Olivier Breton’s Post

Coding-agent stacks changed in 2026. Most teams are still buying like it's 2025. Is your team evaluating a tool or choosing an operating model? The expensive mistake isn't picking the wrong vendor. It's running a 2025 evaluation process on a 2026 category. Because most teams still ask: which model feels smartest, which UI is nicest, which demo looked best. Those questions made sense when AI coding meant better autocomplete. They don't hold when OpenAI, GitHub, Anthropic, and Cursor are now shipping multi-agent supervision, parallel worktrees, background automations, and GitHub-native delegation. The category matured. The buying process didn't. I've watched technical leaders standardize on one tool and then force every workflow into it. That's the wrong design for most serious engineering organizations. The more durable pattern emerging from the article: Terminal-first agent for deep repo work and direct execution Supervisory workspace for parallel tasks and orchestration GitHub-native layer for issue-to-PR flow and review handoff Remote background lane for async experiments and sandboxed work Shared context layer for controlled access to systems GitHub's own responsible-use guidance states human validation is still required because Copilot can miss issues or make mistakes. OpenAI frames the core challenge as how people direct, supervise, and collaborate with agents at scale. Not what agents can do. That reframe matters. The real question isn't whether the tool can generate code. It's whether your team has a credible review model for delegated work. The threshold most teams haven't defined: what approval criteria separates agent-suggest from agent-execute. Domain leads have different answers. That gap is where rework and governance exceptions accumulate. Buying discipline beats vendor hype. Operating model clarity beats feature comparison. Let's map which of your current workflows already have agent exposure and which ones still lack a defined review threshold. #AIStrategy #EngineeringLeadership #AgentOps

Lately I almost never just sit down and solve something myself. First instinct: tool it. Spin up the AI, wire a progressive loop against a quality target, then let it run. Results get better every week to the point where they frequently surpass what I could only accomplish after at least three full drafts. That's been working. But a new tradeoff has crept in, and I'm confident I'm not alone. Sometimes I'm faster. The AI takes a few passes, orbits the problem, gets there eventually, and I already knew the answer, but it stuck the landing soundly. So now there's this constant background calculation running in my head: > Is this worth the tokens, or should I "just do it?" < The shift isn't "can I automate this?" because I have and will continue to do this. AI tooling routinely elevates my work product and allows others to contribute similarly across the team. Big equalizer! I've been building toward this for a while — this repo is where that thinking lives: https://lnkd.in/gfhgSGQ6. Frankly, though, the more interesting stuff is what happens when you layer real workflows on top of it. That's what we're working on at CallBox, and it's where the actual gains are showing up. We have some great internal adoption by Product and BizDev folks as well as Software Engineers. How are others thinking about this tradeoff? #AIFirst #AIAssistants #WorkflowAutomation #EngineeringLeadership #TechLeadership #DeveloperExperience #Productivity #FutureOfWork #SoftwareEngineering #BuildInPublic

Is System-Driven Specification the next step in software engineering? --- I’ve been experimenting with this idea in a project I recently published on GitHub: 🔗 https://lnkd.in/dk52uhj2 --- 👉 ia-system-spec-framework Most developers see AI as a way to write code faster. --- I’ve been exploring a different angle: 👉 What if we use AI to design better systems first? Instead of starting with a specific language, you define the system using structured specifications. From that, you can: → Generate a backend in .NET → Or Node.js → Or any other stack 👉 The design becomes portable — not the code. --- 🧠 This approach connects with ideas we’ve seen before: → Model-Driven Development (now finally viable with AI) → Domain-Driven Design (focus on domain before code) → Infrastructure as Code (but applied to systems) --- In a way, this becomes: 👉 System as Code --- 🚀 Pros I’ve observed: ✔ Strong architectural consistency ✔ Better alignment between backend and frontend ✔ Massive improvement in AI-assisted development (tools like GitHub Copilot work much better with structured context) ✔ Reusable and versionable system design ✔ Clearer domain modeling before implementation --- ⚠️ But there are real trade-offs: ✖ Higher upfront cognitive load ✖ Specs can become outdated if not maintained ✖ System quality depends heavily on spec quality ✖ Not everything can (or should) be generated ✖ Requires discipline and a mindset shift --- 📉 This is probably not ideal for: → Small projects → Quick prototypes → Solo hacking without structure --- 📈 But it becomes very powerful for: → Multi-tenant SaaS platforms → Distributed systems → Event-driven architectures → Teams that need consistency at scale --- We’ve seen similar ideas before (like MDD), but they didn’t scale well due to tooling limitations. Now, with tools like GitHub Copilot and ChatGPT, the equation is changing. --- 👉 The real challenge is not generating code. 👉 It’s keeping the spec as the single source of truth. --- Curious to hear your perspective: Are you adapting your system design for AI-assisted development, or still starting from code? #SoftwareArchitecture #AIEngineering #SystemDesign #DotNet #FullStack #phyton

𝐓𝐡𝐞 𝐧𝐞𝐱𝐭 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬 𝐰𝐨𝐧’𝐭 𝐬𝐭𝐚𝐫𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐭𝐞𝐫𝐦𝐢𝐧𝐚𝐥. AI coding is moving fast. We now have agents, Claude Code, GitHub Copilot workflows, and reusable AI skills that encode how experienced engineers think and work. Even leading developer educators like Matt Pocock are showing how powerful this becomes when we move from random prompting to repeatable AI skills: PRDs, issues, TDD, code quality, architecture and review loops. That is a big signal. But here is the gap: Most of these workflows still assume the user already understands the developer world. Repositories. Issues. Tests. Pull requests. Architecture. Terminals. Frameworks. What about the people who have the business problem, the customer insight, the process knowledge — but not the developer identity? That is why I believe VS Code, GitHub Copilot, Claude Code and the AI coding ecosystem need a new entry point: 𝐍𝐨𝐧-𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐌𝐨𝐝𝐞. Not “write code faster.” But: “I’m not a developer — help me build.” A guided mode that asks: What do you want to create? What data or tools should it connect to? Who will use it? Should this become a prototype, automation, internal tool, or app? Developer Mode is for control. Agent Mode is for autonomy. Non-Developer Mode should be for translation. From idea → requirements → workflow → prototype → usable solution. Microsoft made productivity tools accessible to knowledge workers. The next opportunity is making AI building accessible to non-developers. So my question is: Are we building better tools for developers — or the first real building environment for everyone else? Curious how Microsoft, GitHub and Anthropic think about this next layer of AI building. #AICoding #AIAgents #GitHubCopilot #ClaudeCode #VSCode

People are worried that GitHub might use developers' code to train AI 🤖 But honestly… what’s wrong with that? If AI learns from more real-world code: • Tools will get smarter • Development will get faster • And bigger companies competing means more benefits for us And we all know one thing 👇 👉 More competition = better products + lower costs Instead of fearing it, maybe it’s time to adapt and take advantage of it. What do you think is a threat or opportunity? Learn More Here: https://lnkd.in/dKfzq3ZS #AI #GitHub #Developers #Tech #Innovation #Engineers #coding

I started using GitHub Copilot seriously… and it changed how I code. Not because it replaces me — but because it removes 𝘧𝘳𝘪𝘤𝘵𝘪𝘰𝘯. Here are 5 practical ways I use Copilot daily 👇 1. Writing boilerplate in seconds 2. Debugging faster with quick suggestions 3. Learning new frameworks by doing, not just reading 4. Refactoring messy code into something cleaner 5. Generating test cases (seriously underrated) But here’s something interesting 👇 Copilot vs other AI tools: • Copilot → feels like a pair programmer inside your IDE • ChatGPT → better for deep explanations & problem breakdowns • Cursor / other AI IDEs → more control + full-codebase awareness So it’s not about “which AI is best” anymore… It’s about 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘤𝘰𝘮𝘣𝘪𝘯𝘦 𝘵𝘩𝘦𝘮. The real shift in 2026: Developers who know how to collaborate with AI > those who don’t. Curious to know 👇 Which AI tool is your go-to right now? #GitHubCopilot #AI #SoftwareDevelopment #Developers #Productivity

🚀 Announcing CodeFlow AI — Today's Launch https://www.codeflowai.app For the last few months, our team has been building something we genuinely believe will change how teams do code review. The Problem: Code review bottlenecks are destroying engineering productivity. Your best people spend 6-12 hours per week just reviewing PRs. Meanwhile, 42% of code is now AI-assisted, but validation is even slower—it requires careful review from experienced engineers. The result: Your team's velocity is capped by the availability of your most expensive people. The Solution: CodeFlow AI—an AI-powered code review platform that integrates with GitHub in 30 seconds. How it works: ✅ Install the GitHub App (30 seconds) ✅ Push a PR (nothing changes for your workflow) ✅ CodeFlow AI reviews it automatically ✅ Comments appear directly on GitHub with actionable suggestions What it catches: 🐛 Logic errors and edge cases 🔒 Security vulnerabilities (OWASP top 10, SQL injection, XSS) ⚡ Performance bottlenecks (N+1 queries, memory leaks) 📊 Code quality issues and refactoring opportunities Why it's different: Unlike generic tools, CodeFlow learns YOUR codebase. Your architecture. Your standards. This reduces false positives by 65% compared to traditional tools. More importantly: it validates AI-generated code properly. Not just flagging surface-level issues, but catching the subtle edge cases and assumptions that humans usually handle. Real traction from 500+ beta users: - 40% reduction in average review time - 65% fewer false positives vs traditional tools - 89% adoption rate after day one (that's our key metric) - Critical vulnerabilities caught that human reviewers initially missed We're launching today with 30 days free, no credit card required. This isn't about replacing code review—it's about augmenting it. Your team focuses on architecture and design decisions. We handle the technical checks that waste senior engineer time. If your team is struggling with code review bottlenecks, or if AI-generated code validation is painful, CodeFlow is built exactly for that problem. What's your biggest code review challenge right now? I'm genuinely curious. #AI #CodeQuality #SoftwareEngineering #DeveloperTools #GitHub #Productivity

🚀 Announcing CodeFlow AI — Today's Launch https://www.codeflowai.app For the last few months, our team has been building something we genuinely believe will change how teams do code review. The Problem: Code review bottlenecks are destroying engineering productivity. Your best people spend 6-12 hours per week just reviewing PRs. Meanwhile, 42% of code is now AI-assisted, but validation is even slower—it requires careful review from experienced engineers. The result: Your team's velocity is capped by the availability of your most expensive people. The Solution: CodeFlow AI—an AI-powered code review platform that integrates with GitHub in 30 seconds. How it works: ✅ Install the GitHub App (30 seconds) ✅ Push a PR (nothing changes for your workflow) ✅ CodeFlow AI reviews it automatically ✅ Comments appear directly on GitHub with actionable suggestions What it catches: 🐛 Logic errors and edge cases 🔒 Security vulnerabilities (OWASP top 10, SQL injection, XSS) ⚡ Performance bottlenecks (N+1 queries, memory leaks) 📊 Code quality issues and refactoring opportunities Why it's different: Unlike generic tools, CodeFlow learns YOUR codebase. Your architecture. Your standards. This reduces false positives by 65% compared to traditional tools. More importantly: it validates AI-generated code properly. Not just flagging surface-level issues, but catching the subtle edge cases and assumptions that humans usually handle. Real traction from 500+ beta users: - 40% reduction in average review time - 65% fewer false positives vs traditional tools - 89% adoption rate after day one (that's our key metric) - Critical vulnerabilities caught that human reviewers initially missed We're launching today with 30 days free, no credit card required. This isn't about replacing code review—it's about augmenting it. Your team focuses on architecture and design decisions. We handle the technical checks that waste senior engineer time. If your team is struggling with code review bottlenecks, or if AI-generated code validation is painful, CodeFlow is built exactly for that problem. What's your biggest code review challenge right now? I'm genuinely curious. #AI #CodeQuality #SoftwareEngineering #DeveloperTools #GitHub #Productivity

🚀 I tried automating PR reviews with AI… but the real learning wasn’t what I expected. Earlier, I used tools like “@Claude” inside GitHub PRs. 👉 You just tag it 👉 It understands context 👉 Starts reviewing instantly Feels like magic. Almost like a senior dev jumping into your PR. --- Then I moved to **n8n automation for PR reviews**. And reality hit differently. With n8n: * You need to pass PR link manually (or configure webhook) * Fetch diffs via GitHub API * Structure prompts yourself * Decide how AI should review (security, performance, best practices) * Post comments back manually via workflow Basically… YOU design the brain. --- 💡 But here’s the twist: n8n is not “less powerful” — it’s just a different philosophy. 👉 Claude = *agent (you give intent, it figures out execution)* 👉 n8n = *workflow (you define execution step by step)* And that difference is HUGE. --- ⚙️ Example of what n8n can actually do: * Trigger on every PR automatically * Fetch code changes (diffs) from GitHub * Run AI review (OpenAI / Gemini / Claude) * Add structured comments on PR * Label PRs (bug, enhancement, risky change) * Notify team on Slack * Store review logs in DB All fully automated 🔥 (Yes — once configured properly) ([N8N][1]) --- 🤯 My biggest realization: Claude feels *smart* n8n makes *systems* Claude helps you review faster n8n helps your team never miss a review --- ⚖️ So what should you use? 👉 If you want quick, intelligent help → use AI agents (Claude-style) 👉 If you want scalable engineering workflows → use n8n The real power? Combine both. --- 💭 Future is not “AI vs automation” It’s: **AI + Automation = Autonomous Engineering Systems** And we’re just getting started. --- #AI #n8n #GitHub #CodeReview #Automation #Developers #DevOps #StartupTech

𝐀𝐈 𝐯𝐬. 𝐓𝐡𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫: 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐒𝐡𝐢𝐟𝐭 🚀 The conversation shouldn't be about 𝘈𝘐 𝘳𝘦𝘱𝘭𝘢𝘤𝘪𝘯𝘨 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 it should be about how 𝘈𝘐-𝘦𝘮𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 are redefining the industry. In a landscape that moves this fast, staying relevant isn't just about writing code; it’s about architectural thinking, problem-solving, and using the right tools to amplify our output. I’ve been exploring how modern AI workflows can streamline complex logic, allowing us to focus on what truly matters: Innovation. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐨𝐫 𝐌𝐲 𝐍𝐞𝐭𝐰𝐨𝐫𝐤: 𝑳𝒐𝒈𝒊𝒄 𝒐𝒗𝒆𝒓 𝑺𝒚𝒏𝒕𝒂𝒙: AI can generate code, but it still struggles with high-level logic and unique system constraints. That’s where we come in. 𝑪𝒐𝒏𝒔𝒊𝒔𝒕𝒆𝒏𝒄𝒚 𝒊𝒔 𝑲𝒆𝒚: Whether it’s Git hygiene or code quality, tools help, but the developer’s discipline defines the project’s success. 𝑻𝒉𝒆 𝑯𝒚𝒃𝒓𝒊𝒅 𝑭𝒖𝒕𝒖𝒓𝒆: The most "industry-ready" developers in 2026 aren't fighting AI; they are leading it. I’m curious to hear from the community: How are you integrating AI into your daily development cycle without losing the "human touch" in your architecture? Let’s connect and share insights on building more resilient, efficient software. #AndroidDevelopment #SoftwareEngineering #TechInnovation #AIinTech #FullStack #DevOps #CareerGrowth #Networking