Anant Jain

AI Coding Tools (May 2025)

Tech

Over the past couple months, I took a mini break to explore the incredible developments in AI-powered coding. We're obviously experiencing a transformative shift in how engineering teams operate. Mastering a change like this requires intentional effort, and in my opinion, becoming AI-native is perhaps the highest-leverage activity available to anyone right now.

My goal was simple: try out as many AI-powered IDEs, coding agents, and similar tools to become proficient with them. I wanted to develop a deep understanding of today's innovation frontier and, if lucky, find both an opportunity and a potential co-founder to launch a startup in this space.

This post is to log my learnings about everything I learned about the future of software engineering. The current set of AI coding tools falls into three categories, which I’ll cover next, followed by a brief section on what I think is missing and I’m most excited about.

1. AI-assisted IDE’s

AI-assisted IDE’s that boost individual developer productivity through a local IDE. These function as AI pair programming partners:

  1. Synchronous and iterative
  2. Best for higher ambiguity/complexity tasks
  3. GTM: Individual adoption, leading to consumer (bottom-up) go-to-market
  4. UX: An app installed on your computer

I would further divide this category into three smaller sub-categories:

Terminal based:

  1. Warp: This isn't just a tool within your favorite terminal (typically, iTerm2), but a whole new AI-powered terminal itself that comes with a coding agent. I didn't use Warp's coding agent extensively, but it successfully replaced iTerm2 for me—the ability to type what you want to do in plain English without memorizing unix commands comes in handy once in a while.
  2. Claude Code: This is my favorite tool on this list. It's the best-designed terminal tool I've seen. The Claude Code agent works well and I like how it uses Claude.md as a shared scratchpad in the repo to remember codebase preferences over time. My only wish is that Anthropic open sources it, especially after the code leaked via sourcemaps and someone created a multi-provider version from it (that Anthropic recently DMCA’ed as well 🤷🏻‍♂️)
  3. OpenAI Codex: Claude Code's first-mover advantage was significant for me, and Codex didn't feel like an improvement in my limited experimentation. There's probably truth to the floating tweets about OpenAI's struggles to build great products in this space (hence the Windsurf acquisition). Massive props to them for making it open source, though!
  4. Aider: While I didn't explore this as much as intended, it has received considerable praise in open-source-loving Reddit communities.

Editors (IDEs):

  1. Cursor: My most-used tool on this list. All things considered, I believe this is the strongest product and team in the IDE space. I just wish their growing pains were not so evident.
  2. Windsurf: My initial experience a few months ago was underwhelming, but trying it again this past month was decent. While it wasn't better than Cursor for my use cases, they claim superior performance with larger codebases.
  3. Zed: The latest entrant in this category, created by the Atom editor team. While incredibly fast (built from scratch in Rust), betting on speed as a differentiator seems risky when the improvement margin over VSCode-based tools isn't substantial.

VSCode Plugins:

  1. RooCode (forked from Cline): I quite enjoyed RooCode, and it makes me my favorite in this category. I especially liked the Architect mode being separate from Code mode. However, the "bring your own API key" approach is expensive—especially when tackling ambiguous problems where growing context diminishes model performance while token usage (and costs) keeps climbing.
  2. Github Copilot: Though they obviously fumbled a ton since 2022, they remain well-positioned to capture the enterprise market through Microsoft's proven bundling strategy.
  3. AugmentCode: Deserves a mention for being on top of the SWE-Bench Verified leaderboard as of today.

2. AI “Engineers”

AI agents that handle complete tasks independently. These tools operate asynchronously with a "ticket to pull request" workflow, similar to delegating work to a junior engineer:

  1. Asynchronous processing with single or limited interactions
  2. Ideal for straightforward, well-defined tasks
  3. GTM: Team-wide adoption, implying an enterprise (top-down) sales motion
  4. UX: Ambient agents with Agent Inbox (Dashboard)

Here are the key products I experimented with in this category:

  1. Devin: This team is extremely impressive and on a great trajectory. While the initial version didn't amaze me, v2 was significantly better—provided you set appropriate expectations.
  2. Charlie: I got a chance to try out the private beta, and have to say, Charlie is truly impressive. The team pivoted into this space earlier this year, and from my limited testing, they seem to outperform Devin.
  3. Factory: I haven't tested this one. In such a competitive space, I'm generally skeptical of products that require talking to a Sales AE before trying them out, especially in this space.
  4. [Update 05/16] OpenAI's Codex: Powered by codex-1, a version of OpenAI o3 optimized for software engineering, and can perform tasks in parallel, such as refactoring, bug fixing, and documentation. I’m yet to test this extensively!

3. Prototying tools

I include this category here, but this wasn’t the focus of my exploration. I’ve extensively only used v0.dev here, and that too purely to work with PM’s/Designers as we explored ideas (i.e., Figma replacement). The three other notable tools here are Bolt.new, Lovable, and Replit. There are minor differentiators between these four tools, but each one of them wants to figure out a way to provide hosting or sell a Figma-replacement to Enterprises to make the revenue stickier.

What’s missing?

Here are a few products/ideas that I’m excited about in this space:

  1. Building your own agents: I think we’re very close to nailing down the UX of building your own agent for non-developers. Folks broadly realize that this could be useful (eg., YC’s Internal Agent Builder RFS for Summer ‘25) and I wouldn’t be surprised if one of the current AI Agent frameworks pivots into this.
  2. Staff engineer agent: I think we’re missing an agent fine-tuned to take larger scope and ambiguous problems and scope them. In particular, I think the UX here should resemble what we humans do when we kick off significant projects: write a doc → share it with the team and iterate on it via comments → decision maker approves and a plan with milestones/issues get made. I think Devin is well positioned to incorporate this, but they seem to be focused on building a “junior engineer that codes” for the most part.
  3. Issue tracking: Linear remains my absolute favorite here — it's great to see them setting up an MCP server and maintaining clean APIs for integrations, and I think they can become the dashboard and orchestration layer for AI agents. However, when it comes to "traditional SaaS apps getting disrupted by AI-native ones" thesis, I think project/issue tracking could be a ripe space to go into.