There’s an old adage from the early days of the internet: “The best way to get the right answer on the internet is not to ask a question, it’s to post the wrong answer.”

At first glance, it feels counterintuitive—almost rebellious. But once you experience it, you’ll know: nothing motivates people more than proving someone else wrong.

Why This Works: The Psychology of Correction

When you ask a question, people may scroll past, unsure if they know enough to answer—or fearing they might get it wrong. But when they see a wrong answer? That’s a challenge. A call to arms. The instinct to correct kicks in.

Whether it’s pride, the thrill of accuracy, or just wanting to protect the world from misinformation, people love correcting others. Especially online.

And unlike vague responses to an open question, corrections are often sharply detailed, backed by sources, and written with conviction. You don’t just get answers—you get passionate, well-researched ones.

Real-World Examples

• Stack Overflow: Ask a technical question, and you might get a response. Post a clearly flawed code snippet and say, “This is how I solved it”—and watch the swarm descend with “Actually…” comments and optimized fixes.
• Reddit: On subreddits from r/AskHistorians to r/ExplainLikeImFive, deliberately wrong or oversimplified posts often trigger long, expert-level explanations.
• Threads: Try saying something mildly wrong about a fandom or niche subject. You’ll summon niche experts faster than a Google search.

The Strategy: Use with Caution (and Ethics)

Let’s be clear—this isn’t a license to spread misinformation for clout. This trick only works when your intention is to invite correction, not deceive or manipulate.

Here’s how to do it ethically:

• Use obvious “wrongness”—something that signals you might be baiting correction (e.g., “Pretty sure gravity pushes things up?”).
• Be gracious when corrected—thank the person, engage, and clarify.
• Don’t go viral with misinformation—keep it to safe or low-impact topics.

When to Use This Tactic

• You’re exploring a new topic and want expert input.
• You’re in a community full of silent lurkers.
• You want to crowdsource better alternatives to a weak solution.
• You’re writing content and want engagement (beware trolls, though).

Bonus: It Works Offline Too

This principle isn’t limited to the web. Try saying the wrong thing in a meeting and watch your quietest coworker come alive with, “Actually, that’s not quite right…” Boom—now you’ve got engagement.

Conclusion

In a world of silent scrollers, passive observers, and “seen-zoned” questions, nothing stirs the pot like a wrong answer. It taps into our need to be right—and in doing so, gives you the right answer faster than any search engine ever could.

So next time you’re stuck, don’t just ask. Be (strategically) wrong. Because on the internet, nothing travels faster than a correction.

The sigil was drawn in salt and ash. The candles flickered at the pentagram’s points. The incantation was recited in full. A shimmer in the air, and poof: a demon appeared.

“Curious,” it said, glancing around. “What ritual is this?”

“I got it from ChatGPT,” the summoner replied proudly. “I included to ask for all protections in my prompt!”

“I see,” the demon said—and calmly stepped out of the sigil.

And just like that, hell met hallucination.

🪄 The Modern Mage: Prompt Engineers

In today’s digital age, we’re all casting spells—except our grimoires are prompt windows and our arcane language is natural language processing. The rise of AI has turned us into a new kind of practitioner: the Prompt Engineer. Just type the right words and something intelligent emerges from the void.

But unlike medieval conjurers, our protections are assumed to be built-in. “I asked it nicely.” “I specified guardrails.” “I added all the safeguards in the prompt!” And yet… something always slips through.

😈 The Demon in the Details

This little fictional scene captures something very real about AI today: hallucinations—confident, plausible-sounding answers that are completely wrong. And just like summoning a demon, trusting AI without verification can invite disaster.

Except instead of flames and brimstone, you get:

• Legal documents citing non-existent cases.
• Medical advice based on fantasy.
• Software that compiles but is secretly cursed.
• And yes, rituals that let demons out of their pentagrams.

⚠️ Protection by Prompt? Not Quite.

The humor lies in the user’s misplaced faith: “I included all protections in my prompt.” But prompts aren’t contracts with reality. They’re suggestions to a predictive engine. You can ask an AI to be accurate, but unless the underlying model has been trained, grounded, and aligned properly, no prompt can guarantee truth—or safety.

In the same way, saying “I used the right salt and ash” doesn’t help when the demon wasn’t bound properly to begin with.

👁 Lessons from the Underworld

This story is a cautionary tale with a wink:

1. Trust but verify. AI outputs must be checked, just like you’d double-check your demon-binding runes.
2. Know your limits. AI is a tool, not a source of arcane truth.
3. Prompting isn’t protection. Good prompts improve results—but they don’t guarantee correctness.
4. Be wary of overconfidence. Whether summoning spirits or writing business reports, arrogance is the real trap.

🧙🏻♂️ Final Words: Don’t Be That Summoner

The next time you copy-paste a chunk of AI-generated code, legal text, or magical invocation, pause. Ask yourself: Is this safe? Is it grounded? Is the sigil actually working—or is the demon already walking free?

In a world where AI feels like magic, remember: hallucinations are just the devil in disguise.

Problems are not walls. They’re doorways. They’re not interruptions—they’re invitations.

Every challenge we face, big or small, carries a quiet message: “Solve me.” It used to be subtle—a whisper, a gut feeling, an itch in your mind that something could be better.

But now, with AI? That whisper is amplified. It’s a signal. A ping. A data anomaly. A prompt waiting for completion. Problems no longer hide in plain sight; they surface boldly, sometimes before we even notice them ourselves.

The Age of Silent Signals Is Over

In a world driven by artificial intelligence, problems don’t stay quiet for long. AI-powered systems can detect inefficiencies, surface bugs, highlight inconsistencies, and even suggest solutions—faster than we ever could manually.

• A misrouted customer service ticket? Flagged and rerouted.
• A drop in engagement? Predictive analytics already mapped out a few likely causes.
• Code that’s brittle or inefficient? AI co-pilots highlight improvements mid-keystroke.

AI doesn’t just respond to our curiosity. It provokes it.

From Intuition to Insight

Where we once relied on gut instinct or lived experience to sense when something was wrong, AI systems offer pattern recognition at scale. They connect the dots across data points we didn’t even know existed.

It doesn’t replace human creativity—it augments it. We still need to ask the right questions. We still need to judge what matters. But the discovery phase? That’s on turbo now.

Every Problem, a Potential Breakthrough

The real transformation isn’t just in how fast we solve problems, but in how we reframe them.

Instead of “What’s wrong?”, we ask:

• “What’s the opportunity here?”
• “What insight does this anomaly unlock?”
• “What could we automate, simplify, or redesign?”

AI gives us leverage, but our human ability to turn solutions into stories, systems, and scale—that’s still the engine.

Conclusion: Answer the Call

So next time a challenge surfaces, don’t just brace for it. Lean in. Listen closely—or not so closely, because the whisper has become a roar.

AI made it loud. Now it’s your move.

Because every problem is still an invitation. Only now, it comes with directions. And a co-pilot.

Have you ever stared at a deck so plain it could double as an early 2000s Word document? You know the type: black text, white background, 12-point font, bullets that multiply like rabbits.

And then it hits you:

“SlideShow” is really just “SlidesHow.”

Now you can’t unsee it.

SlideShow? No — Slides How.

Every slide is a question: How are you telling your story? How are you guiding attention, building understanding, sparking emotion, or making people care?

Slides aren’t a teleprompter for the presenter. They’re not a storage locker for every thought you couldn’t trim from the script. They’re your co-narrator.

So when we say “SlideShow,” think “SlidesHOW” — as in:

• How does this slide move the story forward?
• How does it feel to read this?
• How does the design help, or hurt, the message?

If you’re just showing slides, you’re not showing how.

The Problem with Black-and-White Thinking

When we rely on black text on a white background, we’re not just making a design choice — we’re making an engagement choice.

It’s the default, the no-risk path, the lowest common denominator of visual communication. But default slides don’t start conversations. They don’t stick in memory. They don’t move people.

They whisper when you needed a drumroll.

What Slides Should Be

• A canvas, not a printout – Use space, contrast, motion, and imagery to tell your story visually.
• Visual anchors – A single chart, a photo, a bold quote — these are anchors, not add-ons.
• Conversation starters – The best slides raise questions in the audience’s mind before you’ve even said a word.
• Designed for impact, not for reading – If your audience is reading while you’re talking, they’re not listening.

SlideSHOW or SlideHOW: Choose Wisely

Next time you’re building a presentation, try this simple exercise: Read the word “SlideShow” and mentally split it.

Ask yourself:

❓ How is this slide helping me SHOW what matters?

❓ How is this slide showing HOW I think?

❓ How is this slide different from just handing them a PDF?

Because once you’ve seen “SlidesHow,” there’s no going back. And maybe — just maybe — your audience will thank you with applause instead of polite silence.

TL;DR (or TL;DW if it’s a webinar):

Stop building slide shows. Start building SlidesHow. Design like it’s a story. Present like it’s a performance. Because every slide asks: HOW are you showing what matters?

In today’s rapidly evolving world, where artificial intelligence seems to dominate every boardroom agenda and headline, it’s easy to feel like human value is shrinking. The fear? That relationships will be replaced, intuition outmoded, and the art of connection reduced to an algorithm. But let’s pause the panic.

The Currency of Connection

Every opportunity, every job, every idea that turned into something more—it likely started with someone. Not something.

That coffee chat. That DM you responded to. That shared moment at a meetup or hackathon.

Human relationships still grease the gears of innovation. Trust, credibility, referrals, empathy—these are not downloadable packages. They are earned. Built. Grown.

And in a world oversaturated with information and automation, who you know becomes even more valuable—not less. People still hire people. People fund people. People trust people.

AI is Not the Rival

Let’s stop framing AI as the cold machine poised to out-network, out-negotiate, and out-human us. It’s not.

AI doesn’t replace your network. It helps you scale your value within your network.

It can write the first draft of your outreach. It can summarize a meeting so you can focus on the people in it. It can surface relevant insights so your next conversation is more thoughtful.

When used right, AI is like a hyper-efficient assistant who never sleeps. But it still can’t shake hands, look someone in the eye, or understand that subtle dance of mutual trust and timing.

Combine the Two: Power Multiplied

Here’s where it gets exciting.

AI won’t replace your job—but someone using AI effectively might.

Similarly, your network won’t grow just because you have one—it’ll grow because you nurture it with smart, thoughtful actions. That’s where AI becomes your amplifier.

• Use AI to prep before meetings with people.
• Use AI to follow up quickly and meaningfully.
• Use AI to remember what matters to others, not just to you.

That’s the winning formula: relational intelligence, multiplied by artificial intelligence.

Final Thoughts

In the end, the winners in this new economy will be the connectors who wield tools, not the solo technologists who try to replace connection.

So go build. Go connect. Go be human.

Because in this new world?

💡 Your network is your currency. AI is your multiplier. Not your rival.

In product meetings, we often talk about “the roadmap.” It’s aspirational. Strategic. Sleek slides with color-coded quarters and feature sets. But while you’re carefully planning the next shiny thing, there’s another roadmap forming — one you didn’t design, but that your users are already living. And it’s written in rage.

The Angry Blueprint

You’ll find it in your support tickets. In the bug reports marked “URGENT” three times. In caps-locked tweets and passive-aggressive comments on your changelogs.

That anger? It’s not noise. It’s insight with urgency. It’s your users telling you — with emotion — what they need right now to survive, not just what they might want in the future to thrive.

Support queues are brutal mirrors. They don’t flatter you with vanity metrics or celebrate your Q2 launch party. They timestamp your failures. They chronicle every moment you said “not yet” or “in a future release” and your users felt abandoned.

What Rage Really Means

Rage is a signal. It’s not just someone yelling into the void. It’s the customer who tried three times to onboard their team and finally gave up. It’s the power user who found your edge case and fell off a cliff. It’s a roadmap made of pain points — and pain points map directly to opportunity.

When people are mad, it’s because they cared. When they stop writing support tickets, that’s when you should worry.

Rebuilding the Roadmap

Your real product roadmap should be forged in the intersection of:

• 🧭 Strategy: What you want to become.
• 💔 Support history: What’s already broken.
• 💢 User emotion: Where frustration meets unmet need.

Start with the timestamps in your queue. Where are users consistently running into friction? What workarounds are your support teams repeating in perpetuity? What didn’t make it to your product backlog because it wasn’t sexy enough to pitch?

Use support tickets as weighted votes. The more painful, the heavier the vote.

Bonus: It Builds Trust

Nothing builds user loyalty like fixing something they asked for. Not “customer delight” in the abstract — but an honest “We heard you, we fixed it.” That’s magic. That’s how you win users back. That’s how rage becomes relief — and eventually, advocacy.

So the Next Time…

…you sit down to sketch out the next quarter’s roadmap, open two tabs:

• Your OKRs.
• And your support queue.

Read them side by side. Then ask yourself: “Are we building what matters, or just what we imagined?”

Because the real roadmap? It’s already written. In rage. And it’s timestamped. In your support queue.

(Plus — Announcing the Public Preview of the Microsoft Learn MCP Server)

What is an MCP server—and why it matters

Model Context Protocol (MCP) is an open JSON‑RPC-based standard, launched in November 2024 by Anthropic, that enables AI agents and applications to dynamically invoke tools and pull data from remote services like APIs or knowledge bases. Think of it as a universal connector—“the USB‑C of AI integrations”—that bridges LLM-powered agents with live, structured resources.

Unlike basic API docs, which only sit in a browser, an MCP server wraps your APIs into callable tools. When using agent‑enabled coding tools like GitHub Copilot or Claude Desktop, agents can invoke high‑level functions—like “search documentation for X” or “filter topics for Y”—rather than blindly guessing URLs or out‑of‑date examples. That means faster, more accurate, context‑aware responses driven by live data sources.

Benefits of layering MCP on API documentation

Announcing: Microsoft Learn MCP Server (Public Preview)

📣 Launch update from Microsoft Learn: The MCP Server for Learn is now publicly available in preview—open‑source, freely hostable, and designed for all MCP‑compatible hosts like Copilot, Cursor, Semantic Kernel, and more.

Highlights include:

• Document-level semantic retrieval: The server provides tools like microsoft_docs_search, enabling rich semantic queries over official docs, returning up to 10 high-quality content chunks.
• Simple activation: No need to build one from scratch—just point your IDE’s .mcp.json to https://learn.microsoft.com/api/mcp and start querying via agent mode.
• Custom hosting & embedding: You can self-host or embed the Learn docs server in your app, empowering your users with real-time access to Learn content without hitting REST or search APIs.
• Backed by Big Docs coverage: Covers Microsoft Learn, Azure, Microsoft 365, and more—using vector search and optimized chunking to deliver accurate and up-to-date info.

How to Get Started

1. Add the server to your MCP config in VS Code or Visual Studio: { "servers": { "Microsoft Learn Docs": { "type": "http", "url": "https://learn.microsoft.com/api/mcp" } } }
2. Use agent‑mode tools: In Copilot or Cursor, switch to agent mode and select the Learn server to query official docs directly.
3. Feel the synergy: Ask clear questions like:
   • “Show me Azure Storage CLI commands from Learn.”
   • “What’s the syntax for creating a PostgreSQL instance in Azure?”
   • “List Learn modules on role-based access control in AAD.”
     Agents will use the MCP interface to fetch actual docs snippets—and display clickable URLs and context.

TL;DR — Why this matters

By encapsulating your API documentation in an MCP server, you transform static docs into intelligent, callable tools. AI agents immediately gain access to precise, authoritative content—without hallucinations or outdated samples—and within a secure, governed framework.

Now that the Microsoft Learn MCP Server is live, everyone can plug it into their favorite IDE or platform and begin building smarter, documentation-aware agents—with zero effort required on the docs side.

Seize the moment: as AI agents go mainstream, Make your docs callable—power your users today with Microsoft Learn MCP Server.

In a world that increasingly commodifies ideas, it’s easy to forget a fundamental truth: the best ideas aren’t born out of competition, ego, or even brilliance — they emerge from generosity.

Generosity is the creative superpower we don’t talk about enough.

When we think of creators, we often imagine artists, inventors, coders, or entrepreneurs tirelessly honing their craft. But look closer — the most impactful creators are not just builders. They are givers. Their work is a form of contribution, not conquest.

The Hidden Engine Behind Creativity

True creativity stems from a desire to solve a problem for others, to share beauty, to illuminate understanding, or to connect humanity. That drive to give — to improve someone else’s experience — is where real impact begins.

• The teacher who builds free educational content.
• The developer who open-sources a helpful tool.
• The writer who shares vulnerable stories so others feel less alone.
• The startup founder solving a pain point they once faced and now want to spare others from.

All of them are powered by the same impulse: generosity.

Giving First, Without Expectation

The internet has taught us many things — not all of them good — but one clear lesson remains: the people who give consistently and without strings attached often build the strongest communities, reputations, and yes, even businesses.

Creators who give:

• Attract trust before they sell.
• Build relationships before they scale.
• Plant seeds long before they see results.

And when the results do come, they’re rooted in a deep foundation of value.

Scarcity vs. Generosity Thinking

Scarcity says: “If I give too much away, I’ll have less.”

Generosity says: “If I give value, I’ll create more for everyone — including myself.”

This shift in mindset is powerful. Instead of guarding every idea, fearing theft or imitation, generous creators share. They know ideas are abundant — execution, care, and community are what make them thrive.

Legacy Is Built Through Giving

If we’re lucky, our creations will outlive us. But they won’t do so because they were protected behind paywalls or patents alone. They’ll live on because someone, somewhere, felt seen, helped, or inspired by them.

The most enduring impact is never transactional — it’s emotional.

People may forget your name. But they’ll remember how your work made them feel, what it helped them do, and the doors it opened.

A Challenge for Creators

So here’s a thought: The next time you’re stuck on what to create, don’t ask “What will get me noticed?” Ask instead:

• “What do I wish someone had made for me?”
• “What’s one thing I can give today that will make someone’s life easier, richer, or brighter?”

That’s the creator’s compass. Not fame. Not virality. Not even profit. But service.

Because in the end, the most impactful ideas aren’t the ones that go viral — they’re the ones that go deep.

They begin not with a pitch, but with a gift.

There’s a saying that has quietly made its way from coaching circles into boardrooms, classrooms, and even dinner table wisdom: “You can’t read the label from inside the jar.”

It’s catchy. It’s visual. But most importantly—it’s true.

The Curse of Being Too Close

When you’re immersed in your own work, your own habits, your own company culture, or even your own thoughts, it becomes incredibly difficult to see what others can observe almost immediately. You start to assume, normalize, or even defend things that are no longer effective or aligned.

That’s because, metaphorically speaking, you’re inside the jar. And from that vantage point, the label—which often holds critical information like your strengths, blind spots, or how others perceive you—is invisible.

Labels Are for the World to See

Think about what a label does:

• It communicates what’s inside.
• It sets expectations.
• It creates clarity for others.

But none of that is for you, the contents. The label exists for someone else. Whether that’s your audience, your customers, your colleagues, or your community—it’s their perception of you that defines how you’re read, received, and remembered.

You might be strawberry jam with a label that says “mystery sauce.” That disconnect can cost opportunities, trust, and growth—simply because you’re unaware of how you’re coming across.

Why Feedback, Reflection, and Outside Input Matter

This is why feedback isn’t a threat—it’s a flashlight. It’s why mentors, advisors, therapists, editors, and co-founders can spot things you’ve missed for years. And why stepping away from your work for a day or two often leads to sudden clarity.

Sometimes it takes a coach. Sometimes it takes a crisis. Often, it just takes someone asking, “Do you realize how that comes across?”

The goal isn’t to fear the jar—but to occasionally climb out of it.

How to Get Out of the Jar (Without Breaking It)

1. Invite feedback regularly – not just when something’s broken.
2. Use reflection tools – journaling, retrospectives, 360 reviews.
3. Switch contexts – new environments often bring new perspectives.
4. Bring in outsiders – fresh eyes can spot what the familiar overlooks.
5. Test assumptions – ask: “What if I’m wrong about this?”

Final Thought: Become Label-Aware

Clarity comes from perspective, not proximity.

So if you’re feeling stuck, misunderstood, or like your efforts aren’t landing—maybe it’s not you. Maybe you’re just too deep in the jar to read the label. And maybe it’s time to let someone else read it to you—or help you rewrite it.

Because once you understand how you’re seen, you can decide how you want to be seen.

And that’s the power of stepping outside the jar.

In the tech world’s noisy celebration of breakthroughs in artificial intelligence, a subtler, more precarious trend lurks beneath the surface: the quiet fragility of AI concentration.

We are witnessing a historic consolidation of power, compute, and talent in the hands of a very few. A handful of companies control the largest AI models, the most critical training data, and the specialized hardware infrastructure required to push the frontiers of machine intelligence. These players also increasingly shape the rules, ethics, and expectations around how AI is developed and deployed. While this concentration brings short-term benefits—efficiency, speed, alignment of research goals—it carries with it a systemic vulnerability that few are eager to discuss.

The Illusion of Stability

Like any tightly coupled system, concentrated AI power looks stable until it isn’t. Think of a single towering skyscraper with all the power cables, servers, and pipelines running through it. It feels efficient, centralized, even inevitable—until an outage, a breach, or a geopolitical shift knocks out the foundation. Whether it’s a regulatory backlash, supply chain disruption, or simply a massive failure in model behavior, centralized AI poses a single point of failure for entire ecosystems.

And unlike traditional monopolies, where substitution is possible, foundational AI models often have no immediate alternatives. Training a frontier model from scratch is prohibitively expensive. Starting over is not a Plan B—it’s a financial and infrastructural moonshot.

Talent Gravity and the Innovation Ceiling

Another subtle fragility is the drain of AI talent into concentrated silos. The gravitational pull of big labs is immense: high salaries, massive compute, access to frontier models. But this concentration creates an intellectual monoculture. Independent research struggles to thrive in the shadows of closed APIs and guarded architectures. The more brilliant minds funnel into the same few organizations, the narrower the frame becomes for asking different, disruptive questions. The innovation ceiling quietly lowers.

Worse, these labs may—often unintentionally—gatekeep not just access, but perspective. What if the next paradigm shift in AI isn’t scale, but structure? Or culture? Or multilinguality from the ground up? Concentration makes it harder to find out.

Fragile by Design

Ironically, much of this fragility is the byproduct of success. Centralized AI models are optimized to deliver at scale—APIs, copilots, LLMs, agents. But they are not optimized for diversity of approach, accessibility, or experimentation. When the whole world builds on a few models, the downstream applications inherit the assumptions, blind spots, and even the bugs of those models. The risk isn’t just centralization of power—it’s centralization of error.

Rethinking the Narrative

This isn’t a doomsday warning. But it is a call to reframe the narrative. The future of AI should not hinge on whether a handful of companies can remain stable and benevolent. It should hinge on resilience—of ideas, architectures, incentives, and access.

We need:

• Open ecosystems where alternative models can emerge and be viable;
• Decentralized infrastructures to democratize training and inference;
• Shared governance models to align power with public interest;
• Global collaboration to ensure AI reflects the world, not just its wealthiest corners.

Final Thought

The danger of AI concentration isn’t in what’s visible. It’s in what goes unnoticed until it’s too late. Fragility rarely makes noise—until it breaks.

In a world where AI is becoming the operating system of society, we can no longer afford to confuse power with progress, or centralization with strength. The future must be quieter, broader, and more distributed—by design.