A New Batch of Tech Terms Explained (July 2026 Edition)
Plus a fresh Dice report on U.S. tech hiring myths vs. reality
Hey community,
Dice has just released a report that compares some of the most common myths about the U.S. tech hiring market with what their latest research actually found.
Many of you are reading this newsletter from the United States, so I thought you might be interested in checking it out. Here’s the link:
👉 Tech Hiring Myths vs. Reality
In the report, you’ll find insights on:
common misconceptions about today’s U.S. tech hiring market
how AI is changing the hiring process for both recruiters and candidates
what tech professionals think about AI-powered hiring and resume screening
where perception and reality differ in today’s tech hiring market
and more.
And of course, below is another batch of tech terms explained in plain English, so when you’re assessing or interviewing candidates, you’re not left guessing what those terms mean.
👉 Dapper
Dapper is a lightweight, open-source object-relational mapper (ORM) for .NET.
It helps developers execute SQL queries and map the results directly to C# objects with very little overhead, making database access simple and highly performant.
Unlike full-featured ORMs such as Entity Framework, Dapper doesn’t generate SQL queries or track changes to objects. Instead, developers write SQL themselves, giving them more control over how data is retrieved and updated while still avoiding much of the repetitive data-mapping code.
Dapper is commonly used in high-performance backend applications, APIs, and microservices built with C# and .NET.
On a resume, Dapper usually indicates experience building .NET backend applications, writing SQL directly, and working with relational databases.
👉 Weaviate
Weaviate is an open-source vector database designed for AI and machine learning applications.
It stores and searches vector embeddings (numerical representations of data generated by AI models), allowing AI applications to find semantically similar text, images, audio, and other types of data. This makes Weaviate well suited for semantic search, recommendation systems, RAG pipelines, and AI agents.
In addition to vector search, Weaviate supports hybrid search, combining vector similarity with traditional keyword search to improve search quality.
Weaviate is commonly used as part of modern AI infrastructure alongside large language models (LLMs).
Other popular vector databases you may also see listed on resumes include Pinecone and Qdrant.
👉 Combine
Combine is Apple’s reactive programming framework for Swift.
It provides a declarative way to handle asynchronous events and data streams, making it easier to manage tasks such as network requests, user input, timers, and UI updates.
Instead of relying on callbacks or delegates, Combine lets developers create pipelines that automatically process and react to changes in data over time. This can make application code more predictable, reusable, and easier to maintain.
Combine is most commonly used in iOS, macOS, watchOS, and tvOS applications, often alongside SwiftUI but also with UIKit.
You can think of it as Apple’s first-party alternative to third-party reactive programming frameworks like RxSwift and ReactiveSwift.
On a resume, Combine usually indicates experience building modern Apple applications with Swift, particularly applications that rely on reactive programming and asynchronous data flows.
👉 Polars
Polars is an open-source DataFrame library for data manipulation and analysis.
Designed for high performance, it uses a columnar memory format and parallel execution to process large datasets efficiently. It also supports both eager and lazy execution, allowing queries to be optimized before they run.
Polars has native support for both Python and Rust and is commonly used for data engineering, analytics, machine learning pipelines, and other data-intensive apps built with these languages.
In plain English, Polars helps developers clean, transform, and analyze large datasets quickly and efficiently.
Polars is often used as a high-performance alternative to Pandas for data processing.
👉 TanStack Query
TanStack Query (formerly React Query) is a data-fetching and server-state management library for JavaScript and TypeScript applications.
It helps developers fetch, cache, synchronize, and update data from APIs while automatically handling common tasks such as caching, background refetching, retries, loading states, and error handling.
Unlike general state management libraries such as Redux or Pinia, TanStack Query is specifically designed to manage server state, reducing the amount of code needed to keep application state in sync with backend services.
TanStack Query is most commonly used with React, but it also supports frameworks such as Vue, Angular, Solid, and Svelte.
On a resume, TanStack Query usually indicates experience building modern frontend applications that interact with APIs efficiently, particularly with React and TypeScript.
👉 Temporal
Temporal is an open-source workflow orchestration platform for building reliable, long-running workflows in distributed systems.
It helps developers manage complex business processes by automatically handling task coordination, retries, timeouts, state management, and recovery from failures. This allows applications to continue running reliably even if services or infrastructure experience interruptions.
Unlike traditional job schedulers, Temporal is designed for durable application workflows that may run for minutes, days, or even months while maintaining their state.
Temporal supports multiple programming languages, including Java, Go, TypeScript, Python, and .NET, and is commonly used to orchestrate microservices, backend systems, data processing pipelines, and other distributed applications.
In plain English, Temporal helps developers build applications that can reliably complete long-running tasks, even if parts of the system fail or restart.
👉 LlamaIndex
LlamaIndex is an open-source framework for building AI applications powered by large language models (LLMs).
It helps developers connect LLMs to external data sources (such as databases, documents, APIs, and cloud storage) so AI applications can retrieve relevant information and generate more accurate, context-aware responses.
LlamaIndex provides tools for data ingestion, indexing, and retrieval, making it a popular framework for building RAG pipelines, AI assistants, chatbots, and AI agents.
LlamaIndex is commonly used alongside vector databases and frameworks such as LangChain and LangGraph.
That’s it for today. Thanks for reading, and have a productive day!
Andrew,
Founder of GlossaryTech