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The race to bring AI development back to the desktop is getting interesting. AMD's Ryzen AI Halo Developer Platform and NVIDIA's DGX Spark are both targeting the same goal: running larger AI models locally without relying on cloud infrastructure. A few things that stood out to me: • Both support up to 128GB unified memory • Both claim support for models up to 200B parameters • AMD comes in at a lower price point • NVIDIA continues to benefit from its strong CUDA ecosystem • AMD offers more flexibility with Windows and Linux support For developers building AI agents, experimenting with local LLMs, or looking to reduce cloud costs, these compact AI workstations are worth watching. We're moving toward a future where serious AI development doesn't always require a data center. Which would you choose today: AMD or NVIDIA? #AI #GenerativeAI #LLM #AIAgents #MachineLearning #AMD #NVIDIA #LocalAI #TechInnovation

I cleared the Claude Certified Architect (CCA) exam in just two weeks — here's a quick look at exactly how I did it. In this videoI share the multi-modal prep approach that worked for me: reading the official Exam Guide multiple times, building my own revision notes, working through the Anthropic courses, turning the guide into audio for revision on the go, and taking two full practice exams before the real thing. I also cover why many capable candidates still fail, and the exam-day habits that make the difference. My exact 2-week study plan and the materials I used The "must-read, must-do, good-to-do" resource list Why candidates fail — and how to avoid those traps Exam-day tips: depth over breadth, revision, repetition, and pacing Whether you're an aspiring Claude Certified Architect, a PM moving into AI, or just exploring the certification, this should save you time and point you in the right direction. If this helps, a like and subscribe means a lot — and drop your questions in the comments. #ClaudeCertifiedArchitect #CCA #Anthropic #Claude #AICertification #GenAI #AgenticAI #TechCertification #AICareers #ClaudeAI My Linkedin: https://www.linkedin.com/in/jacinthp/ --------------------------------------------- Github: https://github.com/jacinthpaul --------------------------------------------- Website: https://www.programstrategyhq.com/ ---------------------------------------------

Spent some time over weekend with Claude Fable and generated a Tutorial for the Claude Certified Architect Foundations exam. If you're preparing for the certification, actively using Claude Code, or simply trying to understand Agentic AI, MCP, tools, prompts, and context management, this is a great resource—even if you're not a developer. I particularly liked how it explains complex concepts in plain language without assuming deep engineering expertise. Feel free to share it with anyone preparing for the exam or looking to level up their AI knowledge. #ClaudeAI #ClaudeCode #Anthropic #AIAgents #MCP #AIArchitecture #PromptEngineering #TechnicalProgramManager #TPM

If you're looking for one course to get up to speed on AI-Assisted Software Development, this is it 🚀 I've just completed "Mastering AI-Assisted Development" by Addy Osmani, and it's one of the most practical summaries of modern AI-powered engineering practices that I've come across. Linkedin Learning - https://lnkd.in/gzRaVn3m Some key areas covered:  - Apply advanced AI workflows across the entire development lifecycle. - Design robust AI-human collaboration patterns. - Confidently debug, evaluate, and improve AI-generated code - Implement AI-enhanced quality, security, and reliability practices. - Build fast, iterative, AI-driven prototypes that scale. - Master tooling ecosystems for AI-assisted development. - Establish personal and team-level AI standards. The biggest takeaway: AI is an engineering collaborator. The real skill is learning how to orchestrate AI effectively while maintaining strong engineering judgment and ownership.  For anyone leading software delivery, platform teams, digital transformation programs, or AI initiatives, this is a worthwhile resource to understand where modern engineering practices are headed. #TechnicalProgramManagement #TPM #EngineeringLeadership #AIAssistedDevelopment #ArtificialIntelligence #SoftwareEngineering #AICoding #AIAgents #GenerativeAI #EngineeringManagement #TechLeadership #DigitalTransformation #ProductManagement #AddyOsmani #AINativeEngineeringTeams #ProgramManagement

How to Reduce AI Token Usage in 2026 || Top 10 Cost Saving Techniques Are your AI bills growing faster than your productivity? In this video, I break down the Top 10 techniques to reduce AI token usage and cut LLM costs in 2026 without sacrificing quality. Whether you're using ChatGPT, Claude, GitHub Copilot, OpenAI APIs, Anthropic APIs, Cursor, Cline, or enterprise AI agents, these strategies can help you save 60–90% on AI costs. You'll learn: ✅ Prompt Caching (The #1 Cost Reduction Technique) ✅ Batch APIs for Instant 50% Savings ✅ Smart Model Routing & Cascading ✅ Claude Code Context Management ✅ GitHub Copilot Cost Optimization ✅ Structured Outputs & JSON Schemas ✅ Semantic Caching Strategies ✅ Using Open-Weight Models Effectively ✅ Lean Prompt Engineering Techniques ✅ AI Cost Observability & Monitoring By the end of this video, you'll have a practical playbook for building cost-efficient AI applications, coding agents, and enterprise AI solutions. Key Topics Covered AI Token Optimization Prompt Caching Claude Code Best Practices OpenAI Cost Reduction Anthropic Cost Optimization GitHub Copilot Pricing Changes AI Agent Cost Management LLM Engineering AI FinOps Enterprise AI Governance Semantic Caching Batch Processing APIs Who Should Watch? AI Engineers Prompt Engineers Software Developers Technical Project Managers AI Product Managers Enterprise Architects CTOs & Engineering Leaders Anyone building AI-powered products 📌 What's your biggest AI cost challenge today? Let me know in the comments. 👍 If you found this helpful, Like, Share, and Subscribe for more AI, Technical Project Management, and Enterprise Technology insights. #AI #LLM #ChatGPT #ClaudeAI #PromptEngineering #AIAgents #OpenAI #Anthropic #GitHubCopilot #AITools #ArtificialIntelligence #TokenOptimization #AICostOptimization #LLMEngineering #AI2026 #TechnicalProjectManager #AITPM Follow Jacinth P | AI TPM for practical AI implementation, AI strategy, and enterprise technology insights. My Linkedin: https://www.linkedin.com/in/jacinthp/ --------------------------------------------- Github: https://github.com/jacinthpaul --------------------------------------------- Website: https://www.programstrategyhq.com/ ---------------------------------------------

🚀 Most teams are using AI. Few are becoming AI-Native. Today, many engineering teams use AI as: ✅ Coding assistants ✅ Chatbots for developers ✅ Prototyping tools ✅ Documentation helpers But that's only scratching the surface. The real transformation happens when AI is embedded across the entire Software Development Lifecycle (SDLC)—from planning and design to development, testing, reviews, deployment, and operations. OpenAI recently published an excellent guide on How to BUild AI Native Engineering Teams, where engineers delegate routine work to AI agents while humans focus on judgment, strategy, architecture, and business outcomes. 📖 OpenAI Guide: How to Build an AI Native Engineering Team https://lnkd.in/gPdTMEhp How to Get Started 1️⃣ Connect AI agents to your codebase 2️⃣ Integrate with issue trackers and documentation systems 3️⃣ Use specification templates for agent-driven development 4️⃣ Start with one pilot feature or team 5️⃣ Measure productivity and quality outcomes 6️⃣ Scale successful practices across the organization The future of software engineering is about enabling engineers to delegate routine work to AI agents, move faster, improve quality, and spend more time on solving meaningful problems. Teams that master this shift will build software faster, learn faster, and innovate faster. #AI #ArtificialIntelligence #AINative #SoftwareEngineering #EngineeringLeadership #DeveloperProductivity #AgenticAI #AIAgents #GenAI #OpenAI #TechLeadership #DigitalTransformation #DevOps #PlatformEngineering #EngineeringManagement #TechnicalProgramManagement #FutureOfWork 👇 How far along is your team on the AI-Native journey? Are you still using AI as a tool, or are you embedding it across the SDLC?