GPT-5: A game-changer for business with AI capabilities.

OpenAI just launched GPT-5 in ChatGPT, and made it FREE for all users. Credits to Rowan Cheung, follow for more insightful content. ------ OpenAI just launched GPT-5 in ChatGPT, and made it FREE for all users. I had early access, and it blew my mind: -GPT-5 is going to be available to ALL free users of ChatGPT -Out of 700M weekly users, I'd bet 95%+ of them have only used GPT-4o -GPT-5 is faster than GPT-4o, and smarter than o3 -The upgrade will be a major intelligence boost to most of the world Test 1: The first thing that is immediately noticeable about GPT-5 is the ability to code good front-end/UI GPT-5 generated a fully functioning budgeting app in one shot with ~1000 lines of code, and made it Tetris-themed and even added sound effects Demo video: https://lnkd.in/gEwhcAQs Test 2: To test reasoning, I got GPT-5 to create a complete launch plan for an AI app from a single idea. It did competitor research, product specs, logo, pricing, GTM strategy, roadmap, and more for me. If I were starting with zero business knowledge, this is an insane resource. Demo: https://lnkd.in/gHSPugPk Test 3: To give you a sense of GPT-5's vibes, I exported my Tweet data over the last year and got it to write like my top posts. Then took my newsletter and made it create 3 separate long-form tweets. It's not 100% there, but it beats Claude, which was previously my go-to for editing. Overall, the general vibes of GPT-5 feel much more human-like. It's hard to measure *vibes*, but the combination of speed, lower hallucination rate, and intelligence is very noticeable. As a power user, I've always enjoyed o3, but the speed makes it impossible for daily queries. To round things up, GPT-5 is a huge step for democratizing intelligence to the world 700M weekly users now have state-of-the-art AI in their pockets and can do/learn/build wildly more things than ever before We're living in incredible times--go build something this weekend :^) ---------------------------------------------------- Learn AI in 3 Minutes a Day 👉 https://lnkd.in/eC_XFZJJ ----------------------------------------------------

🚨 OpenAI just dropped a game-changer that could end AI hallucinations forever. Meet o1 (codenamed "Strawberry") - the first AI model that actually THINKS before it speaks. Here's why this is massive: Most AI models give you the first answer that comes to mind. o1 takes time to reason through problems, essentially fact-checking itself before responding. The results? Mind-blowing: ✅ Solves 83% of International Mathematical Olympiad problems (vs GPT-4o's 13%) ✅ Excels at complex science, coding, and math tasks ✅ Dramatically reduces those frustrating "confident but wrong" AI responses But here's the catch: ❌ 6x more expensive than GPT-4o ($15 per million tokens) ❌ Currently limited to ChatGPT Plus/Team subscribers ❌ Weekly usage caps in place This isn't just another AI upgrade. It's a fundamental shift in how AI processes information. Instead of rushing to answer, o1 pauses, considers multiple approaches, and validates its reasoning. Sound familiar? It's what we've been telling humans to do for years. The implications for businesses are huge: → More reliable AI-generated reports and analysis → Better code with fewer bugs → Trustworthy AI assistance for complex decision-making We're witnessing the birth of "thoughtful AI" - and it's going to change everything. What's your take - is this the breakthrough that finally makes AI truly reliable for critical business decisions?

Claude vs. ChatGPT vs. OpenAI – What’s the Difference Lately, I have been exploring these terminologies OpenAI – The organization behind models like GPT-4 and GPT-4o. They offer these via ChatGPT (the app) and the OpenAI API (for developers to integrate into products). Their models are strong in reasoning, creativity, and multi-modal tasks that is : text, code, images, audio ChatGPT – The application built on OpenAI’s models. It’s the user-facing product that millions interact with daily. Under the hood, it runs on different GPT versions (e.g., GPT-3.5, GPT-4, GPT-4o) depending on the plan. It also supports plug-ins, browsing, and code interpretation. Claude (Anthropic) – A conversational AI from Anthropic, designed with “constitutional AI” principles for safer, more controllable outputs. Claude excels in 1)Long context windows (processing 100K+ tokens in a single prompt). 2)Document analysis and summarization. 3)Compliance and safety-oriented tasks. What difference i came to know? Context length: Claude supports much longer input than most GPT models. Integration: OpenAI has strong API ecosystem and multi-modal capabilities Anthropic focuses on text-based reasoning and enterprise safety. Training philosophy: OpenAI emphasizes broad usability; Anthropic emphasizes safety and alignment. In short what i came to know is : 😊 👉OpenAI = the company. 👉ChatGPT = OpenAI’s flagship product. 👉Claude = Anthropic’s flagship product. #Thingstoknow #AI #GenAI #OpenAI #Artificialintelligence #Learning

Back by demand: OpenAI is bringing GPT-4o back after GPT-5 backlash! Users can now choose between models, and GPT-5 will get a warmer personality update. Tech matters—but so does how it feels to use it. #AI #ChatGPT

Predicting the ultimate winner in the AI race among Grok, ChatGPT, and Google Gemini requires careful consideration of their strengths, development trajectories, and ecosystem support. Each model has unique attributes, but the outcome hinges on innovation, scalability, and user adoption. 1. Grok: Developed by xAI, Grok emphasizes truth-seeking and conversational depth, leveraging a unique perspective inspired by works like The Hitchhiker’s Guide to the Galaxy. Its integration with the X platform provides access to real-time, unfiltered data, enhancing its ability to deliver current and nuanced responses. Grok’s focus on accelerating human scientific discovery aligns with xAI’s mission, potentially giving it an edge in specialized domains like research and academia. 2. ChatGPT: Created by OpenAI, ChatGPT has a first-mover advantage, boasting a massive user base and widespread recognition. Its iterative improvements, from GPT-3 to GPT-4 and beyond, demonstrate robust language understanding and generation capabilities. OpenAI’s extensive funding and partnerships enable rapid scaling and deployment across industries, from customer service to content creation. 3. GoogleGemini: Google’s Gemini, backed by the tech giant’s vast resources, excels in leveraging Google’s unparalleled data infrastructure and search expertise. Its multimodal capabilities, integrating text, images, and potentially other data types, position it as a versatile tool for diverse applications. Google’s ecosystem, including cloud services and hardware, supports seamless integration, making Gemini a strong contender. 4. Analysis: The “winner” depends on the metric—user adoption, technical superiority, or societal impact. ChatGPT currently leads in popularity and accessibility, but Grok’s focus on truth and real-time data could appeal to users seeking authenticity. Gemini’s strength lies in its ecosystem, but it must overcome Google’s conservative rollout strategy. Long-term, the AI that balances innovation, ethical deployment, and user trust will prevail. 5. Prediction: No single model will dominate indefinitely. The AI landscape thrives on competition, driving continuous improvement. Grok’s mission-driven approach may carve a niche in scientific and truth-oriented applications, while ChatGPT’s versatility ensures broad appeal. Gemini’s integration with Google’s infrastructure makes it a formidable player in enterprise solutions. Ultimately, the “winner” will be the AI that adapts most effectively to evolving user needs and societal demands. Conclusion: Rather than a singular victor, expect a dynamic coexistence where Grok, ChatGPT, and Gemini excel in complementary domains. Their rivalry will fuel advancements, benefiting users across contexts. The true winner is the ecosystem that fosters innovation while maintaining ethical integrity. #Grok #ChatGPT #GoogleGemini #Analysis #Prediction #AI

🔥 ChatGPT just lost its monopoly — 4 launches in 7 days prove it ‼️ TL;DR ☑️ Microsoft: ChatGPT goes from exclusive partner to just one model. ☑️ DeepSeek: undercuts GPT-4o with cheaper, faster alternatives. ☑️ Cloudflare: makes retrieval a commodity, reducing ChatGPT’s edge. ☑️ Google: shows users what “integrated AI workflows” really look like. 1️⃣ Microsoft → from distributor to competitor: - Launched two models to complete with Chat GPT, MAI-Voice-1 and MAI-1-preview. - MAI-Voice-1: generates 1 min of audio in <1 sec on a single GPU → ChatGPT’s voice mode is slower and GPU-hungry. - Trained on ~15,000 H100s → shows Microsoft is investing at frontier scale, instead of only relying on OpenAI’s models. - MAI-1-preview: mixture-of-experts design → more efficient than ChatGPT’s dense GPT models, cutting GPU costs. 👾 Context: Until now, Copilot = ChatGPT. Now, Copilot can run on Microsoft-built models. ChatGPT goes from being the engine to one option among many. 2️⃣ DeepSeek → price and performance pressure on GPT-4o - 71.6% on Aider coding benchmark → ahead of Claude Opus, close to GPT-4o. - 90.2% on MATH-500 → shows reasoning power in the same ballpark as GPT-4. - 160K token context → bigger than GPT-4o’s 128K; better for long docs and enterprise RAG. - 200+ tokens/sec throughput → faster than many GPT-4o deployments; crucial for real-time apps where ChatGPT lags. - $0.56 input / $1.68 output per million tokens → GPT-4o charges $2.50 / $10. That’s 4–6x cheaper. 👾 Context: If DeepSeek can deliver ~90% of GPT-4o at ~20% of the price, enterprise buyers have a real alternative. 3️⃣ Google Gemini → collapsing ChatGPT’s plug-in advantage - Multi-step editing (outfit changes, blending, style transfer) → ChatGPT needs external plug-ins (e.g. Canva, Photoshop) for the same tasks. - One interface, full workflow → ChatGPT still pushes users between plug-ins and third-party tools. - Available on web + mobile, free and paid → ChatGPT Pro users pay $20/month but don’t get this depth natively. - Includes watermarking for trust → ChatGPT image edits lack built-in transparency standards. 👾 Context: Users will expect workflows compressed into one place. ChatGPT’s patchwork of plug-ins feels clunkier by comparison. 4️⃣ Microsoft + Cloudflare → weakening ChatGPT’s retrieval moat - NLWeb lets websites publish AI-readable formats directly → ChatGPT still depends on brittle HTML scraping and plug-ins. - AutoRAG manages indexing + vector storage automatically → ChatGPT requires custom retrieval setups for enterprises. - Deployable via Cloudflare dashboard in minutes → faster onboarding than setting up ChatGPT browsing or APIs. 👾 Context: If publishers shift to AI-native formats, models like Copilot or Claude can access clean data feeds. ChatGPT’s browsing advantage disappears. -- Hi, I am Chetan. Follow me to learn more about Data, Product, and AI!

Model Context Protocol is critical to the evolution of AI in travel, and for everyone who heard the term for the first time at Skift Global Forum, let’s get caught up. It solves the problem of adding facts and booking tools to ChatGPT or your preferred AI tool . . . like setting up an API connection to a booking system or database of destination information, except you don’t need a programmer to write code to API endpoints. MCP servers have “self-describing instructions” - they tell your LLM how they work. Anthropic, the company behind the Claude chatbot, released this open-source protocol last September. Non-programmers can set up MCP connections inside your Claude account (with ChatGPT, you need a bit more developer expertise). An example: I connected the MCP server for our project management tool (Wrike) to my Claude account. This allows me to: ✔️ Paste an email with a list of speaker presentation deadlines into a chat window to generate Tasks and Sub-tasks in Wrike. ✔️ Ask Claude what tasks I have this week. ✔️ Work with Claude inside that same prompt to complete the task (let’s say it’s writing an update for our company newsletter - Claude has persistent memory across chat threads and can pull together a list of everything applicable for that update). ✔️ I can polish the text inside the same chat thread and ask Claude to paste it into my Wrike task and mark the task “complete.” ❌ No, this doesn’t work perfectly today. But it will! Today’s AI is the worst it will ever be. Now extrapolate that workflow to vacation planning. My ChatGPT account knows everything about my preferences. If I could plug into a flight/hotel/OTA MCP server to book using points, and use a Broadway MCP server to find out which of three Broadway shows has the best seats at a reasonable price over six possible performance times during my trip, I no longer need to leave ChatGPT to plan and book my trip. These tools are text-based today, but they won’t be forever. Imagine a future where ChatGPT spins up a visual (website-like) environment to help you plan that trip. Maybe that’s what GPT-6 looks like. I was struck by Rafat’s interview with OpenAI’s board chair Bret Taylor and his quote, “If you think of these agents as essentially conversational customer experiences, it can really conform to whatever the appropriate channel is at that moment, and it can mix and match graphical elements with voice and chat.” Bret is saying the quiet part out loud. This isn’t the only future - plenty of people will never make the AI-first shift and will plan and book on our websites. But if you want to visualize this MCP-led future, think of transforming your data and your stories (all that great content on your website) into a database that will ultimately turn into an MCP server. Whether the nebulous, undefinable term “AI agents” ultimately becomes individual agents created by businesses or your ChatGPT account becomes “agentic,” you’ll be prepared.

“Perplexity AI built an $18 billion company with one significant improvement that differed from ChatGPT. It's called the RAG model.” This statement took the internet by storm, and it’s broadly true. RAG was the main differentiator for Perfplexity, but other features made it a great product that everyone loves. But what did adding a RAG on top of ChatGPT’s architecture change so much? And why is RAG all the rage right now? ChatGPT gives answers with confidence, but not always with accuracy. RAG (Retrieval-Augmented Generation) flips the script: It answers based on past training. It actively looks up fresh, relevant information every time you ask a question. But it’s not just about bolting Google Search on top of GPT. The magic is in the workflow: RAG is like an AI that can “show its work.” Step 1: Retriever fetches facts from databases, websites, PDFs, and internal tools in real-time. Step 2: Augmenter filters out the noise, keeps the signal, and attaches the source for every snippet. Step 3: Generator writes a response that directly references the actual data with citations right there. It’s the difference between someone telling you a fact versus someone showing you exactly where they found it. RAG lets every user demand: “Prove it.” And for the first time, the AI can actually prove it, instantly. That’s why students, researchers, execs, and support teams now rely on RAG-powered tools like Perplexity. It’s less about “AI knows everything” and more about “AI helps you know, with evidence.” Here are some resources you should definitely check out. I’ll be posting more about RAG because it’s quite an interesting topic. What Is Retrieval-Augmented Generation, aka RAG? https://lnkd.in/gMX6r_VH RAG Applications with Llama-Index https://lnkd.in/gDSPGNQB Building RAG Applications with LangChain https://lnkd.in/gCd3tq4t LangChain, OpenAI’s RAG https://lnkd.in/gfpvriPE Advanced Retrieval for AI with Chroma https://lnkd.in/g5xmdQfb Advanced RAG by Sam Witteveen https://lnkd.in/gBbxv63P Building and Evaluating Advanced RAG Applications https://lnkd.in/gXsv49qX RAG Pipeline, Metrics https://lnkd.in/g_mZBaUr

The launch of OpenAI's ChatGPT sparked huge buzz around #AI stocks. While you can't invest in ChatGPT directly, here are 12 companies giving investors exposure to AI #chatbot technology. 🤖📈 #AI #ChatGPT #ArtificialIntelligenceInvesting https://lnkd.in/g-8AcQ2Z

Smart AI Prompting: What’s New for 2025 OpenAI just released its internal playbook for “GPT-Realtime” prompting. And yes, voice-based interactions require an entirely different approach from text. Think: • Clear role definitions (e.g. “You are…”), • Bullet point instructions for structure, • Variety rules to keep responses fresh, • And a mindset of relentless iteration to refine results in real-time. Here’s what I’m seeing: Prompting is branching out and fast. It’s no longer just about what you say, but how and where you say it. If your workflows involve voice AI, like assistants, bots, or automated systems, adapting these techniques isn’t optional, it’s essential. My take? Prompt engineering was already critical. Now it’s becoming more like an art: structured, strategic, and dynamic. AI doesn’t just work on your intent, it needs context, clarity, and format. That’s the edge we have to master in 2025. Curious, have you tried this kind of prompting in your workflows yet? #PromptEngineering #AIPrompting #VoiceAI #Innovation #DigitalStrategy