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| 🔗 Stories, Tutorials & Articles |
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| Agentic Coding is a Trap |
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| AI-driven coding agents are the hot new trend, but beware of the trade-offs: increased complexity, skills atrophy, vendor lock-in, and fluctuating costs. Only skilled developers can spot issues in the vast lines of generated code, but paradoxically, AI tools are impacting critical thinking skills negatively. To avoid this, demote AI's role, leverage it for planning, and stay actively engaged in implementation to maintain coding skills and mitigate comprehension debt. |
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| When upserts don't update but still write: Debugging Postgres performance at scale |
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| The Datadog team introduced a new upsert query to track inactive hosts, but it unexpectedly increased disk writes and WAL syncs due to row locking. By digging into Postgres's Write-Ahead Logging (WAL) and rewriting the query using a Common Table Expression (CTE), they avoided unnecessary overhead and improved performance. The new query eliminated the need for locking and minimized WAL activity and allowed for more efficient host tracking without sacrificing correctness. |
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| How We Reduced Median Memory Estimation Error by 99%, With the Help of AI |
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| The compaction pipeline at Mixpanel ran into memory estimation issues causing OOMKills. By implementing AI-assisted analysis, they were able to reduce median estimation error by 99%, leading to a significant improvement in memory estimation accuracy. Through thorough analysis and exploration of alternative approaches, they were able to find a simple solution that outperformed more complex alternatives. |
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| How To Make a Fast Dynamic Language Interpreter |
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| Zef's AST-walking interpreter posts a 16.6× speed-up. The gains come from surgical changes: 64-bit tagged values, AST node & RMW specialization, symbol hash-consing, inline caches, and a shaped object model. Developers built it on Fil-C++ and later ported it to Yolo-C++. The Yolo build adds ~4x speed, at the cost of unsound GC behavior. Zef now challenges CPython, Lua, and QuickJS. |
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| A Couple Million Lines of Haskell: Production Engineering at Mercury |
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Mercury runs ~2M lines of Haskell in production.
They chose Temporal to retire cron and DB-backed state machines. Durable workflows replace brittle coordination.
They open-sourced a Haskell SDK for Temporal, wired in OpenTelemetry hooks, and pushed records-of-functions plus domain-error types. |
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