Daniel Green (@dgrreen) header
Daniel Green (@dgrreen) profile picture

AI Enablement & Implementation Strategy | @AICollectiveCo Kansas City Lead | @OpenAIDevs Codex Ambassador

Kansas City, MO
Joined February 2009
Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)Photo by Daniel Green (@dgrreen)
Daniel Green retweeted
By popular request, you can now text your dot. Give it a task to take off your plate or something to keep an eye on, using text, photos, or even voice notes. It will text you proactively with relevant updates. Safe, secure texting via iMessage or RCS. You’re the only one who can text your dot, and your dot can only text you. Rolling out now as a preview for Pro users in the US.
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Daniel Green retweeted
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Daniel Green retweeted
Much requested, and now yours to connect: iCloud Mail and Calendar plugins are available in ChatGPT. Ask ChatGPT to find that email, catch up on your inbox, check what’s next, or update a calendar event. Phew. Available on all ChatGPT plans on web, desktop, and iOS.
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Daniel Green retweeted
🎙️ The AI Collective Podcast goes live at 9 AM PT Live on LinkedIn, YouTube, X and Instagram with co-host Catherine: this week in AI, what's happening inside The AI Collective, and three guest interviews. - 9:15 AM PT: Daniel Green, KC Chapter Lead at The AI Collective, on closing the AI adoption gap for operators outside the coasts - 9:45 AM PT: Carlo Edoardo Ferraris (@carloAI), Founder & CEO of Natura Umana, on Interface, the $99 AI ring that hands tasks to Claude, ChatGPT, Grok Bot and more - 10:10 AM PT: Apheth D Almeida of Lucen Robotics, on an open-source modular kit that rebuilds from the same actuators into a biped, wheeled base, quadruped or arm From adoption to wearable agents to open robotics, live this morning 👉 luma.com/genai-hwiv
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Daniel Green retweeted
If OpenAI’s post below leaves you unsure when to use GPT-6.1 Terra vs. Sol or Astra, OAI researcher @sandersted has some helpful guidance (via HN):
Replying to @OpenAI
GPT-6.1 Terra substantially outperforms GPT-5.6 Terra on Agents’ Last Exam, a test of how well agents use software at work. It even matches GPT-6 Astra at one-tenth the price per token and finishes tasks over 3× faster on average than both Astra and GPT-6.1 Sol.
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What do people use ChatGPT for at work and how does that differ around the world? Our new OpenAI Signals data lets researchers compare activities from writing and coding to teaching and business analysis across nearly 100 countries. Download the data and tell us what you find: openai.com/index/chatgpt-exp…
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Daniel Green retweeted
GPT-6.1 Terra: delivering fast and capable computer use at a lower price. GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it.
🤖 Made with AI
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Daniel Green retweeted
Streamers, devs, designers, chat enthusiasts! The Codex x TwitchCon Hackathon with @TwitchDev and @OpenAIDevs is live! Build something streamers and their community on Twitch could really use. Codex helps you get set up with the Twitch API, EventSub and chat, so you can focus on the idea. $25,000 in prizes and the top 5 get shown on the Codex Booth at TwitchCon San Diego on Nov 15. Deadline: Mon Nov 2, 2:00 PM PT. Register today: bit.ly/twitchcodex26t
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Daniel Green retweeted
My general advice for using AI for any purpose is to ask yourself: "how do I make what I am doing even more ambitious?" You do this until you either hit a wall and then you know the shape of the frontier (useful!) or you find out that the AI can do more than you think (common!)
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Daniel Green retweeted
how to get a tech job in 2026 - never ever use a job board - dm/email people directly - don't use AI to write the email - build something with the company tech publicly - write about the company publicly - go to conferences and meet people from the company - github doesn't matter anymore - create educational content about the company tech - make sure people recognize you when you apply - develop some social skills - if startup dm the founder - everyone is looking for technical people with distribution - develop a brand - move to the US (SF/NY)
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Google and AI are completely different, and understanding how they work changes how you use them. *To be specific: by “AI” here I mean Large Language Models, or LLMs. Google has technically always been AI – its PageRank algorithm, which decides which links to return to you, has always been a form of AI. And yes, Google is now augmented with LLMs. What we’re comparing in this post is traditional Google (using PageRank) and LLMs.* Lets break down the differences in plain English & answer some of the most commonly asked questions below (so you know when to use which tool): Q: What’s the biggest difference between Google and LLMs like ChatGPT? Google searches. LLMs predict. Google scans the internet and returns the most relevant websites using a ranking system. LLMs don’t search. They learn from massive amounts of text — and then predict what word comes next based on probability. Q: So are LLMs just looking things up? No. They’re word generators. They learn patterns in language, and then generate text that follows those patterns. That’s why they can sound confident — even when they’re wrong. Many - crucially, not all!!! - models today have been augmented with the ability to pull some data from the internet and insert it into its context window (i.e. the input into the LLM, the prediction machine) which reduces the risk of “hallucination,” (i.e. making things up) but even so, the model is still a predictor. It can (and often does) still mess up EVEN with web search added. Q: What’s the biggest tradeoff to watch out for? Hallucinations. LLMs are generators (& not a fact checkers). So they can make things up. That’s why tools like web-browsing and retrieval-augmented generation (RAG) are so important. They anchor generation to real data. But they’re not foolproof. Bottom line: If Google is a librarian, LLMs are improv actors who’ve read the entire library. Sometimes brilliant. Sometimes also wrong. But when you understand *how* they think — you can use them way more effectively. Leave your questions or comments in the section below 🥰👇 & share to spread the word!! This concept is simple and yet so important. You guys rock, sending love to you all!
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Daniel Green retweeted
I made a song for our Kansas City Codex Community event. Featuring (a quote by) @romainhuet and (a mention of) @thsottiaux. Nov 5 · 6–8:30 PM CT · Downtown KC You Can Just Build Things in Kansas City.
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Daniel Green retweeted
Day 19/ Small updates. Calls connect much faster for dots, changing reasoning in codex & work mid conversation does not invalidate cache anymore and a we've also updated the codex cli.
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Daniel Green retweeted
Since the beta, we’ve added new capabilities to the Decisions API so you can now use: • Native GPT-Live integration so voice apps can react as people speak. • Prompt caching to help lower costs when reusing context. • Native JSON inputs so you can pass structured app data directly. • Images in individual question instructions so you can compare multiple images against a shared reference. • Up to 400 questions per request so you can make twice as many decisions in a single call. openai.com/index/decisions-a…
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Daniel Green retweeted
Messi asked ChatGPT to teach him a chess opening that matched his style. It suggested Goat Horns 🤘
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Learn to build evals, folks. Highly recommended reading on using decision models for evals. From our internal evals, System One models like Jev are a great fit for LLM Judges. If you want to go beyond Hamel's great write-up, I've also put together a few resources on applying decision models to LLM judges. (bookmark it) 1) Jev-as-a-Judge for Agent Evaluations - academy.dair.ai/dashboard/re… 2) Jev-as-a-Judge: Evaluating Agents with a Decision Model - academy.dair.ai/labs/jev-as-… Bonus paper I would recommend, with a few other ideas that combine System One and System Two models: Jev-as-a-Judge paper - arxiv.org/abs/2609.26550
New blog post 🎉: How To Use Decision Models For Evals In this post, we show you how to choose a decision model for your task, tune its threshold (don't assume the 0.5 default is good!), and interpret its scores. Lots of cool interactive charts here.
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Daniel Green retweeted
ChatGPT is now the largest medical record sync from hospitals to consumers, passing Apple. I'm glad we can all access our medical records through the apps we choose. Rightly so! We're working with Epic and hospitals to reduce the API load on their servers. The current setup is archaic - the equivalent of refreshing your inbox instead of getting a push when email arrives. FHIR Subscriptions can solve this & reduce strain on the system.
Incredibly, hospitals are now paying for AI companies to disrupt them. 🤦‍♂️ Patients linking their MyChart accounts to @OpenAI's ChatGPT rose more than 7,000% in a single month at 69-hospital @AdvocateHealth, its CIO Bobbie Byrne told @Becker’sHR. Patients set this up themselves in ChatGPT, so that the AI can explain their lab results, scans, and doctors’ notes. You know, instead of one of Advocate's doctors. To add insult to disruption, every time a patient asks ChatGPT a question, Advocate’s Epic system has to look up and send the records and pay for the computing resources that make that happen. AND — brace yourself — Federal law says Advocate can't charge patients, or their apps, for that access to the patient's own records. So a health system is paying for a competitor’s access to its own patients’ records. The competitor takes away the followup visit, and the healthcare system pays for that to happen — both in lost revenue and in increased computing costs. That’s Interpretation Migration, subsidized by the disruptee. buff.ly/DlyODMb #DrYou #InterpretationMigration #Disruption
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👁️ Why would we deceive you? Now live on our YouTube channel and wherever you get your podcasts, watch/listen to our #NYFF64 Q&A with YOU CAN SEE EVERYTHING filmmakers Nathan Fielder and Lance Oppenheim! Lock in: filmlinc.org/nyff2026/daily/…
Tomorrow afternoon....you can see (and hear!) everything about YOU CAN SEE EVERYTHING. Subscribe to our 💻 YouTube channel (youtube.com/filmlinc) and to 🎧 Film at Lincoln Center wherever you get your podcasts to be the first to experience our #NYFF64 conversation between Nathan Fielder, Lance Oppenheim, and @gumyoulike about the film everyone's talking about: YOU CAN SEE EVERYTHING.
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Daniel Green retweeted
You Can Eat Anything (by tommcgovern27 on IG)
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Nathan Fielder is on the cover of WIRED, and the funniest part of the interview is that the reporter seems to have missed the entire point of his movie. 'You Can See Everything' isn't really about whether Elizabeth Holmes is a liar. It's about how easily YOU can be manipulated into believing someone is a liar, a victim, a villain, or a hero depending on how their story is told. And here we have the interviewer doing exactly what the movie is warning us about.
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