Quick question - what specific languages or frameworks are you mostly dealing with in that legacy codebase? I mean, GitHub Copilot is basically the standard and it's like $10 a month which is a STEAL for the time it saves, but it can struggle with really weird, niche older stuff. If you're on a tight budget, Tabnine Basic has a free tier that's actually decent for local autocompletes, but the Pro version ($12/mo) is where the real context-awareness kicks in. I've found that knowing your tech stack helps figure out if these tools will actually save you those 10 hours or just give you more bugs to fix lol.

Seconding the recommendation above about Cursor, it basically saved my sanity. But if you want to keep using VS Code, you should check out Amazon Q Developer. It's surprisingly good at security scans and catching vulnerabilities before you commit, which is huge for legacy stuff. Another one I've used for years is Bito AI. It's great for generating documentation and unit tests on the fly without being too intrusive. Honestly, both work well and actually feel like they're helping, not just adding noise. gl! 👍

Nice, didn't know that

oh man, I feel u on the boilerplate burnout. I've been through a bunch of these tools and honestly, most of them are kinda mid when you're dealing with a massive legacy codebase.

In my experience, here's the deal:

1. Cursor AI Code Editor is literally a game changer right now. Since it's a fork of VS Code, your setup stays the same, but the way it handles context is sooo much better than a plugin. It actually 'sees' your whole project, so it doesn't suggest as much outdated junk. It's basically the only thing that's saved me those 5-10 hours a week.

2. GitHub Copilot is the reliable standard, but sometimes it feels a bit basic compared to Cursor. It's great for unit tests tho!

3. Be careful with Tabnine—it's decent for privacy but i think the suggestions aren't quite as smart as the others lately.

So basically, just try Cursor. It handles refactoring way better than anything else I've tried. gl! 👍

Just sharing my experience: I went through this last year when I was doing some market research for my team to see what actually works for security and legacy code. Honestly, I'm pretty CAUTIOUS about these tools cuz you dont want them leaking your repo or suggesting weird stuff.

1. GitHub Copilot: It's the standard for a reason, but it's kinda "black box" and sometimes hallucinates on old libraries.
2. Sourcegraph Cody: This one felt better for context because it actually indexes your whole codebase... definitely helped with the legacy spaghetti I was dealing with.
3. Amazon Q: Really direct on security and AWS stuff, but the UI is a bit clunky compared to the others and feels less intuitive.

It's a lot to weigh up but basically it depends on how much you trust the AI with your context... I ended up sticking to ones that have better privacy controls tbh.

Quick questions for you:
- Are you mostly worried about logic bugs or just catching security vulnerabilities??
- How big is this legacy codebase you're talking about... like are we talking thousands or millions of lines?

So, I've been geeking out on the benchmarks from the developer community lately and there are a few tools that arent getting enough love here but are absolute beasts for legacy context. If ur tired of that annoying lag in VS Code, you gotta look at how these things handle the context window specifically. Tbh, it's not just about the IDE plugin anymore, it's about the tools ability to ingest the whole repo without choking or hallucinating functions that dont exist. Here are a couple of gems from the community that actually deliver:

• Supermaven: This one is insane because of the 1-million-token context window. It feels way faster than Copilot because they use a custom integration that minimizes latency.
• Aider: If youre comfortable with the terminal, this is basically the gold standard for multi-file refactoring right now. It uses git history and file maps to understand where ur logic is buried.
• CodiumAI: They have a really solid PR Agent that handles the documentation and unit test generation you mentioned without being as intrusive as a standard autocomplete. Definitely check out the Roo Code project on GitHub too if you want to see how agents are evolving. It is open source and the community is going wild over how it handles complex tasks compared to standard chat assistants. Hope that helps you reclaim those 10 hours!!!

🙌

Honestly, I’ve been taking a super cautious approach to this because I’m terrified of these tools actually *slowing* me down in the long run. I started doing some informal benchmarking on the tool I’m currently testing just to see if it’s genuinely worth the subscription, you know? I’ve been looking at a few specific performance metrics:
- **Context Latency:** If the tool takes more than a second to 'think' about my legacy files, it breaks my flow completely.
- **Rejection Rate:** I keep a rough tally of how many suggestions I actually accept. If I’m hitting 'esc' more than 40-50% of the time, it's basically just noise.
- **System Overhead:** One tool I tried basically turned my laptop into a space heater, which ended up tanked my actual build speeds. I’m still a bit uncertain if any of them are truly a 'magic bullet' yet. I find that unless the tool handles large file indexing without hanging my UI, it’s more of a hindrance than a help. Real-world performance is everything when you're already stressed about deadlines, right?

Ok so, one thing that often gets overlooked when picking these tools is the actual underlying architecture and how they hook into your environment. If you want to avoid that intrusive feeling while maintaining high context, you need to look at how these extensions interact with your Language Server Protocol (LSP) setups.

• Continue.dev is basically the gold standard for flexibility right now. It is an open-source framework for VS Code that lets you plug in any LLM you want via API. This is HUGE for compatibility because you can use Claude 3.5 Sonnet for complex refactoring and then swap to a local Llama 3 model for basic boilerplate if you are worried about data privacy or vendor lock-in.
• Supermaven is another one that is blowing people away lately. It has a massive 1-million-token context window which is VITAL for those legacy codebases you mentioned. Most tools hit a wall when they cant see the related utility classes three folders deep, but Supermaven handles it with almost zero latency. It is basically the fastest autocomplete I have tested.
• For the security side, dont just rely on the LLM. You should integrate Snyk into your IDE. It is a dedicated security engine that catches vulnerabilities much more reliably than a general-purpose chat bot ever will. It is all about building a specialized stack rather than hoping one tool does everything perfectly.

To add to the point above: honestly, you gotta be super careful about how much you trust these things with a legacy codebase. Its easy to get lazy and just tab through the suggestions, but thats how bugs crawl into production. I've been doing this a long time and I've seen some messy stuff. Make sure to watch out for:

• Data leakage: double check if your code is being used to train their models. Usually, you want an enterprise tier for privacy or a local-first setup.
• Security blind spots: AI is great at boilerplate but often misses logic flaws that lead to SQL injection or broken auth.
• Context drift: after a long session, the model might start forgetting your specific style or project constraints. I would suggest always doing a manual review on every single line it generates... dont let the shiny new toys make you skip the basics. Tbh, the biggest productivity killer is having to debug hallucinated code that looked right at first glance. Be careful with those auto-generated unit tests too, they can sometimes just test that the code does what the AI thinks it should do, not what it actually needs to do. Its a bit of a trap if you arent careful.