Generative AI

How AI goes from babbling to brilliant, the 4 stages of LLM training Every large language model starts off like a toddler with a keyboard: random words, full confidence, zero clue. But through a few key stages, it learns to talk, help and even reason. Here’s how it grows up: Stage 0: Random Model Straight out of the box, it knows nothing. Ask it anything and you’ll get enthusiastic gibberish. Stage 1: Pre-Training It binge-reads the internet. Books, articles and code. It starts to mimic human language but still doesn’t understand what you mean. Stage 2: Instruction Fine-Tuning Now it learns to follow directions. With question–answer examples, it starts being actually useful. Stage 3: Preference Fine-Tuning (RLHF) Humans step in to judge its answers, “this one’s good, that one’s off.” The model adjusts tone, clarity, and logic to sound more human. Stage 4: Reasoning Fine-Tuning Finally, it learns to think through problems, justify choices, and explain its reasoning. This is where conversation starts feeling like cognition. From gibberish to genius, this is how an LLM grows up. Each stage brings AI closer to understanding but are we moving toward AGI, or just building a smarter imitation of it? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PdxNz0 #llm #artificialintelligence #machinelearning #ai #generativeai

We’ve stopped asking “Can AI do this?” Now it’s “Which agent does it best?” The AI agent landscape just split into six domains and most teams are still trying to use one generic tool for everything. Here’s what’s actually happening in 2025: → Voice Agents manage full conversations with customers → Agentic RAG retrieves and generates information in real time → Coding Agents write and ship working code → CUA (Computer-Using Agents) operate apps like real users → DeepResearch Agents produce comprehensive reports autonomously → AI Agent Protocols help agents collaborate across platforms It's about the right agent for the right job, i.e specialised agents working together as an ecosystem. Companies winning in 2025 understand this: you don't need one powerful agent. You need the right agent for each job, coordinated through protocols like A2A. Which agent type would solve your biggest bottleneck right now? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PdzmX0 #ai #aiagents #artificialintelligence #machinelearning #llm

Ignoring China is how you miss half of the AI story. Most people still think AI is a Silicon Valley sprint, a race between founders, startups and hype. But while the West was busy posting hot takes, China was building. Along with prototypes and research papers, it has shipped whole ecosystems of products. → Today, China’s AI is accelerating through: → Models are getting dramatically cheaper to run. → Video generation is already mainstream. → Companies are adopting AI at scale. → Open-source AI is exploding, pushed by developers who move fast and break nothing. The map is bigger now. Anyone who stops at San Francisco risks getting blindsided by the real competition. Are we watching the AI race or missing where it’s actually happening? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03Pf8PP0 #llm #artificialintelligence #machinelearning #geopolitics #generativeai

Meta’s Chief AI Scientist just shared what comes after large language models. Yann LeCun, one of the founding minds of modern AI and a creator of deep learning says he’s no longer interested in LLMs, calling them a dead end for building real intelligence. He’s working on what he calls Advanced Machine Intelligence (AMI), AI that learns by doing, exploring and interacting with the world around it. Here’s what that means: → Machines that understand the physics of how things move and change → Systems that can remember, reason and plan over time → AI that learns from observation and experimentation → Models that make decisions and improve through experience LeCun says a four-year-old can learn more about physics in an afternoon than today’s largest models learn from the entire internet. A child watching a ball roll downhill learns why it moves: cause, motion, gravity. A language model only learns the words that describe it. That’s the kind of grounded learning AMI aims to recreate. It moves AI from recognizing patterns to building understanding, from text-trained systems to world-trained ones. If machines start learning through experience, what new kinds of intelligence will we build next? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03Pf8Jn0 #ai #deeplearning #innovation #futureofwork #machinelearning

Stack Overflow's real downfall was not realizing humans like to have conversations. When ChatGPT arrived, Stack’s activity fell by 16% within weeks. By 2024, Stack agreed to supply data to OpenAI’s models through an official partnership. AI made it easy to ask questions without fear of being judged. It gives instant responses, so community waiting loses appeal. For Devs and AI builders, a few lessons matter: • Design your model to respond humanely, not just accurately • Trust is earned through safety, clarity, and speed • Data sourcing, curation, and tone control will define your model’s edge Maybe Stack doesn’t need to compete with AI. It needs to remember what made people ask in the first place. We have to wonder, what happens to shared knowledge when every lesson begins in a chat window? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03Pf87q0 #llm #openai #machinelearning #artificialintelligence

Researchers around the globe just defined how AGI can be measured. A group of scientists from Oxford, MIT, Cornell, Berkeley, and more than 25 global institutions came together to publish “A Definition of AGI”, a paper proposing a structured way to measure progress toward Artificial General Intelligence. It treats AGI as a scientific, testable concept rather than a buzzword. According to the paper, AGI is “an AI that matches or exceeds the cognitive versatility and proficiency of a well-educated adult human.” Key insights from the paper: → The framework evaluates ten components of intelligence such as reasoning, memory, language, perception and creativity. → AGI progress is measured across domains, creating an “AGI score” that reflects overall capability and adaptability. → When tested, models like GPT-4 and GPT-5 show large variability across domains, with estimated AGI scores around 27% and 58%. Interestingly, parts of the paper itself have been called into question, several of its cited references appear to be fabricated or AI-generated, a reminder that even research on intelligence isn’t immune to AI’s own hallucinations. The framework still offers value as a first attempt at measuring cognitive breadth but it also shows why verifying sources matters as much as defining intelligence. Could defining AGI be the first real step toward creating it? Link to the full research in the comments. Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PdG7M0 #agi #machinelearning #artificialintelligence #ai #ainews

Notice how some people got way better at their jobs after AI and others just… didn’t? A new Wall Street Journal analysis found AI is blowing open performance gaps inside companies faster than anyone thought. In just six months, top performers boosted their output by 40%, while average performers barely moved. What the top 1% does differently: ➣ Test smarter workflows and refine what sticks. ➣ Automate repetitive tasks and buy back 2–3 hours a day. ➣ Use AI to break down big problems into steps. ➣ Build feedback loops so quality compounds over time. ➣ They scale learning instead of hoarding it. The same technology is available to everyone, yet it rewards people who think differently about how they work. The next promotion will go to the person who turns AI from a tool into an advantage. When AI becomes part of the job, is it still fair to compare those who use it with those who don’t? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PddrH0 #llm #ainews #artificialintelligence #agenticai #generativeai #aiagent

Sam Altman was only 19 when speed became the foundation that led to OpenAI. He left Stanford to build Loopt, an app for sharing real-time locations. It never scaled widely, but the $43.4 million exit taught him the value of momentum. That instinct carried into everything he built next At Hydrazine Capital, he sought out founders who could learn and execute quickly and at Y Combinator, he built a culture around speed and clear decision-making. By the time he founded OpenAI, speed became the system itself. Now, OpenAI has scaled faster than any software company in modern history. What momentum looks like at OpenAI: • Hit $12B annual revenue in under three years. • Scaling toward 1M+ GPUs by 2025. • Used its own models to design chips in weeks. • Released major models every six months. • Training compute doubles twice a year, outpacing Moore’s Law. If human speed built OpenAI, AI speed now builds the future. Everyone’s racing to move faster with AI. Does anyone stop to ask faster toward what? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03Pdf3F0 #artificialintelligence #samaltman #openai #venturecapital #llm

Skipping AI verification is the new “I agree to the terms and conditions.” AI generates numbers with the kind of confidence that could win gold at the Olympics and sometimes we just nod along like, “sure, that looks right.” What separates great operators is simple: they pay attention. Here’s what they do differently: • Copy-paste habits lead to blind errors • Overprompting creates noise that sounds smart • Review gaps turn small slips into public mistakes • Teams that skip checks lose trust the fastest AI isn’t a shortcut for thinking. It’s like a friend helping you find your keys but you still need to know what door they open. AI works best when curiosity stays in the loop. How do you make sure your AI’s confidence matches its accuracy? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PddqS0 #generativeai #artificialintelligence #AIethics #agenticai #llm

Did the world just change its currency? NVIDIA is now worth more than every major bank in the US and Canada. One company that makes chips is bigger than the entire banking system. It’s a wild moment. Because it says something about where value lives now. Here’s what that chart really tells us: • The new vaults are humming in data centers. • Processing power has become the world’s favorite asset. • Intelligence is being traded, the way oil once was. • Every part of modern life now runs on that invisible power source even your bank app. Why this matters: • The next wave of infrastructure is silicon and energy. • “Smarts,” human or machine, have become something you can invest in. • The biggest shift in economic power is unfolding behind server racks. You don’t need to be a tech person to care. You just need to see where the value chain starts now and where it’s headed. That little green box on the chart is a preview of the future being built. What part of your world do you think will be rewritten by this shift first? Want your AI product to grow faster? Let’s talk about how we can scale adoption through our 13M+ community. https://hubs.li/Q03PddP70 #ai #innovation #artificialintelligence #ainews #nvidia