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Gemini is Now the Best All-in-One AI & More AI Use Cases
In this video, I go over the various updates and releases from Google and Anthropic, discusses the upcoming AI hardware releases from Apple and OpenAI, tests out a frankly creepy demo of a live interactive AI avatar, and more. Enjoy!
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šŸ”ā±ļø Stop Re-Explaining Your Job: Build a Prompt Library That Cuts Rework in Half
We do not lose the most time doing hard work. We lose time repeating ourselves. We re-explain the same context to teammates, to new hires, to stakeholders, and to our own tools and templates. Then we act surprised when cycle time stays high and rework keeps showing up. A shared prompt library is not a ā€œnice to have.ā€ It is an operational asset that turns repeated thinking into reusable leverage. When we build it well, we stop paying the setup cost every time we open a task. We get time back through faster starts, fewer revisions, and shorter handoffs. ------------- The Hidden Cost of Starting From Zero ------------- Most teams have recurring work that looks unique on the surface but is structurally the same underneath. Weekly updates. Client emails. Meeting agendas. Project briefs. Job posts. Performance notes. Training docs. Risk reviews. The categories are predictable, but we treat each instance like it is brand new. That is why context becomes the bottleneck. Someone begins a task, then spends 20 minutes remembering what ā€œgoodā€ looks like. They hunt for last month’s version, copy it, patch it, and hope they did not miss a key detail. They ask someone else for examples. They send a draft that is close but not aligned, and then they get feedback that could have been avoided if we had a shared baseline. This is not just wasted writing time. It is wasted coordination time. Every time we start from zero, we create more back-and-forth. People react to style differences, missing sections, or unclear ā€œdefinition of done.ā€ Rework rate rises because the first draft is not wrong, it is inconsistent. Inconsistent work triggers extra review. AI makes this problem more obvious because it can generate so much so quickly. Without a shared library, we end up generating new versions of the same thing, each slightly different. That creates confusion and more time spent arguing about format and tone instead of substance. A prompt library is how we standardize the starting line. Standardizing the starting line is one of the fastest ways to shorten cycle time.
šŸ”ā±ļø Stop Re-Explaining Your Job: Build a Prompt Library That Cuts Rework in Half
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You failed. Now what?
You failed. Okay. Take a breath. First, let’s just acknowledge something. You were in the arena. You put something out there. You risked looking stupid. You risked it not working. That already puts you ahead of the majority of people who are still ā€œthinking about itā€ or ā€œgetting ready.ā€ Failure has a way of messing with your head. It makes you question yourself. It makes you wonder if maybe you’re not cut out for this. But almost every time, it’s not about who you are. It’s about what you did. There’s a big difference. When something doesn’t work, it’s usually a strategy issue, a clarity issue, a focus issue, or just not enough reps. It’s rarely an identity issue. But if you make it about your identity, you’ll shrink. If you make it about the approach, you’ll grow. So instead of asking, ā€œWhat’s wrong with me?ā€ ask, ā€œWhat can I learn from this?ā€ What broke? What did I assume that wasn’t true? Where did I hesitate? Where did I rush? If you paid the emotional price of the failure, at least get the lesson out of it. That’s where the value is. The only real danger isn’t failing. It’s quitting. It’s deciding that this one outcome defines you. It doesn’t. It defines a moment. And moments can be adjusted. Sometimes you don’t need more effort. You need a different angle. Sometimes you don’t need a new dream. You need more reps. Sometimes you just need to stay in the game longer than the discomfort. Failure isn’t the opposite of success. It’s the path to it. And once you stop being afraid of it, once you realize it can’t actually hurt you unless you let it stop you, you start playing differently. You start playing to win instead of playing not to lose. That’s the shift. So let me ask you this...What did your last setback teach you and what are you going to adjust because of it?
Changing to Claude from Chat GPT
Hello everyone, I want to change my paid version from Chat GPT to Claude. ĀæDoes anyone have recommendations? ĀæWill I loose my projects and the GPTs that people share with me? I don’t have created personal GPTs, those are GPTs people created and shared with me. Thanks
šŸ“° AI News: OpenAI Locks In A Massive $110B Round, The AI Infrastructure War Just Leveled Up
šŸ“ TL;DR OpenAI just finalized a jaw dropping $110 billion funding round, valuing the company around $730B pre money, roughly $840B post money. This is not ā€œstartup funding,ā€ it’s an industrial scale power grab for chips, data centers, and dominance in AI agents. 🧠 Overview OpenAI has closed what appears to be one of the biggest private funding rounds in history. The money is coming from a trio of heavyweight backers and it is aimed at one thing, scaling AI infrastructure and distribution fast enough to stay ahead in the model and agent race. This also signals a shift in the AI era. The competitive moat is no longer only model quality, it’s who can secure compute, energy, and deployment channels at global scale. šŸ“œ The Announcement OpenAI confirmed it has finalized a $110B raise at an estimated $730B valuation before the investment, translating to roughly $840B after the new money. The funding is led by three major players: • Amazon, reportedly $50B total, with a portion upfront and the rest tied to conditions. • SoftBank, reportedly $30B. • Nvidia, reportedly $30B. The round is framed as fuel for OpenAI’s next phase, scaling infrastructure, expanding access, and pushing deeper into enterprise and agent deployments. āš™ļø How It Works • Mega funding for mega compute - This round is built to bankroll the most expensive part of AI, chips, data centers, networking, and ongoing inference at scale. • Strategic investors, not passive money - These are not just financial backers, they are infrastructure and platform giants that benefit when OpenAI scales. • Cloud and chip leverage - The deal structure points to more tight coupling between OpenAI and the hardware and cloud ecosystems needed to serve models cheaply and reliably. • Agents are the prize - The biggest winners will not just answer questions, they will run workflows, handle tasks across apps, and become the default interface for work.
šŸ“° AI News: OpenAI Locks In A Massive $110B Round, The AI Infrastructure War Just Leveled Up
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