Good morning, {{ first_name | AI enthusiasts }}. Google just added three more models to the Gemini family. Somehow, that made the missing one even harder to ignore.

A new Flash trio covers speed, low-cost use, and cybersecurity, and the company’s “most ambitious pre-training run yet” for Gemini 4 is already in progress. But the delayed 3.5 Pro model expected to challenge frontier rivals? Still nowhere to be found.

Reminder — Our next live workshop is today at 12 PM EST! Join and learn how to go from idea to working product without a technical team using AI tools for research, prototyping, iteration, and more. RSVP here.

The Rundown: Google just released a trio of Gemini models, led by 3.6 Flash, alongside a speed-focused 3.5 Flash-Lite and a security-fine-tuned 3.5 Flash Cyber, prioritizing efficiency over intelligence, as the long-awaited 3.5 Pro remains in testing.

Why it matters: While a lot of people use Gemini models, which makes efficiency upgrades useful, these scores and the absence of a strong frontier model only add to the perception that Google is falling behind (with even Elon chiming in to take a jab). A lot is riding on the company’s 3.5 Pro and its 4 class that is now in training.

The Rundown: Unwrap is a customer intelligence platform that pulls your feedback (surveys, reviews, support tickets, social comments) into one view, using AI and NLP to surface actionable insights and deliver them straight to your inbox.

The Rundown: Anthropic secured court approval for a $1.5B settlement with book authors, clearing payouts of nearly $3,000 a title across 482K works pulled from piracy sites — a record for U.S. copyright cases that leaves AI training standing as fair use.

Why it matters: $1.5B sounds like a lot until you weigh it against a trial with potential damages in the hundreds of billions, and a fair-use ruling that now goes untouched while Anthropic keeps training on (legally purchased) books. With other labs facing similar suits, $3,000 a work may be the new rate for making copyright issues disappear.

The Rundown: In this guide, you will learn how to turn your AI knowledge into a focused consulting offer. Using Claude, you will choose a narrow problem, collect client context, and create a one-page opportunity brief leading to an implementation project.

Pro tip: Before the audit, have Claude research the best tools, templates, and examples for your niche, and keep that library ready to match against bottlenecks.

The Rundown: Bridging the gap between a multimodal sandbox demo and a scalable product is a massive engineering challenge. The Startup technical guide on generative media provides the architectural blueprint to actually build and deploy your ideas.

Inside the guide, you’ll learn how to:

The Rundown: Poolside launched Laguna S 2.1, a new open-weights coding model that tops the Western open-source field while staying small enough to live on a single desktop — the strongest sign of America’s response to China's open-model lead.

Why it matters: TML's Inkling made self-hosted coding AI more interesting for U.S. buyers last week, and Laguna now adds a smaller option with even stronger results. Two releases don’t erase China's deep lead (and Kimi is still lapping the field), but they at least show that American open-source has momentum instead of just a gap.

Mozilla released its 2026 State of Open Source AI Report last week, revealing open models now rival Big Tech's best. Learn more.*

Twitter and Block founder Jack Dorsey unveiled Buzz in early access, an open-source workspace where AI agents join team chats as coworkers.

Substack introduced a new partnership with Pangram that embeds its AI writing detector tool into the platform for both readers and publishers.

Anthropic rolled out Record a skill, a feature (similar to Codex’s upgrade) that allows users to screen-record a workflow to let Claude create a skill around the task.

Sakana AI released Fugu-Cyber, a security model that bundles specialist agents behind one API, claiming benchmarks on par with GPT-5.5-Cyber and Mythos Preview.

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 Jonathan in Waxhaw, NC:

“I'm writing a novel. Asking AI 'is it good?' receives flattering positive feedback — annoying.

I've developed an evaluation protocol which prompts multiple AIs to assess the material through market research simulation, from four perspectives: reader acceptance, publication acceptance, production acceptance, and marketability. The protocol instructs the AI to generate four personas — some antagonistic, some YA-biased, some literature aficionados, and some business-biased.

I've input prose and pitch decks, and results have identified gaps, inconsistencies, and positives. Running across multiple AIs gives results that differ but frequently identify the same strengths and weaknesses. These results are LLM training-biased, but different draft revisions and comparing across AIs gives valuable feedback.”

How do you use AI? Tell us here.

Rowan, Zach, Shubham, and Jennifer — the humans behind The Rundown