Category: Here’s What I Found Today

A Homelab Perspective on Backup Strategy

“Data must always be restorable (and as quickly as possible), in an open format, and consistent.”
— Stefano Marinelli

Why Your Homelab Needs More Than Just “Copies”

If you’re running a homelab—whether it’s for learning, hosting services, or managing family data—you’ve probably told yourself “I’ll back it up later” or “I’ve got my files on another disk, so I’m safe.” But after reading Stefano Marinelli’s “Make Your Own Backup System – Part 1: Strategy Before Scripts,” it’s clear that many of us (myself included!) have been lulled into a false sense of security by confusing “backups” with mere file copies.

Marinelli’s core message?
True backup starts with a plan, not with scripts, disks, or the latest cloud storage.

Key Takeaways for Homelabbers

  • Plan First, Script Later:
    Don’t just whip up a cron job to rsync your /home directory. Start by asking: What do you really need to protect? How much downtime can you live with if something breaks? Where should your most precious data actually live?
  • Full Disk vs. File Backups:
    Do you back up the entire drive (system and all), or just the irreplaceable stuff? Full disk images are great for quick, all-in-one restores—especially for VMs—but can eat up tons of space. File-level backups (using rsync, tar, etc.) give you granularity, but restoring a borked system is way harder unless you know exactly what you’re doing.
  • Snapshots Are Essential:
    Filesystems like ZFS and BTRFS aren’t just for big enterprise setups—they’re your friend! Snapshots freeze your data at a specific point, so you’re not backing up half-written databases or files mid-change. This is the difference between a backup that works and one that silently fails.
  • Push or Pull?
    Marinelli makes a strong case for the “pull” model: your backup server fetches data from your machines, not the other way around. This means your main server never has to open up ports or risk exposure, and you keep one central point for management and restores.
  • Own Your Data:
    The article strongly advocates for keeping backups out of the “big tech” cloud. For homelabbers, that resonates—part of the homelab spirit is self-reliance and not being beholden to someone else’s infrastructure or fine print.

What’s Missing (and What to Ask Next)

Marinelli doesn’t dive (yet) into the weeds of scripting, automation, or how much this might cost you in hardware and time. He’s laser-focused on strategy—which is honestly what most homelabbers skip in their rush to install the next shiny tool.

But if you’re like me, you might be wondering:

  • How do I automate snapshot-based backups in a way that’s easy to restore?
  • What’s the best way to test that my backups actually work—without nuking my main system?
  • Are there open-source tools that make “pull” backups easier for a home environment?
  • What’s the smartest way to mix local and “cloudy” (maybe self-hosted) offsite storage?

Should You Read the Original?

If you’re running a homelab—whether you’ve got a single Raspberry Pi or a rack of old enterprise gear—you owe it to yourself (and your data) to rethink how you do backups. Marinelli’s post is a reminder that strategy trumps technology. The how-tos are coming in his later posts, but even as a stand-alone, this first part is pure gold for anyone who wants to sleep better at night knowing their family photos, media libraries, or home services are safe.

Final Word

Don’t wait for disaster to figure out if your backups work. Start with a plan, learn the difference between copying and true backups, and—most of all—make sure you can restore what you care about, when it matters most.

In Defense of Sharing AI Output: Why “AI Slop” Isn’t the End of Meaningful Communication

Rethinking proof-of-thought, noise, and the upside of a more open AI culture.

Is sharing ChatGPT output really so rude?
A recent essay compares AI-generated text to a kind of digital pollution—a “virus” that wastes human attention and diminishes the value of communication. The author proposes strict AI etiquette: never share machine output unless you fully adopt it as your own or have explicit consent from the recipient.

It’s a provocative take, inspired by Peter Watts’ Blindsight, and it raises important questions about authenticity, value, and digital trust. But does it go too far? Is all AI-generated text “slop”? Is every forward or paste a violation of etiquette?

Let’s consider another perspective—one that recognizes the risks but also sees the immense value and potential of a world where AI-generated output is more freely shared.

“Proof-of-Thought” Was Always a Mirage

The essay’s nostalgia for a lost era of “proof-of-thought” is understandable. But let’s be honest: not every piece of human writing was ever insightful, intentional, or even useful. Spam, boilerplate, PR releases, and perfunctory office emails have existed for decades—long before AI.
Authenticity and attention have always required discernment, not just faith in the medium.

AI may have made text cheap, but it has also made ideas more accessible and the barriers to entry lower. That’s not a bug—it’s a feature.

Sharing AI Output: Consent, Context, and Creativity

Of course, etiquette matters. But to frame sharing AI text as inherently rude or even hostile misses some crucial points:

  • AI output can be informative, creative, and valuable in its raw form. Sometimes a bot’s phrasing or approach offers a new angle, and sharing that output can accelerate understanding, brainstorming, or problem-solving.
  • Explicit adoption isn’t always practical. If I ask ChatGPT to summarize a dense technical paper or translate a snippet of code, sometimes the fastest, most honest way to help a friend or colleague is to share that result directly—with attribution.
  • Consent can be implicit in many contexts. In tech, research, and online forums, sharing logs, code snippets, or even entire AI chats is often expected and welcomed—especially when transparency and reproducibility are important.

The Upside of “AI Slop”: Accessibility, Efficiency, and Learning

What the “anti-slop” argument underplays is just how much AI has democratized expertise and lowered the cost of curiosity:

  • Non-native speakers can get better drafts or translations instantly.
  • Students and self-learners can access tailored explanations without waiting for a human expert.
  • Developers and researchers can rapidly prototype, debug, and collaborate with a global community, often using AI-generated code or documentation as a starting point.

Yes, there’s more noise. But there’s also far more signal for many people who were previously shut out of certain conversations.

Trust and Transparency, Not Gatekeeping

Rather than discouraging the sharing of AI output, we should focus on transparency. Label AI-generated text clearly. Foster norms where context—why, how, and for whom AI was used—is always provided. Give people the choice and the tools to ignore or engage as they see fit.

Blanket prohibitions or shame about sharing AI content risk re-erecting barriers we’ve only just started to dismantle.

Questions for the Future

  • How do we build systems that help us filter valuable AI output from true “slop”?
  • What new forms of collaborative authorship—human + AI—will emerge, and how do we credit them?
  • How can we leverage AI to reduce noise, not just add to it?

A Call for a More Open, Nuanced AI Etiquette

AI is here to stay, and its output will only become more sophisticated and pervasive. The solution isn’t to retreat or treat all shared AI text as digital poison. It’s to develop a culture of honesty, clarity, and context—so that AI can amplify, rather than degrade, our collective intelligence.

So yes: share your ChatGPT output—just tell me where it came from. Let’s make etiquette about agency, not anxiety.

The 3 Roles That Build Great Strategy Talent: A Review of Bandan Jot Singh’s Insights

In the fast-moving world of product management, crafting and executing a solid strategy is often more complex than simply delivering features. Bandan Jot Singh’s recent article, “The 3 Roles That Build Great Strategy Talent,” offers a fresh and practical framework that product managers and leaders can adopt to navigate this complexity more effectively.

Singh identifies three critical roles that shape strong strategy talent: The Realist, The Investor, and The Challenger. These aren’t formal job titles but behavioral stances that individuals can embody at different points in the strategy process to ensure it’s robust, well-supported, and adaptable.

Why These Roles Matter

Much like mapping customer journeys involves planning for “unhappy paths” or edge cases, product strategy requires anticipating risks, securing resources, and revisiting assumptions continuously. Singh highlights how many teams neglect these “unhappy paths” in strategy, leaving their plans vulnerable to market shifts, stakeholder dynamics, and operational realities.

Breaking Down the Roles

  • The Realist: This role is about spotting cracks early — the misalignments between what’s planned and what’s happening on the ground. For junior PMs especially, who are close to customer feedback and delivery challenges, raising early red flags backed by data builds trust and prevents costly surprises.
  • The Investor: Getting buy-in and resources isn’t just about asking; it’s about making a persuasive business case. Framing requests in terms of impact, ROI, and alignment with company goals can move leadership to commit people, budget, and support.
  • The Challenger: Strategy should never be set in stone. When priorities or market realities shift, challenging assumptions and advocating for pivots keeps the strategy alive and relevant. This role requires courage and a culture that welcomes questioning without fear.

Leadership’s Role

Singh also emphasizes how product leaders must embody these roles with greater finesse. They set the tone by encouraging dissent, packaging strategy in business language for executives, and demonstrating that revisiting strategy is a sign of strength, not failure.

What’s Missing?

While Singh’s framework is clear and actionable, the article doesn’t deeply address how organizational culture or hierarchy can impede these roles, especially the Challenger. Psychological safety and navigating internal politics are crucial elements for enabling these behaviors in practice.

Why You Should Read the Original

If you’re a product manager, leader, or anyone involved in strategic planning, Singh’s article offers a valuable lens to rethink how you engage with product strategy. It reminds us that strategy isn’t a static plan but a living, breathing process that requires a balance of realism, investment, and challenge — and that each of us can step into these roles to drive better outcomes.

You can read the full article here: The 3 Roles That Build Great Strategy Talent by Bandan Jot Singh

Daily Links: Saturday, Jul 19th, 2025

I’ve shared an array of topics on my blog recently—perfect for tech enthusiasts and budding leaders. Dive into AI with an expansive glossary, learn strategic insights for product managers, explore tools for autonomous systems, and discover the revolution of AI-assisted coding with Claude Code. Plus, get practical advice for new managers and insights into why self-taught engineers often excel. Let’s explore these intriguing topics together!

Why Every Product Manager Should Make Strategy Their Side Project

If you’re a product manager constantly juggling delivery deadlines and leadership expectations, Amy Mitchell’s recent article, “Make Strategy Your Side Project,” is a must-read. Rather than treating strategy as a distant, high-level exercise, Mitchell offers a fresh and practical take: strategy is something you build right alongside your day-to-day product work.

What Sets This Article Apart

Most advice on strategic thinking can feel overwhelming or disconnected from the reality of busy product teams. But Mitchell cuts through that noise by emphasizing small, solution-level strategy — the kind that solves recurring patterns or friction points within your product or team.

She debunks the myth that strategic projects come fully formed on your plate. Instead, you need to spot opportunities in customer feedback, cross-team friction, or delivery bottlenecks — and then build a case for them carefully, framing these projects as hypotheses rather than “big strategies” to manage skepticism and risk.

The Power of Starting Small and Following Through

One of the most compelling insights is how starting small and staying close to delivery work can set you apart. Mitchell points out that many product managers have ideas or decks, but few follow through when the work gets messy or unrewarded in the short term. This follow-through — involving stakeholders, tracking progress, and closing the loop — is what builds trust and influence.

Her “Billboard Test” is a simple but effective tool: Would your team be proud to say, “We figured that out. That changed how we operate”? If yes, you’re on the right track.

Why This Matters for Product Managers Today

In today’s fast-paced environments, leadership demands both immediate results and strategic thinking. Mitchell’s approach offers a way to reconcile those pressures, making strategy less of a distant moonshot and more of a continuous, manageable side effort.

Whether you’re trying to stand out in a crowded product team or earn more visibility with senior leaders, the article provides actionable advice for embedding strategy work into your routine without losing focus on delivery.

Final Thoughts

“Make Strategy Your Side Project” is a timely and practical guide for product managers who want to grow their strategic impact organically. By focusing on small, product-rooted projects and following through with rigor, you can earn the influence and visibility leadership is looking for.

If you’re ready to rethink how you approach strategy and want actionable steps to start today, I highly recommend reading Amy Mitchell’s full article.

Daily Links: Friday, Jul 18th, 2025

Hey there! This blog post is a treasure trove of diverse content. I dive into some intriguing reads and discoveries, like Bridget Read’s history-packed book “Little Bosses Everywhere”, a nifty programming trick for writing better code, a look at enhancing RAG with ELO Scores, and even how to artisanally craft your own git repositories. Come explore these topics with me!

Daily Links: Tuesday, Jul 15th, 2025

In my latest blog entry, I delve into a mysterious new law that’s causing quite a stir in the gambling world. With a tax change looming, the industry faces significant shifts. Click the link to explore how this could play out for gamblers and the industry alike.

Daily Links: Sunday, Jul 13th, 2025

In this blog post, I highlight some intriguing reads! Discover insightful business metrics, tips for rapid software development, automating oral arguments, and crafting stellar newsletters. I also explore tools like Jukebox for music sharing, handy self-hosted utilities, and advanced SEO tactics for the LLM era. Plus, learn about the mystical user-interface journey of Apple legend Bill Atkinson and consider switching to Folio, an exciting Pocket alternative.

Daily Links: Saturday, Jul 12th, 2025

In this blog post, I dive into some intriguing topics like how algorithms can enhance our bookshelves, the pitfalls of AI’s false promises, and innovative tools for project collaboration and data ownership. There’s also a spotlight on interesting developments like open-source alternatives to Trello, the impact of AI tools on developer productivity, and the importance of context in AI agent operations.

Daily Links: Tuesday, Jul 8th, 2025

In this blog post, I dive into the buzz around AI and what large language models (LLMs) can genuinely achieve. While there’s excitement about AI-first companies, I point out that discussions often feel too abstract to be practical. I explore the real opportunities AI presents, but also highlight the importance of having concrete conversations rather than lofty, vague claims. Check it out!

  • What can agents actually do?: There’s a lot of excitement about what AI (specifically the latest wave of LLM-anchored AI) can do, and how AI-first companies are different from the prior generations of companies. There are a lot of important and real opportunities at hand, but I find that many of these conversations occur at such an abstract altitude that they border on meaningless. Sort of like saying that your company could be much better if you merely adopted more software. That’s certainly true, but it’s not a particularly helpful claim.