{{first_name | Leader}}, welcome back.
The next frontier for AI looks a lot like memory. Models are getting better at holding context, understanding visuals, and acting on larger streams of information.
These are today’s updates.
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Claude adds 1M token context window
Nvidia launches NemoClaw agent platform
Memories.ai builds visual memory AI
Tools, resources, and a prompt to build the adoption fix framework. ⬇️
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Anthropic made the 1 million token context window generally available for Claude Opus 4.6 and Sonnet 4.6, now included at standard pricing. Opus is priced at $5 input and $25 output per million tokens, while Sonnet is $3 and $15. The expanded window supports up to 600 images or PDF pages per request, up from 100 previously.
On long-context recall benchmarks like MRCR v2, Opus 4.6 scored 78.3%, among the highest reported for frontier models. The larger context lets teams process full codebases, thousands of contract pages, or extensive research material in one session without summarization.
Startup Memories.ai, spun out from Meta’s Ray-Ban AI glasses team, is developing infrastructure that lets AI systems store and recall visual experiences. Its Large Visual Memory Model (LVMM) indexes and retrieves information from unstructured video data rather than text.
The company raised $16 million and released LVMM v2 after its initial launch in 2025. It uses custom LUCI wearable devices to collect video data for training and is working with Qualcomm on on-device processing later this year.
Nvidia introduced NemoClaw, an enterprise platform built on the open-source OpenClaw framework for developing and deploying AI agents. The system integrates with Nvidia’s NeMo tools and NemoTron models and can run cloud models on local infrastructure.
The platform adds enterprise features like security controls, privacy safeguards, and on-prem deployment options. Currently in early alpha for developers, Nvidia describes NemoClaw as a foundation layer for building and managing AI agent systems at scale.
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Useful Resources
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What Perplexity’s AI computer could look like and cost.
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Why some founders leave $100M ARR companies to build AI startups.
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💰 Funding
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The Adoption Fix Framework
When to use this?
When something has launched, but usage, engagement, or compliance is lower than expected.
I’ll describe an initiative that has already launched but isn’t being adopted as expected.
Build an Adoption Fix Framework covering:
Friction Analysis
Where users are dropping off
What feels hard or unnecessary
Incentive Analysis
Why teams may not care
Misaligned KPIs or rewards
Leadership Signals
Where leadership behavior contradicts the initiative
Immediate Interventions
3 actions that can increase adoption in 30 days
Keep it practical and enterprise-ready.
Initiative + current reality: [describe clearly]P.S. Get more such prompts in the Prompting Playbook (free for you)
Stay curious, {{first_name | leaders}}
PS. If you missed yesterday’s issue, you can find it here.