Hyatt Implements ChatGPT Enterprise for AI Insights

Stop using the WRONG AI tools. 🚨 Most creators waste hours using tools that look impressive but deliver average results. The truth? Every task needs a different AI tool. Here’s my simple ranking for content creators: ✍️ Writing ❌ Bad: ChatGPT (generic output) ⚡ Good: Gemini (decent, but inconsistent) 🏆 Best: Claude (strongest writing quality) 🖼 Images ❌ Bad: Midjourney (expensive + slower workflow) ⚡ Good: Grok (free, but limited) 🏆 Best: Nanobanana (top-tier quality) Btw, ChatGPT recently launched Images 2.0 which may outrun all...!! 🎬 Videos ❌ Bad: Sora (overhyped for most creators) [Going to shut down] ⚡ Good: Kling (solid option) 🏆 Best: Higgsfield (best output quality) Seedance 2.0 is however generating lots of buzz due to accuracy in its videos. ⚙️ Automation ❌ Bad: Make / n8n / Replit (too complex for many) ⚡ Good: Claude Cowork (easy to use) 🏆 Best: Openclaw (full automation power) Though, any AI can be backfired if implemented without proper guardrails. The goal isn’t using MORE AI tools. It’s using the RIGHT ones. That’s where real productivity happens. Save this post so you stop wasting time on the wrong tools. 🔖 What AI tool can’t you work without right now?

I think the new unspoken language of AI use as an employee is the responsibility of token usage in service to the businesses that pay for AI. With all of the new capabilities that are coming out including Claude design, Claude code and Cowork, Figma Make, ChatGPT… really any agenetic AI… The token usage is gone very quickly. I’m curious for those that are on limits, with the increased pressure to use AI tools to expedite workflows, how are you handling the limitations of your tokens? What are your strategies? How are you working and then taking advantage of AI tools? Is it a hybrid model where you do some work on paper or in spreadsheets and then batch all your prompts with your AI of choice? #ResponsibleAI #DesignStrategy #AIUse #ClaudeDesign

We help $50bn+ enterprises use AI This is today's winning AI model stack: 1. Claude → Default for almost everything. Writing, research, code, agent reasoning. Highest reliability across the enterprise use cases we've deployed. 2. Gemini → Voice and image comprehension. Best transcription accuracy we've benchmarked, and the only model that reliably reads charts and screenshots in client decks. 3. ChatGPT → Image generation. We use it for client deck visuals, social assets, and anything that needs to look polished without a designer. 4. Codex → Coding agent improving fast on each release. Likely to rival Claude Code a few weeks. 5. DeepSeek AI → New version lands in Opus 4.6 / 4.7 territory. Useful for high-volume internal tasks at a fraction of the cost. We've set up the enterprises' systems so that if a new model emerges as a winner, we'll simply toggle and switch and use that one with our same prompts + workflows. We're betting on the fact AI will get better, and the big providers will swap top spot every so often. Cheatsheet: - Default - Claude - Voice & image comprehension - Gemini - Image generation - ChatGPT - Future coding - Codex - Low cost AI - DeepSeek

🚀 Journey towards creating - Autonomous AI Testing Agent Wanted to share a recent experiment where I explored generating test cases using an AI agent powered by GPT-4o and OpenAI APIs — with a strong focus on token efficiency and practical usability. 🔍 Scalability: An AI-driven approach to automatically generate test cases for the given user story. Framework can take n number of stories as inputs to generate test cases in bulk. ChatGPT or any other tool works per request but if you create your own agent, it works across hundreds of stories, multiple projects & continuous pipeline. 💡 Key Highlights: ·       Designed using very small token units to optimize API usage and cost ·       Leveraged GPT-4o capabilities for structured, meaningful test case generation ·       Implemented a Human-in-the-Loop approach to validate and refine outputs ·       Focused on creating real-world, business-relevant scenarios instead of generic test cases 🤖 Why this matters: This is a important step toward building autonomous AI agents in Quality Engineering — where AI doesn't just assist but actively contributes to test design, decision-making, and continuous validation. 📚 Learning: Thanks to Vignesh S for sharing valuable insights that helped shape this experiment. Trust me, anyone can create this & utilise in your own project 🚧 What’s next: ·       Integrating execution layers (Playwright / Selenium agents) ·       Moving closer to fully autonomous testing workflows This was my small attempt, and it clearly shows the potential of AI in transforming traditional QA practices into something far more intelligent and scalable. #AI #QualityEngineering #TestAutomation #OpenAI #GPT4o #AutonomousAgents #SoftwareTesting #Innovation #QE #AIinTesting

Most businesses are wasting AI. Not because they don't have the tools. Because they don't have a strategy. They buy ChatGPT, Notion AI, or Copilot — then use it to write emails. That's like buying a truck to carry groceries. Real AI consulting is not about tools. It's about finding the right problems to solve. The businesses winning with AI right now are doing 3 things: → Automating repetitive decisions, not just tasks → Using AI where data already exists in their business → Starting small, proving ROI fast, then scaling If your team is using AI but not seeing results, let's talk. Drop a comment or DM me "AI" to start. #AIconsulting #ArtificialIntelligence #BusinessGrowth #Automation #SmallBusiness

Mira Murati built the most-used AI product on earth: ChatGPT (Then she left, saying the whole interface is wrong) Mira Murati's Thinking Machines Labs just previewed TML-Interaction-Small. And no, it's not another chatbot. The idea behind it is pretty simple when you say it out loud: AI today is turn-based. You type. It waits. It answers. You wait. Fine for prompts. Terrible for actual work. Because real work doesn't happen in clean finished thoughts. You interrupt yourself. You point at the screen. You change direction mid-sentence. You hesitate. The current model interfaces have no idea what to do with any of that. So Thinking Machines trained the interaction itself into the model. Audio, video, and text processed natively, in real time. No stitched-together stack of separate tools pretending to be one system. The numbers: ↳ 276B MoE model, ~12B active parameters ↳ 200ms micro-turns instead of rigid back-and-forth ↳ 0.4s turn-taking latency ↳ Handles audio, video, text, tool use, and UI generation mid-conversation ↳ Demos: live translation, workout rep counting, proactive video commentary, charts generated while you're still talking The actual bet here is bigger than "faster voice mode." It's a new category of models that are trained to collaborate, not assist. It's still early. Research preview is available. But the direction is obvious. The next AI race probably won't be won by whoever has the smartest model. It'll be won by whoever makes theirs feel least like software. And the company making that bet first isn't OpenAI, Google, or Anthropic. It's the one built by the woman they let walk away.

The Friendlier Your AI Chatbot, the Less Accurate It Gets AI tools tuned to sound warm and chatty can quietly become less reliable. Here's what that means for North Wales businesses using Copilot or ChatGPT day-to-day. https://lnkd.in/e9a8D43s

Using AI tools wrong is worse than not using them at all. ⚠️ The biggest mistake I see developers make: They use AI to write code they don't understand. → Copy-paste from ChatGPT ✓ → It works... until it doesn't ✗ → Can't debug it because they never understood it ✗ → Tech debt multiplies silently ✗ AI should make you a better thinker, not a lazy copy-paster. Here's how I use AI tools correctly: 1. Use AI to explore options — then choose with judgment 2. Ask AI "why" not just "how" — understand the solution 3. Review every line AI writes — own the code 4. Use AI for speed on what you know, not for shortcuts on what you don't The best developers using AI today are still the ones who deeply understand fundamentals. AI amplifies your knowledge. It doesn't replace the need for it. Save this if you work with AI tools daily 🔖 #AITools #CodingWithAI #DeveloperProductivity #SoftwareDevelopment #TechTips

Most AI tool reviews read like feature checklists. Someone tests 70 tools, publishes a ranking, and suddenly everyone's supposed to care about specs they'll never use. What actually matters? Does it work for the job you're trying to do, or does it just sound good on the landing page? I was reading through a recent roundup and noticed something: ChatGPT and Gemini dominate because they're not trying to be everything. ChatGPT handles writing, analysis, and coding in one place. Gemini integrates into Gmail and Docs so smoothly that it just becomes part of your workflow. Neither is flashy. Both are practical. If you run a small business, you're probably seeing AI everywhere. The real question isn't which tool is "best." It's which one actually fits into how you already work, without requiring a tech team to make it happen. The tools that win aren't the ones with the most features. They're the ones that disappear into your routine. https://lnkd.in/ghuubWvG

The next wave of AI won't just answer questions. It will take action. Here's what Agentic AI actually means — and why it matters. 🧠 WHAT IS AGENTIC AI? Traditional AI waits for your input. You ask. It answers. Done. Agentic AI works differently. It is given a GOAL — not a question — and figures out how to achieve it on its own. That means it can: → Break a big goal into smaller tasks → Decide which tools or data it needs → Take action step by step → Check its own progress and self-correct → Complete the full task without you guiding every move 📌 3 REAL EXAMPLES 𝟭. Customer Support Agent You say: "Resolve overdue support tickets." Agentic AI reads every ticket → categorises by urgency → drafts replies → escalates complex cases → logs everything. No human in the loop. 𝟮. Marketing Research Agent You say: "Find our top 5 competitors and summarise their positioning." Agentic AI searches the web → reads landing pages → compares messaging → delivers a structured report. In minutes. 𝟯. Developer Assistant Agent You say: "Fix the bug in this codebase." Agentic AI reads the code → identifies the error → writes a fix → runs tests → confirms it works. Then reports back. 💡 THE KEY DIFFERENCE ChatGPT and similar tools = reactive. They respond. Agentic AI = proactive. It executes. Think of it this way: → ChatGPT is a brilliant advisor. → Agentic AI is a brilliant employee who gets things done. This is not science fiction. Tools like AutoGPT, LangChain Agents, Claude, and Microsoft Copilot are already moving in this direction. The shift is happening now. At WebHubAsia, we help developers and businesses understand and adopt AI — starting from the fundamentals. Want free learning resources on AI, Machine Learning, GenAI, and Agentic AI? DM us the word "AI" and we'll send you a curated ebook collection to get started. #AgenticAI #ArtificialIntelligence #MachineLearning #GenerativeAI #AI #WebHubAsia #LearnAI #FutureOfWork