Today's Guide to the Marketing Jungle from Social Media Examiner...
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The weekend is almost here, Alluser! Here's a recap of the most important insights, trends, and updates from the week. Catch up in minutes and go into next week prepared.

In today's edition:

An easy fix to improve the views on your Instagram posts
Facebook updates for marketers
A simple way to build complex AI agents
🗞️ Industry news from Facebook, LinkedIn, and more

Strategic Distribution Plan for More Views on Instagram Content

Could the timing of your Instagram Stories be limiting the reach of your feed posts?

Many marketers instinctively share new content to Stories right after publishing, but this common practice could cut your post's momentum short.

A small adjustment in how you sequence content distribution can lead to more visibility in Explore and Recommendations, extending your post's life without changing the content itself.

Jenn Herman shares the timing that makes the difference. Watch more here.

On this week's Social Media Marketing Talk Show, host Jerry Potter breaks down the latest Facebook news and what it means for marketers like you.

Attribution Window Changes Coming in 2026

Starting in 2026, there will be major changes to Facebook's attribution windows. They're completely sunsetting the seven-day view attribution window. You'll need to adjust your reporting and optimization strategies if you've been relying on this specific attribution model to measure your campaign performance.

Additionally, marketers will lose the ability to compare attribution windows directly within the Facebook interface. If you've been using comparisons between different attribution windows to understand your campaign performance, you'll need to find alternative methods or potentially use third-party tools through the API to maintain this capability.

The platform will still maintain several default attribution windows, including one-day click, seven-day click, twenty-eight-day click, one-day engaged view, and one-day view.

Historical Data Limitations

Facebook is also limiting the history of some breakdown metrics. Metrics like unique actions, hourly stats, and frequency value will be restricted to thirteen months at most, with frequency specifically limited to six months of historical data. Other variables have different limitations, so marketers should be aware that they won't be able to go back as far in their history with these metrics starting in January.

This change means that if you rely on year-over-year comparisons or long-term trend analysis using these specific metrics, you'll need to export and store this data externally before the limitations take effect. It's a significant change for marketers who use historical data for strategic planning and performance benchmarking.

The Phase-Out of Automated Ads

Facebook is beginning to phase out automated ads, with full discontinuation planned for 2026. Automated ads were a great idea because you could create them, and they would just do their thing, operating with minimal manual intervention from marketers.

The phase-out is already beginning, and you may already not be able to create new automated ads, though existing ones will continue to run until the full discontinuation. This represents a significant shift in Facebook's advertising strategy, pushing marketers toward more sophisticated campaign management tools and AI-driven optimization features.

New Placement Flexibility for API Users

For marketers using third-party tools through the API for Meta ads, a new feature allows up to five percent of campaign spend on placements that you haven't explicitly selected. This feature is called placement spend flexibility (though as noted in the original content, "that's a tricky one to say").

Essentially, you'll be able to allocate up to five percent of your budget, and if Meta's AI determines that it can improve performance by putting your ads in some of these placements that you did not select, it will be able to do that automatically. This is a way to potentially get a little more out of your ad money.

It's important to note that this feature is not enabled by default. You will need to explicitly give it permission to use this flexibility. So you don't need to worry about it just getting turned on automatically and changing your placement strategy without your knowledge.

The Push Toward Advantage Plus Placements

Overall, Meta is fully recommending that people continue to use Advantage Plus placements for most advertisers because the AI is just getting smarter and smarter. If you're unfamiliar with Advantage Plus, it's making good ads more accessible to more people by using AI to optimize placement and delivery automatically.

This trend toward AI-driven campaign creation and management is already visible across the industry. Google announced a new tool this week that will scan your website and turn it into campaigns in your voice, so we're sure to see similar developments from many more parties as well, including continued evolution from Meta.

More Features, Tools, Changes, and Updates Discussed Include:

New Social Features for Reels
AI-Driven Content Suggestions Within Reels
AI-Powered Content Creation Features
Updates to Facebook Events
Messenger Desktop Discontinuation

Watch it on YouTube

Building AI Agents: A Proven Process

Businesses shouldn't rush to give AI agents full autonomy over sensitive information or critical processes immediately. Many concerns about AI agents going wrong stem from organizations that skipped crucial steps in building trust and reliability before granting autonomy.

Just because tools like AgentKit from OpenAI make it incredibly accessible to build an agent doesn't mean those agents will work properly in real-world applications. The tools are easy to use, but the methodology behind building agents that behave correctly requires substantial work and expertise. The accessibility of agent-building tools has created a dangerous gap between the ease of creating an agent and the difficulty of creating one that actually works reliably.

Together with co-founder Tyler, Sara Davison has established a 4-step systematic methodology for developing AI agents that perform reliably in production environments. Here's a look at step 2.

The Mock-up: Creating a Minimum Viable AI Agent

The challenge many clients face is that AI agents remain somewhat abstract. People understand AI is important, but they struggle to grasp why it matters specifically to their business or how it would actually work in their environment. Showing up with a working mockup solves this problem immediately. Because the available tools have become so accessible, creating a mini MVP to demonstrate concepts is now relatively straightforward.

After completing their discovery work, Sara and Tyler can quickly pull together a simple version of what the final system might look like. They give this to the client with instructions to test it and provide feedback about what works, what doesn't, and what's missing. This hands-on experience becomes the foundation for meaningful feedback.

The mockup reveals crucial information. Does the workflow need more agents because additional specialists need to be involved? Or is the system overcomplicated and trying to do too much? What happens when processes break in ways that weren't anticipated during discovery? Often, testing reveals gaps in the process that weren't apparent during the interview phase.

This creates a circular feedback loop. The mockup generates insights that inform how to improve the agents. The improved agents get tested again, revealing new refinements. This iteration continues until the system reliably handles the real workflow with all its complexity and edge cases.

For their day-to-day mockup testing, Sara and Tyler have found certain tools particularly useful. They use a tool called TypingMind, which functions as a centralized playground for multiple large language models. It connects all the major AI providers like OpenAI and Anthropic in one interface, making it easy to build and test agents quickly using model context protocols. This allows rapid experimentation without committing to any particular platform or provider.

They also work extensively in a tool called Cassidy AI, which they specifically teach in their courses because it balances accessibility with power. Someone without a technical background can learn to build functional agents in Cassidy, while the platform still provides enough capability for sophisticated implementations.

However, Sara emphasizes a critical principle that guides all their work: remain large language model, tool, and platform agnostic. The AI landscape is moving incredibly fast. A business that commits its entire infrastructure and development costs to relying on one tool or platform creates dangerous fragility. When that tool changes, gets discontinued, or gets superseded by something better, the business faces a crisis.

Their systems are designed to allow easy switching between large language models. If OpenAI releases a new version, they can integrate it into their builds and their client builds without rebuilding everything from scratch. This flexibility protects against obsolescence and allows businesses to take advantage of improvements as they emerge.

Both TypingMind and Cassidy function as interfaces that allow developers to choose the underlying large language model on the backend, whether that's Google Gemini, Claude, ChatGPT, or others. They also enable quick mockups that people can interface with directly, without needing their own accounts with specific AI providers. This makes testing with actual users straightforward and removes technical barriers.

Sara stresses that success with AI agents isn't really about the tools themselves. The tools are just plumbing, the infrastructure that makes things work more easily. The real determinant of success is the mental models, frameworks, and approaches you understand and apply. You could use N8N, Make, Copy, or numerous other tools to build functional agentic systems. What matters is understanding the operational workflow and procedures required to make agents work reliably.

Other topics discussed include:

How AI Agents Are Different From AI Assistants
The Benefits of Properly Implemented AI Agents
Step 1: Documenting Workflow and DocGen
Set 3: Deploying AI Agent Teams
Set 4: Establishing Human-in-the-Loop and Evaluation Systems to Ensure Quality

Today's advice provided with insights from Sara Davison, a featured guest on the AI Explored podcast.

Watch the full interview on YouTube

Facebook Lets Private Groups Go Public Without Exposing Member History: Facebook is rolling out a noteworthy update that gives group admins the ability to convert a private group into a public one, while protecting the privacy of long‑standing members. When the change is initiated via the group's settings, existing posts and comments remain visible only to existing members and moderators, and the member list remains hidden from the public. The conversion triggers a three‑day review period during which other admins can cancel the change, and members are notified of the upcoming shift. Once a group becomes public, new content is visible to anyone, helping expand reach, but prior content remains shielded—striking a balance between growth and privacy. Meta

Instagram Adds Competitive Insights Tool for Benchmarking: Instagram has rolled out a new "Competitive Insights" feature for business and creator accounts, allowing users to compare their profile's performance with up to 10 similar accounts. The tool provides comparative data on follower growth, post and Reel frequency, and boosted ad activity—excluding campaigns run through Meta Ads Manager. While it offers helpful benchmarking, it currently lacks deeper engagement or conversion metrics. The feature is accessible under the Professional Dashboard and is being released gradually to eligible users. Sarah Roizman via Threads

LinkedIn Reports Strong Q1 FY26 Momentum & Engagement Gains: LinkedIn, in conjunction with Microsoft's Q1 FY26 earnings announcement, highlighted robust growth and deeper platform engagement. Over the past twelve months, LinkedIn's revenue reached USD 18 billion, up 10% year‑on‑year, while its global membership base approached 1.3 billion. Engagement is rising meaningfully: comment volumes grew 24%, and video uploads have expanded in double‑digit fashion for three straight quarters. LinkedIn

Threads Adds Reply Approval Tool for Post Moderation: Threads has rolled out a new moderation feature allowing users to review and approve replies before they appear on their posts. When enabled via the "Reply options" during post creation, all replies are held for manual approval. Pending replies can be managed through the activity feed or beneath the post, and unapproved ones are sent to an "Ignored" tab. While waiting for review, replies can't be edited but can be deleted. Users replying to such posts are notified that their response requires author approval. Threads

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We publish updates with links for our new posts and content from partners. Your information: Email: tukangpostemel@gmail.com Opted in on: 2021-09-06 17:03:43 UTC.