AI Coding Assistant — Write, Explain, Debug
Want GitHub Copilot without IDE lock-in? Ryna AI writes, explains, and debugs code on web and mobile. Deep Thinking mode handles algorithm design. Near-unlimited on the free plan.
GitHub's 2024 Octoverse reports 92% of developers now use AI coding assistants — but 43% of those report friction with Copilot's IDE-tied model: they don't want to open an IDE for a quick SQL query in the browser, they want to review code on mobile, they want to discuss architecture in chat.
Ryna AI fills that gap. No IDE lock-in — chat.rynaai.com handles 50+ programming languages: Python, JavaScript/TypeScript, Java, C/C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin, SQL (PostgreSQL/MySQL/SQLite), Bash, PowerShell, R, MATLAB, Scala, Elixir — at depth. Deep Thinking mode (Plus) handles algorithm design, system architecture, and complex debugging with 30-60 seconds of structured reasoning.
The difference: Copilot autocompletes — fast but line-level. Ryna AI is chat-based — for bigger problems ('this function is O(n²), how do I get to O(n log n)?'), explanations ('why use async/await, isn't callback enough?'), multi-approach reviews ('write this both OOP and functional, compare'). Best when used together. Free plan covers near-unlimited code Q&A; Plus ($12/mo, 399.99 TRY) adds Deep Thinking + file upload.
Why use Ryna AI for this
IDE-independent: works in browser and mobile; no plugin or extra software needed.
50+ language depth: niche languages (Elixir, Crystal, Nim) included; framework-aware (Django, Spring, Rails, Next.js).
Code + reasoning: ask 'why this pattern?' and AI explains the design decision.
Context-aware debugging: paste a stack trace + code, AI finds the cause, fixes it, shares prevention tips.
Multi-approach: 'write this OOP and functional, compare performance + readability' — fantastic for learning.
Tests and security: 'write unit tests for this function', 'review this code against OWASP Top 10' — strong as a review tool too.
Example prompts
Copy any prompt below and paste into chat.rynaai.com. Each prompt is tuned for a different scenario — try them all to see how Ryna AI adapts.
How it works — step by step
- 1
State the problem / goal
Open chat.rynaai.com. Specify language + framework + version: 'Python 3.11, FastAPI 0.100, writing an async DB query'. Prevents AI mixing old APIs with new ones.
- 2
Paste existing code or error message
If you have code, paste it in a markdown code block. For errors, paste the full stack trace — AI finds the cause in the lower frames.
- 3
Ask for reasoning, not just code
Instead of 'write this function', say 'write this function and explain your design decisions'. Async vs sync, recursive vs iterative — these choices are learning moments.
- 4
Request tests and edge cases
'Write 5 unit tests: happy path + null + empty input + max size + wrong type'. AI returns all edge cases at once; you just plug them in.
- 5
Pre-prod security review
Ask 'review this code against OWASP Top 10', 'any race conditions?', 'is this SQL-injection-safe?' AI surfaces issues with cause + fix.
Example: input → output
You ask
PostgreSQL: customers with 3+ purchases in last 30 days + total spent. Suggest indexes.
Ryna AI returns
```sql SELECT c.id, c.email, COUNT(o.id) AS purchase_count, SUM(o.total_amount) AS total_spent FROM customers c INNER JOIN orders o ON o.customer_id = c.id WHERE o.created_at >= NOW() - INTERVAL '30 days' AND o.status = 'completed' GROUP BY c.id, c.email HAVING COUNT(o.id) > 3 ORDER BY total_spent DESC; ``` Index recommendation: ```sql CREATE INDEX idx_orders_customer_status_created ON orders(customer_id, status, created_at DESC) WHERE status = 'completed'; ``` Why this index? • `customer_id` for the JOIN, `status='completed'` filtered via partial index, `created_at DESC` accelerates the 30-day range scan. • Partial index only stores completed orders — ~30% smaller on disk. • EXPLAIN ANALYZE should show 'Index Scan using idx_orders_customer_status_created'; if you see 'Seq Scan', run ANALYZE first.
Pro tips
- Put language + framework + version in the first 10 words of every prompt. Reduces AI mixing old APIs with new.
- Paste the full stack trace, not just the last 5 frames — the cause is often in lower-level framework frames.
- Don't say 'write this code' — say 'write this code with test cases'. Production-ready in one prompt.
- For complex algorithms, enable Deep Thinking (Plus). Standard mode tries shortcuts and can miss on NP-hard or graph problems.
- When asking for code review, give specific criteria: 'OWASP Top 10', 'memory leak', 'race conditions', 'big-O analysis'. Generic 'review this' stays shallow.
- Run every AI-generated code through your own linter and test suite before prod — 95% correct, but the 5% can surprise you.
Ryna AI vs GitHub Copilot
| Feature | Ryna AI | GitHub Copilot |
|---|---|---|
| Where it runs | Web + mobile (chat.rynaai.com) | IDE-locked (VS Code, JetBrains) |
| Architecture and large-problem support | Chat-based, deep discussion + Deep Thinking | Autocomplete-focused, limited architecture chat |
| Monthly price | Free or Plus $12/mo (all AI features) | $10/mo (individual), $19/mo (business) |
| Speed (autocomplete) | Chat — slower than inline autocomplete | Inline autocomplete, milliseconds |
Common mistakes to avoid
- ✕Shipping AI code without tests. 5% error rate can blow up in prod. Always review + test.
- ✕Skipping version: 'write Python' lets AI choose between 2.7 and 3.12. Say 'Python 3.11'.
- ✕Not enabling Deep Thinking for complex algorithms — standard mode tries shortcuts and misses on NP-hard or graph problems.
- ✕Truncating stack traces. 'Just the last line' hides the cause — paste the full trace.
- ✕Asking 'is this code safe?' generically. Be specific: 'SQL injection', 'XSS', 'race conditions' — category-by-category audit.
- ✕Mixing projects in one chat — context blurs and AI guesses the wrong framework. New project = new chat.
Who this is for
CS students, junior-to-senior developers, data scientists, DevOps engineers, hobbyists.
FAQ
How does it compare to GitHub Copilot?
Copilot is IDE autocomplete (faster iteration). Ryna AI is chat-based — better for bigger problems, architecture, explanations. They complement each other. Ryna AI Plus ($12) vs Copilot ($10) — close in price but Ryna AI includes all other AI capabilities.
Which languages does it support?
Python, JavaScript/TypeScript, Java, C/C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin, SQL (PostgreSQL/MySQL/SQLite), Bash, PowerShell, R, MATLAB, Scala, Elixir — plus niche languages. Framework-aware: Django, FastAPI, Spring, Rails, Next.js, Vue, Angular, .NET.
Can I use generated code in production?
Never ship AI code as-is. Review, write tests, security-check. Ryna AI excels at drafts and boilerplate; domain logic and edge cases require your expertise.
How do I use it for debugging?
Paste the stack trace + relevant code, ask 'explain cause and fix, plus prevention tips'. AI finds the cause (often hidden in lower frames), proposes a fix, and adds prevention guidance.
Can it design algorithms?
Yes — with Deep Thinking (Plus). Questions like 'can I get this from O(n²) to O(n log n)?' or 'BFS or DFS for this graph traversal?' get 30-60 seconds of structured reasoning with multiple approaches.
Can I use it for code review?
Yes, strongly. Provide specific criteria: 'OWASP Top 10', 'memory leaks', 'race conditions', 'big-O analysis'. AI surfaces issues with cause + fix. Not a replacement for a senior dev's PR review, but a strong support tool.
Does it support niche languages (Elixir, Nim, Crystal)?
Yes, though not as deep as the mainstream languages. Solid on basic syntax, idiomatic patterns, and stdlib. For ecosystem-specific bugs (e.g., a tricky Phoenix LiveView issue), consult the official docs as well.
Can I upload code as a file?
Yes, with Plus. Upload .py or .js files (or a zip); AI reads the architecture and answers questions. For very large projects, enable Deep Thinking.
Related use cases
Free — near-unlimited daily messages
No credit card. Plus at $12/mo (399.99 TRY) unlocks image analysis, file analysis (PDF/Word/Excel), deep thinking, web research, and assistants.