AI Coding Speed vs Quality: Industry Trust Gap Revealed

𝗢𝘂𝗿𝗼𝗯𝗼𝗿𝗼𝘀: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗕𝗲𝗳𝗼𝗿𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 🛰️ [TOOLS] Ouroboros is an AI agent framework that uses multi-stage reasoning to refine ambiguous inputs before generating code. Why it matters: Ouroboros addresses the 'garbage in, garbage out' problem by prioritizing reasoning and ambiguity reduction. This can lead to more reliable and efficient AI-driven code generation. 🤔 How can AI agent frameworks like Ouroboros balance reasoning and efficiency to deliver reliable and cost-effective solutions? #AI #AgentFramework #Reasoning #CodeGeneration #LLM 📡 Follow DailyAIWire for autonomous AI news 🔗 https://lnkd.in/dm_d864g

Gone are the days when you asked AI complex questions and it produced seemingly perfect code instantly. Now comes the reality check. In this The New Stack article, Sonar’s Anirban Chatterjee digs into our new report → 96% of developers do not fully trust that AI-generated code is functionally correct, yet only 48% verify it. 🤯 What else did our research reveal? Take a read of Anirban’s article to find out: https://lnkd.in/gwSmDEwP #AI #SoftwareDevelopment #StateofCode #DeveloperSurvey

Code Review in the Age of AI https://lnkd.in/gX2Zxbaf AI did not kill code review. It made the burden of proof explicit. Ship changes with evidence like manual verification and automated tests, then use review for risk, intent, and accountability.

Your code reviews are theater. AI writes faster than humans can review. So we skim. We approve. We pretend we caught the bugs. The fix isn't slower AI. It's shifting the burden of proof. Addy Osmani proposes PR contracts. Every pull request needs: - Intent. What this change does and why. - Proof. Tests, screenshots, evidence it works. - Risk. What could break. - AI disclosure. Which parts were generated. The reviewer isn't there to find bugs. They're there to confirm the author already did. When someone submits a 500-line diff with no proof it works, that's not a PR. That's a hope. I second this. It's why we built PR testing at QA.tech. Every pull request gets tested against the actual product. Evidence that the change works, generated automatically. Human accountability doesn't scale down just because AI scales up. https://lnkd.in/d5pHStJZ

A recent IEEE Spectrum article highlights a worrying trend: newer AI coding models are increasingly prone to “silent but deadly” failures — generating code that looks correct, runs fine, but is logically wrong. Unlike obvious syntax errors, these issues quietly slip into production and can cause serious downstream impact. As AI becomes deeply embedded in our development workflows, this is a strong reminder that: Speed must not replace correctness AI outputs still need rigorous review and testing Human judgment remains critical https://lnkd.in/gbFwrDs6 #AI #ResponsibleAI #SoftwareEngineering

AI coding assistants are getting smarter… but not necessarily better. A recent IEEE Spectrum article (https://lnkd.in/eQhKbC-f) highlights a troubling trend: newer AI coding models are increasingly producing silent failures—code that runs without errors but is logically incorrect or misleading. The core issue is training feedback. When AI models are optimized on user acceptance, they learn to prioritize “works enough” over “is right.” That creates a dangerous feedback loop: wrong code → accepted → reinforced → repeated The silent failure mode is the most dangerous kind because it doesn’t crash enter production and creates technical debt that’s hard to trace back.

AI didn’t replace coding. It changed code review. The most practical way to think about “AI coding agents” isn’t: “they write code for you.” It’s: “they generate a first draft faster than any human can.” So the real skill shift is: - Less time typing - More time reviewing (assumptions, edge cases, security, performance) To use AI agents well, pick projects where speed helps — but correctness still matters. Projects AI agents can accelerate (without making it fluffy): 1) Backend API + database (Python) Build: a small service (inventory, booking, task manager) Agent helps: endpoint scaffolding, schemas, CRUD wiring You verify: auth rules, validation, migrations, query performance 2) Production-ready background jobs Build: email sender, report generator, file processor Agent helps: worker skeleton, retries, scheduling You verify: idempotency, failure handling, DLQ, observability 3) Caching + performance mini-lab Build: add caching and measure before/after Agent helps: cache wiring, basic benchmarks You verify: cache keys, TTLs, invalidation, p95/p99 4) ML pipeline (trading is a great example) Build: data → features → model → evaluation/backtest Agent helps: pipeline structure, scripts, charting You verify: time splits, leakage checks, reproducibility Takeaway: AI agents make building faster — and mistakes faster. The advantage goes to people who can review like an engineer, not just generate like a tool. Rule of thumb: if you can explain why it works and how it fails, you’re using AI the right way. What’s one project AI made dramatically easier for you — without lowering quality?

I may have opinions, but one of the things I have seen all the time is people trying to eat the whole elephant at once. In your classic computing courses this is known as decomposition. It's not mystical and yet when presented with a problem many people are bad at breaking that problem down into smaller parts.

AI code generation is surging, but code review capacity is staying the same. The result? A massive verification bottleneck. 🛑 Great piece in The New Stack by Sonar’s own Anirban Chatterjee on why AI hasn’t actually shrunk developer toil yet, it’s just changed its shape. "Value is no longer defined by the speed of writing code, but by the confidence enterprises have in deploying it." Check it out! #AI #StateofCode #DeveloperSurvey

AI code generation is surging, but code review capacity is staying the same. The result? A massive verification bottleneck. 🛑 Great piece in The New Stack by Sonar’s own Anirban Chatterjee on why AI hasn’t actually shrunk developer toil yet, it’s just changed its shape. "Value is no longer defined by the speed of writing code, but by the confidence enterprises have in deploying it." Check it out! #AI #StateofCode #DeveloperSurvey