
AI Enablement & Implementation Strategy | @AICollectiveCo Kansas City Lead | @OpenAIDevs Codex Ambassador
Kansas City, MO
Joined February 2009
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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):

ALT In the case of GPT-6.1 Terra, I'd say Terra is probably better than Astra and Sol at technical communication, similar on vision and computer use (while being faster / cheaper), and a bit worse on most other things. For API developers, it's a nice midrange option if you want something cheaper than Sol but a lot smarter than Luna (especially for vision / computer use / communication). For subscribers, it's not a bombshell release - if you're hitting usage limits, maybe it helps you go a bit further, and if you want clearer communication or faster computer use, it can be a nice option to select.
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.
Daniel Green retweeted
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…
Between shipping Codex updates and resetting limits, @thsottiaux somehow hasn't found time to upload an X banner.
So I made him one. 🚢
Terra-fying in time for Halloween 2026🎃
Replying to @OpenAI
GPT-6.1 Terra is rolling out now and is available to all Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.
Developers can also access it through the OpenAI API as gpt-6.1-terra.
openai.com/index/introducing…
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
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
I've heard tomorrow's episode of @AICollectiveCo live podcast is rumored to be the best one yet...
The AI Collective Podcast with AJ & Catherine xitter.cf/i/broadcasts/1yKAPwLYm…
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!)
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)
Daniel Green retweeted
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!
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.
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.
woah(!)
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.
block.xyz/dessa
What if OpenAI dots were IRL?
This is Build-a-Dot workshop made with Codex & 💚.
buildadot.daniel-green.chatg…
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…
TIL ⏱️
It's not a new feature, but everyone I've shown this Apple Watch tip to has fallen in love with it. It's a great, subtle way to check you're not running late for a meeting while staying focused on a conversation. support.apple.com/guide/watc…

















