Languages & Frameworks
Adopt
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83. OpenTelemetry
OpenTelemetry is quickly becoming the industry standard for observability. The release of the OpenTelemetry protocol (OTLP) specification established a standardized way to handle traces, metrics and logs, reducing the need for multiple integrations or major rewrites as monitoring distributed solutions and interoperability requirements grow. As OpenTelemetry expands to support logs and profiling, OTLP ensures a consistent transport format across all telemetry data, simplifying instrumentation and making full-stack observability more accessible and scalable for microservices architectures.
Adopted by vendors like Datadog, New Relic and Grafana, OTLP enables organizations to build flexible, vendor-agnostic observability stacks without being locked into proprietary solutions. It supports gzip and zstd compression, reducing telemetry data size and lowering bandwidth usage — a key advantage for environments handling high volumes of telemetry data. Designed for long-term growth, OTLP ensures OpenTelemetry remains a robust and future-proof standard, solidifying its position as the de-facto choice for telemetry transport.
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84. React Hook Form
We noted React Hook Form as an alternative to Formik. By defaulting to uncontrolled components, it delivers significantly better out-of-the-box performance, especially for large forms. React Hook Form is well-integrated with various schema-based validation libraries, including Yup, Zod and more. Additionally, React Hook Form offers a lot of flexibility, making it easy to integrate with existing codebases and other libraries. You can use React Hook Form with external controlled components libraries such as shadcn or AntD. With strong performance, seamless integration and active development, it’s a solid choice for building large form or form-heavy application.
Trial
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85. Effect
Effect is a powerful TypeScript library for building complex synchronous and asynchronous programs. Web application development often requires boilerplate code for tasks such as asynchrony, concurrency, state management and error handling. Effect-TS streamlines these processes using a functional programming approach. Leveraging TypeScript’s type system, Effect helps catch hard-to-detect issues at compile time. Our team previously used fp-ts for functional programming but found that Effect-TS provides abstractions that align more closely with daily tasks. It also makes code easier to combine and test. While traditional approaches like
Promise/try-catchorasync/awaitcan handle such scenarios, after using Effect, our team found no reason to go back. -
86. Hasura GraphQL engine
The Hasura GraphQL engine is a universal data access layer that simplifies building, running and governing high-quality APIs on different data sources. It provides instant GraphQL APIs over various databases (including PostgreSQL, MongoDB and ClickHouse) and data sources, enabling developers to fetch only the data they need quickly and securely. We found Hasura easy to implement GraphQL for server-side resource aggregation and have applied it in multiple data product projects. However, we remain cautious about its powerful federated query and unified schema management. A noteworthy recent addition is Hasura's PromptQL feature, which allows developers to leverage LLMs for more natural and intuitive data interactions.
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87. LangGraph
LangGraph is an orchestration framework designed to build stateful multi-agent applications using LLMs. It provides a lower-level set of primitives like edges and nodes compared to LangChain’s higher-level abstractions, offering developers fine-grained control over agent workflows, memory management and state persistence. This graph-based approach ensures predictable and customizable workflows, making debugging, scaling and maintaining production applications easier. Although it has a steeper learning curve, LangGraph's lightweight design and modularity make it a powerful framework for creating agentic applications.
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88. MarkItDown
MarkItDown converts various formats (PDF, HTML, PowerPoint, Word) into Markdown, enhancing text readability and context retention. Since LLMs derive context from formatting cues like headings and sections, Markdown helps preserve structure for better comprehension. In RAG-based applications, our teams used MarkItDown to pre-process documents into Markdown, ensuring logical markers (headers, subsections) remained intact. Before embedding generation, structure-aware chunking helped maintain full section context which improves the clarity of query responses, especially for complex documents. Widely used for documentation, Markdown also makes MarkItDown’s CLI a valuable developer productivity tool.
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89. Module Federation
Module Federation allows for the specification of shared modules and dependency deduplication across micro frontends. With version 2.0, it has evolved to function independently of webpack. This update introduces key features, including a federation run time, a new plugin API and support for popular frameworks like React and Angular as well as popular bundlers like Rspack and Vite. By adopting Module Federation, large web applications can be divided into smaller, manageable micro frontends, allowing different teams to develop, deploy and scale independently while sharing dependencies and components efficiently.
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90. Prisma ORM
Prisma ORM is an open-source database toolkit that simplifies working with databases in Node.js and TypeScript applications. It offers a modern, type-safe approach to database access, automates database schema migrations and provides an intuitive query API. Unlike typical ORMs, PrismaORM uses plain JavaScript objects to define database types without decorators or classes. Our experience with Prisma ORM is positive; we find it not only better aligns with the general TypeScript development landscape, it also neatly integrates with the functional programming paradigm.
Hold
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105. Node overload
A few years ago, we observed Node overload: Node.js was often used for questionable reasons or without even considering any alternatives. While we understand that some teams prefer a single-language stack — despite the trade-offs — we continue to advocate for polyglot programming. At the time, we noted that Node.js had a deserved reputation for efficiency in IO-heavy workloads, but we mentioned that other frameworks had caught up which offered better APIs and superior overall performance. We also cautioned that Node.js was never well-suited to compute-heavy workloads, a limitation that remains a significant challenge. Now, with the rise of data-heavy workloads, we’re seeing teams struggle with these as well.
Unable to find something you expected to see?
Each edition of the Radar features blips reflecting what we came across during the previous six months. We might have covered what you are looking for on a previous Radar already. We sometimes cull things just because there are too many to talk about. A blip might also be missing because the Radar reflects our experience, it is not based on a comprehensive market analysis.
