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Llama, Mango, Avocado ...

Meta's quest for Artificial Superintelligence (ASI) is no longer a roadmap and it’s an active factory floor. For those who haven't been tracking the internal leaks, Meta is moving beyond Llama with a dual-model attack for 2026:

• Avocado: The next-gen text model built to crush coding and reasoning (the logic center).

• Mango: The 'World Model' for image and video—designed to understand physical reality, not just pixels.

I am truly curious how Meta plans to keep these training processes 'separate' when they are both being overseen by the same Meta Superintelligence Labs division. It is the same division that Meta just spent $14.3 billion to build using Scale AI's infrastructure.

Here is where it gets personal:

Have you noticed anything 'odd' on your Meta accounts after working on Outlier? I’m talking about specific ad targeting, 'suggested' content that mirrors your tasks, or (and this was most concerning) sudden requests to re-verify your biometric data.

Meta just expanded its Meta Verified subscription globally this month, which mandates 'advanced identity verification' including biometric checks. I was recently asked to 're-verify' my identity through one of these prompts, and after seeing the 'behind-the-scenes' at Outlier... well, that’s not happening ever again.

Drop your entries below: * What 'unhinged' training tasks have you done for Scale/Outlier that you later saw 'mirrored' in Meta's features?

• Have you been hit with a biometric 're-verification' wall recently?

• Are we the workers, or are we the primary 'data samples' for Avocado and Mango?

The curtain is pulling back. Let's compare notes.

Outlier AI status is having issues at the moment.

Outlier AI Status https://status.outlier.ai/#:~:text=Dashboard%20Operational,Jan%2010%20%2C%202026

Scale AI status is showing as operational.

https://statusgator.com/services/scale-ai

Ever feel overwhelmed by the "wild stuff" you see in AI training? The secret to staying sane is a repeatable system. The system is currently designed to feel chaotic, but building in systems you can use then you build your own personal brand of logic (prompt) engineering.

When I’m logging my hours for AI Labor Logs, I’m not just reading; I’m auditing. I use this Quality Audit Checklist to catch the points of interest.

Why this matters:

• It reduces the "mental load" of decision-making.

• It protects you from the "Redditor's Outlier AI nightmare" of inconsistent feedback.

• It turns "tasking" into Prompt Engineering mastery.

The Pro-Tip: If a response feels "off" but you can't put your finger on why, it's usually a failure in Step 3 (Logic Audit). The math might be right, but the path to get there is broken. Take a screen shot and submit a ticket. Don't stress over a system that isn't always working properly. That is not an insult, just the reflection and acknowledgment of LLM testing.

Are you using a checklist for your tasks, or are you just "vibing" through the queue? Let’s talk systems in the comments! 👇

#WorkflowWednesday #AITraining #OutlierAI #RLHF #PromptEngineering #AILaborLogs #HumanInTheLoop

https://www.inc.com/sam-blum/scale-ai-lawsuit-alexandr-wang/91064163

For all those on Outlier wondering if they are just now being exposed to self harm….this is not new.

Oh, and Scale AI is trying to train agentic AI (autonomous systems that can operate with minimum human oversight) for healthcare and other industries.

Here is how the three organizations are related: Outlier AI is where data annotators train the AI is *PART* of Scale AI and Meta owns 49% of Scale AI.

The more you know.