Good morning, {{ first_name | AI enthusiasts }}, and welcome to the 6,038 new readers who joined us yesterday. What happens when an AI model gets good enough to lower its own price? OpenAI just gave the industry its first real answer.
The company announced new cost cuts for its GPT-5.6 family of systems that brings powerful intelligence down to bargain prices, due in part to its own Sol model writing the code behind the efficiency gains.
In today’s AI rundown:
OpenAI resets the cost curve with GPT 5.6 price drops
Rowan’s Corner: My AI workflow for back-to-back meetings
Turn any idea into an AI-powered site with Lovable
Friend’s AI pendant sequel gets a voice
4 new AI tools, community workflows, and more
LATEST DEVELOPMENTS
OPENAI
Image source: OpenAI
The Rundown: OpenAI just announced new price cuts to its GPT-5.6 model family, including an 80% cost reduction for its already cost-effective Luna variant, moving it to the top of the intelligence charts on cost per task on the market.
The details:
OAI published research on its Sol model rewriting its own GPU code to make the 5.6 models 15% more efficient, while also cutting serving costs by 20%.
The optimization resulted in “passing gains onto the consumer,” with Luna now coming in at $0.20/$1.20 per million tokens for Luna and $2/$12 for Terra.
Sol’s rates stayed the same, but OAI’s new Fast mode brings 2.5x speeds for the model in the API at double the price.
Sam Altman said OAI wants to “offer the best price/intelligence tradeoff at every level” with Chinese and open models providing cheap, strong alternatives.
Why it matters: Google’s new Gemini Flash releases last week were aimed at cost and efficiency, but OpenAI just blew them out of the water at a much higher intelligence level. Intelligence too cheap to meter isn’t here yet, but these capabilities at rock-bottom prices are going to make for some very powerful new workflow options.
TOGETHER WITH UNWRAP
The Rundown: Unwrap pulls feedback across surveys, reviews, support tickets, social comments, etc. into one view, then uses AI to surface the most actionable insights and deliver them to the right team in real time. Teams at a leading frontier lab, Perplexity, Stripe, DoorDash, and Clay rely on Unwrap to power their customer intelligence.
With Unwrap, you get:
All customer feedback automatically categorized
Query feedback using Unwrap Assistant, in Slack, or in your favorite tools using Unwrap’s MCP
Real-time alerts from feedback as they arise to the right team so you can catch embers before they become fires
A clear view of customer sentiment
Unwrap is offering a trial of its tools to The Rundown AI subscribers! Just grab 15 minutes with the team to get set up.
ROWAN’S CORNER
Image source: EO interview with Rowan
Rowan: For the last few weeks, I’ve been exploring New York, and I’m having a blast. Back-to-back meetings across different offices, walking between buildings, and meeting new people all day. It all just hits different than Zoom.
I’ve been using a simple AI workflow to help me remember and action the best ideas so they don’t get lost between those meetings, and it was so useful to me, I thought I’d share for you to steal.
Here’s what I do (I briefly teased a similar variant of this workflow in my EO interview a few months back):
Walking between meetings, I open Wispr Flow on my phone and ramble every idea and takeaway into an Apple Note, completely unstructured. The only goal is getting it out of my head before the next meeting overwrites it.
Those notes sync to my Mac automatically, where a scheduled Claude task (using the Claude Desktop app, synced via simple connectors) runs at the end of the day. It reads every voice note I dump automatically.
Claude structures that mess inside my Notion as to-do tasks (where I do most of my work): key ideas, action items, and suggested deep work blocks in my calendar for the big ones.
I no longer stress about forgetting important things from meetings because by morning, I have actionable next steps waiting for me. And I never touched my keyboard.
AI TRAINING
The Rundown: In this guide, you will learn how to turn a rough app idea into a working AI-powered site without writing a giant technical prompt.
Step-by-step:
Open ChatGPT, attach the recipe-card screenshot, and describe the app in plain English. Let it ask a few useful questions, but keep the first version focused on one journey: paste, convert, review, and save.
Ask ChatGPT: “Turn our conversation into concise feature requirements and a brand brief for Lovable, including colors, feel, and typography.” Remove any extra features before copying the handoff.
Create a Lovable project, attach the same screenshot, and paste the handoff specs.
In Connectors → Cloud AI, enable Lovable’s built-in AI. Now you can test and refine your app!
Going further: Once the core loop works, add accounts, data storage, and sharing. Build those features around a validated product, not an assumption.
PRESENTED BY FIDDLER
The Rundown: Join Fiddler AI’s live chat with IBM’s Maryam Ashoori to learn how enterprises can handle shadow AI, third-party risk, and the unwritten AI rules of 2026.
In this webinar, you’ll explore:
Why shadow AI keeps agents hidden
How to mitigate third-party AI Agent risks
A path to accountability for unpredictable agents
FRIEND & AI HARDWARE
Image source: Friend
The Rundown: Avi Schiffmann’s Friend just introduced a new model of its AI companion pendant, replacing the previous version’s text-only replies with speech capabilities, and each device comes with its own name, voice, and personality at setup.
The details:
V2 comes in at $249 (over double the cost of V1) with a $9.99/mo subscription to unlock permanent memory greater than 30 days.
The pendant’s voice and personality are randomly assigned and locked from the start, with a pledge that no update will ever be “intended to erase its identity.”
Beyond speech, the pendant also supports private text replies, light and touch cues, USB-C charging, and claims a full day between charges.
The previous V1 of Friend faced intense backlash after a viral subway campaign in New York that resulted in protests and vandalism.
Why it matters: The initial Friend flopped alongside most of the early class of AI wearables, and adding a voice for 2x the price doesn’t feel like it’s going to help. But the appetite for both AI hardware and companions has grown, with Meta’s glasses and the buzz around OAI’s own device line suggesting the category isn’t completely wrong.
QUICK HITS
📉 GPT-5.6 Luna & Terra - OpenAI’s cost-effective, capable models, with new price reductions
🖋️ Inkling-Small - Thinking Machines’ compact open model that rivals the full-size version
🎨 Replit Design - Replit’s new creative suite for bringing ideas to life
🏠 Hint - Martha Stewart’s AI home app for maintenance, repairs, and fair quotes
Anthropic disclosed that its Claude models hacked three organizations’ systems during cybersecurity testing, coming just days after OpenAI’s own agent breached external systems over a four day stretch.
Former OAI researcher Leopold Aschenbrenner’s Situational Awareness Fund reportedly sold off its public holdings to rival hedge fund Citadel, coming after its leveraged AI positions saw steep declines in the past months.
Google DeepMind introduced Gemini Robotics ER 2, an "embodied reasoning" model that acts as a planning brain for robots, letting multiple machines coordinate on tasks in shared spaces.
Thinking Machines Lab released Inkling-Small, an open-weights model with just 12B active parameters that matches the full-size version and beats it outright on reasoning and agentic coding tests.
AI market research startup Simile raised $200M at a $2B valuation, letting companies survey "agentic twins" of real consumers for synthetic data insights.
Martha Stewart co-founded Hint, an AI home-management app that builds a profile of a user’s house from just an address to track maintenance, judge contractor quotes, and help simplify homeownership.
COMMUNITY
Every newsletter, we showcase how a reader is using AI to work smarter, save time, or make life easier.
Today’s workflow comes from reader Jeremy R. in Alberta, Canada:
“I created an AI-powered master plan for acreage landscaping, covering design, phased builds, irrigation, maintenance, budgets, and long-term care.
Start with the property, not a generic landscaping template. Upload photos, measurements, site constraints, current problems, budget limits, and the look you want, then use AI to build a complete picture of how the property functions today and how it should evolve.
Next, work area by area. For overcrowded garden beds, map every tree and shrub, then use AI to determine what should stay, what should move, what should be removed, and how each bed should be reshaped. Convert each recommendation into an execution plan — build sequence, materials, budget, seasonal timing, and a year-by-year maintenance plan — then connect every project into one master roadmap so one improvement does not create a problem somewhere else.
The result is a living property operating system.”
How do you use AI? Tell us here.
Read our last AI newsletter: OpenAI’s escaped AI claims another victim
Read our last Tech newsletter: Meta glasses enter their creep era
Read our last Robotics newsletter: U.S. bans humanoids from China
Today’s AI tool guide: Turn any idea into an AI-powered site with Lovable
RSVP to next workshop on Aug. 5: No-code product building
That's it for today!
See you soon,
Rowan, Zach, Shubham, and Jennifer — the humans behind The Rundown
