Cursor

What do you like best about the product?

What I like best about Cursor is how seamlessly it integrates AI into the coding workflow. It makes writing, understanding, and debugging code much faster and more intuitive. The AI suggestions are generally context-aware and helpful, especially when working across larger codebases.

What do you dislike about the product?

One thing I dislike about Cursor is that the AI suggestions can sometimes be inconsistent or require additional review and corrections. For larger or more complex codebases, it may occasionally misunderstand the context and suggest changes that aren’t quite what I intended.

What problems is the product solving and how is that benefiting you?

Cursor helps solve the problem of spending too much time on repetitive coding tasks, debugging, understanding unfamiliar code, and writing boilerplate. It speeds up development by providing context-aware suggestions, generating code, explaining existing code, and helping with refactoring.

What do you like best about the product?

Cursor provides a UI that’s very similar to VS Code, so I can use it without any difficulty. In Cursor, I’ve been using the AI agent, and it completes my code in a few seconds. If any bug comes up, I just tell the AI by giving a prompt, and it fixes the bug within a second. Basically, I give a prompt and Cursor completes my task. I also like that whenever Cursor wants to run any command or perform a command-related task, it asks me first, and then I can approve or reject it.

What do you dislike about the product?

They provide only a very limited number of tokens and an AI agent for code completion, but they don’t offer other tools. It also requires a cursro login. Meanwhile, antigravity provides much more AI with higher limits, along with code suggestions as you type. antigravity is free, while cursro does not provide this feature.

What problems is the product solving and how is that benefiting you?

We needed a way to leverage AI automation for coding tasks without sacrificing security or operational control in our environment. Cursor addresses this by offering intelligent, command-focused task automation while still keeping a clear human-in-the-loop approach. When the AI agent proposes running system commands, it asks for manual approval or rejection first, which helps keep our development workflow fast, safe, and transparent.

What do you like best about the product?

I use Cursor at my organizer for writing code, and it's like our default IDE. Cursor helps me analyze the code, write it better, debug and fix bugs, and even fix the build. I find it indispensable for coding. I really like its ease of effect, with the IDE and chat being inbuilt, so there's no need to switch back and forth. It creates the context and remembers everything, and it's fast, allowing me to switch models easily.

What do you dislike about the product?

So there is a thing like I have integrated Cursor with my Jira and conference MCP server. But almost every time, it loses the context and asks me again to link them, and it takes time. If there are more connectors that we can connect the Cursor to and sometimes, like, the chat window gets freeze.

What problems is the product solving and how is that benefiting you?

I use Cursor to analyze and debug code, help fix bugs and build processes, and it simplifies code writing. It feels essential to my coding.

What do you like best about the product?

I appreciate Cursor's natural language interaction. Setting it up was smooth and straightforward.

What do you dislike about the product?

I feel Cursor could improve on better requirements interpretation.

What problems is the product solving and how is that benefiting you?

Cursor helps me with unclear technical requirements by creating command line utilities from plain English descriptions.

What do you like best about the product?

The AI integration in Cursor feels genuinely woven into the coding workflow instead of bolted on as an afterthought. Inline code generation and chat-based editing pull context from the whole codebase, not just the open file, so suggestions actually match existing architecture and coding patterns rather than generic boilerplate. Tab-to-accept autocomplete is fast and often predicts multi-line edits correctly, saving a lot of repetitive typing during daily development.
The interface stays close to a familiar VS Code layout, so there's almost no learning curve coming from that ecosystem — getting started took minutes rather than a real onboarding process. Extensions and settings carry over smoothly, and the editor stays responsive even in larger projects with many open files. Integration with existing Git workflows and terminal usage feels seamless, and referencing specific files or symbols directly in a prompt makes debugging and refactoring noticeably quicker than switching to a separate AI tool.
On pricing, the value holds up well against the time it saves — faster iteration and fewer context switches easily justify the subscription cost for a small technical team. Support has been reliable when needed, with documentation that covers most common issues, so there's rarely a wait to keep moving. Overall it's become a core part of the day-to-day coding process.

What do you dislike about the product?

Pricing gets frustrating once usage scales — the fast request limits on the standard plan get consumed quickly on larger codebases, and hitting that ceiling mid-task means either slowing down to conserve requests or upgrading to a higher tier sooner than expected. More transparency around real-time usage consumption would help, since right now it's often only clear after the fact.
Performance can also dip on very large repositories — indexing takes noticeably longer, and the context window occasionally doesn't fully capture relevant files scattered across a big project, so suggestions miss dependencies living outside the immediate working directory. That means double-checking generated code more carefully on bigger builds than on smaller ones.
The AI still occasionally hallucinates function signatures or library APIs that don't actually exist, especially with less common packages or internal libraries it hasn't seen much of, so verification against actual documentation remains necessary rather than optional. Multi-file refactors sometimes need manual cleanup afterward since the model doesn't always catch every downstream reference that needs updating.
Onboarding new team members to the AI-specific features (custom rules, context management, model selection) takes more explanation than just picking up a standard editor, since getting real value out of it requires understanding how to prompt and scope context effectively. Minor learning curve, but it's there.

What problems is the product solving and how is that benefiting you?

Cursor is solving the friction of context-switching between writing code and getting AI assistance — instead of copying code into a separate chat window and pasting suggestions back, everything happens inline within the actual editor. This has meaningfully sped up day-to-day development, especially for repetitive tasks like writing boilerplate, generating test cases, and drafting initial implementations of well-understood patterns.
It's also cut down significantly on time spent debugging, since the AI can scan across multiple files to spot the source of an error rather than manually tracing through the codebase file by file. For a small technical team without dedicated resources for every specialization, this has been a real force multiplier — junior or less experienced developers can move faster on unfamiliar parts of the stack because the AI helps bridge knowledge gaps in real time, whether that's an unfamiliar library, a new framework pattern, or legacy code someone else wrote.
Refactoring large codebases has become considerably less tedious too. What used to mean manually updating dozens of related files can now be scoped and handled in a single pass with AI assistance, with far fewer missed references than doing it by hand. On the documentation side, having AI help generate and maintain inline comments and docstrings has improved code readability across the team, which matters a lot given multiple people touch the same codebase over time.
The net benefit has been faster iteration cycles overall — less time spent on mechanical, repetitive work means more time available for actual architecture decisions, business logic, and problem-solving that requires real judgment.