Myth 1: One AI model can handle all my code
The myth: Developers pick one assistant—Copilot, Cursor, or a ChatGPT tab—and route every task through it. Writing functions, debugging race conditions, generating tests, explaining legacy code. One model, one interface, done.
What actually happens: You get decent autocomplete and mediocre everything else. The model that writes clean React components might hallucinate package names when you ask it to debug a segfault. The one that's fast enough for inline suggestions chokes on architectural questions.
I learned this the expensive way. I spent two months using Claude Sonnet for everything: new features, refactoring, debugging, documentation. Sonnet is brilliant at reading large files and suggesting clean rewrites. But when I hit a gnarly Postgres query that returned the wrong rows only on Tuesdays, Claude gave me three different "fixes" that all missed the actual bug—a timezone cast buried in a subquery.
Then I tried DeepSeek R1 on the same problem. R1 is a reasoning model: it thinks out loud before answering, showing each logical step. It spotted the cast in thirty seconds and explained why Tuesday mattered (the query ran in a cron job that reset every Monday at midnight UTC). One model for architecture, another for debugging. Both good at their jobs; neither good at both.
What works: Match the model to the task. Use Claude Sonnet 4.5 when you're refactoring a module or writing new features—it follows instructions well and keeps style consistent across hundreds of lines. Switch to DeepSeek R1 or DeepSeek V3 when you're stuck on logic: race conditions, off-by-one errors, why the recursion blows the stack. Use Gemini Flash with web grounding when you need to check current API docs or find out if a package still works with Node 22. Keep Llama 3.3 or Amazon Nova Micro in a scratch tab for quick questions—"What does this regex match?"—where speed matters more than perfection.
Kryotta's Compare mode lets you send the same prompt to three models at once and see which answer is clearest. I use it when I'm not sure which model fits: paste the buggy function, run Claude + DeepSeek + Gemini, pick the explanation that makes sense. Two minutes, one workspace, no tab-switching between five different tools.
Myth 2: AI code review is just fancy autocomplete
The myth: AI code review means the assistant suggests the next line while you type, maybe catches a missing semicolon. Useful for boilerplate, not much else.
Reality: The best AI code review happens after you write the code, not during. You paste a pull request, a refactor, or a function you inherited from someone who left six months ago, and you ask the model to explain what it does, spot edge cases, suggest a cleaner structure, or write the tests you should have written.
I reviewed a 400-line Python script last week—data pipeline, lots of nested loops, zero comments. I dropped it into Claude Sonnet 4.5 and asked: "Explain this in plain English, then tell me what breaks if the input CSV has duplicate IDs." Claude walked through the logic in numbered steps, flagged two places where duplicate IDs would silently overwrite earlier rows, and suggested a dict check that took three lines. That's not autocomplete. That's a second pair of eyes who doesn't get bored.
Gemini Pro is excellent when the codebase is huge. It handles 2 million tokens of context—entire repos—so you can ask, "Where else do we call this function?" or "What happens if I change this return type?" and it actually knows. I used it to trace a bug through eleven files in a Django project. Gemini found every place the function was imported, explained how each caller expected the old return type, and listed what I'd need to update. Saved me two hours of grep and guesswork.
What works for AI code review:
- Refactoring: Claude Sonnet. Paste the messy function, describe what it should do, ask for a rewrite. It keeps variable names sensible and doesn't invent libraries.
- Explaining legacy code: Gemini Pro or Claude. Both handle long files. Gemini is faster; Claude gives clearer explanations.
- Spotting bugs before commit: DeepSeek R1. Ask it to "think through edge cases" or "find the bug in this function." The reasoning trace often catches things you missed.
- Writing tests: Claude or GPT-OSS 120B. Describe the function, ask for pytest or Jest cases. You'll still need to tweak assertions, but the structure is solid.
One more thing: debugging with AI works better when you give the model the error message, the relevant code, and what you already tried. "This throws a KeyError" gets a generic answer. "This throws KeyError: 'user_id' on line 47 when the request comes from the mobile app but not the web app; I checked that both send JSON" gets a useful one.
Myth 3: Reasoning models are too slow for real work
The myth: Models like DeepSeek R1 that "think out loud" take forever to answer and produce walls of text. Fine for research papers, useless when you're trying to ship.
Reality: Reasoning models are slower—R1 might take twenty seconds where Sonnet takes five—but they're faster than being wrong. When you're debugging a subtle issue or designing a tricky algorithm, the extra fifteen seconds buys you an answer that actually works. You're not reading the reasoning trace for fun; you're reading it to understand why the solution is correct, so you don't break it again next week.
I use DeepSeek R1 for:
- Logic bugs: Off-by-one errors, wrong loop conditions, anything where "it should work but doesn't."
- Algorithm design: "What's the fastest way to find overlapping intervals in this list?" R1 thinks through time complexity, suggests two approaches, explains the trade-off.
- Tricky SQL or regex: When I need to be sure the query does what I think it does.
I don't use R1 for autocomplete, boilerplate, or quick questions. That's what Claude Haiku 4.5 or Amazon Nova Micro are for—fast, cheap, good enough for "generate a Flask route that accepts JSON."
Claude vs DeepSeek for coding comes down to this: Claude is better at writing and refactoring; DeepSeek is better at solving and explaining. If you're building something new, start with Claude. If you're stuck, switch to DeepSeek. If you're not sure, run both in Compare mode and see which answer you trust.
Myth 4: You need expensive subscriptions to use AI for code
The myth: Serious coding with AI means paying for GitHub Copilot ($10/month), ChatGPT Plus ($20), Claude Pro ($20), maybe Cursor Pro ($20). That's $70 a month, or ₹5,880, before you've written a line.
Reality: Most of that overlap is waste. You're paying four times for similar capabilities because each tool locks you into one model.
Multi model coding platforms give you access to six or eight models for less than the cost of two subscriptions. Kryotta costs $19/month (₹1,599) and includes Claude Sonnet, Gemini Pro, Llama 3.3, Mistral Large, DeepSeek V3 and R1, GPT-OSS, and Amazon Nova—plus image and video models if you need them. You're not locked into one assistant's interpretation of your prompt; you can try three models on the same function and pick the best answer.
The cheap models—Llama 3.3 70B, Amazon Nova Lite, Gemini Flash Lite—are shockingly good for everyday tasks. I use Nova Lite for commit messages, Llama for code comments, Flash Lite for "explain this error." They're fast, they cost almost nothing per request, and they're right often enough that I only escalate to Sonnet or R1 when I need to be sure.
If you're freelancing or bootstrapping in India, this matters. ₹1,599 is manageable; ₹5,880 is not. And you get better results because you're using the right model for each job instead of forcing one model to do everything.
Myth 5: AI hallucinates too much to trust with production code
The myth: AI invents function names, imports packages that don't exist, suggests code that compiles but does the wrong thing. You can't trust it with anything that matters.
Reality: AI does hallucinate. But reasoning models for debugging hallucinate less, and you can catch most hallucinations in thirty seconds by running the same prompt through two models and comparing answers.
When Claude suggests a fix, I paste the same prompt into DeepSeek V3 or Gemini Pro. If all three models agree, the answer is probably right. If one invents a library or skips a step, I know to double-check. This isn't paranoia—it's faster than testing blind.
Compare mode makes this trivial. One prompt, three models, three answers side by side. I used it yesterday on a React hook that wasn't cleaning up properly. Claude said to move the cleanup into useEffect's return function. DeepSeek said the same thing and explained why (the dependency array was stale). Gemini suggested useLayoutEffect, which was wrong for this case. Two votes for the right answer, one outlier. Done in two minutes.
The other trick: ask the model to explain its reasoning. "Why did you suggest this?" or "What could go wrong with this approach?" A model that's guessing will waffle or contradict itself. A model that understands the problem will give you a clear, specific answer.
AI for developers India is growing fast—more freelancers, more startups, more people building products in tier-2 cities with small teams and tight budgets. The developers who win are the ones who treat AI as a junior colleague: useful, fast, needs supervision, gets better when you ask good questions. Not a magic box, not a replacement. A tool that makes you faster when you use it right.
Questions people ask
Can I use AI to review my own code before I push it?
Yes, and you should. Paste your changes into Claude or DeepSeek, ask it to spot bugs or suggest improvements. It's faster than waiting for a human review and catches obvious mistakes—missing null checks, unclear variable names, functions that do too much. You'll still need a human to review architecture and business logic, but AI handles the mechanical stuff.
Which model is best for debugging production issues?
DeepSeek R1 if you need step-by-step reasoning and you're not in a rush. Claude Sonnet 4.5 if you need a fast, reliable answer and the bug isn't too subtle. Gemini Pro if the issue spans multiple files and you need to trace calls across a large codebase. Try all three in Compare mode if you're stuck—one of them will spot what you missed.
Do I need to learn prompt engineering to get useful code from AI?
Not really. Be specific: paste the actual code, describe what it should do, include the error message if there is one. "Fix this" gets a lazy answer. "This function should return the sum of even numbers, but it's returning zero when the list is empty—why?" gets a useful one. Clarity beats cleverness.
Is it worth switching from Copilot to a multi-model platform?
If you only use AI for autocomplete, probably not. If you also use it for debugging, refactoring, documentation, or design questions, yes—you'll get better answers and save money. Copilot locks you into one model; a platform like Kryotta lets you pick the right model for each task and compare answers when you're not sure. Most developers who switch don't go back.
