Opus 4.6 Advances Reasoning in Coding Agents

Claude AI 👾 🚀 Claude is an advanced artificial intelligence model developed by Anthropic that is widely used in the software development field. From a development perspective, Claude acts as an intelligent coding assistant that helps developers write, review, and improve code efficiently. It supports multiple programming languages such as Python, JavaScript, Java, C++, and many others, making it useful for developers working in different technology stacks. Developers can use Claude to generate code snippets, explain complex code logic, debug errors, and suggest optimized solutions. This helps reduce development time and improves productivity, especially when working on large or complex projects. One of the important advantages of Claude in development is its ability to understand long code files and large documentation. Developers can provide entire code blocks or project files, and Claude can analyze them to identify issues, suggest improvements, or explain how the code works. This is particularly useful for beginners who are learning programming concepts, as well as experienced developers who need quick insights into unfamiliar codebases. Claude can also assist in tasks such as API integration, database queries, algorithm explanations, and software architecture suggestions. 🚀 In addition, Claude can help developers follow best practices in coding by recommending clean code structures, proper naming conventions, and efficient algorithms. It can also generate documentation, comments, and technical explanations, which are essential for maintaining large software projects. Many development teams use AI tools like Claude to speed up development cycles, reduce manual effort, and improve code quality. As AI-assisted development becomes more common, tools like Claude are becoming valuable companions for programmers, helping them focus more on solving problems and building innovative applications rather than spending excessive time on repetitive coding tasks. #snsinstitutions #snsdesignthinking #designthinkers

"Expectation Based Programming" is my way of writing code, reviewing it, designing for it, debuging, prompting, and all things agentic and AI, for all things that include at least two humans. It applies not only to systems that include code, but also any architecture, or infrastructure that includes more than one human. EBP asks a simple question: "How do we expect our shared systems to behave?" My thinking started from reading Ted Chiang’s short story “What’s Expected of Us.” published 20 years ago in Nature Magazine. In the story, a device called "the Predictor" turns green one second BEFORE its button is pushed. This forces society to confront what it really means for behavior to be “expected” or predetermined. The twist is philosophical: once people believe behavior is predictable, their relationship to taking action changes. If people still read blog posts, then the post that describes EBP ("Expectation Based Programming") would start with this insight exactly. The core exercise of EBP is done with other humans that are not on your team: do the two of us answer the question “How should the system behave in this case?” in the exact same language. If we have an agreement, then the code almost doesn’t matter. When we practice good EBP, writing software is the easy part, and learning how to talk about systems is where the focus is at. This is the exact same way we used to evaluate software engineers in leetcode interviews. Can we reason together clearly about behavior, constraints, and outcomes? In the photo is the result of a simple Google Translate query that gave me a result that I was not expecting.

Is “Vibe Coding” making us forget lessons from the past? When I started my career over 25 years ago, many engineering companies wrote their own software. It was often created by a few very capable people in-house, based on methods they had developed over years.   It worked… until it didn’t.   These systems often had: • Little or no documentation • No formal support • No update roadmap • Limited security considerations • Heavy dependence on the original developer   Over time they became incredibly hard to maintain. Programs couldn’t move to new systems. Sometimes the original source code was lost. And when the developer left, the knowledge left with them.   Languages changed too. Python is everywhere today, but you don’t see many engineers updating legacy Fortran tools anymore.   Over the past 25 years, most organisations moved toward commercial off-the-shelf software - with documentation, support, updates, and quality control. But with the rise of AI coding and “vibe coding”, I sometimes wonder if we’re drifting back toward the same risks.   Uncontrolled scripts. Untested tools. Critical processes dependent on code that only one person understands. To be clear - AI coding isn’t bad. I use it myself to speed up Excel macros or prototype Python scripts. It can also help software companies accelerate development.   But history taught us something important: Once you account for the hidden costs of maintaining home-grown tools, commercial software is often cheaper, and far more sustainable in the long run.   Curious to hear other perspectives. Are we repeating old mistakes - or entering a genuinely new era of engineering software?

𝗧𝗵𝗲 𝗨𝗹𝗧𝗶𝗺𝗮𝗧𝗲 𝗚𝗨𝗜𝗗𝗘 𝗧𝗢 𝗩𝗜𝗕𝗘 𝗖𝗢𝗗𝗜𝗡𝗚 You want to build apps with Vibe Coding tools, but you're not a developer. You've seen the videos: someone types a sentence, presses Enter, and 30 seconds later, a working app appears. You think: I can do that too. Here's what you need to know: - Vibe Coding tools generate code, but you need to understand the structure. - You don't need to be a developer, but you need to know how to use the tools. - There are three main tools: Claude Code, OpenAI Codex CLI, and Google Antigravity. To get started: 1. Choose a tool that fits your needs. 2. Learn the basics of coding and software development. 3. Start small, test often, and commit your changes. Remember: - Vibe Coding is not a replacement for understanding, it's a tool that requires understanding. - The most successful non-developers in Vibe Coding are those who invest time in understanding the systems their AI builds. Source: https://lnkd.in/gjC_g8rn

A strange thing is happening in software development. English is becoming a programming language. For decades, building software meant learning strict syntax: Python. Java. C++. JavaScript. You had to think like a machine. Now the direction is changing. Developers are increasingly writing instructions in plain language, and AI systems are translating that intent into working code. Instead of writing 300 lines of code, a developer might simply say: “Create a REST API that takes product data and returns personalized recommendations.” The AI generates the functions, structure, and even tests. This doesn’t remove engineers. But it changes what engineering means. The real skill is no longer memorizing syntax. It’s: • Clear thinking • Precise instructions • System design • Understanding how machines should behave In other words: The programmer of the future may spend less time writing code and more time telling machines what to build. And in that world, English might quietly become the most widely used programming language on Earth. The real question is not “Can you code?” It’s: Can you think clearly enough to instruct an intelligent machine?

𝗠𝗼𝘀𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘁𝗵𝗶𝗻𝗸 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗚𝗼 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝘆𝗻𝘁𝗮𝘅. It’s not. It’s about unlearning complexity. Control flow in Go is a perfect example. Many languages give you multiple looping constructs:  • while  • do-while  • foreach  • for Go removed almost all of them. There’s only one loop. `𝗳𝗼𝗿`. And that design decision is intentional. Because Go optimizes for clarity and predictability in production systems. Here’s what most people miss: 𝟭. 𝗢𝗻𝗲 𝗹𝗼𝗼𝗽 → 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 The `for` loop replaces everything: • Classic iteration • Infinite loops • While-style loops • Range iteration One construct. Multiple behaviors. Less syntax → fewer mental branches. 𝟮. 𝗦𝗶𝗺𝗽𝗹𝗲𝗿 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗹𝗼𝘄 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗯𝘂𝗴𝘀 Distributed systems fail in subtle ways. When your control structures are minimal, reasoning about behavior becomes easier. That’s why Go codebases often feel boringly readable. And that’s a feature. 𝟯. `𝘀𝘄𝗶𝘁𝗰𝗵` 𝗶𝗻 𝗚𝗼 𝗶𝘀 𝗺𝗮𝘀𝘀𝗶𝘃𝗲𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱 Unlike many languages: • No implicit fallthrough • Cleaner branching • Can replace long if-chains Many senior Go engineers prefer `switch` for readability in production services. 𝟰. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗹𝗼𝘄 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝘀 𝗚𝗼’𝘀 𝗽𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆 Go prioritizes: • Simplicity over cleverness • Readability over abstraction • Predictability over flexibility The language is designed for large teams maintaining systems for years. Not writing fancy code. Uncomfortable truth: Many developers coming from other languages try to write Go like Java or Python. That’s usually when Go code becomes messy. Good Go code is often simpler than you expect. Almost suspiciously simple. Curious how others approach this: When you started writing Go, what control-flow habit from another language was hardest to unlearn? #golang #softwareengineering #backenddevelopment #programming

Last weekend while talking with a friend, he asked me what my workflow is for programming with AI. I hadn’t had time to answer him until today. Here’s what I wrote to him in case it helps anyone. AI-Assisted Development Workflow A Structured Approach to Building Software with LLMs Introduction Over the past moths of working with Large Language Models (LLMs) in software development, I have adopted a structured workflow that significantly improves the quality and reliability of AI-generated code. Instead of directly asking an LLM to implement a feature, the process introduces validation loops at each stage of the development lifecycle. The workflow separates development into four core phases: 1. Specification 2. Understanding 3. Planning 4. Implementation The Workflow 1. Start with a User Story 2. Refine the story until it is clear and complete 3. Ask the LLM to explain the story back 4. Validate that the explanation is correct 5. Ask the LLM to produce an implementation plan 6. Refine the plan if needed 7. Ask the LLM to implement the solution Workflow Diagram (below) Strengths of This Workflow Clear Requirements First The workflow forces the refinement of the User Story before coding begins. Verification of Understanding The LLM explains the requirement in its own words to detect misunderstandings. Planning Before Implementation Generating a plan forces the model to reason about architecture and dependencies. Iterative Refinement Each phase includes a feedback loop allowing improvement before moving forward. Separation of Cognitive Phases Specification, understanding, planning, and implementation are treated as independent reasoning steps. Why This Workflow Works LLMs perform better when complex reasoning is separated into stages. Instead of asking the model to understand requirements, design architecture, and write code simultaneously, the workflow splits the process into understanding, planning, and execution phases. Summary This workflow provides a structured way to collaborate with LLMs when building software and improves accuracy, reliability, and maintainability of generated code.

I would like to offer my perspective on Coding with AI. Some background on me: I’ve been a profession coder since 1990. I was there when the web came to life; when the first web browser was available for Solaris but not for the Mac. I was there when Javascript was introduced and when CSS was released. I was an early adaptor of Java. I was a Smalltalk developer and I worked in Objective-C back in the early 90s. I was an early adopter of web technologies and have used a LOT of them in my career. Regardless of my experience, there are some things that developer do so infrequetnly that it’s like setting the clock on your kitchen microwave. I mean, you’re not an idiot, but it might take you a few mins to remember what final button to press. I have had sales teams ask me, more times than I can count, if I have ever used X or Y API before. So that feeling of, um, “can I do this?” in the face of real skepticism is real. One of the most freeing aspects of AI-assisted coding is that I really don’t fear that fringe tech anymore. If I haven’t used it in years, no problems, a little back-and-fourth with claude code and, oh yeah! That’s how that works; I am back up to speed in less than 5 mins.  I haven’t used that exact API before but I have all the documentation…. what’s quirkey—boom! AI helps me figure out the blindspots pretty quick. The conversations on Linked In lately, the back-and-fourth-scortched-earth-sliced-bread sentiments that have been expressed here—I’ll be honest, I have been ignoring because it’s working very well for me. I’ve said it before: Senior developers are at a massive advantage because we know what good looks like and we can recognize bad pretty quick. (this is what I look like as I am trying to remember what buttons to press to reset the time on the microwave)

I really appreciate this perspective. The microwave analogy is real. Tech stacks move so fast that it is impossible to keep every API and fringe tool fresh in your head. What stood out to me most is your point about senior developers having the advantage because they know what good looks like. That is exactly why AI works so well in the hands of experienced engineers. It does not replace judgment. It multiplies it.

"Senior developers are at a massive advantage because we know what good looks like and we can recognize bad pretty quick." ^ 1,000,000% this!