Learn2Earn AB’s Post

🌟𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜/𝗠𝗟-𝗱𝗿𝗶𝘃𝗲𝗻 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗱𝗲𝗯𝘂𝗴 𝗳𝗹𝗼𝘄 𝗳𝗿𝗼𝗺 𝗮 𝗦𝗮𝗺𝘀𝘂𝗻𝗴 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 + 𝗖𝗮𝗱𝗲𝗻𝗰𝗲 𝗗𝗩𝗖𝗼𝗻 𝟮𝟬𝟮𝟱 𝗽𝗮𝗽𝗲𝗿 - 𝘂𝘀𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹𝘀. 🌟 Verification can consume a huge chunk of the chip design cycle, and debug is often the slowest part: engineers triaging failures one by one, manually, for days. I wanted to explore: 𝗖𝗮𝗻 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗯𝗲 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜/𝗠𝗟 — 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? So I built 𝗔𝗣𝗕-𝗗𝗧𝗧 (𝗔𝗜/𝗠𝗟 𝗗𝗿𝗶𝘃𝗲𝗻 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝗲𝗯𝘂𝗴 𝗧𝗼𝗼𝗹) 👇 It includes: 🔍 ASD — detects abnormal simulations using regression models. 📚 DHF — finds similar past failures using string similarity. 🧠 DGS — classifies new failures using TF-IDF + Random Forest. Plus a lightweight 𝗥𝗧𝗟 𝗱𝗶𝗳𝗳 𝗲𝗻𝗴𝗶𝗻𝗲 to highlight likely bug locations. 📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗼𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗔𝗣𝗕 𝗨𝗩𝗠 𝘁𝗲𝘀𝘁𝗯𝗲𝗻𝗰𝗵: ✅ 5/5 failure classification accuracy. ✅ 4/4 known failures matched with exact fix + RTL line. ✅ Fully local, no external dependencies. ✅ Deployed as a Streamlit app. 💡 𝗪𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱 𝗺𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴: You don’t need a fully mature environment to get started. ➡️ Early signal comes from just a few clean regression runs. ➡️ Knowledge base builds naturally during debug. ➡️ Models improve incrementally with labeled failures. ✅ The system becomes useful in weeks —  𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗘𝗗𝗔 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗲𝗺. This project combines my DV background with independent reskilling in Python, scikit-learn, PyTorch, and LLM engineering. It was my way of exploring how AI/ML can be made practical for verification teams in real semiconductor environments. 🔗 Full technical breakdown in the PDF 👇 Happy to discuss approaches, limitations, or extensions (LLM-assisted debug is next). If you're working on scaling verification or exploring AI-assisted debug, I’d love to connect. #DesignVerification #DV #Semiconductor #AIinEngineering #MachineLearning #Python #UVM #SystemVerilog #EDA #OpenSource #Verification #Streamlit #LLM #SDET #CareerBreak #TechPortfolio #TechCareers #AppliedAI

Rust is underrated for agentic engineering: - Agents remove much of the learning curve barrier - Types + Compiler catch many bugs agents (and humans) commonly write - AI is good at Rust and you get high perf systems Move fast with stable infrastructure.

My message to AI doomers who think AI-generated code will end software development: "Civilization advances by extending the number of operations we can perform without thinking about them." — Alfred North Whitehead, mathematician and philosopher AI, in the right hands and used correctly, will arguably be one of the best things to happen to software development in the 21st century. #ArtificialIntelligence #AI #SoftwareDevelopment #Programming #TechFuture #Automation #DeveloperLife #Innovation #FutureOfWork #MachineLearning #Coding #TechTrends

Big codebase. Better understanding? What improves contextual understanding in large codebases? • Context window • Cache memory • CPU threads Drop your answer in the comments and share your reasoning. Understanding code at scale is the real advantage today. #AI #Coding #Developers #Tech #Upskilling #Evolvv

📚 New article just published on SYUTHD! 🔖 Beyond REST: How to Design Semantic APIs for Autonomous AI Agents 🏷️ Category: API Development 📖 Full article → https://lnkd.in/gtjdgrhd 👉 Follow our page for more tech tutorials: https://lnkd.in/gsJDptPM 💬 Telegram: https://t.me/nisethtechno 👍 Facebook: https://lnkd.in/gsKv3Dyn #APIDevelopment #Tech #Tutorial #Programming #TechBlog #2026

The landscape of programming is undergoing a significant transformation with the rise of AI. Contrary to some predictions, top programmers are poised to become even more critical. Their value will lie not just in coding, but in their ability to strategically direct and manage AI systems, shaping the future of software development. This evolution highlights the enduring importance of human ingenuity and strategic oversight in technological advancement. #AI #Programming #SoftwareDevelopment #TechLeadership #FutureOfWork

Heap Sort: A Reliable and Efficient Sorting Technique Heap sort is a powerful comparison-based sorting algorithm that leverages the structure of a binary heap to organize data efficiently. It works by first building a max heap from the input data, ensuring that the largest element is always at the root. Then, it repeatedly extracts the maximum element and rebuilds the heap until the entire array is sorted. One of its key advantages is its consistent time complexity of O(n log n), regardless of the initial order of the data, making it a dependable choice for performance-critical applications. Unlike some other efficient algorithms, heap sort does not require additional memory for sorting, as it operates in-place. However, it is not a stable sort, which can be a limitation in scenarios where preserving the original order of equal elements matters. Despite this, its predictability and space efficiency make it highly valuable in systems where memory usage is constrained. Understanding heap sort is essential for developers looking to strengthen their grasp of fundamental data structures and algorithms. #algorithms #datastructures #heapsort #programming #softwareengineering #coding #computerscience #tech #developers

Reading the code is the LAST step of debugging. Not the first. I've seen engineers spend 3 days reading through a codebase trying to find a bug. Meanwhile the fix was a config change pushed 2 days earlier. It wasn't in the code at all. The best debuggers I've worked with never start with code. They start with behavior. → What changed? → When did symptoms start? → Who's affected — all users or some? → What's the blast radius? Code is 200,000 lines of possibilities. Behavior is a finite set of symptoms. Start with the smaller search space. Form hypotheses about behavior. Then use code to validate or kill those hypotheses. Think of it as binary search on the system: each observation should eliminate half the problem space. AI can read code faster than any human. It can't observe production behavior and form contextual hypotheses about what went wrong. That's the skill. Skill #4 of 12 AI-proof engineering skills. → Follow for the full series. — #AIProofSkills #SoftwareEngineering #Debugging #SystemsThinking #EngineeringLessons #Engineering #ProductionDebugging #BuildInPublic

Most developers try to remove duplicate code. But what if duplication isn’t the problem? What if it’s a signal? In this video, I explore a simple but powerful idea: Redundancy isn’t something to eliminate— it’s something to understand. When we see the same intent expressed in different ways, it often means we’re missing an abstraction. And when we find it… patterns emerge. This is one of the key thinking skills behind good software design—and something AI can actually help us see faster. Watch the video, the link is in the first comment. #softwaredevelopment #designpatterns #cleancode #ai #programming

Some engineers are still debating whether AI coding works in the long run. Anush E. is running 10 agents in parallel, consuming 6.5 billion tokens a week, and shipping production code without opening an editor once. Just wrapped recording with Anush, CVP of AI Software at AMD, on what happens when someone goes truly all-in on agentic development. Anush founded nod.ai, led the Shark compiler project, and now runs AI software strategy across AMD's full silicon portfolio. He also went viral for building a pure-Python AMD GPU user space driver entirely through Claude Code Some of what we covered: - Why testing is replacing code review as the quality gate - How he rebuilt Slurm in Rust overnight (it's deployed in production) - What it means that his HR partner is now fixing engineering bugs with Claude Code - Why an open-source software stack is jet fuel for the agentic era - How to actually upskill yourself and your team instead of just talking about it His line: "Software is just tokens." Episode dropping April 22nd on the Chain of Thought Podcast! Subscribe so you don't miss out 👇 https://lnkd.in/gfmsf3Px What have you shipped with AI agents that you wouldn't have attempted six months ago? #AIAgents #AgenticCoding #AMD #SoftwareEngineering #OpenSource