Trends in Personal AI Development

O'Reilly's Technology Trends for 2025 report, published today, is based on analyzed data from 2.8 million users on its learning platform, and giving insights into the most popular technology topics consumed - identifying emerging trends that could influence business decisions in the year ahead. The outlook for AI technologies is marked by dramatic growth in key areas. The percentages describe the growth in interest or usage of specific areas within the field: Prompt Engineering surged by 456%, AI Principles by 386%, and Generative AI by 289%. Additionally, the use of GitHub Copilot skyrocketed by 471%, highlighting a robust interest in tools that boost productivity. In terms of security, there was a significant 44% increase in interest in governance, risk, and compliance, accompanied by heightened attention to application security and the zero trust model. While traditional programming languages such as Python and Java experienced declines, data engineering skills witnessed a 29% increase, underscoring their essential role in powering AI applications. * * * Based on these numbers, the report analyses the Technology Trends for 2025 in the field of AI: I. Diverse AI Models: Unlike previous years when ChatGPT dominated, the field now includes a variety of strong contenders like Claude, Google’s Gemini, and Llama. These models have broadened the AI landscape and are each finding their niches within different user bases. II. Skill Growth: There has been a significant increase in interest and development in AI skills, notably in Machine Learning, Artificial Intelligence, Natural Language Processing, Generative AI, AI Principles, and Prompt Engineering. These skills are seeing varying levels of growth, with Prompt Engineering experiencing the most substantial surge. III. Shift in Platform Focus: Interest in GPT has declined as the industry moves away from platform-specific knowledge towards more generalized, foundational AI understanding. This shift reflects a maturation in the industry as developers seek capabilities that are applicable across various models. IV. Future Trends: The report anticipates potential disillusionment with AI, a phenomenon more sociological than technical, often due to overhyped expectations. Nonetheless, advancements continue, particularly in making AI interactions more intuitive and reducing the need for complex prompts. V. Development Tools and Data Engineering: Tools like LangChain and retrieval-augmented generation (RAG) are highlighted as key to building more sophisticated AI applications that can handle private data more securely and efficiently. Moreover, the importance of data engineering skills is underscored, supporting AI applications with robust data infrastructure. * * * The insights of the report can guide strategic planning, investment decisions, and curriculum development, and overall, offer a valuable snapshot of the technology landscape.

Most people trying to learn AI are asking the wrong question. They ask: “Which AI tool should I learn?” But tools change every few months. What actually matters are the skills behind the tools. That’s why I created this visual: “15 AI Skills to Master in 2026.” If you zoom out, modern AI development is no longer about just calling an API. It’s about building complete intelligent systems. Here are some of the most important capabilities emerging right now: 1. Prompt Engineering Crafting structured prompts that guide models toward reliable outputs. 2. AI Workflow Automation Using AI to automate real operational workflows across apps and data. 3. AI Agents & Agent Frameworks Designing goal-driven systems that plan, reason, and execute tasks autonomously. 4. Retrieval-Augmented Generation (RAG) Connecting LLMs to real data so responses stay accurate and grounded. 5. Multimodal AI Systems that understand text, images, audio, and code together. 6. Fine-Tuning & Custom Assistants Adapting models for specific domains, products, and business use cases. 7. LLM Evaluation & Observability Measuring quality, reliability, and performance of AI outputs. 8. AI Tool Stacking & Integrations Combining multiple AI tools, APIs, and systems into a unified workflow. 9. SaaS AI Application Development Building scalable AI products and platforms. 10. Model Context Management (MCP) Handling memory, context windows, and token budgets in agentic systems. 11. Autonomous Planning & Reasoning Techniques like ReAct and Plan-and-Execute that power intelligent agents. 12. API Integration with LLMs Letting models interact with real-world systems and services. 13. Custom Embeddings & Vector Search The foundation of semantic search and knowledge retrieval. 14. AI Governance & Safety Ensuring responsible AI through guardrails, monitoring, and policies. 15. Staying Ahead of AI Trends Because the AI landscape evolves faster than any other technology. The biggest shift happening right now is this: We’re moving from AI as a chatbot to AI as a system of intelligence embedded into products and workflows. And the engineers who understand this full stack will define the next decade of software. If you’re building in AI, which of these skills are you focusing on right now?

As a PhD student in Machine Learning Systems (MLSys), my research focuses on making LLM/GenAI serving and training more efficient. Over the past few months, I’ve come across some cool papers that keep shifting how I see this field. So, I put together a curated list to share with you all: https://lnkd.in/gYjBqVPt This list has a mix of academic papers, tutorials, and projects on GenAI systems. Whether you’re a researcher, a developer, or just curious about GenAI Systems, I hope it’s a useful starting point. The field moves fast, and having a go-to resource like this can cut through the noise. So, what’s trending in GenAI systems? One massive trend is efficiency. As models balloon in size, training and serving them eats up insane amounts of resources. There’s a push toward smarter ways to schedule computations, overlap communication, compress models, manage memory, optimize kernels, etc. —stuff that makes GenAI practical beyond just the big labs. Another exciting wave is the rise of systems built to support a variety of GenAI applications/tasks. This includes cool stuff like: - Reinforcement Learning from Human Feedback (RLHF): Fine-tuning models to align better with what humans want. - Multi-modal systems: Handling text, images, audio, and more. - Chat services and AI agent systems: From real-time conversations to automating complex tasks, these are stretching what LLMs can do. - Edge LLMs: Bringing these models to devices with limited and heterogeneous resources, like your phone or IoT gadgets, which could change how we use AI day-to-day. The list isn’t exhaustive, so if you’ve got papers or resources you think belong here, drop them in the comments.

A teammate recently asked me a thought-provoking question: “With the rise of GenAI, should I consider shifting my career path and start learning it seriously?” For context, he’s spent most of his time in the world of building and deploying e-commerce applications — not in AI or ML. I gave him an honest, off-the-cuff answer in the moment. But later, the question stuck with me. So I decided to dig deeper. And, quite fittingly, I turned to a GenAI companion to help me explore the broader picture. Over the past 15 years, software development has gone through seismic shifts — and we're now on the edge of another massive wave. Looking Back (2006–2022): These trends paved the way for today's GenAI era: * Cloud Computing: Transformed infrastructure and scalability * Big Data: Enabled smarter analytics and real-time insights * Traditional Machine Learning: Powered predictions and personalization * DevOps & CI/CD: Made software shipping faster and more reliable * Zero-Trust Security: Met rising complexity with stronger controls * NLP & Chatbots: Let machines process and respond to language They didn’t just change tools — they redefined how we build, deploy, and secure software. Now, if you consider what is in store for the next 15 years,  The future of software development will be: * AI-Paired & Autonomous: From copilots to agents that build, test, and deploy software * Natural Language-Centric: "Describe, not code" workflows * Composable & Modular: APIs, functions, and logic blocks like Lego * Self-Healing Systems: Bugs that detect and fix themselves * Intent-Driven Infra & DevOps: "I want 99.99% uptime" → system adapts * Zero-Trust by Default: Secure supply chains, SBOMs, AI-native security * Edge + Cloud-Native Dev: Building for everywhere, from devices to data centers The next 10 years won't just be about writing better code — they'll be about orchestrating intelligence, collaborating with AI, and reimagining developer experience from the ground up. Are we ready for a world where developers don’t just write software — they design ecosystems of intent? Curious to hear from others: Which of these trends are you already seeing? What are you most excited (or worried) about? #SoftwareEngineering #DeveloperTools #FutureOfWork #AI #DevOps #LLMs #EdgeComputing #DeveloperExperience #TechTrends #Coding #GenAI #PlatformEngineering

Based on recent advancements in AI world, I feel the overall landscape is shifting from general-purpose bots to more specialized and action-oriented systems. Here is an overview of what happened last week in AI. Let’s start with research topics.. - Agents That Do Your Research: A new framework called AIRA-dojo is setting the stage for AI that can autonomously conduct machine learning research. The key finding is that the operators or tools given to the agent are more critical to its success than the specific search strategy it uses. - Expanding Memory for Vast Contexts: Researchers introduced MEMAGENT, an approach that allows LLMs to handle incredibly long texts up to 3.5M tokens with minimal performance loss. - A New Approach to Sequence Modeling: The H-Net model proposes a move away from fixed tokenization. Instead of relying on pre-defined tokens, it learns to dynamically chunk raw data into meaningful segments. Tech Updates & Product Launches.. - Open-Source Coding Gets a Boost: DeepCoder, a new 14-billion-parameter model, has been released, claiming performance similar to OpenAI's o3-mini. - Cloudflare's AI Security Focus: Cloudflare focus on securing AI workflows includes new features to control employee use of AI apps, scan services like ChatGPT for data exposure, and protect original content from AI crawlers, addressing the growing "Shadow AI" problem in enterprises - Specialized Models for Medicine: The MedGemma suite of open models, based on the Gemma 3 architecture, is optimized for medical vision and language tasks. These models excel at analyzing chest X-rays, answering medical questions, and performing histopathology, demonstrating the power of domain-specific foundation models . What's Brewing for the Future... Looking beyond the news could see several trends signal where AI is heading next. - Following Anthropic's Model Context Protocol (MCP), Google has announced its Agent2Agent (A2A) protocol, designed to facilitate communication, discovery, and task management between intelligent agents. This development is critical for building a future where different AI agents can work together seamlessly. - Multimodal seem to become the default: The ability for AI to process and understand multiple types of input text, images, audio, and video simultaneously is quickly shifting from a premium feature to a standard expectation. Typical Kano model cycle.  - Google's Gemini 2.5 Flash is a "hybrid reasoning model" that allows users to specify a "thinking budget." This gives developers direct control over the computational cost (and therefore time and money) spent on solving complex reasoning problems. Per me AI innovation is accelerating on 3 parallel tracks: core research is tackling fundamental challenges like memory and reasoning, the tech industry is racing to build secure and specialized tools, and the groundwork is being laid for a future of interconnected, multimodal agentic systems. What trends do you see?

We are only one month into 2026, and the Personal #AI Revolution is already in full swing. We’ve moved past the era of generic chatbots and entered the age of #PersonalIntelligence. This is no longer gray theory or tech-bro hype; it is the new backbone of your daily life. By securely weaving together the data from your emails, calendars, documents, and even your photo library, #AI is evolving into a hyper-personalized companion that understands your context, your preferences, and your world. How this changes your game: - Vacation Planning, Reimagined: Forget spending hours scrolling through reviews. Your AI analyzes your past travel patterns and preferred vibes to suggest destinations you’ll actually love. It builds the itinerary while you just pack the bags. - Connecting the Dots: Tired of digging through threads? Ask a complex question, and your #AI pulls facts from a three-month-old email and a forgotten PDF to give you the exact answer in seconds. - Proactive Life Admin: It’s the ultimate concierge. From finding the specific tires that fit your car model to alerting you when a subscription you don’t use is about to renew, it handles the "mental load" so you don’t have to. What really sets my pulse racing? It’s the end of the "Digital Noise" era. We’ve spent years being slaves to our devices—managing notifications, organizing folders, and losing ourselves in the search. Now, the roles are reversing. You are finally becoming the director, not the librarian. I am beyond energized by the fact that this technology is finally human-centric. It’s not about making machines smarter; it’s about making your life larger. It’s about reclaiming the hours lost to mundane tasks and using that mental space for creativity, family, and what truly matters. We aren't just using tools anymore; we are gaining cognitive freedom. The Golden Rule: You stay in control. You decide exactly which apps to connect and which boundaries to set. Your data stays protected, ensuring your digital twin works exclusively for you. We are witnessing a massive shift: moving away from a "one-size-fits-all" AI toward an intelligence that is uniquely yours. The future isn’t just smart—it’s personal. Google Gemini3 #AI #Personalization #AgenticAI #FutureTech #GoogleGemini #Innovation #PersonalIntelligence

𝐓𝐡𝐞 𝐦𝐨𝐬𝐭 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐬𝐬𝐞𝐭 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐰𝐨𝐧’𝐭 𝐛𝐞𝐥𝐨𝐧𝐠 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐲. 𝐈𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞𝐥𝐨𝐧𝐠 𝐭𝐨 𝐨𝐧𝐞 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬. That sounds absurd today. 𝐈 𝐝𝐨𝐧’𝐭 𝐭𝐡𝐢𝐧𝐤 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐢𝐧 𝐟𝐢𝐯𝐞 𝐲𝐞𝐚𝐫𝐬. Right now, something fascinating is happening. The best knowledge workers aren’t just using AI. 𝐓𝐡𝐞𝐲’𝐫𝐞 𝐪𝐮𝐢𝐞𝐭𝐥𝐲 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐏𝐞𝐫𝐬𝐨��𝐚𝐥 𝐀𝐈 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦. It starts small. A few prompts. Then AI agents. Then a personal knowledge system. Then automated workflows. Every conversation makes it smarter. Every project makes it better. 𝐄𝐯𝐞𝐫𝐲 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐬. Eventually, it becomes a digital extension of how that person thinks, decides, learns and creates. For decades, companies owned the operating system of work. The knowledge. The processes. The playbooks. 𝐀𝐈 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐭𝐡𝐚𝐭 𝐞𝐪𝐮𝐚𝐭𝐢𝐨𝐧. For the first time, individuals can build an intelligence system that grows with them every single day. And that’s where I think most executives are looking at the wrong KPI. They measure: ✔️ AI licenses. ✔️ AI adoption. ✔️ AI governance. 𝐁𝐮𝐭 𝐭𝐡𝐞𝐲 𝐝𝐨𝐧’𝐭 𝐚𝐬𝐤 𝐚 𝐦𝐮𝐜𝐡 𝐛𝐢𝐠𝐠𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: 𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐨𝐟 𝐨𝐮𝐫 𝐛𝐞𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐭𝐡𝐚𝐭 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐛𝐞𝐥𝐨𝐧𝐠 𝐭𝐨 𝐮𝐬? Because one day, those people won’t just leave with experience. 𝐓𝐡𝐞𝐲’𝐥𝐥 𝐥𝐞𝐚𝐯𝐞 𝐰𝐢𝐭𝐡 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐚𝐜𝐜𝐮𝐦𝐮𝐥𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐧𝐝 𝐀𝐈-𝐞𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐰𝐚𝐲𝐬 𝐨𝐟 𝐰𝐨𝐫𝐤𝐢𝐧𝐠. 𝐓𝐡𝐞 𝐧𝐞𝐱𝐭 𝐂𝐕 𝐰𝐨𝐧’𝐭 𝐛𝐞 𝐚 𝐏𝐃𝐅. 𝐈𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐀𝐈 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦. Most companies haven’t realized that yet. 𝐈 𝐭𝐡𝐢𝐧𝐤 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐝𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐝𝐞𝐜𝐚𝐝𝐞. #AI #Leadership #FutureOfWork #EnterpriseAI #Innovation #PersonalAIOperatingSystem 𝘝𝘪𝘥𝘦𝘰 𝘤𝘳𝘦𝘥𝘪𝘵𝘴 𝘵𝘰 𝘵𝘪𝘯𝘬𝘦𝘳𝘵𝘢𝘪𝘭𝘰𝘳𝘢𝘳𝘵

Personal AI is shaping up to be one of the defining themes of 2026, and that says a lot about where real value in AI is heading. In a recent conversation on CNBC, Doug Clinton, founder and CEO of Intelligent Alpha, highlighted a clear shift in how capital and attention are moving across the AI landscape. The takeaway wasn’t about bigger models or flashier demos. It was about who AI is being built for. We’re seeing a bifurcation in AI spending: 🔵 One side chasing massive infrastructure and generalized platforms 🔵 The other focusing on AI that works at an individual level, adapting to how you think, decide, and operate Personal AI isn’t just about productivity hacks or copilots. It’s about systems that understand context, preferences, and decision patterns deeply enough to become a true extension of the user. That’s where the next wave of ROI will likely come from; not from AI that replaces humans, but from AI that compounds human judgment. The winners won’t be the loudest tools, they’ll be the ones that quietly become indispensable. #ArtificialIntelligence #AITrends #FutureOfWork

Meta is reportedly developing an advanced AI assistant designed to carry out personalized, everyday tasks for users. That is a major signal. Personal agents are moving from theory to mass-market reality. The key question is not whether these agents will be useful. They will be. The key question is whether they will truly represent the individual. A personal AI controlled by a platform is not necessarily personal. It could become one of the most intimate interfaces ever created between human intention and commercial influence. This is why Identic AI matters. If an agent learns who we are, acts on our behalf, filters information, makes recommendations, and interacts with the world around us, then it must be aligned with our values and accountable to us. The future of AI should not be a better advertising engine. It should be a new foundation for human agency.

Your AI knows you've been researching burnout symptoms. It sees you've canceled workouts three times this week. Should it suggest taking a day off? Current Personal AI can't make that call because it's missing the infrastructure. We've been focused on making AI better at answering "what did I say about X?" through RAG and vector search. But personal AI needs three things retrieval might not thoroughly provide: - Understand intent: What you actually care about, not just what you typed. - Track evolution: How your preferences and goals change over time. - Anticipate needs: Knowing when to be proactive versus waiting for a prompt. This is bigger than retrieval and delves into relationship management, which led me to think about enterprise CRMs. Enterprise CRMs store customer data AND actively manage relationships. Every interaction is contextualized within a history, and sentiment shifts trigger different strategies. Imagine applying that same architecture to personal AI. Not just storing your calendar and health data, but actively managing your relationship with your goals, behaviors, and wellbeing. For example, an agentic system sees you stayed up until 2 AM working and logs it as a "productive session." A "Life CRM" connects that to a high-stress workday and recognizes it as panic-driven deadline work, not genuine productivity. It understands you need a sense of autonomy, not more productivity hacks. Making this work—especially on-device for privacy—requires rethinking our stack: - Multi-modal data fusion: Connecting what you say, what you do, and what your biometrics show in real-time. - Intent-aware annotation: Moving beyond "User stayed up late working" to "User is working late to compensate for a high-stress day." That level of context is what makes proactive AI useful. - Storage beyond vector search: We need relational, trigger-based architectures designed for anticipation, not just semantic similarity. As edge AI makes truly personalized (world) models possible, I keep coming back to this: Are we building the right infrastructure layer? I'm curious—are others exploring CRM-style architectures for personal AI? I know knowledge graphs are a thing but I’m not sure if someone has the architecture to connect it with personalized, multi modal data. Or have you seen other approaches that solve this proactive, relationship-management problem? What am I missing here? #AI #DataEngineering #EdgeAI #PersonalAI #LifeCRM #SoftwareArchitecture #TechTrends #ConsumerAI