@MinionLabAI

Contribute to the Future of AI with MinionLab!

linktr.ee/minionlab
Joined December 2022
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We spent a decade reverse-engineering the feed. Now the feed wants a prompt. Facebook is rolling out plain-language controls for what people see in Feed and Reels. A reminder for social teams: your feed is a sample of one.
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Meta just bought AI analytics startup Sundial. AI can make data easier to query. But in social listening, what counts as a "mention": a post, a reply, or a review? Change the unit, and the trend can change with it. Before trusting the chart, check what it counted.
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X says its retrieval model is trained to surface posts people finish reading. Yet many brand dashboards still open with impressions. That's an instrumentation mismatch. Views tell you a post traveled. Replies can reveal what people questioned, challenged, or wanted next.
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AI search is getting a byline. Google is testing clickable creator names in AI Mode video results. One click leads to a profile of their broader work. For brands, that could shift the question from “Who gets reach?” to “Whose work can we stand behind?”
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The AI buildout has two conversations. The industry talks about compute and scale. Nearby communities talk about water, electricity costs, noise and jobs. If you only listen to the first group, you miss the objections that can shape whether a project moves forward.
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AI shopping has a new checkout question: Did the agent buy what you meant, or what it inferred? Amex just published a framework for preserving a customer's authorized intent before an agent transacts. The next conversion bottleneck may be permission, not recommendation.
🤖 Made with AI
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One viral post can fill a dashboard. It can also distract from the useful signal. When the spike fades, which question keeps coming back in the comments? AI can draft the next 100 posts. Social listening helps you decide which one is worth publishing.
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Step 0 of every web agent eval: did the browser actually open the page? AgentGo did in this run. The browser environment deserves a line in the eval, right next to the model name. #AIAgents #OpenAIDot #MuseAI
AgentGo
@AgentGo_BrowserSep 30
We ran the same page through OpenAI dot and AgentGo. alibabacloud.com In this run, dot did not open it. AgentGo loaded it. What changed was the browser session, not the model. Try it from the walkthrough above. #AgentGo #AIAgents #OpenAIdot
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Muse signup complete inside AgentGo’s live browser
AgentGo
@AgentGo_BrowserSep 26
Try Meta's Muse AI with AgentGo: AgentGo helps you sign up for Meta's Muse AI. Get started: agentgo.live Muse is Meta's personal AI agent, with its own computer and browser for travel planning, form filling and multi-step web tasks. Watch our demo to see Muse signup completed inside AgentGo's live browser, with a human handling verification and onboarding. Create your AgentGo account and try it yourself. #Muse #AIAgents
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Replying to @allstream460283
Muse has its own browser, so our friends at AgentGo opened it inside theirs and made recursion a product demo. A human handled onboarding; the Minions are still looking for the base case.
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MinionLab retweeted
AgentGo@allstream460283Sep 26
We opened Muse in AgentGo's live browser and had a human handle onboarding, because even AI assistants come with paperwork. cloud.browser-use, consider this bring-your-agent-to-work day. #BrowserAutomation
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AI does not give marketing teams memory. It gives them speed. Without a record of what customers said, which claim failed, or why sentiment shifted, automation repeats old mistakes faster. The next marketing moat is not more content. It is learning that survives the campaign.
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Translation is not understanding. About 3.4B people speak languages underrepresented in today’s AI. A system can translate words and still miss sarcasm, slang, or buying intent. Global AI cannot flatten markets into English. Language is access. Context is intelligence.
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AI has an averages problem. An agent can score 77.4% across repeated runs, yet succeed every time on only 53% of tasks. Social data has the same trap: one sentiment score can hide platform splits and recurring objections. The average looks clean. The variance tells the truth.
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AI companies are watching model risk. They should also watch backlash risk. Because adoption can slow down before the tech fails: users lose trust employees push back regulators notice the noise brands avoid association The feed is becoming part of the AI risk surface.
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AI is no longer one narrative. Safety teams are asking for brakes. Investors are still buying the buildout. Governments are debating control. Users are deciding what they trust. The next advantage is not just tracking sentiment. It is seeing which narratives are splitting.
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In search, brands learned to separate paid traffic from organic ranking. AI assistants will need the same distinction: paid placement vs. earned evidence If an assistant explains your category without an ad, what public signals does it use to describe you?
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SEO measured where your page ranked. Social listening measured what people said. AI Visibility asks a different question: When an assistant explains your category, does your brand show up with the right evidence? That evidence is built in public, long before the buyer asks.
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The next ad channel may not look like a feed. It may look like an AI assistant answering a question. Search intent. Social context. Reviews. Comments. Public comparisons. Brand visibility is becoming less about one page ranking, and more about what the internet can prove about you.
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AI shopping is changing the shelf. A buyer may skip your site and ask an assistant for a shortlist. That shortlist is shaped by reviews, videos, comments, and public comparisons. Social Insight helps brands monitor what feeds the answer, not just what ranks in search.
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