When AI Meets Authenticity: How Hoffman Media Keeps Creativity at Its Core

When I stumbled across Samir “Mr. Magazine™” Husni’s interview with Eric Hoffman—“Eric Hoffman To Samir ‘Mr. Magazine™’ Husni: We’re Fundamental Believers In The Power Of Our People, Our Creativity, Our Brands Being Authentic”—thanks again to BoSacks’s latest newsletter, I was struck by how this family-run publisher has managed to honor its print legacy while eyeing the AI revolution with both curiosity and conviction. In our industry, talk of automation often sends shivers down spines: Will robots steal our jobs, dilute our voice, turn our carefully crafted recipes into bland text? Eric’s perspective cuts through the noise: protect the heart of your content, but let AI tackle the busywork.

Why AI Doesn’t Have to Be the Enemy

I get it—there’s something soulful about flipping a magazine page or reading a cookbook you can hold. Eric’s promise to “never create content with AI” is both a rallying cry and a branding gem. But when he says, “if you think about processes and things that can be automated, sure,” you hear a savvy operator, not a Luddite. I found myself nodding: why not let AI do the heavy lifting on rote tasks, freeing our creative teams to dream up that next viral baking retreat?

The Four Quick Wins I Can’t Stop Talking About

  1. Metadata Tagging & SEO: Imagine an AI that reads every recipe you’ve ever published—tagging gluten-free, vegan, weeknight-friendly—and then suggests SEO-optimized headlines before you hit “publish.” No more manual spreadsheets, just better discoverability.
  2. Video Transcription & Captioning: Those live “Bake from Scratch” classes are gold, but manually captioning each one takes hours. A smart transcription tool can draft the subtitles; an editor polishes the quirks and brand tone. Suddenly, your videos are more accessible, more searchable, more binge-worthy.
  3. Personalized Emails: We all hate generic blasts. With AI-driven segmentation, your die-hard sourdough fans get a “New Starter Tips” note, while pastry obsessives hear about next month’s French-baking retreat. It’s like having a mini-marketing genius on your team.
  4. Event Reminders & CRM Workflows: No more sending “Oops, you’re on the waitlist” emails by hand. AI can trigger confirmations, nudge no-shows, even tailor follow-up offers based on the exact retreat someone attended—right in your CRM.

Keeping It Real: Ethical Guardrails

Here’s the kicker: trust is earned. If your readers suspect you’ve replaced human care with cold algorithms, you’ll lose more than time—you’ll lose loyalty. So let’s bake in these rules from the start:

  • Human-in-the-Loop: Every AI output—whether it’s a tag, a caption, or an email draft—gets a human’s eyes before it goes live. No “set it and forget it.”
  • Transparent Disclosure: A small note—“AI-generated captions, reviewed by our team”—means your audience knows you’re mixing horsepower with heart.
  • Copyright Respect: Only use your own licensed content to train or feed models. If your AI partner can’t sign off on that, it’s a no-go.
  • Data Privacy: Make sure any behavior-tracking for personalization is covered in your privacy policy, and give folks an easy opt-out.

What I’m Going to Try Next

I’m itching to pilot auto-captioning on our next live demo—measure the editor hours saved versus the fine-tuning required. If that goes smoothly, imagine rolling out metadata tagging across our entire back catalog: instant boost to SEO, minimal human grunt work. And yes, I’ll be drafting an “AI Usage Policy” this week—because nothing spells “we’ve got our act together” like a clear, company-wide guideline.

Why It Matters for You

Whether you’re running a niche culinary magazine in Birmingham or a global events brand in New Orleans, this hybrid approach can supercharge your growth without sacrificing authenticity. The parents of Hoffman Media taught their kids that people—and their stories—come first. Now, AI is playing sous-chef: invisible when it needs to be, instrumental when it matters.

So here’s to a future where we work smarter, not harder, with technology as our teammate rather than our threat. And big thanks to BoSacks for flagging Samir’s interview—sometimes the best insights arrive via a trusted newsletter.

Daily Links: Thursday, Jul 31st, 2025

Hey there! In my latest blog post, I dive into some fascinating topics: exploring patterns in “Context Engineering for Agents,” picking up tips from Ethan Evans on how to effectively manage up at work, and a guide on building a disaster-prep kit to keep you safe during emergencies. You won’t want to miss these insights and practical advice!

What If News Avoiders Are Right—and Journalism Needs to Get Real?

Let’s face it: a staggering number of people are tuning out the news on purpose. Recent reports say that in some countries, up to 60% of the public avoids the news entirely. That’s not just a sign of audience fatigue—it’s a flashing red light for anyone who still believes journalism is “essential.” (Hat tip to BoSacks, whose newsletter first put this article on my radar.)

The Practitioner’s Dilemma: “But How Do We Fix It?”

Having spent years working with and around newsrooms, I’ve had a front-row seat to the cycle: new tools, new platforms, fresh engagement strategies—all launched in the hope of winning back audiences. But let’s be honest: none of it really matters if the news itself doesn’t fit into people’s lives. The recent piece, What if news avoiders are right and you don’t need journalism? confronts this crisis head-on.

The authors argue that journalism has been missing the mark, producing for peers or vague notions of “the public” while ignoring how people actually use—or don’t use—what we publish. The JR3 project, with folks from the Knight Lab and News Alchemists, gathered a group to ask two deceptively simple questions:

  • “What is the purpose of journalism?”
  • “What should journalism enable us to feel, think, or do?”

When journalists answered honestly, their responses shifted from the usual talk of “watchdogs” and “guardians of truth.” Instead, people wanted journalism to help them feel better, take meaningful action, and connect with others. Not exactly the classic playbook—but maybe that’s the point.

But the Contrarian in Me Isn’t Satisfied

Now, here’s where my skeptical side kicks in. If we only create journalism to make people feel good or “empowered,” do we risk turning away from the hard truths that journalism is supposed to shine a light on? The world isn’t always a comfortable place. Sometimes, the news is negative because reality is negative.

Let’s not fool ourselves: there’s a danger in softening every edge or chasing popularity at the expense of uncomfortable, necessary stories. Journalism isn’t about customer service or crafting content that never offends. It’s about surfacing what matters—even when that means unsettling people, or challenging their views.

So Where’s the Sweet Spot?

For me, the real opportunity here isn’t about throwing out the old model for the shiny new one. It’s about balance. Yes, journalism should be more in tune with its audience: listen more, communicate with empathy, design stories that matter in the real world. But at the same time, it can’t become an echo chamber or a comfort zone.

The best journalism serves both the audience’s needs and the public interest—even when those don’t perfectly align. That tension? That’s where the real work happens.

Want to Go Deeper?

  • How would a newsroom look if it truly put audience needs at the center, every day?
  • Should every story be “empowering,” or do some just need to be true?
  • How do we measure impact without reducing journalism to a popularity contest?
  • Where does audience input strengthen journalism, and where does it dilute its mission?

Who Should Care?

If you work with media, study journalism, or have simply given up on the news because it feels irrelevant or exhausting—now’s the time to get involved. This isn’t just about keeping journalism alive as a business; it’s about making it matter to people again, even when that means making us all a little uncomfortable.

Memorable Takeaway:
“The pre-existing mental model for journalism falls apart when you center the audience.” But maybe it holds together best when you center both the audience—and the uncomfortable truth.

Daily Links: Wednesday, Jul 30th, 2025

In my latest post, I dive into a range of fascinating topics, from creating my own ultra-fast game streaming video codec called PyroWave, to navigating the evolving tech landscape with AI tools! I also explore the changing role of design in the AI era, why some projects demand lots of energy, and principles for production AI agents. Join me on this tech-fueled journey!

A Personal Take on Driving AI Adoption (and Why Mindset Matters More Than Tech)

I recently discovered Yue Zhao’s insightful article, “What Most Leaders of AI Get Wrong About Driving Adoption” and was reminded how often the human side of change gets overlooked. As an AI advocate, I’ve seen even the most promising initiatives stall—not because the technology failed, but because people weren’t ready.

Why Technical Focus Alone Isn’t Enough
It’s tempting to believe that once teams learn the latest AI tools, adoption will naturally follow. Yet time and again, projects falter not for lack of skill but because fear and uncertainty go unaddressed. When people feel anxious about what AI means for their roles, they hesitate to experiment or speak up—even when the technology could help them thrive.

Three Simple Shifts with Big Impact
Yue outlines a change-management approach that puts people first. Here’s how I’m applying it:

  1. Acknowledge and Address Fear. Instead of glossing over concerns, create dedicated forums—like quick “AI myth-busting” discussions—where everyone can voice questions and get clear answers. It demystifies the technology and validates genuine worries.
  2. Share Your Thinking. Transparency builds trust. I maintain a lightweight “AI decision diary” that outlines which tools we’re evaluating, why, and what trade-offs matter. This openness invites feedback and keeps everyone aligned.
  3. Build Together. Co-creation beats top-down edicts every time. Host hands-on sprints with diverse team members to prototype AI-enabled workflows. Even a short, focused session can spark ideas that stick—and foster ownership.

Real-World Reflections
After running these inclusive sessions with various teams, I’ve seen a noticeable shift: participants move from skepticism to genuine curiosity. The simple act of co-designing experiences turns apprehension into enthusiasm.

Why This Matters for You
True AI adoption isn’t about deploying the flashiest model; it’s about empathy and collaboration. When you weave in conversations about fear, share your rationale openly, and invite people into the process early, you transform AI from a mandate into a shared opportunity.

Your Turn
What’s the biggest roadblock you’ve faced when introducing AI? Reply with your experiences, and let’s explore solutions together.

Partnering with AI: How I Learned to Let Claude Code Handle the Busywork

In Sajal Sharma’s insightful guide Working Effectively with AI Coding Tools like Claude Code, she distills how AI assistants shift our role from writing every line to orchestrating systems. Inspired by her lessons, I dove in by asking Claude Code to scaffold a simple customer CRUD API—endpoints, models, tests, even CI workflows. Seconds later, I had files everywhere. Excited? Absolutely. Prepared for quality checks? Not yet.

That moment mirrored one of Sajal’s key points: AI clears the runway, but humans still pilot the plane. It can generate code at lightning speed, but someone has to ensure alignment with security policies, performance budgets, and team conventions. In short, AI is the horsepower; we’re the pilots.

The Real Magic Trick: Specs Before Code

Jumping into a prompt like “Build customer API” is like ordering a pizza without toppings—you might get bread and sauce, but no pepperoni. Taking a cue from Sajal’s spec-first approach, I always start by drafting a clear spec in /docs/specs/.

Here’s a slice of my customer-api-spec.md:

# Customer API Spec

- CRUD operations for Customer entity
- Fields: id, name, email, createdAt, updatedAt
- Input validation: email regex, name length
- 200 vs 404 vs 400 status codes
- Logging: structured JSON with requestId
- Rate limiting: 100 reqs/min per IP

Then I prompt: “Claude, scaffold the customer API based on customer-api-spec.md.” The result closely matches my intentions—no unwanted extra toppings.

Why You Must Play Code Quality Cop

Sajal warns that vague prompts often lead to shortcuts: any types, skipped tests, or generic error responses. I block 30 minutes every Friday for my “AI Code Audit” sprint. I scan new files for weak typings, missing edge-case tests, and logging inconsistencies. Then I ask Claude Code: “Please refactor duplicate helpers into a shared module and enforce our error-handling middleware.” It’s like giving your codebase a weekly checkup.

Double-Checking with a Second Brain

As Sajal recommends, no single LLM should have the final word. For thorny questions—say, whether to shard the Customer table—I generate a plan with Claude Code, then run it by GPT-4o. It’s like having two senior engineers politely debate over which approach scales best.

When both agree, I move forward. That extra validation step takes minutes, but Sajal shares how it’s saved her from invisible tech-debt traps more times than she can count.

From Boilerplate to Brainwork

With the busywork automated, I follow Sajal’s advice: I spend my time on strategy—mentoring teammates, aligning with product goals, and making key architectural decisions. For instance, when our data team needed a real-time import pipeline, Claude drafted ETL scripts in seconds, but only I knew our SLA: analytics data must surface within two minutes. So I guided the solution toward streaming events instead of batch jobs.

Your Turn: One Tiny Experiment

Inspired by Sajal’s guide, pick one experiment for your next sprint:

  • Draft a spec first. Create a one-page markdown with clear requirements.
  • Audit weekly. Reserve 30 minutes to review AI-generated code.
  • Seek a second opinion. Validate your plan with another LLM.

Share your spec or prompt in the comments—let’s build better workflows together!

AI coding tools aren’t a gimmick—they’re a paradigm shift. As Sajal concludes, our true value lies in asking the right questions, crafting clear specs, and safeguarding quality. Keep the horsepower running; stay firmly in the pilot’s seat.

What was your first “Whoa” moment with AI? Drop a comment—I’d love to hear!

Daily Links: Tuesday, Jul 29th, 2025

Hey there! In my latest post, I dive into some nifty resources. First, I share a useful link for comparing the pricing of various LLM APIs like OpenAI GPT-4 and more. Plus, you’ll get a kick out of my adventure hacking my washing machine—it’s where the big words find their home. Lastly, check out how to upscale images using generative adversarial neural networks, CSI-style!

AI Is Fueling a Fake Content Flood — Even People You Know Can Be Caught

In the past week, at least two people close to me unknowingly reshared fake content on Facebook. These aren’t people who fall for chain emails or post conspiracy theories—they’re thoughtful, curious, and fairly tech-savvy. But that’s the reality now: it’s getting harder to tell what’s real online, even for people who usually know better.

The reason? AI is making it fast, cheap, and easy to generate fake stories, headlines, graphics, and even entire videos. And bots are spreading it all before we even realize it.

Take a moment to watch this clip from Rachel Maddow on MSNBC:
https://www.msnbc.com/rachel-maddow/watch/maddow-debunks-weird-fake-news-a-i-slop-stories-about-her-and-msnbc-infect-social-media-243601477992

Whether or not you’re a Maddow fan is beside the point. This segment shows how AI-generated nonsense—fake news stories, bot-written posts, and junk links—are showing up in our feeds, using her name and likeness to push made-up narratives. These aren’t even deepfakes. They’re low-effort, high-impact content designed to manipulate, outrage, and spread like wildfire.

Why People Fall for It

Here’s the tricky part: fake content doesn’t look fake anymore. Logos are copied, images are AI-generated, and the writing sounds just believable enough. AI tools are trained to mimic real news formats, which means many of the visual cues we used to rely on—like headlines, layout, or even tone—can’t be trusted the same way.

Add to that how fast we all scroll, how emotionally charged most social feeds are, and how much trust we put in content shared by people we know… and you’ve got a recipe for misinformation.

What You Can Do

I’m still a believer in AI’s potential, but I’m also realistic about how it’s being used right now. If you’re on social media, you need to assume you’ll be exposed to fake content—because you already have been.

Here are a few habits that help:

  • Pause before you share. If something triggers a strong reaction, that’s a good time to stop and investigate.
  • Check the source. Is it a reputable outlet? Does the link go where it says it does?
  • Reverse image search. Tools like Google Lens can help identify whether a photo has been altered or recycled.
  • Cross-check. If no one else is reporting it, there’s probably a reason.

Fake content is cheap. Your attention—and trust—are not. Stay sharp out there.

If this post helps even one person slow down before clicking “share,” it was worth writing. Let’s keep each other honest.

Rewiring AI: Putting Humans Back in the Loop

I’ll admit it—I used to love the promise of “one-click magic” in my observability dashboard. Who doesn’t want the AI to just fix that pager alert for you at 2 AM? But after reading Stop Building AI Tools Backwards by Hazel Weakly, I’ve come around to a stark realization: those “auto” buttons are exactly what’s hollowing out our edge as practitioners.

Here’s the thing—I’m a firm believer that we learn by doing, not by watching. Cognitive science calls it retrieval practice: you solidify knowledge only when you actively pull it from your own brain. Yet most AI assistants swoop in, do the work, and leave you wondering what just happened. It’s like teaching someone to bake by baking the cake for them. Fun for a minute, but no one actually masters the recipe.

Instead, imagine an AI that behaves like an “absent-minded instructor”—one who nudges you through each step of your incident playbook without ever taking the wheel. Using the author’s EDGE framework, it would:

  1. Explain by surfacing missing steps (“Have you considered rolling back that deploy?”), not just offering “click to fix” tooltips.
  2. Demonstrate with a 15-second animation of how to compare time ranges in your monitoring UI—turning your rough query into the exact syntax you need.
  3. Guide by asking Socratic questions (“What trace IDs have you checked so far?”), ensuring you articulate your plan instead of mindlessly pressing “Continue.”
  4. Enhance by watching your actions and suggesting incremental shortcuts (“I noticed you always filter by five-minutes-pre-alert—shall I pin that view next time?”).

Every interaction becomes a micro-lesson, reinforcing your mental models rather than eroding them.

I’ve started riffing on this idea in my own workflow. When I review pull requests, I ask our AI bot not to rewrite the code for me, but to quiz me: “What edge cases might this new function miss?” If I can’t answer, it highlights relevant docs or tests. Suddenly, I’m more prepared for production bugs—and I actually remember my review process.

What really blew me away in Stop Building AI Tools Backwards was the emphasis on cumulative culture—the fact that real innovation happens when teams iterate together, standing on each other’s shoulders. By capturing each developer’s on-the-job recalls and refinements, AI tools can become living archives of tribal knowledge, not just glorified search bars.

Of course, building these “human-first” experiences takes more thought than slapping an “Auto Investigate” button on your UI. But the payoff is huge: your team retains critical reasoning skills, shares best practices organically, and feeds high-quality data back into the system for ever-smarter suggestions.

So next time you’re tempted to automate away a few clicks, ask yourself: am I strengthening my team’s muscle memory—or erasing it? If you want to see how to do AI tooling the right way, check out Stop Building AI Tools Backwards and let’s start rewiring our interfaces for collaboration and growth.

Read the full article here: Stop Building AI Tools Backwards.

Riding the AI Wave: Why Marketing Pros Must Pivot or Perish

I came across Maarten Albarda’s electrifying piece in the latest BoSacks newsletter, originally published on MediaPost: “AI Is Not The Future — It Is Here To Take Your Job” (https://www.mediapost.com/publications/article/407506/ai-is-not-the-future-it-is-here-to-take-your-jo.html?edition=139243). Eric Schmidt’s warning that AI could elbow aside programmers, mathematicians, and entire marketing teams in mere months isn’t sci-fi—it’s next quarter’s boardroom debate. Here’s why embracing AI now feels more like grabbing a lifeboat than steering into a storm.

From where I sit, the real magic (and madness) lies in AI’s leap from “helpful chatbot” to “autonomous strategist.” Imagine a system that doesn’t just draft your ad copy but plans the campaign, allocates budget, and optimizes in real time. That’s not some distant beta test—it’s happening. We’re talking productivity boosts economists haven’t even charted yet. And if you’re thinking, “Nah, that’s years away,” Schmidt’s blistering timeline—full automation of coding tasks within months, general intelligence in 3–5 years—is a gut-check you can’t ignore.

So, what do you do? First, audit your playbook. Map every repetitive task and ask: “Could an algorithm do this faster (and cheaper) than my intern?” Spoiler: the answer’s often “yes.” Next, retool your team for human-only superpowers—ethical oversight, pattern-breaking creativity, and relationship-building that no AI can fake. Finally, make AI fluency part of your culture. A five-minute daily demo, a lunchtime “what’s new” session, even AI peer groups—whatever it takes to demystify the tech and keep curiosity front and center.

Every revolution creates winners and losers. If you lean into AI as a teammate—albeit a supercharged one—you’ll surf this wave instead of wiping out. And trust me, that’s way more fun than reinventing the agency model on the fly while your competitors pull ahead.