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Conf42 JavaScript 2025

frontend developers, javascript developers, full stack developers, engineering managers, data engineers, ai and machine learning engineers, accessibility engineers

TypeScript game development; WebGL2 and WebGPU; WebAssembly and AssemblyScript; agentic coding in the IDE; docs-as-context for LLMs; ARIA accessibility techniques; prioritized task scheduling (scheduler.yield); browser-mode component testing with Vitest; Module Federation micro frontends; real-time data pipelines with Databricks; API integration with OAuth and connectors; confidential computing for VM security

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Level Up: Why TypeScript/JS for game development?Annie Tsai, Adam Ringhede12m
Engine work uses web tech for two years and targets web/mobile genres from hypercasual to FPS, with platforms like Poki and CrazyGames already hosting complex…
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Making Docs and AI Work Together: Agentic Coding in the IDESharon Yelenik26m
Built a pipeline that turns API docs into IDE-executable context: Cloudinary MCP servers plus an LLM client in Cursor, with Context7 indexing and short rules…
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Accessible JavaScript: it's easier than you think!Gian Wild47m
Clear, actionable checklist: implement 3 types of JavaScript functionality (binding, non-interactive, custom), follow 10 accessibility principles (text…
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The 2025 State of JavaScript TestingDaniel Afonso29m
Survey data shows mocking is the top pain, 62% of companies use Jest + React Testing Library (Storybook common) and 13% report no tests; eight company cases…
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Let Your Browser Take a Breather with a Scheduler.yield()Aleksandr Tkachenko28m
Claim: scheduler.yield() from the prioritized task scheduling API pauses long JavaScript chunks and resumes them as a prioritized microtask so UI events run…
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Introduction to ARIA: A Developer's GuideAnisat Ahmed34m
ARIA is presented as a set of attributes that fill semantic gaps when HTML alone isn’t enough, with the central claim that use native HTML first and reach for…
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Transforming Knowledge Systems with Embedded AnalyticsHarshita Dubey05m
Enterprise knowledge systems show >50% failure rates at 100k+ organizations; this case describes a 24‑month overhaul that layers custom JavaScript…
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Generative AI for Claims and FraudAmala Arul Malar Umakanth16m
Tens of millions of claims per day create fragmented data and over $100 billion in costs, and this session lays out a concrete stack: a five-layer architecture…
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Fast-Track AI Integration for Modern Web AppsLinga Annela17m
Claims concrete outcomes: micro frontends speed feature delivery three times faster, raise engagement by 45% and accelerate A/B testing nearly tenfold while…
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API Integration with JS: Scalable, and Future-Ready SystemsDarshini Jayaprakash16m
Argues that REST stays dominant because stateless HTTP scales, and middleware with pre-built Salesforce/SAP/AWS connectors cuts integration work from weeks or…
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Cross-Cloud Campaign Analytics Meets JavaScriptSruthi Erra Hareram14m
Architecture delivers fast trusted answers by pushing heavy work to Azure Data Factory and Azure Databricks, storing daily partitions in a columnar lake and…
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Transforming Healthcare Operations with Measurable GainsRakesh Kumar Yadav14m
Shows a deployed NodeJS and React stack using microservices, Redux state, websockets and server-sent events to power real-time dashboards, chatbot triage and…
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EDI 837 Validation: Automating Claims with Modular JS FrameworksNachi Gudipudi19m
A modular JavaScript framework validates EDI 837 claims at scale by converting X12 837 EDI to JSON, applying metadata‑driven rule engines for eligibility…
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Multi-Agent Intelligence for Next-Gen InsightsAjitha Buvanachandran16m
Claims end-to-end enterprise analysis drops from days to ~20 minutes by orchestrating specialized agents—research, analysis, domain expert, quality—that…
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Agentic AI for Autonomous Micro-Frontend User InterfacesJyoti Kunal Shah13m
An Agentic Control Plane is built that runs RL agents and LLM agents over OpenTelemetry inputs and applies fixes via GitHub pipelines and service mesh…
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JavaScript Text Analytics for User Behavior IntelligenceUjjwala Modepalli16m
Claims client-side NLP runs fully in-browser for privacy, real-time feedback and cost savings, using TensorFlow.js models (lightweight transformers/LSTMs) for…
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Scaling Reinforcement LearningJyotirmoy Sundi17m
Describes a production recipe for RLHF at scale: training on Kubernetes multi-GPU clusters (4–8 GPUs per pod) using DeepSpeed and Ray Train with NCCL/NVLink…
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VM Security with Confidential ComputingPriyadarshni Shanmugavadivelu13m
Cloud VMs use AMD SEV SNP and Intel TDX to encrypt VM memory and create hardware-enforced trust domains with TPM 2.0 measured boot and PCRs for remote…
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AI-Driven Rate Limiting for Scalable and Cost-Efficient APIsRehana Sultana Khan39m
Describes an AI rate limiter that ingests over 14 data points and synthesizes 27 behavioral features, trains a hybrid model (random forest committee +…
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Framework for Frontend & Node.js Vulnerability ManagementSrinivasa Rao Gunda18m
Covers a practical vulnerability-management framework that assesses dependencies, enforces prevention patterns, automates detection and applies risk-based…
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Privacy by Design for JavaScript Data SystemsVivekananda Chittireddy16m
Conference-level claim: 140 countries have strong privacy laws and average breach cost is ~$5M, and 87% of US citizens are reidentifiable from three…
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Revolutionizing JavaScript PerformanceDeepika Annam11m
Claims measured in production: 2.29× throughput gains, 40% bundle size reduction, 60% faster load times and 3× API response improvements. Describes Node.js…
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Real-Time Data Systems: Speed, Scale, and ResilienceLakshmi Bhargavi Mullapudi08m
Real-time pipeline targets 180 gigabytes with ~30% realtime demand and latency goals under 1 second, using event capture at ~10 ms, ML scoring at ~50 ms and…
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Building the Future: Microservices, Edge & Cloud InnovationSurya Prabha Busi18m
Microservices replace monoliths and claim adoption rising from 45% in 2019 to 91% in 2024, enabling a 76% deployment time reduction and ~50% operational cost…
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Building Real-Time Feature PipelinesVenkata Chandra Sekhar Sastry Chilkuri11m
Shows an end-to-end JavaScript pipeline that consumes Kafka events in Node.js, computes windowed, idempotent features, caches them in Redis with sensible TTLs…
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