Intelligent Coding and Predictive Debugging Techniques

Most AI coders (Cursor, Claude Code, etc.) still skip the simplest path to reliable software: make the model fail first. Test-driven development turns an LLM into a self-correcting coder. Here’s the cycle I use with Claude (works for Gemini or o3 too): (1) Write failing tests – “generate unit tests for foo.py covering logged-out users; don’t touch implementation.” (2) Confirm the red bar – run the suite, watch it fail, commit the tests. (3) Iterate to green – instruct the coding model to “update foo.py until all tests pass. Tests stay frozen!” The AI agent then writes, runs, tweaks, and repeats. (4) Verify + commit – once the suite is green, push the code and open a PR with context-rich commit messages. Why this works: -> Tests act as a concrete target, slashing hallucinations -> Iterative feedback lets the coding agent self-correct instead of over-fitting a one-shot response -> You finish with executable specs, cleaner diffs, and auditable history I’ve cut debugging time in half since adopting this loop. If you’re agentic-coding without TDD, you’re leaving reliability and velocity on the table. This and a dozen more tips for developers building with AI in my latest AI Tidbits post https://lnkd.in/gTydCV9b

Want to design better AI agents? Take notes from code writing systems. Techniques include — Multi-agent — Tool choice — Underlying model — Diff format — Innovative Signals — Code retrieval + knowledge graphs — LSP — Fault localization Let's dive deeper with real examples: Multi-agent Use agents with different roles / prompts that have access to different tools and can hand off to another agent. Some roles used in coding: searcher, planner, reproducer, coder, tester, editor Tool choice Figuring out which agent has access to which tools and designing their inputs / outputs effectively. Tools: knowledge graph, search, bash commands, edit, run test, bash Underlying model The model does a lot of the heavy lifting, and different models are good for different things. What to evaluate for: General ability, long context, latency, test-time compute, infinite output with "prefill" Diff format (input / output of tools in general) The standard human-readable diff format doesn't work well for code edits. Cursor and Aider pioneered using LLM-friendly diff formats to reduce error here. At a higher level, each tool has i/o that can boost quality. Language Server Protocol (LSP) LSPs are a language-independent way in VSCode that gives you symbolic references (Ctrl+Click of functions), function definitions and code structure details. Using LSP for static analysis (after diff application) and for retrieval / kg helps. Search and Knowledge Graphs Pre-processing the cross-references of a codebase with LSP which can be searched by keyword, fuzzy and embedding in addition to having a KG of references to other symbols from functions is critical to get the right context for your LLM inference Fault localization Using domain-specific techniques like this, both SBFL (spectrum-based) and MBFL (mutation-based) that are well-researched ways to identify which file might contain a bug is a useful technique. Today, state of the art is 30% on solving the 2294 real Github issues in SWE-Bench full and 55% on verified Some players: OpenHands, Devin, Bytedance (MarsCode), Amazon Q, Aider, SWE-Agent, CodeR, AutoCodeRover Lessons from coding agents are critical to all agents. Source: https://lnkd.in/gEBmvgA5 https://lnkd.in/gWd8R2ZW https://lnkd.in/gc72t9Zu https://lnkd.in/g8mgzUg4 https://lnkd.in/gdJ6gNiy https://lnkd.in/gZFAwKmQ https://lnkd.in/gA6Ywamq

I spent 200+ hours testing AI coding tools. Most were disappointing. But I discovered 7 techniques that actually deliver the "10x productivity" everyone promises. Here's technique #3 that’s saved me countless hours: The Debug Detective Method Instead of spending 2 hours debugging, I now solve most issues in 5 minutes. The key? Stop asking AI "why doesn't this work?" Start with: "Debug this error: [exact error]. Context: [environment]. Code: [snippet]. What I tried: [attempts]" The AI gives you: → Root cause → Quick fix → Proper solution → Prevention strategy Last week, this technique saved me 6 hours on a production bug. I've compiled all 7 techniques into a free guide. Each one saves 5-10 hours per week. No fluff. No theory. Just practical techniques I use daily. Want the guide? Drop “AI” below and I'll send it directly to you. What's your biggest frustration with AI coding tools? Happy to try and help find a solution.

AI coding assistants are changing the way software gets built. I've recently taken a deep dive into three powerful AI coding tools: Claude Code (Anthropic), OpenAI Codex, and Cursor. Here’s what stood out to me: Claude Code (Anthropic) feels like a highly skilled engineer integrated directly into your terminal. You give it a natural language instruction, like a bug to fix or a feature to build and it autonomously reads through your entire codebase, plans the solution, makes precise edits, runs your tests, and even prepares pull requests. Its strength lies in effortlessly managing complex tasks across large repositories, making it uniquely effective for substantial refactors and large monorepos. OpenAI Codex, now embedded within ChatGPT and also accessible via its CLI tool, operates as a remote coding assistant. You describe a task in plain English, it uploads your project to a secure cloud sandbox, then iteratively generates, tests, and refines code until it meets your requirements. It excels at quickly prototyping ideas or handling multiple parallel tasks in isolation. This approach makes Codex particularly powerful for automated, iterative development workflows, perfect for agile experimentation or rapid feature implementation. Cursor is essentially a fully AI-powered IDE built on VS Code. It integrates deeply with your editor, providing intelligent code completions, inline refactoring, and automated debugging ("Bug Bot"). With real-time awareness of your codebase, Cursor feels like having a dedicated AI pair programmer embedded right into your workflow. Its agent mode can autonomously tackle multi-step coding tasks while you maintain direct oversight, enhancing productivity during everyday coding tasks. Each tool uniquely shapes development: Claude Code excels in autonomous long-form tasks, handling entire workflows end-to-end. Codex is outstanding in rapid, cloud-based iterations and parallel task execution. Cursor seamlessly blends AI support directly into your coding environment for instant productivity boosts. As AI continues to evolve, these tools offer a glimpse into a future where software development becomes less about writing code and more about articulating ideas clearly, managing workflows efficiently, and letting the AI handle the heavy lifting.

If you’re using AI coding tools and you find yourself repeating feedback (“still not working”, “same bug”, “UI still broken”)... pause. That’s the signal you need agent-in-the-loop debugging. Instead of more prompting, ask the agent to build a test harness so it can reproduce the issue and validate fixes on its own: - a minimal repro + single command to run - unit/integration tests that capture the failure - a UI smoke test And if it’s a browser problem, let it use MCP tools (e.g. Chrome DevTools MCP) to run the flow, inspect console/network, and assert pass/fail. This flips the loop from: “try again” → “here’s the failing test” Agent-in-the-loop is faster because it comes back with proof and a working solution, not guesses.

Teaching LLMs to generate Unit Tests for Automated Debugging of Code. Unit Tests(UTs) helps in assessing code correctness and provide feedback , helping LLM in iteratively debugging faulty code. However, a trade-off do exist between generating unit test inputs when given a faulty code and correctly predicting unit test output without access to gold solution. To address it, this paper propose UTGEN, which teaches LLMs to generate unit test inputs that reveal errors along with their correct expected outputs based on task descriptions and candidate code. Also, authors further integrate UTGEN into UTDEBUG, a robust debugging pipeline that uses generated tests to help LLMs debug effectively. 𝗨𝗧𝗚𝗘𝗡: Training LLMs for Unit Test Generation i) Overview Starting with training data for code generation (problem description and gold code), create training data for UT generation in three stages: a) perturbing gold code to generate faulty codes b) generating UT inputs and filtering for failing UTs c) generating and relabeling chain-of-thought rationales conditioned on gold code’s outputs ii) Problem Descriptions and Target Codes - collection of coding problems with problem descriptions and gold codes from Tulu 3 dataset, focused on Python code with functional abstractions - yielded total of 48.3K unique code problems - generated incorrect or faulty code solutions by perturbing reference code solution iii) Data Curation for Supervised Finetuning - input to LLM is same prompt used for sampling unit tests with output being adversarial unit test - used post-hoc rationalization procedure: given the entire UT (x, fr(x)),ask LLM to generate rationales supporting why fr(x) is output corresponding to input x  - added these rationales as CoTs prior to output prediction. 𝗨𝗧𝗗𝗘𝗕𝗨𝗚: Debugging with Generated Unit Tests contains two effective ways of mitigating noisy feedback from automatically generated unit tests i) Boosting Output Accuracy via Test-Time Scaling - for a given UT input, sampled k = 8 output completions (including CoT rationales) and took most common final UT output as final answer - upsampled UT inputs and only retain those where final answer gets over 50% of votes (i.e.,4 votes), discarding unit test otherwise ii) Back-Tracking and Cross-Validation -in each round generate n UTs, use one to provide feedback to debugging LLM, and accept debugger’s edits only if pass rate on entire set of unit tests improves; if it does not, backtrack 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - UTGEN outperforms UT generation baselines by 7.59% based on a metric measuring the presence of both error-revealing UT inputs and correct UT outputs - When used with UTDEBUG, feedback from UTGEN’s unit tests improves pass@1 accuracy of Qwen-2.5 7B on HumanEval-Fix and harder debugging split of MBPP+ by over 3% and 12.35% (respectively) over other LLM-based UT generation baselines 𝗕𝗹𝗼𝗴: https://lnkd.in/eWkpb_bE 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/eMPdJUC3 𝗖𝗼𝗱𝗲: https://lnkd.in/egEfWDMJ

Last week I spent 6 hours debugging with AI. Then I tried this approach and fixed it in 10 minutes The Dark Room Problem: AI is like a person trying to find an exit in complete darkness. Without visibility, it's just guessing at solutions. Each failed attempt teaches us nothing new. The solution? Strategic debug statements. Here's exactly how: 1. The Visibility Approach - Insert logging checkpoints throughout the code - Illuminate exactly where things go wrong - Transform random guesses into guided solutions 2. Two Ways to Implement: Method #1: The Automated Fix - Open your Cursor AI's .cursorrules file - Add: "ALWAYS insert debug statements if an error keeps recurring" - Let the AI automatically illuminate the path Method #2: The Manual Approach - Explicitly request debug statements from AI - Guide it to critical failure points - Maintain precise control over the debugging process Pro tip: Combine both methods for best results. Why use both?  Rules files lose effectiveness in longer conversations.  The manual approach gives you backup when that happens.  Double the visibility, double the success. Remember: You wouldn't search a dark room with your eyes closed. Don't let your AI debug that way either. — Enjoyed this? 2 quick things: - Follow along for more - Share with 2 teammates who need this P.S. The best insights go straight to your inbox (link in bio)