Most social media tips are completely out of date, because social media works very differently these days.

Whereas in the past the main focus was on building up a large following so that your posts would have the widest possible reach, that’s no longer as relevant today. The platforms have changed their algorithms, and we publishers need to adapt to that.

We now manage our social media according to these rules:

Rule 1: A large number of followers no longer guarantees reach

These days, the platforms no longer decide based on who follows you, but on how interesting they deem a post to be.

This means: someone who clicked ‘follow’ two years ago might never see your next post. On the other hand, completely unknown users might see your post if the algorithm thinks it could be of interest to them.

So your followers no longer determine your reach, because that’s determined by the algorithm.

Rule 2: Don’t focus on gaining new followers, but on sharing.

(for me, this is the biggest shift in perspective)

Before I publish anything today, I ask myself three questions:

• Would anyone forward this post to a colleague?
• Would anyone save it to come back to later?
• Would it really make someone stop scrolling because it’s useful?

If I answer ‘no’ to all three questions, the post won’t stand out from the background noise. It’s simply too thin.

Here’s what you can do to make it stand out from other posts:

1. A strong hook. Everyone immediately scrolls past ‘Here are three marketing tips’.

‘Most people are optimising for the wrong metric’ is much more likely to pique curiosity.

2. Something worth saving. Checklists, frameworks or step-by-step guides.

Ask yourself: Would anyone search for this post again next month?

3. Content that people enjoy sharing. Informative posts and surprising insights go viral. Nobody shares pure advertising.

4. Your own perspective. You don’t need a controversial ‘hot take’, but you do need a point of view. Don’t just explain what works, but also why.

Rule 3: Every post starts from scratch, no matter how successful previous posts were. Because just because one post did well once doesn’t mean the next one will automatically be successful.

Every single post has to earn its reach all over again.

Copying viral posts isn’t the best tip either, by the way: Posts go viral because they contain something that hasn’t been done before. A copycat version is unlikely to replicate that ‘wow’ factor.

Rule 4: Stop chasing vanity metrics

Followers are easy to measure, but they’re usually the least meaningful metric.

More interesting, on the other hand, are:

• Reach amongst non-followers
• Saved posts
• Shared posts
• Profile views
• Website clicks or leads

A post that reaches 20,000 new people and generates five qualified leads is better than one that generates 300 new followers but has no impact on the business.

Rule 5: Build trust before you try to sell

Most unknown brands face the same problem: they don’t have the trust needed for customers to buy directly from them. It’s therefore advisable to ensure that users have seen helpful content from you time and again.

And another thing: the brands growing fastest today don’t constantly talk about themselves. They explain, help and share their knowledge. By the time someone actually wants to buy, they’re already top of mind.

In a nutshell

It used to be: Build a large community and they’ll see your content.

Today it’s: Create content that people stick with, rather than just scrolling past. The algorithm helps them find it.

Followers are no longer the goal; they’re the result of your content being shared regularly.

I’d be interested to know: Which metrics do you track on organic social channels? Do you feel that metrics such as saves and shares are now more important than new followers? How do you measure the business impact of social media?

Most AI-generated content fails for the same reason most human-written content fails: nothing changes from one paragraph to the next. It explains, covers and ticks boxes. Resulting in the reader quietly leaving before the end.

AI is extremely good at producing complete, thorough, well-structured content. But that's exactly the trap. "Complete" and "thorough" is not the same as "compelling". A piece that answers every question upfront leaves the reader with no reason to keep going. Curiosity needs a gap to survive. Front-load the answer and the gap closes immediately.

The real issue is what people put into the prompt. "Write a blog post about X" gets you a well structured summary of what is already out there. That's what the model optimizes for: coverage. If you want content that actually moves people, the brief has to force movement. What's unresolved at the start? What shifts by the end? What does the reader believe at paragraph one that they should question by paragraph three?

These are storytelling instructions. And most AI prompts skip them entirely.

The output is only as dynamic as the input. Give the model a static brief and you will get static content. Give it a brief built around tension and progress and the output changes noticeably.

What's the one thing you always include in a content brief that most people leave out?

Paraguay. Paraguay. Ranked 31 places below Germany. A team that, by most pre-tournament logic, had no business still being in this competition after 120 minutes against us.

And yet. Here we are. Tuesday morning in Germany, strong coffee, no football left to look forward to.

We are a German company. We pride ourselves on precision, reliability, and getting things done. Apparently none of that made it onto the pitch in Foxborough yesterday.

The Tah goal in the 101st minute was right there. The ball was in the net. People were celebrating. And then VAR did what VAR does, which is exist specifically to make football fans question their life choices. Waldemar Anton brushed a keeper and suddenly the tournament is over.

Genuinely though, Orlando Gill in goal for Paraguay was exceptional. Credit where it's due. the man saved everything that came near him and carried his country into the Round of 16 almost single-handedly.

Now the real question, since we clearly have nothing left to root for and need something to do with all this nervous energy:

Who wins this thing?

France are the favourites at +270. Argentina at +400. Paraguay apparently beats anyone now so who knows anymore.

Drop your pick below. we are a broken people and we need something to believe in.

Here's the thing I keep running into. Half my feed is people arguing about which model won this week. The other half quietly swapped to a free open-weight model months ago and built something around it that the benchmark crowd can't touch.

The benchmark is a snapshot of one model on one task. What actually compounds is the system you wrap around it.

We learned this the boring way at the ai agents company I work for. Our early agents were basically good prompts. Vee for brand voice, Ines for Instagram, a few others. They worked until they didn't, and the moment we scaled, the same prompt produced generic output for every customer because a prompt knows nothing about who it's working for. No amount of prompt polishing fixes that.

The model is the cheap part now

This is where the open-weight shift actually bites. When a free model running 300 agents in parallel beats a paid model 5x its price on real research work, the smart money stops asking "which model" and starts asking "what runs on top of it."

Three questions matter more than the leaderboard:

How many runs can you afford to throw away? Cheap tokens change what you attempt.

Who checks the output? An open swarm is confident and under-cites, so we put one strong model at a single verify gate whose only job is to refute the work, never to praise it.

Does run fifty know anything run one didn't? If every run starts from zero, you have a prompt wearing a costume. A real loop saves what worked as a reusable skill and bakes every caught mistake into a constraints file the next run reads automatically.

That last one is the whole game. The model doesn't retrain between your runs. Your skill library grows. A competitor can clone your prompt in a minute, they can't clone six months of your real runs.

Where I'll admit I'm wrong-ish

Benchmarks aren't useless. If a new open model genuinely jumps a tier on reasoning, that flows straight into the loop and every run gets better for free. So I do watch them. I just stopped treating the top line as the decision.

And the open-weight stuff is rough in real use. Ours over-cites, contradicts itself across sub-agents now and then, and needs that verify gate babysitting it. The gate costs real tokens and it's the part of the setup I'm least happy about.

So genuine question for the people deep in this: is anyone running a fully open-weight loop, verify model included, with no paid model anywhere in the chain? Every setup I trust still has one expensive model guarding the gate, and I want to know if that's actually droppable yet or if we're all quietly paying the same tax.

For people in content/marketing/freelance writing — how do you keep writing style consistent across multiple clients or brands?

Do you rely on style guides, past examples, or just adapt over time? And in teams, how do writers + editors stay aligned?

Also curious:

• What part of this workflow is the most time-consuming or frustrating?
• Does consistency break down when you scale to more clients?

Would love to hear how this works in real setups.

Been noticing a big shift in how brands approach geo and aeo in 2026. it’s not just about ranking on google anymore, it’s more about how often you appear in ai answers and how visible your content is across different discovery channels.

what’s interesting is how marketing strategy tools are now being used way beyond traditional seo. instead of only tracking keywords, brands are starting to look at full funnel visibility, including how users discover them through ai-generated answers, comparison summaries, and indirect referrals.

most teams are also reworking their content structure. instead of long keyword-heavy pages, they’re focusing more on clear explanations, direct answers, updated data, and topic authority. the goal is basically to make content easy for both humans and ai systems to interpret and reuse.

another big change is how performance is being measured. website engagement tracking and broader traffic pattern analysis are becoming more important than just rankings. brands want to understand not only if they rank, but if they’re actually being surfaced inside ai-driven search experiences.

i also noticed a lot of companies now combine multiple marketing strategy tools instead of relying on just one platform. they’re trying to connect visibility data, audience behavior, and content performance into a single view so they can adjust faster.

overall it feels like geo and aeo are pushing everyone toward more adaptive content strategies instead of static seo playbooks.

curious how other people here are adapting their workflows for this shift in 2026.

We’re naturally talking to a lot of marketers on a daily basis and the same sentence keeps coming up:

>

And I don’t think it’s just “the algorithm”.

A few things seem to be happening at the same time:

• feeds are massively overcrowded
• AI made content production easier
• >meaning everyone publishes more
• users scroll faster and trust less
• platforms reward interaction, not just posting
• a lot of brand content starts sounding weirdly similar

Especially on LinkedIn and Instagram, I often feel like I’ve already read the post before after the first 2 lines. One thing we noticed internally:

A lot of brands still think social media = content production problem.

So their main question is: “How do we post more?”

But the better question in 2026 might be: “How do we create posts people actually react to?”

Quite a big difference, no?

Ironically, AI is both helping and hurting here. Helping in terms of faster ideation, easier repurposing, easier testing

Hurting beceause of the generic tone, endless filler content, “safe” opinions, content volume explosion

The result of this is, that the attention becomes even more selective.

What’s been more effective for us on different channels recently is: reacting to existing conversations instead of posting into the void, more specific observations instead of generic advice, showing unfinished thinking (like here - haha) instead of polished thought leadership, adapting content per platform instead of copy-pasting everywhere, using AI as a sparring partner, not as the final writer

And this is where agents start becoming interesting in my opinion. In a way where they are helping marketers work more efficiently due to: monitoring conversations, spotting recurring pain points, adapting angles per platform, helping teams test more variations faster

What are your social media engagement numbers like these days?
And do you think AI is improving social content overall… or making feeds worse?

Over the last months I've been deep in one problem: why does most AI-generated content still sound like AI wrote it?

I've tested voice extraction workflows, multi-agent pipelines, prompt compression techniques and production setups to close that gap. Some of it worked. Some of it was a waste of time. The stuff that actually works in production looks very different from what gets likes on Twitter.

A recent post about our 100-question voice extraction process got way more traction than expected. So I'm doing an AMA.

What I can talk about:

• voice and tone extraction at scale
• multi-agent workflows vs. single-chat setups
• prompt compression and what it does to output quality
• the line between "sounds human" and "sounds like me specifically"
• mistakes I've made building these systems
• where AI content is actually heading in 2026

I'll start answering questions on: Wednesday, May 20 · 8 AM PT / 11 AM ET / 5 PM CET Drop your questions below.

https://preview.redd.it/ssuilmqo7w1h1.png?width=2048&format=png&auto=webp&s=d3eb3c952fde2daa87ded74bdb20f445b85a7236

For transparency: I work on the AI agent team at whaaat ai. We build AI marketing agents, but this post is about something I built for myself that has nothing to do with marketing.

Every morning I used to spend 15-20 minutes before doing any real work. Open Gmail, scan for urgent messages. Open Todoist, check priorities. Open Google Calendar, see what meetings are coming. Open Slack, see if anything blew up overnight. Open Stripe, check if revenue moved. That's five apps, five logins, five context switches before my actual workday even started.

A few weeks ago I found a way to compress all of that into a single screen that takes 2 minutes to check. And this was built with no code, no deployment, no monthly subscription.

What I built

A persistent dashboard inside Claude's Cowork environment. It connects to your existing tools (email, task manager, calendar, payment processor) through API connectors and pulls live data every time you open it. One screen, everything visible at a glance.

The setup took under 5 minutes: connect the data sources, describe what you want in plain language, let Claude build the HTML dashboard. After that it's persistent. Opens instantly, refreshes on demand.

Why this works better than most "dashboard" solutions

Most dashboard tools (Notion dashboards, custom Retool builds, Geckoboard) require either ongoing maintenance or a monthly fee. This approach has two advantages: the dashboard is built once and costs nothing to maintain. It pulls from the same tools you already use without requiring any data migration.

The other thing I noticed: it changed how I start my day. Instead of bouncing between apps and losing focus, I open one thing, see everything, decide what matters and start working. It sounds small. Over a month it adds up to roughly 6-7 hours of reclaimed time.

What it actually shows

My setup has four sections: unanswered emails sorted by date on the left, tasks sorted by priority on the right, calendar timeline across the top and a small Stripe revenue number in the corner. You can customize this to whatever matters for your business.

The key insight that made me choose a static dashboard over a daily AI summary: running an AI agent every morning to compile a report burns through your API budget fast. A dashboard that was built once and only fetches data when you open it is essentially free after the initial build. Same information, fraction of the cost.

If you want to try this

You need Claude with Cowork access and the connectors for your tools (Gmail and Google Calendar are built in, others like Todoist have their own MCP integrations you can add in a minute). Describe your ideal morning dashboard in plain language. Claude builds it. Done.

The question I keep coming back to: how much time does your team spend every day on "checking things" before actually doing things? For us it was close to an hour per person when you add it all up. Now it's under 5 minutes.

Curious if anyone else has found good ways to reduce morning admin time.

Disclaimer: This dashboard setup is unrelated to whaaat ai's product.