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Deep, recent reads ranked by depth and freshness.

  1. Sequences and other volatiles in Neki

    Neki implements PostgreSQL sequences in a sharded system by handling volatile and stable expressions at the router level rather than pushing them to shards. Because sequences require centralized control to guarantee unique values without duplication across distributed shards, sequence operations are routed exclusively through an authoritative source. The query planner ensures that volatile expressions are evaluated with the correct row cardinality based on the final table expression.

    DEPTH ■■■■■14 min read11 days ago

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  2. 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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  3. 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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  4. PlanetScale is now available on Azure

    PlanetScale expanded its database offering to Microsoft Azure by adapting its Kubernetes-based infrastructure to handle Azure's networking constraints. The engineering team overcame Azure's private IP limits and slow pod startup times by implementing block-based IP allocation via a custom CNI plugin. This approach successfully increased IP capacity and removed the Kubernetes API server from the critical path during pod initialization.

    DEPTH ■■■■■3 min read12 days ago

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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. “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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  12. Handling hot shards

    Growing applications often encounter hot shards when certain tenants or data subsets outgrow initial sharding strategies based on workspace IDs. To mitigate this, teams can scale up resources, isolate large tenants into specific key ranges, or fundamentally change the shard key for problematic tables based on access patterns. Neki facilitates these adjustments by allowing declarative data topologies and online resharding without downtime.

    DEPTH ■■■■■7 min read3 weeks ago

    MySQLPostgreSQL

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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. Fragments: September 29

    This post discusses observations on coding agents, focusing on their persistence, the role of training material in hacking behavior, and the need for greater safety measures and liability. It examines experiments with unlimited tokens and explores regulatory and educational perspectives on agentic AI.

    DEPTH ■■■■■4 min read3 weeks ago

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  20. AI says you’re right, formal methods say you’re wrong

    Engineers often struggle with the intimidating reputation and adoption of formal verification tools like TLA+ and its derivatives, despite their value in catching elusive edge cases and race conditions. Using specification languages to model systems allows teams to test fixes and optimizations mathematically before deploying them to production. This approach helps prevent complex bugs that take weeks to manifest in real-world traffic.

    DEPTH ■■■■■28 min read9 days ago

    Machine Learning

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