😺Who is Llama 4 for?

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Here’s what you need to know about AI today:

• We recap Llama 4’s hairy launch.
• Google paid AI staff to prevent them from joining rivals.
• Shopify’s leaked memo shows it wants AI over human hiring.
• US gov required federal agencies to appoint AI officers ASAP.

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Llama 4 is here—but who is it actually for?

Meta dropped its highly anticipated Llama 4 models over the weekend, and let's just say the rollout was… messy.

Catch me up: wtf is Llama? Llama is Meta's AI model series (like OpenAI's ChatGPT or Google's Gemini), but special because Meta releases the "blueprints" (open weights) for researchers and companies to build upon.

Meta released 2 of 4 total planned models, Scout and Maverick, (the smaller of the “herd”), with “Behemoth” coming soon. Anyone else get a kick out of imagining a giant Llama named Behemoth? Because we sure do!

That said, the surprise weekend launch seemed to catch cloud providers off guard and lacked the usual detailed research paper to go with it, leaving many in the AI community scratching their heads.

Issues arose:

• The Saturday release raised suspicions about Meta's motives—to bury bad news, or front-run competitors?
• Maverick ranked #2 on LM Arena leaderboard, but using an unreleased version, not the public model.
• Real-world tests showed Maverick failing coding tasks (16% on the aider polyglot benchmark).
• Unverified claims of “training on test data” circulated, which Meta VP Ahmad Al-Dahle categorically denied, blaming the mixed results on implementation bugs.

Practical challenges abound, too:

• These models require serious hardware—Scout (the smallest) needs 96GB RAM for a 4-bit version on Mac, so you won’t be running it on your gaming PC.
• The advertised 10M token context window is practically speaking much smaller.
• Accessing the full context window on a dedicated deployment is doable, but it’ll cost something like 2-4x more than limited-context deployments.

So, who is Llama 4 for? It seems Meta is targeting three main groups:

1. Cloud users seeking more efficient (cheaper) dedicated AI than competitors like DeepSeek (once optimized).
2. Companies (with under 700M users) wanting to self-host powerful models on their own beefy servers.
3. Existing Llama 3 users in the above categories looking for an upgrade (once things stabilize).

Then again, probably all three of those groups are waiting to get their hands on Behemoth before they jump in…

Our take: Growing pains, am I right? I think we can say the rumors of Llama 4’s death are premature… but the truth is we probably need to give providers more time to optimize, and Meta more time to fix the kinks, and maybe release smaller, more accessible versions like they did with Llama 3 (we're hoping!).

The question is: does Meta actually have time? DeepSeek R2 is right around the corner, new open Chinese models are dropping all the time, and the stock market is in free fall.

With this rocky launch, Meta's bet on sheer scale might be hitting a wall just as nimbler competitors are finding smarter ways forward—and right when investors are demanding practical results (not just bigger models).

We wrote a longer piece on this on the website, which you can check out here.

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Prompt Tip of the Day

Two tips today because apparently yesterday’s got deleted.

First tip:

Here’s the original source of the more natural sounding writing prompt from yesterday. We also added it here along with another related prompt (in case it gets deleted again). Someone shared this, too, which is handy to add:

Put these prompts in your project / system / custom instructions when working with Claude, Gemini, or ChatGPT so you don’t have to remember to include it every time.

In Claude, you could also use these as part of your personal Claude “style” prompt.

Second tip:

Here’s a framework (and prompt) that lets ChatGPT remember everything about you: first you collect all memory entries in one chat, label them by theme, erase outdated info, archive the clean version, and refresh by pasting into a new chat.

Someone already wrote a 16 min read blog about how they applied this techniques to build a second brain for working with AI.

Oh, and just in case this post also gets deleted, here’s just the prompt to be safe.

Treats To Try.

1. *This free guide from Section outlines the 4 most crucial steps to building an AI-ready culture within your organization and successfully deploying new AI initiatives. Get the AI Readiness checklist here.
2. Google Search AI Mode is now multi-modal, meaning you can search what you see by uploading images along with questions and it’ll respond with links.
3. Amazon’s Nova Reel 1.1 turns your text prompts into 2-minute videos with consistent style across shots w/ pay-as-you-go pricing —read more (video)
4. EverTutor helps you master subjects through personalized voice tutoring that adapts to your mood + learning style with real-time feedback—free.
5. Amurex is a Chrome extension (code) that connects your existing tools to search all your knowledge, automate meeting notes, and organize emails without downloading anything—free to try (love their launch video).
6. Refgrow lets you add an affiliate program to your SaaS product, keeping users inside your tool—free trial, then $9/month.
7. Mergify merges your multiple Spotify playlists, removes duplicates, and syncs directly back to Spotify for you to listen to—free.
8. Databutton pairs you with an AI developer that turns your vision into working software by planning, coding, and deploying your apps—starting at $20/month.

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Around the Horn.

• Google is supposedly pulling a Google and paying AI staff to do nothing instead of work for rivals.
• Shopify now expects its employees to hire an AI before they hire a human—if we kick into a recession, get ready for a lot more messaging like this.
• Google announced Sec-Gemini v1, which combines Gemini's reasoning capabilities with real-time cybersecurity knowledge to help security professionals (ask for early access here).
• The US government ordered federal agencies to appoint new “chief AI officers” and expand their AI strategies within six months.

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Tuesday Tube

We can’t possibly recommend this channel (AI Explained) highly enough. Their first point on a stock market crash delaying AI progress is spot on—and more or less the #1 reason why the AI 2027 report might take a bit longer than three years to play out (which is, on net, probably good??).

A Cat's Commentary.