AI-assisted development boosts prototyping speed by 3x

I used to spend hours on Feature engineering best practices for production ML models tasks. Then I tried vibe coding — letting AI handle the scaffolding while I focused on design. Result: 3x faster prototyping, same code quality. The workflow: 1. Describe the architecture in plain English 2. AI generates the boilerplate 3. I review, refactor, and optimize 4. Ship in days instead of weeks The developers who will thrive in the next 5 years aren't the ones who type the fastest. They're the ones who think the clearest. Have you tried AI-assisted development? What was your experience? #DataScience #DataEngineering #BigData

I used to spend hours on Feature engineering best practices for production ML models tasks. Then I tried vibe coding — letting AI handle the scaffolding while I focused on design. Result: 3x faster prototyping, same code quality. The workflow: 1. Describe the architecture in plain English 2. AI generates the boilerplate 3. I review, refactor, and optimize 4. Ship in days instead of weeks The developers who will thrive in the next 5 years aren't the ones who type the fastest. They're the ones who think the clearest. Have you tried AI-assisted development? What was your experience? #DataScience #DataEngineering #BigData

Chiseling AI‑generated code is quickly becoming an essential skill for engineering teams: AI gives us incredible velocity, but it also floods our codebases with “rough drafts” that aren’t ready for prime time. We should treat AI output like a junior developer’s first pass—useful raw material that must be chiseled into shape through deliberate refactoring, clearer abstractions, stronger error handling, and meaningful tests. By making chiseling a first‑class step in our workflow—not an optional tidy‑up—we preserve velocity while protecting code quality, architecture, and long‑term maintainability. #AI #SoftwareEngineering #CodeQuality #CleanCode #LLM #DeveloperExperience #TechLeadership #Refactoring #AICoding #SoftwareArchitecture

I’ve just published a new portfolio project: AI Workflow Observatory. It is a local-first observability dashboard for AI-assisted engineering workflows. The tool scans local Codex session logs and reconstructs the engineering process behind AI work: - context gathering - planning - implementation - verification - debugging / recovery - handoff quality - estimated cost in USD/EUR/PLN - workflow risk and verification quality The idea is simple: AI coding tools should not only produce code. Engineering teams also need visibility into how the work happened, whether it was verified, where the risk is, and how much iteration/cost was involved. This connects directly to the systems I’m interested in building: practical AI engineering, agent workflows, observability, evaluation, auditability, and operator-facing control layers. GitHub: https://lnkd.in/d8qPz5HB #AIEngineering #GenAI #LLMOps #AgenticAI #Python #FastAPI #Observability #RAG #AIAgents #PortfolioProject

176 pages. ~400k words. 1,603 cross-references. 𝐁𝐮𝐢𝐥𝐭 𝐦𝐲 𝐒𝐞𝐜𝐨𝐧𝐝 𝐁𝐫𝐚𝐢𝐧 𝐮𝐬𝐢𝐧𝐠 𝐀𝐧𝐝𝐫𝐞𝐣 𝐊𝐚𝐫𝐩𝐚𝐭𝐡𝐲'𝐬 𝐋𝐋𝐌 𝐖𝐢𝐤𝐢 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 -- a structured knowledge base where the AI writes and maintains everything. The idea is simple, instead of asking an AI to rediscover your knowledge from scratch on every question, you build a structured wiki that the AI maintains. Skills, projects, patterns, decisions, problems solved, and the stories behind all of it. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐫𝐞 𝐰𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐢𝐬 𝐭𝐡𝐞 Obsidian 𝐠𝐫𝐚𝐩𝐡 𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞. Every dot is a page I've lived through. Every line is a connection I would have forgotten without this system. The large clusters pulling everything together are my core engineering skills -- each one linked to every project that uses it, every pattern proven in production, every bug that taught me something. 43 documented projects. 42 production bugs with the full debugging story. 36 engineering narratives and technical lectures I've written. 24 reusable architectural patterns. 16 major Design/Architectural decisions with the reasoning preserved. 9 deep technical skill maps averaging 3,300 words each. All interconnected. The raw material behind this? 2.4 million words of work records, technical lectures, architecture plans, university coursework, project whitepapers, and source code from 33 repositories -- distilled into a knowledge graph that a local AI can navigate in seconds. Two sessions of structured ingestion turned 14 months of engineering, 4 years of university, and a lifetime of ideas into permanent, queryable, compounding knowledge. No vector database. No embeddings pipeline. No infrastructure. Just markdown files in a folder, cross-links, and a schema that tells the AI how to think about my knowledge. Knowledge that compounds over time instead of disappearing into chat history. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐨𝐰𝐚𝐫𝐝𝐬 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐛𝐢𝐠𝐠𝐞𝐫 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬.. #KnowledgeBase #LLMWiki #SecondBrain #Obsidian #SoftwareEngineering #AI #AndrejKarpathy #BuildInPublic

🚀 Just completed *Claude Code in Action* by Anthropic — and honestly, this confirmed something I’ve been thinking for a while: **AI won’t replace engineers, but engineers who don’t use AI will fall behind.** A few practical observations: • Claude Code isn’t just “smarter autocomplete” — it’s useful for structuring multi-step backend tasks • Plan Mode vs Thinking Mode maps surprisingly well to system design vs deep debugging • The real value shows up in **faster root-cause analysis**, not just code generation • When used right, this can significantly reduce turnaround time across services and teams The bigger takeaway: **AI tools like Claude should be treated as part of the engineering workflow — not as optional add-ons.** The teams that figure this out early will have a clear execution advantage. Curious how others are integrating AI into backend systems and architecture decisions. #ClaudeAI #TechLeadership #BackendEngineering #SoftwareArchitecture #AIEngineering #DeveloperProductivity

𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝟵𝟯𝟬𝗠 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗟𝗟𝗠 𝗯𝗲𝗳𝗼𝗿𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗮𝘁𝗵 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁. Here's what I was missing and why it matters. 𝗪𝗵𝗮𝘁 𝗜 𝗱𝗶𝗱: I read Anthropic's research papers. Studied Claude's architecture. Implemented RoPE embeddings, Grouped Query Attention, SwiGLU activation. Trained it on FineWeb-Edu dataset on a free T4 GPU. It ran. It generated text. I thought I understood it. I didn't. 𝗪𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗸𝗻𝗲𝘄: I knew HOW to copy the architecture. I didn't know WHY any of it worked. I was using: • Matrix multiplication → didn't know what it calculated • Transpose → didn't know why it was needed • Attention scores → didn't know what they represented • Vectors → didn't know what they meant geometrically I was a builder who didn't understand his own tools. 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱: I stopped copying and started asking why. Why do we convert words to vectors? → Because computers can't do math on text. Only on numbers. Why matrices? → Because a sentence is just a group of word vectors. Why transpose? → Because matrix multiplication requires columns of A = rows of B. When they don't match, flip one. Why Attention? → Because the word "bank" means something different in every sentence. Context changes meaning. Attention captures context mathematically. Score = Q × K_transpose Output = Score × V That one formula using everything above is the entire reason modern AI works. 𝗪𝗵𝗮𝘁 𝗜 𝗿𝗲𝗮𝗹𝗶𝘀𝗲𝗱: There are two types of AI developers: Type 1 → Copies architectures, runs code, gets results. Type 2 → Understands why every line exists. I was Type 1 pretending to be Type 2. The gap between them isn't talent. It's whether you ever stopped to ask why. 𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: The model I built without understanding? It still worked. But I couldn't improve it. I couldn't debug it properly. I couldn't innovate beyond what I copied. Understanding the math didn't make me start over. It made everything I already built make sense. If you're building AI systems go one layer deeper. Not for the benchmarks. For the understanding. That's where the real leverage is. #MachineLearning #AI #LLM #DeepLearning #Transformer #AIEngineering #Developer #OpenSource

We’ve spent 18 months trying to "whisper" to LLMs. It’s time to stop and build Context !! I’ll be honest at the start of this AI wave, I also thought a better system prompt was the answer to every production bug. We treated these models like black boxes, hoping that adding a few "magic words" would suddenly make them reliable. But here’s the reality I’ve faced while building, You cannot prompt your way out of a bad architecture. In 2026, the gap between a "cool demo" and "production-grade AI" is no longer about Prompt Engineering. It’s about Context Engineering. If you’re relying solely on the prompt, you’re essentially betting that the model can guess your user's intent. When you master Context Engineering, you’re giving the model the "ground truth" it needs to actually work as a system, not just a chatbot. I’m seeing the most serious AI teams move toward four core pillars: 1. Memory Layers: Moving beyond stateless requests. If your system doesn't understand user history or temporal relevance, it’s not an assistant it’s just a script. 2. Structured Context Injection: We have to stop feeding raw, messy text. The secret to low latency and zero hallucinations is feeding optimized, structured data representations. 3. RAG Pipelines (Retrieval Augmented Generation): The model should be the reasoning engine, not the database. Our job as engineers is to optimize the retrieval path so the right data hits at the right time. 4. Tool Access & Guardrails: Stop treating the model like a conversational partner and start treating it like an Agent with hard boundaries. The difference? Prompt Engineering is brittle. It breaks the moment the model updates. Context Engineering is robust. It’s about building a stable foundation that survives the next model version. We need to stop telling juniors to "learn how to prompt better" and start teaching them how to build resilient data pipelines and smarter memory storage. Engineering is about control. And if you don't control the context, you don't control the outcome. To my fellow builders: Are you still stuck in the "prompting" phase, or has your team started moving toward a full AI-native architecture?? #AIArchitecture #GenerativeAI #EngineeringLeadership #MachineLearning #SystemDesign #FullStackAI

I don't brief Claude at the start of every session. Claude briefs me. That shift took about a week to build. It changed how I work permanently. 📁 The vault as working memory Everything lives in Obsidian: project architecture, agent definitions, session logs, pending tasks, rules for how Claude should behave. Plain text, no lock-in. Before any session, Claude reads 3–4 files and knows exactly where I am — what was done last, what's pending, what the constraints are. No re-explaining. No cold starts. 🔄 What this looks like day to day I come back to a project after a week away. Claude tells me the last 3 decisions I made and why. That's not AI magic — that's documented context the system can actually read. Tasks, blockers, open questions — all registered. When I ask "what should I focus on today?", the answer is grounded in my actual state, not generic advice. 🏗️ Where it gets genuinely different With architecture, rules, and project structure documented in the vault, Claude Code can do more than assist — it can replicate a project setup from scratch, validate if the implementation follows defined patterns, or surface improvement points against the architecture I defined. That's not prompting. That's giving an AI a system to operate within. 💡 Most people use AI as a smart chatbot. I use it as an operating layer that reads my architecture, knows my history, and executes within defined rules. Same tool. Different structure behind it. What's your setup for carrying context between AI sessions? #ClaudeCode #AIEngineering #ContextEngineering #GenerativeAI #AIAgents #DeveloperProductivity #Python #SoftwareEngineering #CareerGrowth #AgenticAI

Stop burning money on Claude Code every time you open a terminal. 💸 Most AI coding assistants have a memory problem. Every time you start a new session, Claude has to re-read your files, re-scan your READMEs, and re-map your project structure. That’s thousands of tokens spent just on "onboarding" the AI before it writes a single line of code. Enter Graphify. Inspired by Andrej Karpathy "LLM Wiki" concept, Graphify builds a persistent knowledge graph of your entire codebase. How it saves tokens: - Read Once, Query Forever: It compiles your repo structure into a structured graph summary. Claude reads this map instead of scanning raw files. - Smart Navigation: Instead of blindly grepping, Claude uses the graph to jump straight to the relevant components. - 70x Efficiency: On large projects, this can reduce token usage by up to 70x for architectural questions. If you're using Claude Code, Cursor, or Windsurf, this is the "senior dev" layer your AI is missing. Check it out: pip install graphifyy #ClaudeCode #GenerativeAI #CodingAssistant #KnowledgeGraph #SoftwareEngineering