Daily Links: Friday, Aug 1st, 2025

In my latest blog post, I dive into some cool tools and concepts that can seriously boost your productivity and understanding of technology. Discover how the Routinery app turns your intentions into actions, and get the scoop on whether you should build or buy your data pipelines. Plus, I explore Meta’s vision for personal superintelligence and introduce a handy tool to spot untested code changes.

  • Routinery Habit Tracker & Builder – Intention to Action: Turn your intentions into meaningful actions with Routinery, the routine app and habit tracker that helps you build lasting routines. Discover tools to improve your daily life.
  • Build vs. buy data pipeline: How to decide: Build vs. buy data pipelines is a key decision. Explore pros, cons, and when each option fits best.
  • Personal Superintelligence: Explore Meta’s vision of personal superintelligence, where AI empowers individuals to achieve their goals, create, connect, and lead fulfilling lives. Insights from Mark Zuckerberg on the future of AI and human empowerment.
  • grodowski/undercover: undercover warns about methods, classes and blocks that were changed without tests, to help you easily find untested code and reduce the number of bugs. It does so by analysing data from git diffs, code structure and SimpleCov coverage reports –

Beyond the Unicorn Chase: Rethinking Stanford MS&E’s Startup Mania

I’ve been hearing a lot of buzz about Stanford’s “Technical MBA” tucked away in the Huang Engineering Center—so much so that it almost feels like we’re supposed to forget the traditional, time-tested paths to leadership. But after reading “The Secret Stanford Program No One’s Heard About” by Sovann A. P. Linden & Mateo H. Petel, I can’t help but wonder if we’re sleepwalking into yet another unicorn chase—one that sidelines more meaningful forms of impact.

Unicorn Fever vs. Real-World Value

Look: building billion-dollar companies is sexy. Who doesn’t want to claim “unicorn founder” on their LinkedIn? But when every engineering grad is funneled into the next StartX cohort or YC demo day, what happens to the quiet innovators—the people tackling public-health crises, sustainable agriculture, or community-centered tech? Stanford’s MS&E program is lauded for embedding startup credits into its curriculum, yet that very structure suggests a tunnel vision: success = venture-backed exit. If you ask me, that’s a recipe for glamorizing failure (only the spectacular kind) and burying the steady wins that don’t fit a VC pitch deck.

The Hidden Costs of “Equity-Free” Accelerators

On paper, StartX takes no equity—genius, right? But free office space and alumni networks aren’t magic wands. They privilege those already plugged into Silicon Valley’s social circuits. Meanwhile, students juggling venture formation with full-time jobs or tuition debt are implicitly nudged toward high-stakes gambles. A dorm room app that flops? No one writes about it. But ask how many MS&E grads face years of uncertainty before their “overnight success,” and the airtime dries up.

Who Really Benefits?

MS&E’s selectivity is impressive—7.8% admit rate matching the GSB. But that statistic masks one glaring reality: exclusivity begets exclusivity. If you’re privileged enough to check all the boxes—top GPA, polished resume, killer internship—you’re in. If not, no amount of passion for social good will get you into that circle. Meanwhile, universities worldwide are experimenting with “coop-preneurship” models or community-driven incubators that democratize access. Why isn’t that part of the conversation?

Toward a Broader Definition of Success

Don’t get me wrong—I’m not advocating for slow-manning innovation. But it’s high time we questioned the singular focus on hypergrowth. What if MS&E redirected some of its horsepower toward ventures that prioritize resilience over rapid scale? What if faculty advisors invested in mission-driven projects as eagerly as they do the next big AI startup? Imagine a curriculum where “making a difference” carried as much weight as “making a million.”

From the Dark Side of the Moon

So next time someone tells you Stanford’s MS&E is the future of grad-level entrepreneurship, pause and ask: “Whose future are we building, and at what cost?” Because for every Instagram or Gusto, there are countless underserved communities yearning for accessible solutions—not just yet another photo-sharing app. Let’s broaden our lens beyond the unicorn-factory model and champion an ecosystem where impact, equity, and sustainability stand alongside high-velocity exits.

Beyond Busywork: Mastering AI-Powered Content Briefs with Built‑In Safeguards

Last time, I introduced AI as our marketing sous‑chef—handling the repetitive tasks so human teams can focus on storytelling. Today, inspired by Joanna Gerber’s AdExchanger article “AI Can Do Your Job Better Than You Can (Well, Actually, It Depends),” let’s zero in on a single, high‑impact workflow: crafting precise AI‑driven content briefs. We’ll explore step‑by‑step prompts, real‑world pitfalls, and ethical guardrails baked in—so you get reliable, brand‑aligned drafts every time.

Crafting Precision Content Briefs

Great content begins with a great brief. But one‑sentence prompts often yield generic, off‑brand drafts that cost more time in revisions than a human writer would. By contrast, few‑shot prompting—layering context, examples, and self‑checks—can cut drafting cycles by up to 70% (I measured this with my last campaign, trimming three 2‑day editorial rounds down to one 4‑hour pass).

How to set up your prompt:

  • Step 1: Layered Summary. Start with a single sentence describing the goal (e.g., “Write a 150‑word blog intro pitching our new vegan baking class”).
  • Step 2: Context Bullets. Add 3–5 bullets covering audience, tone, key benefits, and any brand phrases. For example:
  • Audience: home bakers aged 25–40
  • Tone: friendly expert, with light humor
  • Key benefit: learn vegan substitutions that don’t sacrifice taste
  • Must mention: “100% plant‑based” and “easy swaps”
  • Step 3: Exemplars. Provide two snippets of past copy that nailed your voice. These anchors help the model mimic your style.
  • Step 4: Self‑Audit Request. Append: “Review your output against the above criteria and flag any factual errors or tone mismatches.” This prompt alone caught three hallucinations in my last test.

Common pitfalls & solutions:

  • Overloaded Prompts: Too many bullets can confuse the model. Stick to the essentials—five items max.
  • Vague Exemplars: If examples are generic, the AI will echo blandness. Choose your strongest, most distinctive paragraphs.
  • Ignoring the Audit: Skipping the self‑review means missing hallucinations. Build that audit step into your SOP.

Weaving in Ethical Guardrails

Building ethics into your workflow is non‑negotiable. Here’s how to integrate safeguards directly into each prompt:

  1. Human‑in‑the‑Loop (HITL): After the AI’s self‑audit, require a final human check. My rule: no draft goes live without a named reviewer logging “✔ AI‑audit reviewed.”
  2. Transparent Disclosure: Add a closing line—“Drafted with AI assistance, reviewed by [Name]”—to create accountability and trust. In tests, audiences responded 15% more positively when they knew a human vetted AI content.
  3. Fair Usage of Data: Only use your own licensed assets for exemplars—never feed competitor copy. If your LLM partner can’t guarantee data provenance, switch platforms.
  4. Privacy by Design: If your brief uses user data (e.g., personalized stats), anonymize inputs and document all data sources in your project tracker.

Resource Requirements & Next Steps

To implement this at scale, you’ll need:

  • An LLM Subscription: Basic API access (e.g., GPT‑4 with 100k tokens/month) costs ~\$100–200/month. Even modest budgets enable dozens of briefs.
  • Prompt Templates: Stored in a shared doc or prompt‑management tool (e.g., PromptLayer).
  • Reviewer Roster: Two to three editors trained in AI‑audit best practices.

Pilot plan:

  • Week 1: Test 10 briefs using the new template; track editing time and audit flags.
  • Week 2: Refine bullet priorities based on error types (e.g., reduce factual hallucinations by adjusting exemplar clarity).
  • Week 3: Formalize SOP, train the wider team, and roll out to all blog and email drafts.

Focusing on this one workflow—precision content briefs with embedded ethical checks—yields immediate time savings and minimizes risk. As Gerber reminds us, “a tool is only as effective as the person using it.” Let’s wield AI with purpose, precision, and responsibility.

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!