MERGE
CONFLICT
DIGEST
|
September 29, 2025
|
|
Frontend Development đš
|
|
JavaScriptâs shallow copy creates a new topâlevel object but retains nested references, while a deep copy, achieved via structuredClone, cloneDeep, or Immer, recursively clones every layer, avoiding shared references and stale renders, though itâs costlier.
|
|
|
Hydration warnings in React appear only when the first client render mismatches serverâgenerated HTML; subsequent setState updates are normal. Ensure the initial render is deterministic by avoiding browserâonly logic, using useEffect for updates, or disabling SSR, as in Next.js.
|
|
|
Install next-sitemap via npm, create next-sitemap.config.js setting siteUrl and generateRobotsTxt. Hook this into a post-build script to output sitemaps and robots.txt to public/. The tool auto-indexes large sitemaps, supports transform, additionalPaths, APIs for dynamic routes, and robotsTxtOptions for custom policies.
|
|
|
Backend & APIs đ§
|
|
Article portrays Circuit Breaker for a SymfonyâŻ6.4 API gateway, Vue.js UI, and services using MySQL, MongoDB, ClickHouse, Kafka, with gateway, microservice, and client tiers, Kafka, ML thresholds, dashboards, graceful degradation, canary recovery, security throttling, memory logic.
|
|
|
This guide walks readers through building a Kubernetes operator with kubebuilder, from controller generation to reacting to cluster events and emitting stateâchange events. It covers OCI image building, Deployment, CRUD of a Task resource, validation/mutation webhooks, and TLS via certâmanager.
|
|
|
Geleda revisits MachineâPoweredâChat, reimagining it with gRPC to replace JSON payloads with protobuf tool descriptors, providing type safety, reflectionâderived types, protovalidate/CEL checks, TLS/mTLS security, and a GitHub demo of validated calls.
|
|
|
Go map "map_noswiss" achieves O(1) performance through hashing, handling collisions with overflow buckets and incremental growth to maintain performance. A unique seed prevents security issues like hash-flooding attacks by generating distinct hashes for each entry.
|
|
|
AI in Society & Economy đ
|
|
StudyâŻBudâŻisâŻanâŻAIâpowered learning companion for Herokuâs âBackâŻtoâŻSchool,â employing a multiâagent RAG with pgvector for subâ100âŻms semantic search, DjangoâŻREST/React/Tailwind frontâend, delivering faster study plans, realâtime Q&A, and future collaborative extensions.
|
|
|
An AIâdriven QA framework autoâgenerates test cases, pageâobject models, and edge scenarios via FastAPIâorchestrated LangChain calls to LLMs; CV models refine visual locators, while retrievalâaugmented generation and NLP on logs deliver concise summaries, all openâsource on GitHub.
|
|
|
Ollama introduces a web search API, granting realâtime data access via its platform. A free tier supports casual use, while developers may switch to higher rate limits in Ollamaâs cloud plan for expanded capacity.
|
|
|
Browser & Platform đ
|
|
Network topology dictates device connections and data flow, shaping performance, cost, and fault tolerance. Five core stylesâbus, star, ring, mesh, hybridâbalance simplicity, redundancy, and scalability; star dominates modern networks, while mesh suits highâreliability systems.
|
|
|
Industry & Trends đ
|
|
Remote work has shifted to asyncâfirst, letting Nairobi coders, Berlin designers, and SĂŁoâŻPaulo managers collaborate outside of 3âŻAM calls, replacing meetings with documentation, Loom videos, and tools like Notion and Figma, enhancing efficiency, inclusion, and trust in software engineering.
|
|
|
The guide presents a sixâstep framework for reliable LLM apps, integrating evaluation, observability, redundancy, prompt governance, output safety, and data hygiene. It supports automated and human testing, CI/CD, multiâprovider failover, semantic caching, IDEâbased prompt management, guardrails, and collaborative dashboards.
|
|
|
Software Development & Engineering đ»
|
|
The article critiques Kent Beck's 2003 TDD book, noting its narrow claims, unproven benefits, flawed examples, and confusing presentation, while acknowledging his disciplined practice; ultimately, weak examples and unrealistic assumptions curb its teaching value.
|
|
|
Good taste in software engineering, unlike raw skill, hinges on selecting values such as speed, correctness, or resilience tailored to each project; it develops through varied problems, nuanced tradeâoffs, learning from outcomes, and resisting rigid habits that could derail success.
|
|
|
|
Published by Merge Conflict Digest
|
|