Tether Recognized by @OSCE for Supporting Global Efforts to Combat Human Trafficking Read more: tether.io/news/tether-recogn…

Yeah I’m thinking my job is safe.

Let building be your obsession

Name a ticker no one can afford to ignore.

You know that moment when you need access now — and some cloud says no? ☁️❌ Too many gatekeepers. Too many points of failure. 🍐🔒Meet PearPass — your peer-to-peer local password manager, fully private, open-source, and cloud-free. Stay independent. Stay in control. Follow @Pears_p2p and download the App 👉 pass.pears.com

Trade now ↓ coinbase.com/derivatives-tra… Legal stuff: Listed futures and swaps are offered via Coinbase Financial Markets (“CFM”), a NFA member firm. Trading futures involves substantial risk and may result in the loss of your entire investment. Leverage in futures trading can work for or against you. Trade only if you understand the product and believe it is appropriate for your financial situation and objectives. For informational purposes only. Not investment advice or a recommendation.

Tether Data Introduces QVAC Fabric LLM, the Edge-First LLM Inference Runtime and Generalized LLM LoRA Fine-Tuning Framework for Modern AI Models on Heterogeneous GPUs, Smartphones, Laptops, and Servers. Follow @qvac Learn more: tether.io/news/tether-data-i…

Tether and Da Nang City Sign MoU to Advance Blockchain-Powered Digital Governance and Infrastructure Learn more: tether.io/news/tether-and-da…

A good culture is mission fuel. Less distractions, more building.

Quietly, but steadily, OpenAI is advancing the state of the art in AI. Can you see this? The o3 model is amazing. And we just got it, what, seven weeks ago? It’s become my right hand really. It doesn’t take over. I don’t delegate. But it’s the productive engine that provides structure, content, fodder, knowledge, options. 𝗕𝗲𝗹𝗶𝗲𝘃𝗲 𝗶𝘁 𝗼𝗿 𝗻𝗼𝘁 𝘁𝗵𝗼𝘂𝗴𝗵, 𝗶𝘁’𝘀 𝗻𝗼𝘄 𝘀𝗲𝗰𝗼𝗻𝗱 𝗳𝗶𝗱𝗱𝗹𝗲. Introducing “o3-pro.” Last week OpenAI quietly flicked a switch that ChatGPT power‑users immediately noticed: "o1-pro" was gone. In its place? A new model called “o3‑pro.” It’s the same “reasoning-first” architecture as o3, but you get a lot more compute under the hood, a lot more intelligence. Here’s the catch: 𝘄𝗮𝗶𝘁 𝘁𝗶𝗺𝗲𝘀 𝗮𝗿𝗲 𝗺𝗼𝗿𝗲 𝗹𝗶𝗸𝗲 “𝗴𝗿𝗮𝗯 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲” 𝘀𝗰𝗮𝗹𝗲. Why the longer wait? o3 is given a lot more compute per query. It can pause, back-track, verify its steps. And it comes with the expected menu of capabilities; live web search, Python execution, file analysis, image understanding, long‑term memory recall. In complex prompts it might be calling these tools hundreds of times, a far cry from the halcyon days of GPT 3.5! In head-to-head testing it beats o3 and o1-pro on math, science, programming, business writing and general QA. Quick highlights: • 𝗥𝗼𝗹𝗹𝗼𝘂𝘁: Pro and Team get first crack, Enterprise and API access following in days • 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Extra cycles per query give it time to back‑track, verify its thinking • 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Outscores o3 and o1‑pro across code, math, science, business writing • 𝗧𝗼𝗼𝗹𝘀: Ships with web search, Python, file & image analysis, long‑term memory • 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: Sits at $20 / $80 per million tokens, standard o3 dropped to $2 / $8 • 𝗦𝗽𝗲𝗲𝗱: Responses can take minutes; it’s built for depth, not banter • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: Loves complex prompts, treat it like a consultant, not a chat buddy • 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀: Hits new highs on math, science and coding leaderboards • 𝗦𝘂𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Ideal for any task where correctness trumps latency • 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀: No image gen yet, Canvas is absent, temporary chats disabled So, there you have it. o3-pro is basically saying, “Sure I’m slow, but I’m good!” I’ll take it! I needed that coffee anyway! #discoverthefuture

It’s all connected. It may not seem like it is. But it is. The AI news pops every day and we’re seeing it all in a blur; blockbuster deals, huge funding, shiny new apps, tools. But the deeper story is there hiding in plain sight. Here’s a flurry of events that transpired in just the last 24 hours: • IBM bought Seek AI and opened a Watsonx lab for data queries • Microsoft added free Sora video generation to Bing mobile • Character.AI launched AvatarFX video and social sharing • Meta targeted fully automated AI ad creation by 2026 • Creatify AI raised $15.5M for AI-made social video ads • Console netted $6.2M for an AI Slack help-desk bot • Google ships AI Edge Gallery and puts it on device • Snowflake acquired Crunchy Data to blend Postgres and AI • Musk’s xAI chases a $113B valuation in new funding • Neuralink secures $650M, begins human brain-computer trials Just another AI day in 2025 right? Are there consistent through-lines? Perhaps. Theme 1: AI is embedding itself in the tools we already use Bing, Slack, Instagram, Snowflake, Android, Neuralink, none of these were “AI products,” but each one now has a intelligence under the hood. The common thread? Convenience: bring the intelligence to the workflow. The idea? Make the AI feel like a natural extension, not a separate destination. Theme 2: Generative tech is pushing the limits on modality Video clips from Sora, animated avatars from Character.AI, polished ads from Creatify, even signals straight from a neural implant… Every story shows AI crossing modalities. Why is this significant? The addressable surface for creativity becomes accessible to everyone. Theme 3: The distance between data, compute, and model is collapsing IBM snaps up a query engine, Snowflake folds in Postgres, Google packs models on-device. This convergence matters. Why? When inference happens next to the bits, product output feels immediate, and immediacy breeds trust. The wall between “database” and “AI engine” is crumbling fast. Theme 4: Mundane tasks are migrating from humans to machines Ad copy, campaign targeting, database wrangling… jobs once carried out by humans are now being shifted to AI. It's not about firing people; it’s about raising the floor of what counts as baseline productivity. Humans are getting moved up the stack, towards judgment, relationship, oversight. Theme 5: Capital is pouring into “intelligence” conduits that promise scale The numbers are stunning. $113Bfor xAI, $650M for Neuralink. But the logic’s obvious. If you’ve got the data, the talent, the compute, you can raise the capital. Investors can’t risk missing out so they’re investing in optionality. “Money flows downhill” … towards the momentum. I’m sure there’s a dozen other memes, themes, but the bottom line is, “intelligence” is now becoming democratized. #discoverthefuture

There are moments in time that are pivots, doors that open, 𝘀𝗶𝗴𝗵𝘁 𝘁𝗵𝗮𝘁’𝘀 𝗴𝗿𝗮𝗻𝘁𝗲𝗱 𝘂𝘀 𝘀𝗼 𝘄𝗲 𝗰𝗮𝗻 𝗴𝗲𝘁 𝗮 𝗴𝗹𝗶𝗺𝗽𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲, innovation that changes the way we live, the way we move ahead, the way we think and operate. This was true in 1977. Sure, there were other “personal computers” that were around at the time. Radio Shack was pitching the TRS-80 and there was the Commodore PET and of course, the Atari 2600, all popular in their own way. And then there was the Apple II which debuted 48 years ago today. Now, it was priced dramatically higher than all of those above and yet it outsold them by a wide margin. One version with a whopping 48K of RAM sold for $2,638 ($𝟭𝟰,𝟬𝟬𝟬 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗱𝗼𝗹𝗹𝗮𝗿𝘀), and if you needed a 143K floppy drive you had to fork over another $495. How did Steve Jobs put it? “𝗪𝗲’𝗿𝗲 𝗵𝗲𝗿𝗲 𝘁𝗼 𝗽𝘂𝘁 𝗮 𝗱𝗲𝗻𝘁 𝗶𝗻 𝘁𝗵𝗲 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗲. 𝗢𝘁𝗵𝗲𝗿𝘄𝗶𝘀𝗲 𝘄𝗵𝘆 𝗲𝗹𝘀𝗲 𝗲𝘃𝗲𝗻 𝗯𝗲 𝗵𝗲𝗿𝗲?” Dent it they did. It wasn’t long before this interesting software #VisiCalc came along, a “visual calculator” (known to you as a spreadsheet). And then #WordStar for word processing and #dBase for databases. Suddenly, unbelievably, small businesses could perform financial analysis and create written documents and track customers in ways never before possible. They found it easily worth the investment. Somewhere between 5 and 6 million Apple II series computers would be sold before its end of life in 1993. What is today’s Apple II? #discoverthefuture

You’ve heard of Vibe Coding right? 𝗛𝗼𝘄 𝗮𝗯𝗼𝘂𝘁 𝗩𝗶𝗯𝗲 𝗪𝗿𝗶𝘁𝗶𝗻𝗴? I decided to ask ChatGPT to create an image to describe “Vibe Coding” and here’s what it generated. Looks like a neon mind-map of sorts: blocks of color radiating from a bold “Vibe Coding × AI” core. Each block sketches a different way that machines tune themselves, that they “vibe” with your human emotional texture. Then I asked it to write bullets for each item and here's what it came up with: • 𝗔𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝗻𝗮𝗻𝗰𝗲 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 connects output to the user’s emotional state • 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗶𝗻𝗴𝘂𝗶𝘀𝘁𝗶𝗰 𝗰𝗮𝗹𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻 adjusts vocabulary, aligns with cognitive norms • 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 keep the tone persuasive without slipping into bias • 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝘀 remix prompts, sparks novelty not repetition • 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁-𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 evolve based on feedback loops from the audience • 𝗧𝗿𝘂𝘀𝘁 𝗰𝗮𝗹𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 quantify how believable each response feels • 𝗘𝗺𝗼𝘁𝗶𝘃𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗹𝗼𝗼𝗽𝘀 inject affective cues into vector representations • 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝘃𝗶𝗯𝗲 𝘀𝗶𝗴𝗻𝗮𝘁𝘂𝗿𝗲𝘀 merge text, audio, visuals into felt impressions • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝘃𝗶𝗯𝗲 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 groups user segments by emotional style • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗮𝗲𝘀𝘁𝗵𝗲𝘁𝗶𝗰 𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 keeps fonts, palettes synchronized with the mood • 𝗘𝗹𝗲𝘃𝗮𝘁𝗲𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 turning functional flows into satisfying moments • 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 as people stick with tools that feel intuitively “for them” • 𝗗𝗲𝗲𝗽𝗲𝗻𝘀 𝗵𝘂𝗺𝗮𝗻-𝗔𝗜 𝘀𝘆𝗺𝗯𝗶𝗼𝘀𝗶𝘀 moves interactions to be collaborative • 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝘀 𝗯𝗿𝗮𝗻𝗱 𝘃𝗼𝗶𝗰𝗲 through consistent emotional contour across channels • 𝗥𝗲𝗱𝘂𝗰𝗲𝘀 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 by matching wording to the user’s mental model • 𝗗𝗿𝗶𝘃𝗲𝘀 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 via data-driven evaluation of vibe performance • 𝗨𝗻𝗹𝗼𝗰𝗸𝘀 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝗵𝘄𝗮𝘆𝘀 that blend UX heuristics with affective science Finally, I asked it, “Why does this matter?” Why it matters? Because software that senses and mirrors our vibe lowers the cognitive cost of every interaction. When an AI greets you in the tone you need, compliance rises, churn falls, and trust snowballs. Companies used to chase milliseconds of latency; now they’re chasing milliseconds of empathy. Get those milliseconds right and the rest—loyalty, revenue, and ethical alignment—arrive almost automatically. So there you have it, for all you writers out there: 𝗩𝗶𝗯𝗲 𝗪𝗿𝗶𝘁𝗶𝗻𝗴. #discoverthefuture

Your Best Ideas! Actually, your best ideas aren’t always what’s needed. Sure, good ideas are… good. But sometimes success comes from just doing the work. 𝗝𝘂𝘀𝘁 𝗗𝗼 𝗧𝗵𝗲 𝗪𝗼𝗿𝗸. Your ideas, your ingenuity, your values, your heart, might just come out, might just surprise everyone, including yourself. For example, take one Pyotr Ilyich Tchaikovsky. Asked to compose a work to memorialize Russia’s victory over Napoleon, Tchaikovsky didn’t feel much enthusiasm for the work, probably only agreeing to do it for the lucrative commission. 𝗕𝘂𝘁, 𝗵𝗲 𝗷𝘂𝘀𝘁 𝗱𝗶𝗱 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸. Only spending six weeks on the effort, he wasn’t extremely proud of it, calling it “very loud and noisy.” Noisy indeed. Featuring pealing church bells and exploding cannons, the public immediately loved it and its popularity has never waned. 134 years ago today in fact, Tchaikovsky conducted the work himself in a command performance for the opening of the brand new Carnegie Hall. And today the 1812 Overture is standard fare at most celebrations of American Independence. It's never been more popular in fact. 𝗡𝗼𝘁 𝗯𝗮𝗱 𝗳𝗼𝗿 𝗷𝘂𝘀𝘁 𝗱𝗼𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸. #discoverthefuture bit.ly/4k2MurR

You know the arc of every story don’t you? You know, “Once upon a time…”, “And every day…” etc... So, here’s the charming story of o3. 𝗢𝗻𝗰𝗲 𝘂𝗽𝗼𝗻 𝗮 𝘁𝗶𝗺𝗲… Large‑language models ruled the roost with words alone. GPT‑4o could joke in Italian, summarize SEC filings, but, it stared helplessly at a whiteboard. Claude 3.5, Gemini 2.0 - all brilliant of course, but all basically text‑first. 𝗔𝗻𝗱 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆… Teams hacked around the gap: bolting vector databases onto chatbots, copy‑pasting screenshots into Slack, scribbling system diagrams twice - once for humans, once for the model. Useful? Yes. Seamless? Not at all. 𝗨𝗻𝘁𝗶𝗹 𝗼𝗻𝗲 𝗱𝗮𝘆... OpenAI dropped o3. This new “reflective architecture” can think with images, juggle every ChatGPT tool into one coherent plan, and swallow 200K tokens of context without blinking. 𝗔𝗻𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗶𝘀… Day‑one demos shocked even the doubters. A grimy phone‑shot of a microservice tangle came back as a redesign that halved AWS spend. A medical researcher fed in handwritten notes and o3 generated a structured clinical trial protocol. 𝗔𝗻𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘁𝗵𝗶𝘀… Adoption went viral. Competitors scrambled. GitHub. Claude 3.7 Sonnet touted hybrid reasoning, a 200K context (but no native image‑chain logic). Gemini 2.5 Pro boasted 1M context, yet reviewers noted slower tool choreography compared with o3’s “one‑brain” pipeline. 𝗨𝗻𝘁𝗶𝗹 𝗳𝗶𝗻𝗮𝗹𝗹𝘆… The burning question arose: 𝗜𝘀 𝘁𝗵𝗶𝘀 𝗔𝗚𝗜? Nope. o3 can reflect, plan, manipulate tools, but it still chases your objective, not its own. It doesn’t set sub‑goals unprompted or maintain a persistent worldview across sessions. Superhuman at tasks? Yup, but still human‑directed on mission. 𝗔𝗻𝗱 𝗲𝘃𝗲𝗿 𝘀𝗶𝗻𝗰𝗲 𝘁𝗵𝗮𝘁 𝗱𝗮𝘆… The leaderboard reshuffled. Claude 3.7 wins on raw text‑length answers; Gemini 2.5 flexes giant context breadth. But o3’s image‑native reasoning gives it a headline feature rivals don’t yet match. If Sam hasn’t hit a grand‑slam, he’s at least parked one off the wall. #discoverthefuture

What did Mark Twain say? “𝗧𝗵𝗲 𝗺𝗮𝗻 𝘄𝗵𝗼 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱 𝗴𝗼𝗼𝗱 𝗯𝗼𝗼𝗸𝘀 𝗵𝗮𝘀 𝗻𝗼 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗺𝗮𝗻 𝘄𝗵𝗼 𝗰𝗮𝗻'𝘁 𝗿𝗲𝗮𝗱 𝘁𝗵𝗲𝗺.” Well done. Of course with audiobooks, you don’t even have to read, you can just listen. But what if you could not just listen to a book? What if you could talk to it? What if you could ask it questions? You know, about the characters, the setting, the plot, the details in the third chapter that you forgot. What I really mean is, what if the entire book could be dumped into an LLM’s context window? Well, actually, what if talking books were productized? I buy all my digital books from Amazon (doesn’t everyone?). 𝗪𝗵𝗮𝘁 𝗶𝗳 𝗔𝗺𝗮𝘇𝗼𝗻 𝗼𝗳𝗳𝗲𝗿𝗲𝗱 𝗺𝗲 𝗮 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗯𝗼𝗼𝗸 𝗮𝘀 𝗮𝗻 𝗲𝘅𝘁𝗿𝗮 𝗼𝗽𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗜 𝗽𝗮𝘆 𝗳𝗼𝗿? Maybe as part of my Kindle experience. Yes, of course, all permissions granted by the author and the publisher. Then I could talk to my book. Let’s say it’s Hard Times by Charles Dickens. I could ask it questions: • Remind me again, who is Thomas Gradgrind? • What exactly is Gradgrind’s educational philosophy (“facts, facts, facts!”)? • Why did Louisa Gradgrind decide to marry Mr. Bounderby? Of course, the challenge is in the cost. 𝗧𝗼𝗸𝗲𝗻𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗳𝗿𝗲𝗲. You can’t just buy a “talking book” and pay for it once. Every time you chatted with it, you’re paying for tokens, there’d be a cost to Amazon that they’d need to pass on to you. Easy fix though. Talking books could be a monthly subscription fee right? Like Kindle Unlimited. That said, things might get tricky. You might just ask about the content. You might. 𝗢𝗿 𝘆𝗼𝘂 𝗺𝗶𝗴𝗵𝘁 𝗮𝘀𝗸 𝗺𝗼𝗿𝗲 𝗼𝗽𝗶𝗻𝗶𝗼𝗻𝗮𝘁𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. • Does Dickens mock education unfairly? • Does Dickens romanticize poverty? • Are facts really the enemy? What do you think? Cool idea? Already being rolled out somewhere? What book would you choose first if you could talk to it? #discoverthefuture

Remember the world before HTTP? 𝗪𝗵𝗮𝘁? 𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁? That’s because a) that was such a long time ago b) it’s an underlying protocol which we never notice c) it was essentially “pre-web”, and who can remember that? Well, it’s happening again. This time you get to experience it in real time. Introducing MCP. OK, 𝗶𝘁’𝘀 𝘄𝗮𝘆 𝘁𝗼𝗼 𝗲𝗮𝗿𝗹𝘆 𝘁𝗼 𝗰𝗮𝗹𝗹 𝗶𝘁 𝗮 𝘄𝗶𝗻𝗻𝗲𝗿 but if you were to handicap it, it’s well on its way. MCP. What is it? Well Anthropic’s calling it the "USB-C port for AI.” Basically it’s the protocol that hopes to standardize how AI systems (agents!) connect to outside data and tools and each other. Right now, integration's a bit of a mess. It is! 𝗟𝗼𝘁𝘀 𝗼𝗳 𝗰𝘂𝘀𝘁𝗼𝗺 𝗰𝗼𝗱𝗲, brittle non-standard connections, rework galore, 𝘄𝗮𝘀𝘁𝗲𝗱 𝗵𝗼𝘂𝗿𝘀 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹. Hoping MCP takes flight, here’s the highlights… • 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹: One interface (to rule them all!) for AI integrations • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰: Real-time discovery, negotiates capabilities between AI agents, tools • 𝗦𝗲𝗰𝘂𝗿𝗲: Uses OAuth 2.0 authentication, granular permissions, isolation • 𝗢𝗽𝗲𝗻: Fully open-source (yes!), encourages community participation, innovation • 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲: Scale away! Accommodates new integrations without complex coding • 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹: Continuous context across interactions, agents can be context-aware • 𝗠𝗼𝗱𝘂𝗹𝗮𝗿: Reusable integrations, we're talking "plug-and-play" AI components • 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱: Adopted by major industry leaders like OpenAI, Microsoft • 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲: Pick your language! (Python, Java, Kotlin, C#, etc.) • 𝗙𝘂𝘁𝘂𝗿𝗲-𝗽𝗿𝗼𝗼𝗳: Hoping... foundational, evolving, fundamental to "agentic AI" OK, so what’s the down side? 𝗪𝗲𝗹𝗹, 𝘄𝗲’𝗿𝗲 𝗮𝗹𝗹 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗮𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗯𝗲𝘁𝘁𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗵𝗼𝗿𝘀𝗲. Place your bets! #discoverthefuture buff.ly/LjgEXro

Here’s Russian pianist, Elisey Mysin playing Mozart at just 6 years of age. This is absolutely mind blowing.

Happy birthday, Chicago 🌃