How AI can Improve Coding Tasks

RIP coding? OpenAI has just introduced Codex — a cloud-based AI agent that autonomously writes features, fixes bugs, runs tests, and even documents code. Not just autocomplete, but a true virtual teammate. This marks a shift from AI-assisted to AI-autonomous software engineering. The implications are profound. We’re entering an era where writing code can be done by simply explaining what you want in natural language. Tasks that once required hours of development can now be executed in parallel by an AI agent — securely, efficiently, and with growing precision. So, what does this mean for human skills? The value is shifting fast: → From execution to architecture and design thinking → From code writing to problem framing and solution oversight → From syntax knowledge to strategic understanding of systems, ethics, and user needs As Codex and other agentic AIs evolve, the most critical skills will be, at least for SW tech roles: • AI literacy: knowing what agents can (and cannot) do • Prompt engineering and task orchestration • System design & creative problem solving • Human judgment in code quality, security, and governance It’s a new world for solo founders, tech leads, and enterprise innovation teams alike. We won’t need fewer people. We’ll need people with new skills — ready to lead in an agent-powered era. Let’s embrace the shift. The real opportunity isn’t in writing code faster — it’s in rethinking what we build, how we build, and why. #AI #Codex #FutureOfWork #SoftwareEngineering #AgenticAI #Leadership #AIAgents #TechTrends

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.

Agent-assisted coding transformed my workflow. Most folks aren’t getting the full value from coding agents—mainly because there’s not much knowledge sharing yet. Curious how to unlock more productivity with AI agents? Here’s what’s worked for me. After months of experimenting with coding agents, I’ve noticed that while many people use them, there’s little shared guidance on how to get the most out of them. I’ve picked up a few patterns that consistently boost my productivity and code quality. Iterating 2-3 times on a detailed plan with my AI assistant before writing any code has saved me countless hours of rework. Start with a detailed plan—work with your AI to outline implementation, testing, and documentation before coding. Iterate on this plan until it’s crystal clear. Ask your agent to write docs and tests first. This sets clear requirements and leads to better code. Create an "AGENTS.md" file in your repo. It’s the AI’s university—store all project-specific instructions there for consistent results. Control the agent’s pace. Ask it to walk you through changes step by step, so you’re never overwhelmed by a massive diff. Let agents use CLI tools directly, and encourage them to write temporary scripts to validate their own code. This saves time and reduces context switching. Build your own productivity tools—custom scripts, aliases, and hooks compound efficiency over time. If you’re exploring agent-assisted programming, I’d love to hear your experiences! Check out my full write-up for more actionable tips: https://lnkd.in/eSZStXUe What’s one pattern or tool that’s made your AI-assisted coding more productive? #ai #programming #productivity #softwaredevelopment #automation

💡Most AI coding tools are getting better at generating code. But that is not where the real bottleneck is. The hardest part is not writing code. It is executing real workflows across complex systems. I’ve tinkered with Qoder and QoderWork in real workflows recently, and this gap becomes very obvious in practice. If you talk to enough engineers, you start hearing the same story: Code suggestions are cheap. Context is not. Execution is even harder. Specs live in documents. Code lives in repositories. Workflows live across tools. And engineers are left stitching everything together. This is the gap most AI tools still do not solve. They generate - but they do not execute. We are starting to see a shift: From AI as suggestion engines to AI as execution systems. What makes Qoder different is its Spec-Driven workflow. Instead of treating specs as passive documentation, Qoder turns them into the starting point of execution. In Quest Mode, the system first aligns on requirements, generates a structured spec with task breakdowns and acceptance criteria, and then autonomously executes and verifies each step. No vague prompts. No guesswork. Just traceable, production-ready delivery. That is what I find interesting about Qoder. Not just another coding assistant, but an attempt to build an agentic coding platform for real engineering teams. What this means in practice: 🔹Understanding large, real-world codebases across thousands of files 🔹Breaking specs into structured tasks with clear acceptance criteria 🔹Executing tasks in parallel environments 🔹Delivering outputs that are actually usable in production You can test it on a 500-file TypeScript monorepo — Qoder’s RepoWiki will index the entire codebase, mapped dependencies across modules, and the agent completed a cross-module refactor that would have taken me hours. And importantly, this is designed for teams and enterprise environments. Not just individual developers experimenting in isolation, but engineering organizations that need to ship reliably at scale. And beyond engineering, QoderWork pushes this even further. Turning AI from something that “helps” into something that actually completes real business workflows. From working with local files to automating repetitive knowledge work across teams. For example, you can use QoderWork to process a batch of local PDFs — extracting key insights, structuring them into a report, and organizing outputs automatically. Instead of manually coordinating multiple steps, the agent handled the workflow end-to-end. This is not a tooling upgrade. It is a shift in how work gets done. The question is no longer: Can AI generate code? It is: Can AI take a spec and ship something usable end-to-end? Explore how agentic AI moves from suggestions to real execution: 👉https://aisecret.co/greg Where does your workflow still break down — generation, context, or execution? #Qoder #QoderWork #AgenticCoding #EnterpriseAI #DevTools #AIProductivity

How well does AI write code? According to medalist judges, AI’s code is not so great. But there were a few surprises buried in this paper. This is the most critical and comprehensive analysis of AI coding agents so far. I expected Claude 3.7 to be near the top, but OpenAI’s o4 and Gemini 2.5 Pro scored significantly higher. Both can solve most coding problems that the judges ranked as ‘Easy’, and the solutions cost pennies to generate. OpenAI’s o4-mini-high delivered solutions that only required human interventions 17% of the time for $0.11. Compare that to the cost of a software engineer implementing the solution, and the benefits are obvious. It generated complete implementations for medium problems 53% of the time, also at a significant cost savings. However, its reliability drops to 0 for hard problems. Researchers found that AI coding assistants are exceptionally useful if they are given access to the right tools and focused on simple or medium difficulty problems. With tools and multiple attempts, solution accuracy doubled for some LLMs, and they were able to solve a small number of hard problems. However, programming skills and software engineers are still required. AI coding tool users must be able to identify flawed implementations and know how to fix them. Even with tools and multiple attempts, AI coding assistants still fumble problems at all difficulty levels. Code reviews and validation continue to be critical parts of the workflow, so the hype of vibe-coding and AI replacing software engineers is still just a myth. At the same time, the software engineering workflow is changing dramatically. Multiple researchers have attempted to determine how much code is written by AI vs. people, but accurate classification methods are proving elusive. At the same time, research like this makes the trend undeniable. $0.11 per implementation represents a cost savings that businesses won’t pass up. The future of software engineering is AI augmented. An increasing amount of code will be written by AI and validated by people. Most code required to implement a feature falls into the medium or easy category. AI coding assistants can’t do the most valuable work, but their impact on the time it takes to deliver a feature will be bigger than the benchmarks indicate. Now that we’re seeing research into the root causes of implementation failure, like this paper, expect AI coding tools to accelerate their capabilities development rate in the next two years. For everyone in a technical role, it’s time to think about how to adapt and best position yourself for the next 5-10 years.

𝗗𝗼 𝗔𝗜 𝗖𝗼𝗱𝗶𝗻𝗴 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗕𝗼𝗼𝘀𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆? 𝗔 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 𝗳𝗿𝗼𝗺 800+ 𝗚𝗶𝘁𝗛𝘂𝗯 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 New research from Carnegie Mellon University just dropped and the results are fascinating. The team studied the impact of 𝗖𝘂𝗿𝘀𝗼𝗿, a popular LLM-based agentic IDE, across 807 real-world repositories using causal inference methods. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗳𝗼𝘂𝗻𝗱: 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 gains are real, but 𝘀𝗵𝗼𝗿𝘁-𝗹𝗶𝘃𝗲𝗱 - +281% more code in month 1 - +48% in month 2 - Back to baseline after that 𝗖𝗼𝗱𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘁𝗮𝗸𝗲𝘀 𝗮 𝗵𝗶𝘁 and it sticks - +30% static analysis warnings - +41% increase in code complexity - Long-term slowdown due to accumulated tech debt => 𝗦𝗲𝗹𝗳-𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲: 𝗠𝗼𝗿𝗲 𝗰𝗼𝗱𝗲 -> 𝗠𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 -> 𝗦𝗹𝗼𝘄𝗲𝗿 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 LLM coding agents like Cursor can supercharge productivity, for a moment. But without process changes, they may speed you toward an unmaintainable codebase. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Build in quality assurance from day one. Test coverage, refactoring sprints, smarter prompts. AI won’t save your codebase unless you save it first. #AI #LLM #SoftwareEngineering #Productivity #TechDebt

The question I keep hearing: If AI can write code, what’s left for engineers to do? I’ve spent sometime evaluating AI coding assistants and LLM-based development tools like GitHub Copilot and Cursor, and I’m excited about the possibilities. AI isn’t replacing engineers, it’s supercharging us, handling the routine so we can focus on the creative, high-impact work that drives innovation. Current AI models are getting remarkably good at local optimization, swiftly translating clear requirements into solid implementation. They manage syntax, boilerplate, and common patterns with efficiency that frees up our time. Yet, the real magic happens in partnership: AI excels where specs are solid, while human engineers bring the vision to define those specs in the first place. The work that truly elevates engineering is increasingly a human-AI collaboration, upstream and downstream of code: 1. Architecture and system design : We define service boundaries, select consistency models, and map failure domains. AI can then generate the microservice, but our judgment ensures it’s the right one for the system. 2. Constraint analysis : Balancing latency budgets, scaling needs, and operational tradeoffs becomes smoother with AI simulations, yet our experience spots the nuances that turn good designs into great ones. 3. Problem decomposition : We transform vague goals like ‘boost performance’ into actionable specs, drawing on domain knowledge; AI then iterates rapidly to refine them into working solutions. 4. Code review as system validation : Together, we assess if code aligns with the big picture, minimizes debt, and adds real value, not just compiling, but contributing meaningfully to the ecosystem. AI bridges specs to code with growing sophistication, opening doors to faster iteration and bolder ideas. What it amplifies is our ability to question, innovate, and decide if code is even the best path forward. In this evolving landscape, the engineers who will lead are those who embrace AI as a trusted partner, leveraging it to amplify system-level thinking. How has AI enhanced your workflow? Share your thoughts below—I’m optimistic about where this is headed! #AIEngineering #EngineeringLeadership #AIinSDLC #AIDrivenDevelopment #FutureOfWork

How AI Boosted Our Engineering Productivity by 18% in Just 30 Days 🚀 That’s exactly what we discovered during a recent pilot program at Cognism, where we tested AI-powered coding assistants. The results were too exciting not to share! Why We Tried AI Our engineering team is always looking for ways to work smarter. We introduced this AI tool with three goals in mind: ✅ Automate repetitive tasks ✅ Accelerate development cycles ✅ Empower our team to focus on innovation We gamified adoption by rewarding our early adopters who showed the greatest productivity gains—and their feedback was key in shaping the rollout. The Numbers Don’t Lie Here’s what the pilot achieved: 📈 31% more issues resolved—less time on repetitive work, more time on creative problem-solving. 🔗 21% more pull requests (PRs) merged—quicker features, faster deliverability. ⏱️ 3% faster PR cycle time—a small win that we know can grow. Overall, an 18% productivity boost for our engineering team. What We Learned 1️⃣ It’s not perfect yet. AI isn’t replacing human developers, but it’s transforming how we approach mundane tasks. 2️⃣ Focus matters. The real value is freeing up time for innovation—our developers can concentrate on solving complex challenges, not repetitive ones. 3️⃣ It’s just the beginning. As these tools evolve, the potential gains could be exponential. The tool we selected : www.cursor.com Please comment below with your own findings and tools you are testing.

The world of AI-driven coding just took another leap forward! I’ve been testing out Bolt by StackBlitz, and I’m genuinely impressed. This AI has blurred the lines between traditional web development and intelligent, prompt-based code generation—allowing developers to build and deploy applications in a matter of minutes. Bolt’s power lies in its integration of AI at every stage of the development process. With just a few prompts, it can spin up environments, code entire components, and even handle deployments. In my experience, I was able to build functional, deployable apps faster than ever before—an efficiency level that fundamentally shifts what’s possible for both seasoned developers and those newer to coding. Here’s why this could be a game-changer: Accelerates the learning curve: Developers can focus on concepts and creativity rather than setup and syntax. Enhances productivity: Complex applications can be created with minimal code, reducing development cycles significantly. Lowers the barrier to entry: Individuals with a solid understanding of app design can quickly bring ideas to life, regardless of coding skill level. AI like Bolt isn't just an incremental improvement; it’s paving the way for a future where more people can build and innovate. We’re moving toward a reality where coding is democratized, sparking a new era of digital creativity. Exciting times ahead! https://lnkd.in/gPzVfHct

AI coding tools can 10x your productivity… But if you use them wrong, they slow you down. Here’s the workflow most people miss 👇 I’m Jean! I spent years as a Tech Lead and a Manager at Meta Managing AI coding agents doesn’t feel that different from managing human engineers. In both cases: ↳ You don’t jump straight into implementation. ↳ You plan first. ↳ You write specs. ↳ You set guardrails. Here’s the workflow: ✅ 1. Create “Memory Files” for AIThink of these as instruction manuals for your agents. Add a file like: ↳ agents.md or ↳ CLAUDE.md inside your repo to store: ↳ Project goals ↳ Coding conventions ↳ Tech stack details ↳ Build + test commands ↳ Style and design rules This prevents the agent from “forgetting context” every step. ✅ 2. Force the AI to Plan First Before any code is written, explicitly ask: ↳ Outline the steps before implementation. ↳ List risks or unknowns. ↳ Tell me which files you plan to change. ↳ Wait for my approval before coding. You want the design doc before the PR. ✅ 3. Write a Lightweight Spec Even a simple AI-generated spec works: ↳ Problem to solve ↳ Scope & non-goals ↳ Approach ↳ Files & interfaces affected Most people skip this because they “just want to start coding.” That’s how projects go sideways. Are you using AI as a careful collaborator Or vibe coding to the max? ♻️ Repost if this helps someone with coding workflow 👉 Follow me, Jean, for real-world AI engineering workflows and career lessons. #AIcoding #AIEngineering #SoftwareEngineering