Eric Vyacheslav’s Post

𝐋𝐋𝐌𝐬 𝐭𝐡𝐚𝐭 𝐬𝐡𝐢𝐩 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬, 𝐧𝐨𝐭 𝐝𝐞𝐦𝐨𝐬   Stimulus Growth designs language systems around business truth: problem → dataset → policy → model → decision → feedback. We start with a measurable KPI and build the smallest reliable loop that can move it. Retrieval for facts, fine-tuning for behavior, tool use for action—each choice is deliberate and observable. We version prompts like code, validate outputs against schemas, and log decisions for audit. No vague “AI assistant”; we deliver narrow, dependable workflows that improve weekly as feedback rolls in. This is how LLMs become revenue infrastructure, not a lab experiment.   #ArtificialIntelligence #GenerativeAI #LLM #LargeLanguageModels #MLOps

Your New Pair Programmer: The AI Assistant Level Up Your IDE: How to Choose the Right AI for Your Multi-File Project Stop coding alone. Start developing with an AI that knows your entire codebase. If your project has more than one file (which is, well, all of them), you need an AI tool that’s truly context-aware. A simple autocomplete t... https://lnkd.in/eth8wfpY By Rashi

AI won’t work. Unless you know how to use it. This is how we vibe code in 2025 in nine steps: 1. Draft a prompt 2. Let Claude Code refine it with context 3. Feed the new prompt back in 4. Say: “Analyze. Make a plan. Then implement.” 5. Review with CodeRabbit CLI 6. Apply fixes provided by CodeRabbit 7. Review code yourself 8. Test the hell out of it 9. Ship. Iterate. Get CodeRabbit CLI here: https://lnkd.in/d8jtKmT8 AI won't replace software engineers. But an engineer using AI will.

AI can’t understand our code. Especially the area where we dynamically render the content, a good example is “partial view” where the the front-end code is generated from the backend. Another example is SPA. In Laravel and Vue, it is a common example that we retrieve the common data and perform pass-through in the blade file through props in vue’s main element container to prevent loading overhead. The list goes on. Our choices are to train LLM to understand our coding structure or we refactor our code so that the available and reliable LLM out there can understand. Training LLM takes time so I think, refactoring the code piece by piece is much better and faster approach. Some might ask “why refactor the code?, our code is well structured.”, you have a point but the main goal is to adapt the shocking evolution of software development. Trust me, I’m not not into AI before but IT industry is embracing the tool so….

Self-Evolving AI Agents ReasoningBank is a new type of memory system for AI agents. It helps them learn from both their successes and mistakes by saving useful reasoning patterns instead of just raw data. The agents can look back at these memories to make better decisions in the future. Over time, they keep improving as they add new lessons. This smarter memory system helps them grow more effectively and perform better than older methods that only store simple records of past actions or only successful attempts. In tests, ReasoningBank worked better for tasks like web browsing and software engineering. https://lnkd.in/gHZSVFgC

Anthropic just released Claude 4.5 Sonnet, and it’s a big shift in what AI can do. In testing, it coded for 30 hours straight and built a complete Slack-like app from scratch over 11,000 lines of code without losing track. Most models stall after an hour. Even the previous Claude version could only manage about 7 hours. On the SWE benchmark, it scored 77.2%, reaching 82% with advanced computing. For comparison, GPT-5 sits at 72.8%. It’s available to free users with limits, and the pricing for developers hasn’t changed. We’re watching AI move from being a coding assistant to acting like an autonomous developer. The question is no longer if, but how quickly we adapt.

🚀 New article out: Why most AI agents never scale — and how to fix that We all love building AI demos — quick, flashy, and fun. But turning those prototypes into reliable, scalable systems (without burning through your budget) is where the real challenge begins. In my latest post, I explore the following topics: 💸 Cost-efficiency 👨🔧Maintainability 🗼Readiness for production Let’s move beyond the hype and focus on sustainable AI engineering. 👉 Read it here: https://lnkd.in/d2DNJ6sD #AI #AIAgents #Koog #LLM #Java #Kotlin #JVM #GenerativeAI #MachineLearning #AIdevelopment #DeveloperExperience #SoftwareEngineering #OpenSource #LLM #Production #Scalability #GenAI #AIEconomics

🌟 New Blog Just Published! 🌟 📌 Go Developers Reject AI-Generated Code 🚀 ✍️ Author: Hiren Dave 📖 When the modern cloud-native era began to crystallize, Go emerged not merely as another language but as a pragmatic manifesto. By 2023 the TIOBE index had placed Go at #7 , and a 2025 developer...... 🕒 Published: 2025-09-28 📂 Category: Tech 🔗 Read more: https://lnkd.in/d64MEP7R 🚀✨ #go #ai #code

We’ve made incredible progress in building powerful ML models — but deploying and maintaining them in the real world is still messy. This article https://lnkd.in/gAtHi-xM reminded me that MLOps isn’t just “DevOps for AI.” It’s a whole new challenge that breaks many of our old assumptions about software systems. Until we rethink how we monitor, test, and manage models in production, we’ll keep patching tools that were never meant for non-deterministic systems. How is your team tackling reliability and maintenance for ML in production?

Claude 4.5 Sonnet has just debuted as what’s being called the “world’s best coding model.” Anthropic’s latest AI demonstrates state-of-the-art coding on real-world benchmarks, builds complex agents, and completes long-duration tasks autonomously. With major improvements in reasoning, security, and code generation, it can produce production-ready applications—not just prototypes. Available via API and Amazon Bedrock, Claude 4.5 Sonnet is setting a new standard for developers and enterprises seeking reliable, advanced AI-powered coding tools worldwide. #AIforDevelopers #CodingAI #GenerativeAI