{"id":4740,"date":"2026-03-25T23:20:53","date_gmt":"2026-03-26T03:20:53","guid":{"rendered":"https:\/\/www.wholetomato.com\/blog\/?p=4740"},"modified":"2026-03-25T23:24:17","modified_gmt":"2026-03-26T03:24:17","slug":"ai-in-your-ide-whats-real-whats-hype-and-where-c-fits-in","status":"publish","type":"post","link":"https:\/\/www.wholetomato.com\/blog\/ai-in-your-ide-whats-real-whats-hype-and-where-c-fits-in\/","title":{"rendered":"AI in Your IDE: What’s Real, What’s Hype, and Where C++ Fits In"},"content":{"rendered":"

It’s been roughly three years since AI tools and large language models (LLMs) hit the mainstream in a real, unavoidable way. In that time, people have put them to work writing emails, summarizing legal documents, generating marketing copy, composing music, tutoring students, answering medical questions, planning vacations \u2014 and yes, generating entire recipes for whatever’s left in the fridge. Some uses are practical. Some are strange. Most of them are here to stay.<\/span><\/p>\n

One of the more contentious applications is code generation. Based on the promises of their creators, LLMs can dramatically accelerate coding workflows for professionals and novices alike \u2014 and even allow complete beginners to produce functional, working apps in a matter of seconds.<\/span><\/p>\n

In this blog and product update, we look at the current state of AI in coding and programming, with a particular focus on C++. We’ll review what’s actually being implemented in popular IDEs and development environments, weigh the real arguments for and against, and share how (spoiler) Visual Assist is joining in on the fun.<\/span><\/p>\n

tldr<\/strong>: AI coding tools are real\u2014but not a silver bullet. They\u2019re excellent for speeding up repetitive tasks like boilerplate, test generation, and code explanation, but they can introduce subtle, high-risk errors\u2014especially in complex languages like C++. The most effective developers aren\u2019t blindly trusting or rejecting AI\u2014they\u2019re using it selectively, combining its speed with human judgment and deterministic tools.<\/p>\n

What’s the vibe on vibe coding?<\/b><\/h2>\n

Vibecoding<\/span><\/i> is one of the terms that has emerged from the AI-in-software world. It describes a development style where AI generates the code while the human acts purely as an orchestrator \u2014 describing what the app should do without writing any actual code instructions.<\/span><\/p>\n

Public opinion on this is sharply divided. Supporters call it a revolutionary leap in productivity. Critics call it a reckless path toward technical debt, since the orchestrators often accept generated code without detailed review.<\/span><\/p>\n

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A redditor laments the enormous task of reviewing code created or mostly created with LLMs. (From Reddit<\/a>)<\/p><\/div>\n

In practice, vibecoding’s conversational nature is especially appealing to non-coders. Building a workable dashboard or a simple app used to require at least some programming fundamentals. Now it mostly requires a clear description. Many professionals, however, are quick to point out that the resulting code often carries hidden risk and accumulates debt that’s invisible\u2014until it isn’t.<\/span><\/p>\n

Even the doubters, though, can’t entirely dismiss what this represents: the ability to get functional, workable code in an extremely short timespan is genuinely remarkable, even if the output requires supervision.<\/span><\/p>\n

The C++ wrinkle<\/b><\/h2>\n

In C++, the appeal of AI-assisted coding is even more enticing. The case for it is easy to make. C++ is verbose, has a steep learning curve, and requires a lot of boilerplate. AI tools can absorb much of that burden. Devs can prototype faster and skip the tedious parts. Let the model handle CMake stubs and standard patterns while the developer focuses on architecture (sounds similar to a core benefit of productivity plugins like Visual Assist).<\/span><\/p>\n

The case against it is equally compelling, though. Since the language is complex enough by itself, the added layer of using AI increases the level of required scrutiny and the importance of expert oversight.<\/span><\/p>\n

C++ is a language where getting things nearly right<\/em> often counts as getting them wrong. Memory management, undefined behavior, manual resource handling, pointer arithmetic\u2014 these are areas where subtly incorrect code can compile cleanly, run for a while, and then cause serious problems.\u00a0<\/span><\/p>\n

Of course, it’s not claiming that non-AI programming (i.e. manual coding) is less prone to the same mistakes. But LLMs are the kings of confident persuasion. AI-generated C++ code can look perfectly reasonable and can\u00a0 actually do what it’s prompted for. But it can still contain vulnerabilities or hidden bugs that only surface under specific conditions. Not something you want apps usually associated with being made with C++.<\/span><\/p>\n

Consider a concrete example:<\/span> an LLM asked to generate a multithreaded C++ module may produce code that compiles and appears to run correctly, but contains a race condition in its resource locking logic. There are no compiler warnings; the tests pass. The bug only appears under load, worst-case, in production. A developer who accepted and shipped that code without careful review now has a problem that is far harder to diagnose than it would have been to prevent.<\/span><\/p>\n

This is not a hypothetical edge case. It is the central risk of vibecoding in a low-level, systems-oriented language. The more complex and performance-critical the codebase, the higher the cost of an unreviewed AI suggestion.<\/span><\/p>\n

Grounded in reality: Where AI fits in today<\/b><\/h2>\n

This pattern of a new technology that arrives with great promise, then gets stress-tested by reality, isn’t new.<\/span><\/p>\n

The dot-com boom of the late 1990s is the closest drawn parallel. It attracted enormous investment, spawned extraordinary promises about what the internet would change and how fast, and then collapsed spectacularly around 2000 when the gap between hype and commercial reality became impossible to ignore. Pets.com. Webvan. Kozmo.com. They were the promised pages that were viewed as the future of the net. Then they just disappeared.<\/span><\/p>\n

But when the dust settled, something more durable remained. The core technologies evolved. The survivors like Google, Amazon, the foundations of e-commerce, social networking, streaming\u2014they went on to define the<\/em> internet. The bubble burst, but the internet didn’t go away. It just Darwin-ed its way and evolved to what it is now.<\/span><\/p>\n

There are some that claim that this is AI\u2019s character arc. The hype is there but the pundits are also proclaiming that the disillusionment is beginning and the AI bubble is about to burst sooner rather than later.<\/span><\/p>\n

If that\u2019s the case, whatever survives the contraction (if the bubble actually does collapse) will likely be the tools and use cases that found actual use. The honest applications and tools that add value. Not the ones promoted ad nauseam by marketing teams to \u201creplace human judgment\u201d entirely.<\/span><\/p>\n

The best apps I’ve ever made have one user; me.<\/p>\n

Personalised apps are the peak. Stop chasing the gold rush (it’s a scam), and just solve your own problems.<\/p>\n

– From a Reddit comment by u\/wardox<\/p><\/blockquote>\n

For coding specifically, we’re already seeing where the floor is. In 2025, a randomized controlled trial by AI research group METR found that experienced developers working on large, complex codebases actually took <\/span>19% longer to complete tasks when using AI tools<\/span> \u2014 despite predicting they’d be 20% faster. The overhead of prompting, reviewing, and correcting AI output can exceed the time saved, particularly on tasks requiring deep contextual knowledge of a codebase.<\/span><\/p>\n

And yet, a report from the Financial Times noted that even major enterprises are not immune to risk \u2014 Amazon Web Services reportedly suffered a 13-hour outage to one of its systems due to employee use of its own AI coding assistant, though Amazon has <\/span>disputed the claim<\/span><\/a>. Whether or not that specific account holds up, the underlying concern is legitimate: AI-introduced errors in complex systems can be expensive, subtle, and hard to trace.<\/span><\/p>\n

Bottom line: Wherever you sit on the AI usage spectrum, avoiding it entirely is becoming increasingly difficult. These tools are bundled into IDEs, suggested by default, and integrated into workflows by businesses looking to cash in on the promise of better labor costs. At some point, most developers will get to try them. A deliberate marketing strat to sow the first seeds of reliance.<\/span><\/p>\n

What the numbers actually show<\/b><\/h2>\n
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Security and accuracy were the top concerns of those using AI in professional work.<\/p><\/div>\n

Adoption is broad and still growing. According to Stack Overflow’s 2025 Developer Survey, 84% of respondents are using or plan to use AI tools in their development process, up from 76% the prior year. Roughly 51% of professional developers now use AI tools daily. Visual Studio and VS Code remain the top IDEs overall, though newer AI-native editors like Cursor, Claude Code, and Windsurf are gaining ground.<\/span><\/p>\n

However, developer sentiment is more complicated. Positive feelings toward AI tools dropped from above 70% in 2023\u20132024 to around 60% in 2025 \u2014 suggesting that the initial wave of enthusiasm is giving way to a more calibrated, experienced view. Roughly 87% of developers surveyed expressed concerns about accuracy, and 81% flagged security and data privacy as ongoing worries.<\/span><\/p>\n

The takeaway isn’t that AI is failing but the industry is getting more honest about what it’s actually good at.<\/span><\/p>\n

Common AI use cases in top IDEs and C++ tools<\/b><\/h2>\n

Back to C++. The major IDEs have converged on a consistent set of AI-powered features, though the depth of implementation is where the battleground is.<\/span><\/p>\n