Lenguajes & Frameworks

OpenTelemetry se está convirtiendo rápidamente en el estándar de la industria para la observabilidad. El lanzamiento de la especificación del protocolo OpenTelemetry (OTLP) estableció una forma estandarizada de gestionar trazas, métricas y registros, reduciendo la necesidad de múltiples integraciones o grandes reescrituras a medida que aumentan los requisitos de interoperabilidad y las soluciones de monitorización distribuidas. A medida que OpenTelemetry se expande para admitir registros y perfiles, OTLP garantiza un formato de transporte consistente en todos los datos de telemetría, simplificando la instrumentación y haciendo que la observabilidad full-stack sea más accesible y escalable para las arquitecturas de microservicios. Adoptado por proveedores como Datadog, New Relic y Grafana, OTLP permite a las organizaciones crear conjuntos de datos de observabilidad flexibles e independientes de cada proveedor, sin depender de soluciones patentadas o privadas. Admite compresión gzip y zstd, reduciendo el tamaño de los datos de telemetría y el uso de ancho de banda — una ventaja clave para entornos que gestionan grandes volúmenes de datos de telemetría. Diseñado para el crecimiento a largo plazo, OTLP garantiza que OpenTelemetry siga siendo un estándar robusto y preparado para el futuro, consolidando su posición como la opción predilecta para el transporte de telemetría.

Hemos identificado React Hook Form como una alternativa a Formik. Al utilizar componentes no controlados por defecto, ofrece un rendimiento considerablemente superior sin configuración adicional, especialmente para formularios de gran tamaño. React Hook Form está bien integrado con varias librerías de validación basadas en esquemas, incluyendo Yup, Zod y más. Adicionalmente, React Hook Form ofrece mucha flexibilidad, facilitando la integración con código fuentes existentes y otras librerías. Puedes usar React Hook Form con librerías de componentes controlados externas como shadcn o AntD. Con un rendimiento sólido, una integración fluida y un desarrollo activo, es una opción confiable para la creación de aplicaciones con formularios extensos o con una gran cantidad de los mismos.

Effect es una potente librería de TypeScript para construir complejos programas síncronos y asíncronos. El desarrollo de aplicaciones web a menudo requiere código repetitivo para tareas relacionadas con asincronía, concurrencia, gestión de estados y manejo de errores. Effect-TS agiliza estos procesos utilizando un enfoque de programación funcional. Aprovechando el sistema de tipos de TypeScript, Effect ayuda a encontrar problemas difíciles de detectar en tiempo de compilación. Nuestro equipo utilizaba anteriormente fp-ts para la programación funcional, pero descubrió que Effect-TS proporciona abstracciones que se ajustan más a las tareas diarias. También facilita la combinación y comprobación del código. Mientras que los enfoques tradicionales como Promise/try-catch o async/await pueden manejar estos escenarios, después de usar Effect, nuestro equipo no encontró ninguna razón para volver atrás.

El motor GraphQL de Hasura es una capa universal de acceso a datos que simplifica la creación, ejecución y gestión de APIs de alta calidad en diferentes fuentes de datos. Proporciona APIs GraphQL instantáneas sobre varias bases de datos (incluyendo PostgreSQL, MongoDB y ClickHouse) y fuentes de datos, permitiendo a los desarrolladores obtener sólo los datos que necesitan de forma rápida y segura. Encontramos que Hasura es un GraphQL fácil de implementar en la agregación de recursos del lado del servidor y lo hemos aplicado en múltiples proyectos de productos de datos. Sin embargo, seguimos siendo cautos con respecto a su potente gestión de consultas federadas y esquemas unificados. Una adición reciente destacable es la función PromptQL de Hasura, que permite a los desarrolladores aprovechar LLMs para lograr interacciones de datos más naturales e intuitivas.

LangGraph es un framework de orquestación diseñado para construir aplicaciones multiagente con estados utilizando modelos LLM. Esta tecnología proporciona un conjunto de primitivas de bajo nivel como aristas y vértices en lugar de las abstracciones de alto nivel de LangChain, lo que permite a los desarrolladores un control más detallado sobre los flujos de trabajo de los agentes, la gestión de memoria y la persistencia del estado. Este enfoque basado en grafos garantiza flujos de trabajo predecibles y personalizables, facilitando la depuración, el escalado y el mantenimiento de aplicaciones en producción. Aunque presenta una curva de aprendizaje más pronunciada, su diseño ligero y modular lo convierte en un framework potente para la creación de aplicaciones con agentes autónomos.

MarkItDown convierte varios formatos (PDF, HTML, PowerPoint, Word) en Markdown, mejorando la legibilidad del texto y manteniendo el contexto. Ya que los grandes modelos de lenguaje derivan el contexto de formatting cues como los encabezados y secciones, Markdown ayuda a preservar la estructura para una mejor comprensión. En las aplicaciones basadas en RAG , nuestros equipos usaron MarkItDown para preprocesar documentos a Markdown, asegurándose que los marcadores lógicos (encabezados, subsecciones) quedaran intactos. Antes de incorporar la generación, la fragmentación consciente de la estructura ayudaba a mantener el contexto de la sección lo cual mejoraba la claridad de las respuestas a las consultas especialmente para documentos complejos. Markdown es ampliamente utilizado para documentación y también convierte la CLI de MarkItDown en una valiosa herramienta de productividad para desarrolladores.

Module Federation permite la especificación de módulos compartidos y la deduplicación de dependencias en micro frontends. Con la versión 2.0, ha evolucionado para funcionar de forma independiente de webpack. Esta actualización introduce características clave, incluyendo un tiempo de ejecución de federación, una nueva API de plugins y soporte para frameworks populares como React y Angular, así como otros empaquetadores populares como Rspack y Vite. Al adoptar Module Federation, las aplicaciones web de gran tamaño pueden dividirse en micro frontends más pequeños y manejables, lo que permite que diferentes equipos desarrollen, desplieguen y escalen de forma independiente, a la vez que comparten dependencias y componentes de manera eficiente.

Prisma ORM es una herramienta de código abierto para bases de datos que simplifica el trabajo con éstas en aplicaciones de Node.js y TypeScript. Ofrece un enfoque moderno y de tipado seguro para acceder a bases de datos, automatiza las migraciones de esquemas de bases de datos y proporciona una API de consulta intuitiva. A diferencia de los ORM tradicionales, Prisma ORM utiliza objetos de JavaScript estándar para definir los tipos de base de datos, sin necesidad de decoradores ni clases. Nuestra experiencia con Prisma ORM ha sido positiva; consideramos que no solo se alinea mejor con el ecosistema de desarrollo en TypeScript, sino que también se integra impecablemente con el paradigma de programación funcional.

.NET Aspire is designed to simplify the orchestration of distributed applications on a developer's local machine. Aspire lets you orchestrate multiple services in a local development environment — including multiple .NET projects, dependent databases and Docker containers — all with a single command. Furthermore, Aspire provides observability tools — including logging, tracing and metrics dashboards — for local development, decoupled from the tools used in staging or production environments. This significantly improves the developer experience when building, tweaking and debugging the observability aspects of any system they are working on.

Google, in collaboration with Samsung and Qualcomm, has introduced Android XR, a new operating system designed for XR headsets. Support is planned for glasses and other devices. Most Android apps are supported with no or minimal changes, but the idea is to build new spatial apps from scratch or to "spatialize" existing apps. The new Android XR SDK is positioned as the go-to SDK for such projects, and Google provides guidance on how to choose tools and technologies bundled in the SDK. It’s available in developer preview now.

Browser Use is an open-source python library that enables LLM-based AI agents to use web browsers and access web applications. It can control the browser and perform steps that include navigations, inputs and text extractions. With the ability to manage multiple tabs, it can perform coordinated actions across multiple web apps. It’s useful for scenarios where LLM-based agents need to access web content, perform actions on it and get the results. The library can work with a variety of LLMs. It leverages Playwright to control the browser, combining visual understanding with HTML structure extraction for improved web interaction. This library is gaining traction in multi-agent scenarios, enabling agents to collaborate on complex workflows involving web interactions.

CrewAI is a platform designed to help you build and manage AI agents that can work together to accomplish complex tasks. Think of it as a way to create a crew of AI workers, each with their own special skills, who can collaborate to achieve a common goal. We’ve mentioned it previously in the Radar under LLM-powered autonomous agents. In addition to the open-source Python library, CrewAI now has an enterprise solution so organizations can create agent-based applications for real-world business cases, run them on their cloud infrastructure and connect to existing data sources such as Sharepoint or JIRA. We've used CrewAI multiple times to tackle production challenges, from automated validation of promotion codes to investigating transaction failures and customer support queries. While the agentic landscape continues to evolve rapidly, we’re confident in placing CrewAI in Assess.

ElysiaJS is an end-to-end type-safe web framework for TypeScript, designed primarily for Bun but also compatible with other JavaScript run times. Unlike alternatives such as tRPC, which enforces specific API interface structures, ElysiaJS does not impose any API interface structure. This allows developers to create APIs that follow established industry practices such as RESTful, JSON:API or OpenAPI and also provide end-to-end type safety. ElysiaJS is highly performant when used with Bun run time, even comparable to Java or Go web frameworks in some benchmarks. ElysiaJS is worth considering, especially when building a backend-for-frontend (BFF).

FastGraphRAG is an open-source implementation of GraphRAG designed for high retrieval accuracy and performance. It uses Personalized PageRank to limit graph navigation to the most relevant nodes among all the related nodes in the graph, enhancing retrieval accuracy and improving LLM response quality. It also provides a visual representation of the graph, helping users understand node relationships and the retrieval process. With support for incremental updates, it’s well-suited to dynamic and evolving data sets. Optimized for large-scale GraphRAG use cases, FastGraphRAG improves performance while minimizing resource consumption.

Erlang/OTP is a powerful platform for building highly concurrent, scalable and fault-tolerant distributed systems. Traditionally, its languages have been dynamically typed, but Gleam introduces type safety at the language level. Built on BEAM, Gleam combines the expressiveness of functional programming with compile-time type safety, reducing run-time errors and improving maintainability. With a modern syntax, it integrates well with the OTP ecosystem, leveraging the strengths of Erlang and Elixir while ensuring strong interoperability. The Gleam community is active and welcoming, and we look forward to its continued development.

GoFr is a framework for building microservices in Golang, designed to simplify development by abstracting away boilerplate code for common microservice functionalities such as logging, traces, metrics, configuration management and Swagger API documentation. It supports multiple databases, handles database migrations and facilitates pub/sub with brokers like Kafka and NATs. Additionally, GoFr includes task scheduling with cron jobs. It reduces the complexity of building and maintaining microservices, and allows developers to focus on writing business logic rather than infrastructure concerns. Even though there are other popular Go libraries for building web APIs, GoFr is gaining traction and is worth exploring for Golang-based microservices.

At the core of asymmetric cryptography, which secures most modern communication, lies a mathematically hard problem. However, the problem used in today's algorithms will be easy to solve with quantum computers, driving research for alternatives. Lattice-based cryptography is currently the most promising candidate. Although cryptographically relevant quantum computers are still years away, post-quantum cryptography is worth considering for applications that must remain secure for decades. There is also the risk that encrypted data is recorded today in order to be decrypted once quantum computers become available.

Java post-quantum cryptography takes its first steps in JDK 24, set for general availability in late March. This release includes JEP 496 and JEP 497 — which implement a key encapsulation mechanism and a digital signature algorithm — both standards-based and designed to be resistant to future quantum computing attacks. While liboqs from the Open Quantum Safe project provides C-based implementations with a JNI wrapper, it’s encouraging to see a native Java implementation emerging as well.

Presidio is a data protection SDK for identifying and anonymizing sensitive data in structured and unstructured text. It detects personally identifiable information (PII) such as credit card numbers, names and locations, using named entity recognition, regular expressions and rule-based logic. Presidio supports custom PII entity recognition and de-identification, allowing businesses to tailor it to their specific privacy requirements. Although Presidio automates the identification of sensitive information, it’s not foolproof and may miss or misidentify data. Exercise caution when relying on its results.

As the technologies to build LLM-backed applications and agents continue to evolve rapidly, frameworks for building and orchestrating such applications often struggle to keep up or find the right timeless abstractions. PydanticAI is the latest entrant in this space, aiming to simplify implementations while avoiding unnecessary complexity. Developed by the creators of the popular Pydantic, it builds on lessons learned from earlier frameworks — many of which already rely on Pydantic. Rather than trying to be a Swiss Army knife, PydanticAI offers a lightweight yet powerful approach. It integrates with all major model APIs, includes built-in structured output handling and introduces a graph-based abstraction for managing complex agentic workflows.

Since the release of Swift 6.0, the language has expanded beyond Apple's ecosystem with improved support for major operating systems, making it more viable to use Swift for resource-constrained applications. Traditionally, this space has been dominated by C, C++ and, more recently, Rust, due to their low-level control, high performance and availability of certified compilers and libraries that comply with standards such as MISRA, ISO 26262 and ASIL. While Rust has begun achieving similar certifications, Swift has yet to pursue this process, limiting its use in safety-critical applications.

Swift's growing adoption is driven by its balance of performance and safety features, including strong type safety and automatic reference counting for memory management. While Rust’s ownership model offers stronger memory safety guarantees, Swift provides a different trade-off that some developers find more approachable. Both Swift and Rust share the LLVM/Clang compiler backend, allowing advancements in one to benefit the other. With its ability to compile to optimized machine code, its open-source development and its expanding cross-platform support, Swift is emerging as a contender for a wider range of applications — far beyond its iOS roots.

Tamagui is a library for efficiently sharing styles between React web and React Native. It offers a design system with reusable styled and unstyled components that render seamlessly across platforms. Its optional optimizing compiler boosts performance by converting styled components into atomic CSS with divs on the web and hoisted style objects on native views.

torchtune is a PyTorch library for authoring, post-training and experimenting with LLMs. It supports single and multi-GPU setups and enables distributed training with FSDP2. The library provides YAML-based recipes for tasks like fine-tuning, inference, evaluation and quantization-aware training. Each recipe offers a focused set of features, avoiding complex flag-based configurations. It prioritizes simplicity, favoring code clarity over excessive abstractions. It also includes a CLI for downloading models, managing recipes and running experiments efficiently.

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.