AI Assisted Software Development Techniques

I’ve been running a quiet experiment: using AI coding (Vibe Coding) across 10 different closed-loop production projects — from minor refactors to major migrations. In each, I varied the level of AI involvement, from 10% to 80%. Here’s what I found: The sweet spot? 40–55% AI involvement. Enough to accelerate repetitive or structural work, but not so much that the codebase starts to hallucinate or drift. Where AI shines: - Boilerplate and framework code - Large-scale refactors - Migration scaffolds - Test case generation Where it stumbles: - Complex logic paths - Context-heavy features - Anything requiring real systems thinking [and new architectures etc]. - Anything stateful or edge-case-heavy I tracked bugs and % of total dev time spent fixing AI-generated code across each project. Here's the chart. My learning is that: overreliance on AI doesn’t just plateau, it backfires. AI doesn't write perfect code. The future is a collaboration, not a handoff. Would love to hear how others are navigating this balance. #LLM #VibeCoding #AI #DeveloperTools #Dev

Recently, I adopted a coding tip from the Anthropic team that has significantly boosted the quality of my AI-generated code. Anthropic runs multiple Claude instances in parallel to dramatically improve code quality compared to single-instance workflows. How it works: (1) One Claude writes the code, the coder - focusing purely on implementation (2) A second Claude reviews it, the reviewer - examining with fresh context, free from implementation bias (3) A third Claude applies fixes, the fixer - integrating feedback without defensiveness This technique works with any AI assistant, not just Claude. Spin each agent up in its own tab—Cursor, Windsurf, or plain CLI. Then, let Git commits serve as the hand-off protocol. This separation mimics human pair programming but supercharges it with AI speed. When a single AI handles everything, blind spots emerge naturally. Multiple instances create a system of checks and balances that catch what monolithic workflows miss. This shows that context separation matters. By giving each AI a distinct role with clean context boundaries, you essentially create specialized AI engineers, each bringing a unique perspective to the problem. This and a dozen more tips for developers building with AI in my latest AI Tidbits post https://lnkd.in/gTydCV9b

👩💻This research on 𝐩𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐚𝐠𝐞𝐧𝐭𝐬 caught my eye for AI-assisted software development in enterprise use cases: CodeMonkeys achieved 57.7% success on real GitHub issues 🙉 𝐂𝐨𝐝𝐞𝐌𝐨𝐧𝐤𝐞𝐲𝐬 just cracked a major challenge in AI software development. Instead of giving AI one shot at fixing code, they built a system that: ✅ Iterates like a real developer - writes code, tests it, refines it ✅ Tries multiple approaches - generates many different solutions in parallel ✅ Self-validates - creates its own tests to verify fixes work ✅ Smart selection - combines voting + testing to pick the best solution 🤓 By scaling both "serial" (more iterations) and "parallel" (more attempts) compute, they let the AI read entire codebases and work more like human developers do. 🔴 Results they highlight in the paper: 57.7% success rate on SWE-bench (real GitHub issues) for ~$2,300. When combined with other top systems, it jumps to 66.2%. This shows us that AI CAN actually maintain and improve real software systems for enterprise use cases. It's exciting and promising. Full paper: https://lnkd.in/djpeC5AN #AI #SoftwareDevelopment #MachineLearning #GitHub #CodeGeneration