How DNS Resolution Really Works: Caches, Failures, and Debugging Layers

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DNS behaviour underlies common engineering problems such as stale IP connections, NXDOMAIN errors after record changes, and certificate issuance failures. Resolution involves three distinct roles: the stub resolver on the client, the recursive resolver that performs iterative lookups and caches results, and the authoritative server that holds zone data. For a cold cache, resolving a hostname like api.acmeapp.io requires a chain of referrals from root servers down to the TLD and finally the authoritative nameserver, with each response cached at its own TTL. Key practical factors include glue records for in-zone nameservers, EDNS0 buffer limits that cause TCP fallback on large responses, and anycast routing that means two clients querying the same resolver IP may receive different cached answers. Understanding zone delegation — where an NS record marks the boundary of a separate zone — is essential for diagnosing split-authority and staging-environment DNS issues.

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Developer catalogs 22 AI coding anti-patterns and builds tool to auto-delete them

A developer has identified 22 recurring patterns of low-quality AI-generated code, dubbed 'code slop', that appears correct but introduces subtle bugs, redundancy, and security gaps. Common issues include swallowed exceptions, hallucinated dependencies, one-implementation interfaces, restating comments, and generic variable names like 'data' or 'process'. A USENIX Security 2025 study of 2.23 million AI-generated samples found that nearly 20% referenced non-existent packages, with 43% of invented names reappearing across separate runs. The developer built an agent skill to detect and remove these patterns, demonstrating a real-world refactor that reduced 54 lines of bloated invoice-processing code to just 4 functional lines. The catalog covers not only code quality issues but also AI-generated prose and commit message anti-patterns that mimic the style of experienced engineers without delivering real substance.

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ProgrammingDEV Community ·

Four Hidden Pitfalls of Postgres Logical Replication for Reporting Replicas

A software developer has documented four critical but rarely discussed problems encountered when setting up a PostgreSQL logical replication-based reporting replica. Unlike physical replication, logical replication decodes WAL changes into row-level operations, allowing selective table replication, cross-version compatibility, and replica-only indexes — but with notable trade-offs. One major issue is that DDL changes like adding columns are not replicated, meaning schema migrations must be manually coordinated on both the primary and subscriber databases to avoid silent data gaps or replication errors. Another overlooked problem involves sequences for SERIAL and IDENTITY columns, which are not replicated at all, leaving the replica's sequence counters out of sync and risking duplicate key errors if the replica is ever written to or promoted. The article urges teams to treat replicated-table migrations as two-database operations and plan for sequence management upfront rather than discovering these gaps during incidents.

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ProgrammingDEV Community ·

How to Handle Encrypted Supplier PDF Decryption Failures in Invoice Pipelines

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ProgrammingDEV Community ·

Team Hacker House Builds GraphRAG Fraud Detection Agent Using TigerGraph and Claude

A developer team called Hacker House Goa 2026 has built an autonomous fraud investigation system named FraudLens, using TigerGraph Savanna as its core graph database. The system addresses two major weaknesses in traditional fraud detection: high false-positive rates that block legitimate customers and an inability to detect coordinated syndicate activity spread across many accounts. FraudLens runs an eight-step state machine that traverses multi-hop graph relationships, measures evidence sufficiency, and recommends regulatory actions with full audit trails. The system integrates TigerGraph's native GSQL algorithms, LangGraph, Anthropic's Claude, FastAPI, and React for its full stack. In benchmark testing across 20 cases, the agent achieved 100% schema and logic compliance, correctly flagging two cases for regulatory filing while safely clearing the remaining eighteen.

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