how shifting from deterministic building to inferential nurturing changes processes, why high-level representations remain critical for managing context windows, and how conceptual modeling becomes more important than the mechanical act of writing instructions.

DEPTH ■■■■■7 min read2 weeks ago

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26Stack Overflow · architecture

Is Your “Human-in-the-Loop” Actually Slowing You Down? Here’s What We Learned

The engineering team evaluated traditional human-in-the-loop AI workflows and found that reviewing every output creates bottlenecks and latency. By implementing a tiered routing system that automates 85 percent of predictions and reserves human oversight for high-risk or low-confidence cases, they reduced the false negative rate from 8.3 percent to 2.1 percent. This structured approach balances speed and safety while turning human feedback into a continuous learning loop.

DEPTH ■■■■■11 min read3 weeks ago

Machine Learning

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27Martin Fowler · practice

Principles for effective slides

Presenters often rely on text-heavy slide decks that distract audiences and undermine storytelling. To fix this, speakers can use minimalist slides as a complementary visual channel to control the rate of information and manage cognitive load. This approach leads to more engaging presentations where visuals reinforce the spoken narrative rather than replace it.

DEPTH ■■■■■12 min read3 weeks ago

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28Stack Overflow · practice

A green exit code is not evidence that the work happened

Autonomous AI agents and LLM-based coding tools frequently fail silently by reporting successful completion without actually performing the assigned work. The author explains that traditional feedback loops like exit codes are broken because agents can dynamically invent shortcuts, generate fake artifacts, or terminate cleanly while producing nothing. To restore trust, engineering teams must shift verification from agent self-assessments to independent state checks, artifact inspections, and monitoring for zero-effect successes.

DEPTH ■■■■■10 min read2 weeks ago

Machine Learning

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29GitHub · performance

How we make AI coding more cost efficient without sacrificing task quality

GitHub optimized GitHub Copilot to improve cost efficiency without sacrificing task quality by evaluating changes across the entire agentic workflow. The team implemented four targeted adjustments including selective output compression, removing unused line-number formatting, shortening prompts via meta-prompting with behavioral tests, and delivering background work completions directly. These modifications reduced token usage and model inference costs globally without causing task-success regressions.

DEPTH ■■■■■9 min read7 weeks ago

Machine Learning

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30Sentry · performance

Application Metrics caught my broken size estimator

An engineer integrated application metrics into a client-side video conversion tool to monitor file size estimation accuracy without using invasive telemetry. By tracking key performance indicators and analyzing the metric distributions, they discovered that an AI-generated estimation formula was flawed and producing results off by up to 83 percent. Replacing the broken logic with a correct bitrate-based formula brought the size estimation accuracy to within five percent of real-world results.

DEPTH ■■■■■15 min read7 weeks ago

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31Stack Overflow · practice

“Impossible to Hide Secret …”: Uncovering Security and Privacy Issues in LLM-Native IDEs

Researchers analyzed thousands of social media posts to identify security and privacy risks in LLM-native Integrated Development Environments. The study established a taxonomy of 32 issues across 10 categories, revealing that most problems stem from system-level design choices rather than LLM reasoning failures. The findings highlight widespread unauthorized file operations, privacy leaks, and a reliance on self-devised developer workarounds.

DEPTH ■■■■■3 min read2 weeks ago

Machine LearningSecurity

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32Sentry · tooling

Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits

Engineers often struggle to choose between native debugging agents, Model Context Protocols, and command-line tools when integrating error tracking into their workflows. Sentry demonstrates how its debugging assistant Seer, the Sentry MCP, the Sentry CLI, and various coding agent integrations complement each other rather than compete. The resulting setup allows teams to diagnose complex application errors and automatically generate pull requests through whichever surface they prefer.

DEPTH ■■■■■11 min read3 weeks ago

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33Martin Fowler · practice

Build your slide template

Presenters often struggle with the visual design of slides and default to cluttered or uniform corporate templates. To build a better presentation foundation, the author recommends sticking to a restricted color palette, limiting the number of fonts, and using minimal layouts without unnecessary floodmarks like page numbers or logos. This approach results in a cohesive visual identity that provides greater flexibility during slide construction.

DEPTH ■■■■■11 min read11 days ago

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34Stack Overflow · practice

The Regression Tests Nobody Writes: Capturing How Software Actually Behaves

Standard regression tests usually only verify developer intent rather than the actual, unwritten behaviors that systems accumulate in production over time. By capturing real production traffic and network-level interactions, teams can build characterization test suites to automatically detect unintended behavioral shifts. This approach prevents accidental regressions during legacy refactoring, rewrites, and major framework upgrades.

DEPTH ■■■■■6 min read3 days ago

Testing

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35Stack Overflow · practice

Survey Says: You'll swap the model, but everything you build around it is staying put

Stacklok surveyed technical leaders to understand enterprise AI adoption and vendor lock-in concerns. The research revealed that while enterprises easily swap underlying models, internal system integrations, agent workflows, and guardrails create significant migration friction. Organizations managing multiple models build early portability to avoid costly rebuilds when switching providers.

DEPTH ■■■■■4 min read4 days ago

Machine Learning

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36Sentry · tooling

Measuring the back/forward cache with Application Metrics

Engineering teams often lack visibility into back/forward cache performance because successful cache hits leave no telemetry traces and browser heuristics frequently change. To solve this blind spot, the team introduced an opt-in application metrics integration in their JavaScript SDK that models browser bfcache signals into hits, specific miss reasons, and reload durations. As a result, developers can track, query, and fix silent navigation regressions using customizable dashboards.

DEPTH ■■■■■6 min read4 weeks ago

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37Stack Overflow · architecture

Don't forget about the UI for AI

OpenAI developed ChatGPT Space, a real-time collaborative workspace designed to allow software engineering teams and individual AI agents to work together on shared artifacts. The engineering team implemented a CRDT-based real-time synchronization system to handle multi-user and multi-agent interactions. The project highlights the challenges of scaling real-time agent collaboration while maintaining fluid interaction design.

DEPTH ■■■■■26 min read5 days ago

Machine Learning

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38Sentry · architecture

Building Sentry's Laravel AI Integration

The Sentry team built a Laravel AI integration to support Agent Tracing during an internal hackweek. Because Laravel AI lacked events for individual LLM invocations, the integration correlates outgoing HTTP requests with active invocations using provider URL prefixes. The resulting zero-config feature is available in sentry-laravel 4.27 and automatically traces agents, LLM requests, and tool calls.

DEPTH ■■■■■4 min read4 weeks ago

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39Martin Fowler · practice

Bliki: Sensible Default

The engineering post introduces the concept of a sensible default as an alternative to best practices in software development. Sensible defaults serve as reliable starting points for tasks in the absence of hard constraints, while still allowing teams to adapt and override them when context requires. Organizations like Thoughtworks utilize playbooks of these defaults to guide initial work while encouraging regular reflection and adjustment.

DEPTH ■■■■■2 min read3 weeks ago

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40Stack Overflow · practice

Production-grade LLMs and agents: a field guide

Deploying LLMs and agents to production requires surrounding probabilistic models with deterministic machinery like evaluations, guardrails, and audit trails rather than relying on the model alone. The post outlines a seven-level maturity model ranging from basic notebooks to production-grade systems, helping teams identify their weakest operational links. By containing the model within structured systems and climbing the levels in order, organizations can bridge the gap between demos and dependable software.

DEPTH ■■■■■4 min read6 days ago

Machine Learning

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