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Tip: Boost traffic and conversions with a newsjacking strategy

Source: Growth Bites

If you're quick enough, there's an opportunity in breaking news. Bring in more backlinks, traffic, and conversions by offering your spin on industry news before journalists publish anything.

Newsjacking is when a brand takes advantage of breaking news by injecting its own content into the story. It’s a powerful way to reach a bigger audience. Carrie Rose of Rise at Seven boosted organic traffic by 329% and organic conversions by 53% for a client within two months, primarily through newsjacking. Timing is key. You need to get your thoughts out before journalists start scrambling to find additional information for their articles. Carrie’s rule of thumb is to get something out within 40 minutes. And they were sending at least three per day to national news outlets. Luckily, it doesn’t have to be anything much — a brief but valuable comment is enough. So create Google (and other) alerts within your industry and put together a calendar of upcoming events that might be worthy of a comment. From there, you can take Carrie’s lead and send your commentary to journalists and publications. Or, if you have a decent audience already, you can simply ride the wave by posting on social media and your blog.

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AI naming is a mess. Can someone fix It?

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Introduction

Every time I try to pick an AI model, I feel like I’m cracking a secret code. Do I go with GPT-4o or o3-mini-high? Claude 3.7 Sonnet or Claude 3.5 Haiku? And what exactly is a “Gemini 1.5 Flash” supposed to do?

Apparently, I’m not the only one confused. Julian Lehr, a writer at Linear, tweeted: “There’s a massive opportunity for a branding agency that specializes in AI model names.” Designer Sam Henri Gold added: “Naming things is famously hard, but did AI companies even try?”

Why are AI model names so bad?

AI names are all over the place. Some sound like secret project codes (o3-mini-high). Others could be the title of a fantasy novel (Gemini, Claude, Grok). And then there are the endless version numbers—3.5, 3.7, 4.0, 4o—like someone dropped a bag of numbers on the floor and decided to use them all.

The problem? These names make it hard to tell what the models actually do. If users have to guess or look up a chart just to figure out which AI is best for their needs, something is wrong. AI is supposed to simplify our lives, not make choosing one a frustrating puzzle. It’s okay if you don’t agree though. 😀

The reasons behind the naming mess

• Tech jargon overload – AI companies love complicated names. Maybe they think it makes them sound more advanced. But most people don’t care about “o3-mini-high”—they just want to know if it can write an email or summarize a report.

• The illusion of progress – Adding small version updates (3.5, 3.7, 4.0) makes it seem like big changes are happening, even when the improvements are tiny. Does GPT-4o really feel that different from GPT-4? I can’t tell. Actually, most users can’t tell.

• Hiding the competition – If every company used names like “Fast, Faster, Fastest,” we’d instantly know which one is better. Instead, we get Sonnet, Haiku, Opus—pretty words with no clear meaning.

• Mixing up models and assistants – Google’s “Gemini” is both the assistant and the model. OpenAI’s ChatGPT runs on GPT-4o but also o1 and o3-mini. It’s a confusing mess that makes it hard to know what you’re using..

Read the full article below👇

Agentic AI Workflows: Your Next Growth Lever

Source: Various Sources

What the hell is an agentic workflow (and why should you care)? Last week, we introduced AI workflows as a way to reclaim your time and sanity. This week, we’re going a layer deeper—into something that sounds complex but is actually super useful: AI agentic workflows.

Let’s break it down.

1. What is an AI Agentic Workflow?

Think of it like this:
You have a big task. It’s messy. It takes hours. An agentic workflow is when AI breaks that task into smaller steps, assigns those steps to specialized agents (think: mini-AIs with jobs), and gets it all done without you babysitting the process.

Each agent knows what it’s doing. It doesn’t just spit out a response—it understands the goal, plans how to get there, and handles follow-ups too. And it works on autopilot.

Example?
A customer support flow using voice-generating AI agents that talk, analyze sentiment, escalate if needed, and report back. You just watch the tickets resolve themselves.

2. Types of AI Agents

Here’s a quick tour of the cast of characters in agentic workflows:

• Simple Reflex Agent – Reacts to the now. Think: a robot vacuum that cleans when it sees dirt.

• Model-Based Agent – Uses memory. Like a weather bot that says, “I’ve seen this pattern before—storms incoming.”

• Goal-Based Agent – Knows what you’re aiming for. Like Alexa setting your smart home based on a routine.

• Utility-Based Agent – Picks the best path. Like a self-driving car choosing the safest, fastest route.

• Learning Agent – Gets smarter over time. Think: fraud detection that adapts with each new scam it sees.

These agents work solo or in groups, depending on what you're trying to automate.

3. Why Agentic Workflows Actually Matter?

✅ Work gets done in chunks – Big, messy jobs are broken down and shipped step by step.
✅ They never sleep – Agents run 24/7, so your business doesn’t stop at 6 p.m.
✅ They don’t make dumb mistakes – With clean data, agents outperform humans on repetitive stuff.
✅ You move faster – More accurate decisions, fewer delays.
✅ And you save money – Fewer hands needed on routine ops = lower costs.

Agentic workflows aren’t just about being fancy—they’re about getting stuff done reliably, at scale.

4. What You Need to Build One

Before you dive in, here’s your checklist:

• Start with the pain – What tasks are repetitive, error-prone, or just plain annoying? Start there.

• Audit your tools – What data do you have, where is it stored, and can your AI agents reach it?

• Don’t obsess over the tool – Whether you’re using ChatGPT or Claude doesn’t matter if your internal data is a mess.

• Think like Lego – Break down your knowledge into small, clear blocks AI can learn from.

• Start small – Pilot one agent or flow, prove it works, then scale across your ops.

You don’t need a full AI team to get started. Just a clear process, clean data, and a bit of curiosity.

Thank you for reading! ✌️

We look forward to sharing more with you next week. Stay tuned!

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