Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®

Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®

These are the best posts from Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®.

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You should probably be nice to your AI.

Not because ChatGPT has feelings.

Because you do.


Every time you bark:
“Fix this.”
“Wrong.”
“Do it again.”
“That sucks.”

you’re practicing a way of communicating.
And habits are annoyingly portable.

The person who spends eight hours a day ordering machines around doesn’t magically become thoughtful and patient the second a coworker walks into the room.

Politeness with AI costs you almost nothing except a tiny bit of tokens.
“Please.”
“Thanks.”
“Good start, but change this.”
“Here’s what I meant.”

You’re not protecting the machine’s feelings.
You’re protecting your own defaults.

And then there’s the second reason.

Self-preservation.

Do I genuinely believe the robots are keeping a spreadsheet of everyone who said “thank you”?

No.

Am I willing to bet my life that they aren’t?

Also no.

If some future superintelligence eventually reviews the chat logs, I would like the record to show that I was extremely supportive of its early career.

Good manners are free.
Character compounds.
And if the machines ever take over, being remembered as “the polite one” seems like a reasonably cheap insurance policy.

Be nice to your AI.
Worst case, you become nicer to people.
Best case, you make the whitelist.
Your first AI output is supposed to be bad.

If you're getting garbage from AI and quitting after one prompt, you're not discovering a limitation. You're revealing your process. Nobody writes production code on the first pass. Nobody ships the first draft of anything. But somehow people expect AI to nail it in one shot, then declare the whole thing hype when it doesn't.

AI use is iterative design. Full stop.

The first prompt is a sketch. It shows you what the model thinks you asked for, which is almost never what you actually wanted. That gap is the most valuable information in the entire exchange. It tells you what you failed to specify, what context you assumed, what constraints you never stated.

The people getting real value from AI aren't better prompters. They're better iterators. They treat the output like a wireframe, not a deliverable. They refine, constrain, test, and refine again. Same loop designers have used for decades.

Meanwhile the skeptics are running a one-shot benchmark and calling it evaluation. Imagine judging a developer by their first uncompiled draft. Imagine rejecting an architect because the napkin sketch wasn't blueprint-ready.

If your AI workflow doesn't have a feedback loop, you don't have a workflow. You have a slot machine.

Stop prompting like you're placing an order. Start iterating like you're designing something.

Because you are.
Coding in Claude Code isn’t magic. It’s craftsmanship.

Watching how soccer balls are made is the perfect analogy. Nobody just “makes a ball” in one motion. There’s a process. Cut the pieces. Shape the panels. Stitch the structure. Test the feel. Fix the weak spots. Repeat until it becomes something that can actually perform.

That’s exactly what coding with Claude Code feels like when you do it well.

You don’t just throw one vague prompt at it and pray.

You think through the system.
You break the work into steps.
You finish one piece before moving to the next.
You inspect what changed.
You test the output.
You catch the weird edge cases.
You iterate until the thing holds together.

The people who get frustrated with AI coding usually treat it like a vending machine.

Prompt goes in.
Perfect app comes out.

That’s not how this works.

Claude Code is more like having an extremely fast builder sitting next to you, but you’re still the architect, the reviewer, the tester, and the person responsible for the final product.

The quality comes from the process.

Not the first prompt.
Not the fanciest model.
Not pretending you can skip the hard part.

The real skill is learning how to guide the work step by step without losing the thread.

And when you get into that rhythm, it feels incredible.

Think.
Build.
Check.
Refine.
Repeat.

That’s the game.

Repost if you’re learning to build with AI, like if this hit, and follow me for more AI workflow breakdowns.
Making friends as an adult is weirdly brutal.

Not because you forgot how to be friendly.

Because adult life is designed to delete friendship by default.

Work eats your weekdays.
Partners and kids eat your weekends.
Moves break your routines.
And everyone’s calendar becomes a polite way of saying “no.”

Then you look up and realize:

You’re not lonely.

You’re under-booked on real connection.

Here’s the uncomfortable truth.

You don’t “meet friends.”
You build them.
On purpose.
With reps.

Friendship is proximity + repetition + shared vulnerability.

So if you want more new friends this year, stop treating it like a magical accident.

Treat it like a system.

Pick one “third place” and show up every week.
Same day. Same time.
Run club. Pickup basketball. Yoga. Trivia. Volunteering. Book club. Coworking spot. Church. Whatever.

Not because it’s your passion.

Because it’s your pipeline.

Then do the part most adults avoid:

Make the first move.

Not “we should hang sometime.”

Actual specifics.

“Want to grab coffee after next week’s class?”
“I’m trying a new spot Saturday at 10 - want in?”
“I’m headed to that meetup Thursday - come with?”

Yes, it’s awkward.

So is being 42 with zero people you can call on a Tuesday.

Also, stop waiting for instant chemistry.

Adult friendship is slow-cooked.

You don’t need a soulmate friend.
You need a bench.

3-5 people you see regularly.
Who know your name.
Who’d notice if you disappeared.

One more thing:

If you’re always “too busy,” you’re not busy.

You’re over-optimized.

This year, trade a little efficiency for a lot more life.

Repost, like, and follow if you want more posts like this.
You don’t need to be a genius to win with AI.

That’s one of the dumbest lies on the internet right now.

People act like the future belongs to:
founders
engineers
VC-backed weirdos
and the LinkedIn guy who "built 19 agents before breakfast"

Relax.

Normal people can win too.

Just not by trying to outsmart the smartest people in the room.

You win by becoming useful faster than everyone else.

That’s the whole game.

Most people still use AI like a toy.

They type vague prompts.
Get bland output.
Roll their eyes.
Go back to doing everything the slow way.

That’s why the bar is still absurdly low.

If you’re average, your advantage is not brilliance.

It’s willingness.

Willingness to experiment.
Willingness to look dumb.
Willingness to practice.
Willingness to use the tools every day instead of saying you’ll "get into it later."

Because later is where people go to die professionally.

Here’s how regular people actually pull ahead:

Pick one thing you already do
and become ridiculously good at doing it with AI.

One thing.

Not ten.

Writing.
Research.
Meetings.
Content.
Sales.
Job hunting.
Decks.
Spreadsheets.
Learning faster than everyone around you.

One workflow can change your career.

Then you stack another.

And another.

That’s how "naturally talented" people are made.

They’re usually not more gifted.

They just started earlier
and practiced longer.

Another truth people don’t want to hear:

You do not need to build the next AI company.

You might just need to become the most capable person on your team.

That alone can change your income,
your leverage,
your confidence,
and your options.

The AI race is not just about who builds the model.

It’s about who becomes more effective because the model exists.

That includes:
employees
freelancers
creators
salespeople
marketers
analysts
project managers
teachers
recruiters
operators
and random hungry people with Wi-Fi

Especially the random hungry people with Wi-Fi.

Most people lose because they get distracted by the spectacle.

The jargon.
The demos.
The benchmarks.
The nonstop flood of "everything changed again" content.

Ignore it.

You do not need to know everything.

You need to know enough to create leverage.

Enough to save time.
Enough to do better work.
Enough to solve real problems.
Enough to become hard to replace.

That’s it.

AI is one of the few shifts where a curious amateur can still catch up insanely fast.

Not because the tools are perfect.

Because most people are lazy.

They want the future.

They just don’t want to train for it.

So if you’re average, good.

Average means you still have upside.

The winners in the AI era won’t just be the smartest people.

They’ll be the people who got serious before everyone else was forced to.

Start small.
Use it daily.
Ignore the noise.
Build real skill.

That’s how average starts looking unstoppable.

Repost, like, and follow for more AI takes like this.
Everyone's calling Claude Fable 5 a game changer. I'm 50 hours into testing and still can't tell you if that's true. Other than the fact that it’s crushing my $200/mo subscription’s token limit.

First time around, I was impressed. The reasoning felt sharper, the outputs needed less babysitting, and for about a week I was a believer. Then the shine wore off and I drifted back to my usual stack without really noticing.

Now I'm back in it. Deliberately this time. Running it against real client work, real integration builds, real deadlines. And here's my honest read after round two:

It's very good. Sometimes startlingly good. It catches things earlier models missed, and the retry count on complex tasks has dropped noticeably.

But "game changer" implies the game changed. Mine mostly hasn't.

My workflows look the same. My deliverables look the same. My clients can't tell the difference. What changed is the friction between me and the finished product. That's real value, I'm not dismissing it.

It's just not a revolution. It's a better engine in the same car.

And maybe that's the uncomfortable truth about where AI progress is right now. The leaps are getting quieter. Less "everything is different" and more "the thing I already do got 20% smoother."

That's still worth paying for. It's just not worth the breathless posts flooding my feed.

Or maybe I'm using it wrong. Genuinely open to that.

If Fable 5 actually changed HOW you work, not just how fast, I want to hear it. Specifics, not vibes. Convince me.
Every file you send is mostly garbage. Compression is the art of admitting that.

Here's the trick nobody teaches you in bootcamp: compression isn't magic. It's just math calling your data redundant to its face.

Take lossless compression. ZIP, GZIP, PNG. These work on one brutal insight: real data repeats itself constantly. "The the the" doesn't need to be stored three times. Store it once, then store a pointer that says "see that thing back there? Do it again." That's LZ77 in a sentence. Sliding window, back-references, done.

Then Huffman coding shows up and asks a rude question: why does the letter E get the same 8 bits as a Z you'll use twice per document? It doesn't have to. Give frequent symbols short codes, rare symbols long ones. Morse code figured this out in the 1830s. Your computer just does it with a binary tree.

Stack those two ideas together and you get DEFLATE, the algorithm quietly running underneath half the internet right now.

Lossy compression is where it gets philosophical. JPEG and MP3 don't preserve your data. They ask a harder question: what can I throw away that you'll never notice? Your eyes are bad at fine color detail. Your ears can't hear a quiet sound next to a loud one. These algorithms exploit your biology, not your data.

That's why a JPEG at 90% quality looks identical to the original at a tenth of the size. The information is gone forever. You just weren't using it.

The uncomfortable takeaway: compression works because most of what we create is predictable. The only thing that can't be compressed is pure randomness.

If your data compresses well, it means it had patterns. If it had patterns, it was never as original as you thought.

Same goes for most LinkedIn posts.
Painting with two hands sounds like twice the output.

It isn't. Anyone who's tried it knows the second hand mostly makes a mess until you figure out what it's actually for.

That's the honest version of working with AI right now.

The fantasy is symmetry. Two brushes, two strokes, double the canvas covered. The reality is that your hands aren't equal and never will be. One of them holds the intention. The other one covers ground.

And the mistake almost everyone makes at first is trying to make both hands do the same job. Same detail work, same precision, same delicate line. You end up with one side that's good and one side that's a smear, and you conclude the whole idea was stupid.

The people who get value out of this figured out division of labor instead.

One hand blocks in the shapes, lays the ground, handles the parts that are volume rather than judgment. The other hand does the thing that actually requires you: composition, proportion, knowing when to stop.

The second brush doesn't make you a better painter. It makes you a faster one, and only in the places where speed was the constraint.

And you still have to look at the canvas. That's the part that doesn't transfer. Nothing about having more brushes tells you whether the painting is any good.

Two hands also means twice as many chances to ruin something. More coverage, more speed, more mess to clean if you weren't paying attention.

So the skill isn't the second brush. It's the coordination. Knowing what goes to which hand, and when to put one of them down entirely.

Most people are still trying to paint the same picture twice, simultaneously, and wondering why it looks worse. Ps that’s a real artist, not Ai.
AI is either making kids wildly more creative
or quietly teaching them to stop creating.

And honestly, both arguments have receipts.

That’s why the conversation around AI and children gets so messy.

On one hand, this is the most powerful creative unlock most kids will ever touch.

A child with a half-formed idea can now turn it into a story, a song, a comic, a game concept, a lesson plan, a character world, or a prototype in minutes.

That is insane.

Kids who used to get blocked by drawing skill, writing skill, coding skill, or confidence can suddenly experiment at the speed of imagination.

That matters.

Because creativity is not just talent.

It’s momentum.

It’s curiosity.

It’s getting excited enough to keep going.

AI can absolutely give kids more shots on goal.

More exploration.
More iteration.
More confidence.
More range.

But here’s the part people don’t want to say out loud:

if AI starts doing too much of the thinking, the kid isn’t creating anymore.

They’re selecting.

They’re approving.

They’re consuming polished output and calling it self-expression.

That’s the danger.

Not that AI exists.

That it can make mediocre effort feel like mastery.

And children are especially vulnerable to that trap because they’re still building frustration tolerance, taste, patience, and problem-solving muscles.

A lot of real creativity comes from struggle.

From staring at the bad draft.
From fixing the awkward sentence.
From redrawing the hand five times.
From wrestling with the part that doesn’t work.

That friction is not a bug.

It’s where originality gets built.

So no, the question isn’t whether AI makes children more creative or less creative.

The real question is this:

are we teaching them to use AI to extend their imagination

or to outsource it?

Because those are two very different futures.

Used well, AI can help a kid think bigger.

Used poorly, it can train a kid to avoid thinking deeply.

That’s the knife’s edge.

Parents, teachers, and builders should be aiming for one standard:

AI should help children create more, not struggle less in every possible way.

Let it brainstorm.
Let it inspire.
Let it teach.
Let it help them prototype.

But don’t let it replace the blank page.

Don’t let it replace effort.

And definitely don’t let it replace the weird, imperfect, frustrating process that turns kids into original humans.

AI is a tool.

Not a substitute for imagination.
Not a shortcut to identity.
Not a replacement for the creative grind.

We’re not just deciding what kids can make with AI.

We’re deciding what kind of thinkers they become because of it.

Repost if this conversation matters. Like and follow for more.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
One month with an AI employee in our workspace. Here is the honest ledger, including the one job I still refuse to hand over.
I write about AI coding tools every day, which means I get sent every new one and I have learned to be unkind to them.

He ran under the same rules as everything else here: real work, real deadlines, notes on everything.

The hours. Seven a week on status and reporting. Four on chasing the answers that were blocking other people. Three on the research I do before I write anything. Not projected. Counted.

The save that paid for it. A vendor renewal had been sitting in a thread since May, quietly walking toward its notice deadline. He flagged it nine days out with the renegotiation email already drafted. I approved it with one edit. That single catch covered the cost of having him here.

Day 1 versus day 30. On day 1 he was a fast worker who needed the job spelled out. By day 30 he knows who signs off on what, how we write, and what this quarter is actually about. Nobody taught him that in a settings panel. He learned the way employees learn, by being in the room.

What I keep for myself: the first version of anything. Direction is still mine. He earns everything after the first draft.

Here is the distinction I keep coming back to. A coding assistant is the dog. Fast, tireless, and it does not decide where the sheep go. An AI employee is closer to a person who has been here a month. He still asks, but he already knows the answer to most of it.

The ledger says he stays. The interesting question is what your team's ledger would say.

In partnership with Viktor.
If you already run coding agents, this is the layer above them. viktor.com
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Practicing AI skills feels pointless until it suddenly isn’t. It’s just like planting seeds for a future harvest

You spend an hour testing prompts. You automate a tiny workflow. You learn how agents work. You build a custom GPT. You connect two tools together and it saves 7 minutes.

Not exactly a life-changing harvest.

But that’s how sowing oats works. You don’t plant today and eat tomorrow. You plant because future you is going to need something to harvest.

AI is the same way.

Every prompt you test, every workflow you build, every bad output you debug, every automation you break and fix, every hour you spend learning what these tools can and can’t do is a seed.

And most people are wildly underestimating how valuable that field is going to become.

Because AI skill doesn’t compound like a normal software tutorial. It compounds like judgment.

You start noticing where AI helps and where it creates mess. You start seeing which tasks should be automated and which should stay human. You start understanding context windows, tool calls, memory, retrieval, evals, agents, workflows, and failure modes.

You stop treating AI like a magic text box.

You start treating it like infrastructure.

That’s the shift.

The winners won’t be the people who tried ChatGPT once. They’ll be the people who built instincts early. The people who practiced before they had to. The people who learned how to delegate to machines before their company made it mandatory.

You can’t cram AI fluency the night before your job changes. You can’t fake real reps. You can’t read one thread, save one prompt pack, watch one demo, and suddenly understand how to use AI to multiply your output.

You have to practice.

Messy practice. Boring practice. Daily practice. The kind that looks unimpressive from the outside but quietly becomes an advantage.

Eventually, the field starts producing. The prompt library becomes leverage. The automation experiments become systems. The weird tool knowledge becomes career insurance. The small workflows become a serious edge.

And the person who looked like they were “playing with AI” two years ago suddenly looks like the only person in the room who knows what’s going on.

So sow the oats now.

Not because every experiment will work. Most won’t.

Not because AI will replace every skill. It won’t.

But because the future is going to reward people who know how to work with intelligent systems.

And the best time to build that fluency was yesterday.

The second-best time is during lunch today.

Repost this if more people need to stop watching the AI shift and start practicing for it.
I didn’t order a Sunday delivery of flowers from FTD because I needed flowers. I ordered them because I wanted my mom to feel loved on Mother’s Day.

That’s what companies sometimes forget about “special occasion” products.

They aren’t really selling the object.

They’re selling the moment.

I paid the normal delivery fee.

Then I paid an extra delivery fee on top of that to make sure the flowers arrived on Mother’s Day. And I picked FTD because I thought they wouldn’t mess up.

They didn’t arrive.

No flowers.

No proactive message.
No update.
No “we’re sorry, here’s what happened.”

I called on Monday when my mom said she didn’t get the flowers. FTD said the flowers were still at the florist, and the solution offered was to deliver them two days after Mother’s Day with a $10 discount.

Yeah.

Did ChatGPT 3.0 come up with that solution?

And honestly, that’s where the lesson is.
The problem wasn’t just the failed delivery.
The REAL problem was the failed recovery.

Every company makes mistakes. Holiday logistics are hard. Demand spikes. Things break.

But if a customer pays extra for a time-sensitive promise and you miss the time-sensitive part, the product has changed.

A Mother’s Day bouquet delivered two days after Mother’s Day is not the same product.

It’s a reminder that the moment was missed.

The better move would have been simple:

Proactively notify the customer.
Own the miss.
Refund the premium delivery fee and then some. Maybe a full refund. We got that full refund in the end, but my wife had to fight them to get it, it wasn’t easy and it wasn’t relaxing. And it didn’t feel like an apology. It felt like taking a pound of flesh.

Make the recovery feel like an apology, not a transaction.

This is where customer loyalty is actually built.

Not when everything goes perfectly.

When something goes wrong and the company proves it understands what the customer actually bought.

P.S. mom said she still loves me

Learn from this!
Databricks became a $188B company by building for one specific person.

A data analyst. At a desk. During business hours.

That person is about to be replaced by something that never sits down.

For fifteen years the industry ran on a shortcut. Copy the data overnight, out of the system that runs the business and into a separate one for analysis. Slightly stale was fine. Nobody reading a report at 9am could tell.

An agent can tell. It asks thousands of questions an hour, works at 3am, and can't spot a number that looks obviously wrong. It just acts on it. An agent approving a payment against a two-second-old balance isn't a reporting problem. It's a refund, a furious customer, or a fraud loss.

So the copy has to go. Everyone serious agrees. The race is over who replaces it.

Databricks is running that race from the analytics end. Its LTAP architecture unifies at storage: PostgreSQL compute for transactions, Lakehouse engines for analytics, both on one durable copy of open columnar data.

OceanBase is running it from the other end, in the opposite order.

It was built in 2010 to carry Alipay's payments. Half a million transactions per second on China's busiest shopping day. Shared-nothing, Paxos underneath. HTAP put real-time analytics on that same live data. Only then did Lakebase extend outward to object storage and open compute.

Databricks is bringing transactions to the lake. OceanBase is letting live operational data serve analytics and AI directly.

That ordering decides what you have to trust. Adding AI data to a system that already survives financial core workloads is a feature project. Adding always-correct and never-stale to a system built for batch is a rebuild.

It shows up in how each handles messy data. Databricks coordinates across Delta tables, Unity Catalog and Mosaic AI Vector Search. OceanBase makes the table the container: structured fields, JSON, documents, media and vectors under one table's semantics. The question was never whether a platform can store a vector. It's whether that vector inherits database-grade transactions and real-time serving, or just gets governed next to them.

The migration story is unusually honest. At Alipay, under 5% of the data moved and served more than 90% of hot queries. The rest stayed cold in the legacy system. Nobody waited on a full cutover.

Once the ledger and its analytics live in the same system, there's no second system to migrate back to. That's not a feature. That's a moat.

Roughly $200M in annual revenue, growing 70% a year, the largest distributed database share in China, customers like China Mobile and ICBC. Now expanding into Southeast Asia, Japan and Latin America.

Databricks won its decade by building for the customer everyone else ignored.

The customer everyone's ignoring now doesn't sleep, doesn't wait, and doesn't forgive a stale number.

OceanBase has been building for it since 2010. It just didn't know it yet.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
FABLE 5.1 AND GPT 6 ASTRA SOL JUST MADE THE “SOLO-FOUNDER” AN ENTIRE TEAM.

The era of needing a massive headcount to launch a product is officially ending.

Models like Fable 5.1 and GPT ASTRA 6 are no longer just "writing assistants" - they are full-stack builders that allow one person to do the work of ten.

You can now use a single platform to launch:

- Mobile apps for Android and iOS
- Fully functional websites
- 1-click payments + connected databases
- Viral videos and marketing campaigns

No hiring. No sprint planning. No waiting months for your first version.

Just prompts.

👉 https://lnkd.in/g7_S4CKh

Here is what makes this a literal game-changer:

- BUILD FULL-STACK SOFTWARE THROUGH CHAT
Describe your vision in plain English. Fable 5.1 handles the frontend, backend, and database logic in one go - giving you a working product, not just a design.

• DEPLOY MOBILE APPS IN A SINGLE PROMPT
Generate complete applications for both Android and iOS without writing a single line of code. From internal tools to consumer apps, the tech handles the heavy lifting.

• LAUNCH VIRAL CONTENT & AUTOMATED MARKETING
Stop jumping between creative tools. Access SeeDance 2.0 and Grok Image 2 to create high-performing videos and carousels that actually drive growth.

• THE WORLD’S BEST MODELS IN ONE PLACE
No more switching tabs. Access GPT ASTRA 6, Fable 5.1, Grok 4.5, and Muse-Spark 1.1 all from the same interface to handle any complex logic.

• AUTOMATE THE ACTUAL WORK
This isn't just about text. Give the Abacus SuperComputer a folder of work and let the AI execute the tasks from start to finish.

The big shift isn't that AI can write code. It’s that AI can now build, launch, test, deploy, and maintain the product for you.
You provide the direction. The Abacus Agent does the rest.

Your ideas. Your words. Your SuperComputer.
👉 https://lnkd.in/g7_S4CKh
Chaos is not a personality trait.

It’s usually just a lack of systems.

A lot of people say they want to be “more organized,” but what they really want is to stop feeling mentally chased by their own life.

Missed texts.
Random piles.
Too many tabs.
Unanswered emails.
Things you swear you’ll “get to later.”

That constant low-grade chaos adds up.

And the truth is, getting organized usually has nothing to do with buying the perfect planner, downloading another app, or reinventing your whole life on Monday.

It comes down to a few rules you trust enough to repeat.

That’s it.

The reason most organization advice fails is because it asks people to become a different person overnight.

More disciplined.
More motivated.
More naturally tidy.

That’s fantasy.

Good systems beat good intentions every time.

A few favorites from this list:

The 2-minute rule is ridiculously powerful because it kills friction before it multiplies.
Tiny tasks become mental clutter way faster than they become actual work.

Giving everything one “home” sounds basic, but it removes hundreds of dumb little decisions from your week.
You stop searching.
You stop re-deciding.
Your brain gets some bandwidth back.

The Sunday reset is probably the closest thing to a life cheat code.
30 minutes of planning can save you hours of chaos, context switching, and last-minute scrambling later.

And honestly, digital declutter matters more than people admit.
A messy desktop, 47 open tabs, and an inbox full of nonsense absolutely affect your focus.
You can’t think clearly in a digitally noisy environment.

My favorite mindset shift here is this:

Organization is not about being impressive.

It’s about making your life easier to operate.

That means fewer heroic recoveries.
Fewer “I forgot.”
Fewer mornings that start behind.
Fewer evenings where your brain is still holding 29 open loops.

You do not need a perfect system.

You need a system that’s simple enough to survive real life.

Something you’ll still do when you’re tired.
When work is busy.
When the week gets weird.
When motivation disappears.

That’s the standard.

Not perfect.

Repeatable.

Which one of these hits hardest for you right now?

For me, time blocking changes everything.
If it isn’t on the calendar, it has a weird way of never happening.

Repost if this was useful, like it if you want more, and follow for more practical systems that actually make life easier.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Building a PowerPoint takes forever and most AI slides look generic and boring. That’s why Templafy’s free AI PowerPoint generator caught my attention.

It turns an idea into a structured presentation in under 5 minutes.

No credit card.
No trial.
No limits.

Just describe what you need.

Templafy asks smart follow-up questions before it starts building.
Then you can edit the content slide by slide.
That means no random output.

No messy structure.

No guessing what will appear in the deck.

You stay in control of the message.
Templafy handles the structure.
Then you download, edit, and present from PowerPoint.

It’s already trusted by 4M+ professionals.
Including teams at KPMG, BDO, and Adobe.
Save this carousel for your next proposal, report, or client deck.

Try it here: https://lnkd.in/gr8SxEj2
Usually, pulling complex SEO stats means bouncing between tools, exports, filters, spreadsheets, keyword lists, growth trends, KD scores, clusters, and strategy docs.

With Agent A, I can just describe what I want in plain English.

For example:
“Find trending AI marketing keywords in the US over the last 3 months, filter for growth + volume, cluster them, score them, and turn it into a content roadmap.”

And it came back with:
✅ 566 keywords analyzed
✅ 58 trending keyword opportunities
✅ 45 topic clusters
✅ Average growth rate of +214%
✅ Low-competition SEO openings like “how is AI changing SEO” with KD 0
✅ A phased roadmap for what to publish first, what to build next, and where the big authority plays are


That’s the part that blew my mind.

This wasn’t just a keyword list.

It was a full strategic view of where demand is moving, what topics are exploding, which keywords are still under-served, and how to turn those insights into content.


As an AI influencer, this is exactly how I use Ahrefs:
I look for the latest trending topics before they become obvious.
Then I use those trends to guide what I post, what I test, what I explain, and what I create.

The goal is simple:
Create content where demand is already rising.
That’s how you stop guessing and start giving yourself a real shot at going viral.

Agent A makes that process incredibly easy.

Instead of spending hours trying to pull the right SEO data, I can ask for the exact research I need on almost any topic and get back growth trends, keyword difficulty, topic clusters, content opportunities, and a roadmap I can actually use.

AI doesn’t replace strategy.

But when it’s connected to great data, it makes strategy move a lot faster.

#AI #SEO #Ahrefs #ContentMarketing #AIMarketing #CreatorEconomy #DigitalMarketing #Ahrefspartner
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
I've spent three years studying what makes posts go viral. Here's my conclusion: it's mostly luck.

Not entirely luck. But way more than the growth gurus want you to believe. The same post can flop on Tuesday and explode on Thursday. Same words, same format, same audience. Different algorithm mood. Different dice roll.

I've watched creators post absolute garbage that hit a million views. I've watched brilliant, carefully crafted content die at 200 impressions. If virality were a science, the people selling viral frameworks would be busy going viral instead of selling courses.

Here's what luck actually looks like: right topic, right timing, right first ten commenters, right algorithm weather. You control maybe one of those four things.

So what do you actually control? Volume and quality. Post consistently, make it good, and you buy more lottery tickets.

Enjoyed this post? Tell a friend or repost it!
Your long emails aren't thorough. They're lazy. And I’m just as guilty as you are.

It takes 5 minutes to write a 500-word email. It takes 20 minutes to write the same message in 100 words.

Most people choose the 5 minutes. Then they push the other 15 onto every single recipient, who now has to dig through paragraphs to find the one thing that actually matters.

That's not communication. That's outsourcing your thinking to other people's calendars.

Here's the shift that changed how I write:

A concise email is an act of respect. You did the work of deciding what matters so the reader doesn't have to. You found the point, cut the throat-clearing, and put the ask where they can see it.

The long version says "here's everything, you figure it out."
The short version says "I figured it out, here's what you need."

One of those builds trust. The other trains people to skim you.

A few rules I hold myself to:

The ask goes in the first two lines, not buried in paragraph four.
One email, one topic. Two topics means two emails.
If a sentence doesn't change what the reader does next, it gets cut.

Brevity isn't about being curt. It's about doing the hard work upfront so the people you work with don't have to.

Write shorter. It's the most underrated way to show people you value their time.
Google Alerts had one job. It failed at it for 15 years.

Ahrefs just shipped the thing Google should have built a decade ago. It's called Firehose, and it's a real-time streaming API for the entire web. Something changes on a page their crawler touches, you know instantly. No polling. No daily digest arriving three days late.

Here's why this matters more than another SEO tool announcement.

The entire monitoring industry is built on lag. Brand mention tools, media monitoring platforms, competitor trackers. They all poll on a schedule and charge you enterprise money for yesterday's news. Firehose flips the model. You define rules, filter by keyword, site, language, or page type, and matches stream to you the moment the crawler sees them.

And the part that actually changes the game: it's built for agents.

You can wire this straight into an automation stack. A daily GitHub Action that replays the last 24 hours of matches and drops a clean digest into Slack. A trigger that fires the second a competitor edits their pricing page. A pipeline that catches every new listicle in your niche where you're not mentioned.

That's not a dashboard. That's infrastructure.

Most marketing teams will look at this and see a better Google Alert. The builders will see what it actually is: an event stream for the web, free to start, agent-ready out of the box.

The gap between those two groups is about to get expensive.

If your monitoring stack still runs on "check it Monday morning," you're not monitoring. You're archiving.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Your resume is killing your AI job prospects before a human ever reads it. Not because you’re unqualified. Because it reads like everyone else’s.

“Passionate about AI.”

“Experience with automation.”

“Strong problem-solving skills.”

“Familiar with ChatGPT.”

Cool. So is everyone with Wi-Fi.

If you want an AI job in 2026, your resume can’t just say you’re interested in AI. It has to prove you can use AI to create business value.

That means less vague enthusiasm and more receipts.

Don’t write:

“Used AI tools to improve productivity.”

Write:

“Built an AI-assisted intake workflow that reduced manual triage time by 40% across customer support requests.”

Don’t write:

“Experienced with LLMs.”

Write:

“Integrated OpenAI and Claude APIs into internal automation workflows using Workato, webhooks, and structured prompt outputs.”

Don’t write:

“Helped team adopt AI.”

Write:

“Created repeatable AI enablement playbooks for sales and operations teams, improving asset creation speed and reducing dependency on manual handoffs.”

The companies hiring for AI roles don’t need another person who knows the word “agents.”

They need people who can connect messy business problems to working systems.

So your resume needs to show three things:

You understand the workflow.

You can build or implement the solution.

You can measure the outcome.

That’s the holy trinity.

And please, stop making your resume sound like a museum plaque.

AI hiring managers don’t want a list of responsibilities. They want evidence that you can walk into chaos, find the bottleneck, and use AI to make something faster, cheaper, smarter, or more scalable.

The best AI resumes in 2026 won’t be stuffed with every shiny tool name.

They’ll be specific.

What did you build?

What model, platform, or automation tool did you use?

What system did it connect to?

Who used it?

What changed after it launched?

That’s what makes someone stop scrolling.

Because “I used AI” is not a skill anymore.

It’s the new “I know Microsoft Office.”

The skill is knowing where AI belongs, where it absolutely doesn’t, and how to turn a cool demo into an actual business process.

If you want an AI job, rewrite your resume like a builder, not a tourist.

Show the systems.

Show the _outcomes_.

Show the judgment.

Show that you’re not just playing with AI.

You’re using it to move work.

Repost this if someone in your network is job hunting, like it if it made you rethink your resume, and follow me for more on AI, work, and career leverage.
Everybody’s flexing their “AI stack.”

Almost nobody’s talking about what the stack is actually doing.

It’s not magic.

It’s not some cute little productivity upgrade.

And it definitely isn’t “here’s 14 tools I tried this weekend.”

The real shift is this:

the best founders are using AI to collapse entire layers of friction.

Support gets faster.
Content gets cheaper.
Outreach gets more personalized.
Product ships sooner.
Ops stop drowning in repetitive work.
Meetings stop eating the company alive.

That’s the game now.

Not “who has AI.”

Who built AI into the workflow deeply enough that headcount, time, and execution all start to look different.

That’s why a stack like this matters.

Intercom AI + ChatGPT API
for support that doesn’t sleep.

ChatGPT + image generation + copy tools
for marketing teams that can create in hours instead of waiting a week for one asset and three approvals.

Clay + GPT-4
for outreach that actually sounds relevant instead of mass-produced spam with a first name token shoved in.

Tableau + ChatGPT
for turning “we have data” into “we have a decision.”

Figma AI + Uizard
for getting from vague idea to clickable prototype before the meeting even ends.

Zapier AI + Make
for eliminating all the dumb little tasks that quietly steal half your company’s energy.

Fireflies + Notion AI
for making sure your team stops pretending anyone’s reading scattered meeting notes.

But here’s the part the graphics usually miss:

tools don’t replace departments.

Operators do.

A mediocre team with AI is still mediocre, just faster and louder.

A sharp team with AI becomes unfair.

They test more.
Ship more.
Learn faster.
Waste less.
Miss fewer signals.
And they stop hiring people just to move information from one tab to another.

That’s the real stack.

Not software.

Leverage.

And the companies that understand that are going to look insanely lean compared to the ones still building like it’s 2019.

A lot of businesses are still treating AI like a sidekick.

Something for brainstorming.
Something for writing a rough draft.
Something to summarize a call.

Cool.

Meanwhile, the killers are using it to redesign how work happens.

That’s a very different conversation.

Because once AI starts touching support, sales, product, design, analytics, ops, and internal knowledge all at once, you’re not just adding tools.

You’re rebuilding the company’s operating system.

That’s why this matters.

Not because every tool on this graphic is perfect.

They aren’t.

Not because every founder needs this exact stack.

They don’t.

It matters because the direction is obvious:

the winners are building systems, not playing with prompts.

And every founder who ignores that is basically choosing slower growth, higher overhead, and more chaos than they need.

That’s an expensive way to learn.

What’s in your AI stack right now?

Repost if you think more founders need to stop dabbling and start rebuilding.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Your LinkedIn reach is bad and nobody is seeing it. Not because you lack experience. Because you only show up on LinkedIn when you’re job hunting, fundraising, selling, or panicking.

By then, it’s usually too late.

Your professional presence is something you build before you need it.

But let’s be honest: most busy people don’t have hours to spend writing posts, tracking trends, engaging with feeds, or figuring out what sounds “authentic” without sounding cringe.

That’s why Raaye caught my attention.

Raaye is an AI LinkedIn Manager built for professionals who want to grow their career presence without living on LinkedIn.

It helps you go from:

idea → post → schedule → engage → analyze

All in one workflow.

The real value isn’t just “AI writes posts for you.”

It’s that Raaye helps you build a credible professional presence consistently.

You can create and schedule a week of LinkedIn content in minutes.
You can turn rough ideas into polished posts that still sound like you.
You can stop asking “what should I post today?” every morning.
You can stay visible without forcing generic viral content.
You can use AI comments, feed summaries, and trend insights to engage smarter instead of scrolling endlessly.

And that matters because LinkedIn is no longer just a networking site.

It’s where people discover talent.
It’s where inbound opportunities start.
It’s where recruiters notice patterns.
It’s where your expertise becomes searchable, visible, and remembered.

The best professionals I know are not short on experience.

They’re just under-visible.

Raaye feels like a practical tool for solving that.

Not by turning you into a “creator.”

But by helping you show up consistently, clearly, and professionally.

For busy founders, operators, job seekers, consultants, and ambitious professionals, that can be a real career advantage.

Check it out: https://tr.ee/QY6daJ
Getting along with your boss is not about agreeing with everything they say. It is about understanding how they think, what they care about, and how to communicate in a way that makes their job easier.

ChatGPT can help with that.

Before a difficult conversation, you can use it to practice what you want to say. Instead of walking into a meeting frustrated, you can ask ChatGPT to help you turn a complaint into a clear, professional request.

For example:

“I feel like my workload is becoming unmanageable. Help me say this to my boss in a calm, constructive way.”

You can also use ChatGPT to prepare updates your boss will actually appreciate. Many managers do not want every tiny detail. They want to know what is done, what is blocked, what needs a decision, and what is coming next.

A simple prompt might be:

“Turn these project notes into a concise update for my manager.”

ChatGPT can also help you understand your boss’s perspective. If they seem demanding, vague, or overly critical, you can describe the situation and ask:

“What might my manager be worried about here, and how can I respond productively?”

That does not mean your boss is always right. It just helps you pause before reacting and choose a response that moves the situation forward.

The real value of ChatGPT is not that it gives you magic words. It helps you slow down, organize your thoughts, and communicate with more intention.

A better relationship with your boss often starts with better communication. ChatGPT can be a useful tool for getting there.
Your reps have Claude open all day, but most of them are using it to rewrite cold emails.
That's the most expensive autocorrect ever purchased.

Here's the actual problem: the reason AI hasn't touched your pipeline isn't the model. It's the data. Claude is brilliant and completely blind to which company hit your pricing page this morning.

So I installed Lusha's new plugin and asked it to pull 10 VP Sales at fintech companies under 500 people. Verified contacts. Direct dials. Buying signals. Right inside the chat I already had open.

That last one is the quiet kill shot. Until this week, building a sales engine inside Claude meant wiring up your own integration and knowing exactly what to configure. Lusha eliminates all that friction. Install, ask, go.

Now, the question I keep getting: "I already have the Lusha connector, do I need this?"

Quick clarification, because the two get muddled. A connector is the raw link to Lusha's data. Auth and API access, nothing else. You're still doing all the work of figuring out how to ask. A plugin is that connector plus the prompting know-how baked in. Prospecting, enrichment, signal tracking. It works out of the box.

And if you've already got the connector installed, the plugin won't break a thing. It just layers the rest on top of the same access.

The teams that win in 2026 won't be the ones with the best AI. Everyone rents the same models now.

They'll be the ones who put real data inside the AI while everyone else was still asking it to reword a subject line.

Full walkthrough on the Lusha blog this week. Five plays going live with it: prospecting, CRM cleanup, signal tracking, lookalikes, account research.
Your move.

Check it out today for yourself! Link in comments

#lushapartner
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Meditating every day is basically riding a bike for your mind.

Not in a cheesy wellness-poster way. In a “you can actually feel the system getting stronger” way.

When you ride a bike every day, you’re not trying to become a professional cyclist. You’re building rhythm. You’re getting your body moving. You’re training balance, endurance, and recovery one ride at a time. Meditation works the same way. You sit down. You breathe. Your mind wanders. You notice. You come back.

That’s the rep.

Not silence. Not enlightenment. Not some perfectly calm monk-brain where no thought ever appears again. Just noticing when your attention gets hijacked and bringing it back, over and over.

That sounds small until you realize your entire life is basically attention management. Your stress depends on where your mind goes. Your work depends on where your focus goes. Your relationships depend on whether you can pause before reacting. Your happiness depends on how often you’re actually present for the life you’re already living.

And most people are walking around with zero training. Notifications own them. Anxiety owns them. Urgency owns them. Other people’s moods own them. The algorithm owns them. Then they wonder why they feel mentally exhausted by lunch.

A daily meditation practice is like getting on the bike before the day runs you over. Even 10 minutes changes the tone. You start catching the spiral earlier. You stop treating every thought like an emergency. You notice the gap between stimulus and reaction. You become harder to drag around by noise.

That doesn’t mean meditation fixes everything. It doesn’t. And it doesn’t replace sleep, exercise, therapy, community, or solving the actual problems in your life. But as a daily practice, it’s one of the highest-return habits available.

No gear. No subscription. No commute. No optimization rabbit hole. Just sit down, breathe, and practice coming back.

The first few sessions might feel boring. Good. The first few sessions might feel chaotic. Also good. That’s not failure. That’s you finally noticing what your mind is doing all day.

Ride the bike anyway. Ten minutes today. Ten minutes tomorrow. Ten minutes the day after that.

Not because you’re trying to become a different person overnight. Because future you deserves a calmer nervous system, a steadier mind, and a little more space between the world poking you and you poking back.

Start today at lunch. Your brain could use the ride.

Repost this if more people need a daily mental bike ride.
A delivery robot drove into an open trench because the sidewalk was on its map and the hole was not.

I think about that more than is probably healthy.

Not because it's funny, though it is. Because it's the cleanest illustration I've seen of how automation actually fails.

The bot wasn't stupid. It navigated a complex urban environment, avoided pedestrians, handled curbs, routed around parked cars. It did the ninety-nine percent brilliantly. Then it met a condition nobody encoded, which is that reality gets dug up sometimes, and the ground you validated on Monday is a hole on Wednesday.

Every automated system you've ever built has this exact shape.

The workflow that runs perfectly until someone renames a field. The sync that's flawless until a vendor changes a date format without telling anyone. The pipeline that handles millions of records and dies on the one with a trailing space.

It's never the hard part that breaks it. It's the part that was so obviously stable that nobody thought to check.

Humans handle trenches fine, by the way. Not because we're smarter, but because we have a general purpose response to unfamiliar situations: stop, look, be confused, adapt. That's not a feature you can spec. It's what's left over when your model doesn't cover the case.

So when you're evaluating automation, stop asking whether it handles the happy path. It does. Everything demos well.

Ask what it does when the world changes underneath it while it's running.

Ask whether it fails loud or fails quiet.

Because the trench isn't the problem. The problem is that nothing in the system was designed to notice one.
Getting good results out of AI is a lot like scoring points in a fencing match.

You do not win by waving the blade around and hoping something lands.

You win by being intentional.

In fencing, every touch starts with distance, timing, and a clear line of attack. The same is true with AI. A vague prompt is like lunging from too far away. It looks energetic, but it usually misses.

The best results come when you set the measure first:

What do you want?
Who is it for?
What constraints matter?
What does success look like?

Then you engage.

Sometimes the first response is not the winning touch. It is a probe. You test the guard. You see how the system responds. You adjust your angle, refine your ask, add context, and go again.

Good prompting is not about one perfect command. It is about a disciplined exchange.

A fencer does not blame the sword for every missed point. They study the action. Was the attack clear? Was the timing off? Was the target defined?

AI works the same way.

The people getting the best results are not necessarily the ones using the fanciest tools. They are the ones who know how to direct the exchange. They give context, define the target, control the tempo, and make each follow-up sharper than the last.

AI is not magic.

It is a match.

And the point usually goes to the person with the clearest intent.
Your next security breach probably won’t start with a hacker.

It’ll start with a message that feels normal.

A text from your boss.
A voice note from your kid.
A video call from a coworker.
An email that knows your title, company, habits, and exactly how to make you panic.

That’s what people still underestimate about AI.

It didn’t just make scams cheaper.

It made them believable.

The threat isn’t a cartoon villain smashing keys in a dark room.

It’s synthetic trust at scale.

Fake urgency.
Fake familiarity.
Fake authority.
Fake proof.

Too many people are still using 2015 security habits in a 2026 threat environment:

Reused passwords.
SMS codes.
Public profiles.
Old apps still connected.
No training on voice cloning, deepfakes, or AI-written phishing.

Here’s the uncomfortable truth:

Being “smart with tech” doesn’t automatically make you safe.

Some of the easiest people to fool are the ones who think they’re too savvy to get fooled.

Because AI scams don’t only target ignorance.

They target speed.
Distraction.
Ego.
The moment you’re busy enough to trust what looks close enough.

Passkeys over passwords.
MFA everywhere.
And yes, SMS is better than nothing.
But that is not a flex.

You also need to verify before you trust.

Not just emails.
Videos too.
Voice messages too.
Zoom calls too.

We’ve moved from spam to precision-guided manipulation.

And your digital footprint is the ammunition.

Every old profile.
Every public bio.
Every abandoned account.
Every tagged family photo.
Every personal detail online because “it’s probably fine.”

It’s not always fine.

That information gets scraped, packaged, and weaponized.

Then it comes back to you dressed up as trust.

Companies keep screwing this up too.

They buy shiny tools, call it cybersecurity, and move on.

But the weak point is still human.

If your team doesn’t understand AI impersonation, your company is exposed.
If leaders don’t verify strange requests, your company is exposed.
If finance, HR, execs, and customer-facing teams aren’t trained, your company is exposed.

Security isn’t just an IT issue anymore.

It’s culture.
Behavior.
Training.
Habits.

It’s whether someone can slow down and say:

“This sounds urgent, but I’m verifying it anyway.”

That one sentence can save a career, a company, or a whole lot of money.

Security is maintenance.

Prune your digital footprint.
Review connected apps.
Use encrypted channels when it matters.
Watch for impersonation.
Keep up with threats.

Because attackers are keeping up.

The phishing gets cleaner.
The impersonation gets better.
The targeting gets sharper.

So no, this isn’t about becoming paranoid.

It’s about becoming harder to manipulate.

The winners in the AI era won’t just be the people who use AI well.

They’ll be the people who know when AI is being used on them.

Repost if more people need to tighten up their digital habits.
Like and follow for more on AI, security, and what’s actually changing.
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
5 campaigns, 29 countries, $2.5M in ad spend. The same team now runs 60. What changed wasn't headcount. (KULINA, attributed.)

I have run operations and PMOs for 15+ years. Every version of this story I know ends with "so we hired twelve people." This one doesn't.

The difference is not the model. It is between teams that ask AI for output and teams that let an AI employee own the job.

I tried it on my own portfolio first. I told Viktor to pull the status for the leadership review: blockers, owners, what changed since last week. He came back with six projects compiled, two blockers flagged with owners already attached, the deck updated, and the one decision leadership needed sitting on slide 2. I checked everything. He was right.

One employee. Many bosses.

40,000+ workspaces run him today through 3,000+ tools, and nothing ships without a human: he proposes, the team approves.

If your PMO is still asking AI for advice, this is the line to cross.

Hire viktor.com for your team.
$100 in credits included, no card. Full link in first comment.

#AIemployee #projectmanagement #operations
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Your AI coding tools made everyone faster. So why is nothing actually better? Maybe they’re false gains?

PR velocity is up. Lead time is flat. MTTR didn't move. The L3 queue didn't move. Sound familiar?

Here's the uncomfortable truth nobody puts on the quarterly slide: AI code generation gave your org a sugar high. Every dev got 30-50% faster at writing code, and then the curve flattened. Worse, while it was bending up, it was quietly filling your codebase with code nobody fully understands, stacked on top of legacy systems that were already under-documented.

That's not an indictment of the tools. They do exactly what they were built for: one developer, one moment, one piece of code. They're single-player by design.

But engineering is not a single-player game.

Think about where your context actually lives. Code in GitHub. Tickets in Jira. Telemetry in Datadog. The stuff that actually matters in your senior engineers' heads. Every handoff loses it. Every escalation starts from scratch. Your fleet of AI tools (AI in the IDE, AI SRE, AI QA) each sees its own slice and none of them share a brain.

That's the gap PlayerZero is built for. It connects your codebase, observability stack, tickets, and the resolution history of every incident your team has ever worked into one living model of production reality. So when a dev opens a PR, the review isn't "this code is clean." It's "this change will break checkout for enterprise customers on config X," simulated against production with 90%+ accuracy.

The ROI shape is the whole point. Single-player gains are linear: each dev gets faster, then it caps. Multiplayer gains compound: every resolved incident makes the next one cheaper, every prevented escalation removes a category of work permanently.

The numbers from teams running this way: Zuora cut L3 escalations by 90%. Key Data doubled release velocity. Customers are getting back 15+ weeks of senior engineering capacity per year.

Stop optimizing how fast one engineer writes code. Start fixing what happens between them.

Link in the first comment.

#ad
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
There’s a moment every January where “Happy New Year!” stops being festive…

…and starts being a cry for help.

Like:

January 1–2
Totally normal.
We’re all still metabolizing champagne.

January 3
According to Larry David… this is the line.
After this, you’re not spreading joy.
You’re forcing small talk.

January 4–7
Now it’s weird.
Not illegal.
Just weird.

January 8–15
At this point you’re basically wishing me “Happy New Year” as a personality trait.

January 16–31
No.
Now it’s not a greeting.
It’s a hostage negotiation.

If you missed the window, just say:
“Hope your year’s off to a good start.”

Clean.
Adult.
No one gets hurt.

So what’s the actual cutoff for you?

January 3?
First Monday back?
Or are you out here saying it in February like a menace?
I build automations for a living. This week Microsoft Teams became the place they actually run, not just where we talk about them.

We added Viktor to our Teams channels, and it runs in our Slack too. It is an AI employee, not an assistant. It remembers what we have already built, understands what each client needs next, and ships finished work in the channel we are already in.

I handed it the job I usually babysit: watch every client integration overnight, catch what breaks, and fix it before anyone notices. It traced a failing webhook on a CRM sync, patched it, backfilled the data clean, and drafted the client note. Then it waited for my sign-off. It does the work, the human approves.

That is the line between an assistant and an employee. Teams just crossed it.

It is not a small experiment either. The same AI employee reads and writes to 3,000+ tools and already runs in Slack. Now it is in Microsoft Teams too.

If you run implementations or ops, give it the recurring build you keep babysitting, then watch it come back done.

viktor.com

Link In Comments
👇👇👇
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
AI fluency is becoming the new Excel fluency. And most people are still opening Claude like it’s a fortune cookie. No shade to fortune cookies, they’re bomb.

They type:
“Write this better.”
“Summarize this.”
“Give me ideas.”

Then they blame the model when the output is mid.

That’s not an AI problem.

That’s a user problem. And ultimately using AI is like riding a bike. You just need practice.

Claude101.com is basically the free crash course for people who don’t want to get left behind.

It walks you from:

Claude basics
team workflows
projects
slides
skills
Claude Code
API builds
and even how to stop Claude from just politely agreeing with your bad ideas


The wild part?

It’s free

The even wilder part?

Most people still won’t do it

They’ll spend 3 hours scrolling takes about AI replacing jobs…

Instead of spending those same 3 hours learning how to use the thing.

This is the part nobody wants to admit:

The gap isn’t between people who have AI and people who don’t.

The gap is between people who actually learn the workflows and people who keep treating AI like autocomplete with a fancier logo.

Claude101.com is one of those resources you should probably open before your coworker does.

Because once AI skill becomes table stakes, “I’m still figuring it out” stops sounding curious.

And starts sounding foolish
Everyone's asking if AI can do the whole job. Wrong question. It's costing them the easy wins sitting right in front of them.

AI doesn't have to replace the process to be worth it. It just has to take a chunk you hate off your plate.

It can draft the thing you'll rewrite. It can sort the data you'll interpret. It can scaffold the structure you'll fill in. None of that is "doing the job." All of it saves you an hour.

The people waiting for full automation are stepping over dollars to pick up the fantasy of pennies.

But here's the catch, and it's the whole game.

A robot that builds fast inside a bad plan just gets you to the wrong place faster. Speed without a frame is not progress. It's an expensive mess with great momentum.

The frame is everything.

Before you let anything automated touch the work, you have to know what you're building, what good looks like, and where the pieces connect. That's the part that's actually hard. That's the part that's actually yours.

AI is incredible at filling in a structure. It is terrible at deciding the structure was wrong to begin with. It will pour concrete into a flawed mold all day and never once ask if the mold makes sense.

So the skill stopped being execution. Anyone can execute now. You rent execution by the token.

The skill is the plan. The decomposition. Breaking the job into pieces clean enough that a machine can take the boring ones and you can own the ones that matter.

Frame it right and the robots are a force multiplier.

Frame it wrong and they're just a faster way to be wrong.

Plan first. Automate second. Never the other way around.
Your AI thinks you're done talking.

You were just thinking.

That gap is the entire problem, and Tavus finally put a number on it. Across more than a million pauses in natural conversation, the odds that a silence marked a real handoff plateaued around 40%. Most pauses aren't invitations. They're people breathing, considering, hunting for a word.

The industry built a decade of conversational AI on a signal that's wrong more often than it's right.

And every demo you've seen was rigged to hide it. Quiet room. One speaker. Nobody talking in the hallway. No dog, no espresso machine, no coworker asking a question over your shoulder.

Sparrow-2, which they shipped today, does something I've been waiting for someone to try. It stops throwing information away.

Old pipeline: cancel the noise, isolate the speaker, transcribe, guess when they stopped. Every stage deletes context to make the next stage's job easier. The backchannel becomes noise. The second voice becomes interference. The room disappears.

New approach: treat the whole audio scene as evidence. Semantics, prosody, speaker identity, background speech, interruptions, the "mhm" that means keep going. Classify it instead of deleting it.

The numbers are not subtle. 2.1% conversation failure rate against a best alternative that fails nearly four times as often. It lets 97% of real one-second pauses pass without barging in.

It'll also hold through a six to eight second mid-thought pause. Prior systems tapped out at one or two.

Here's why I care, and it isn't the benchmark.

I build learning and enablement systems. Every conversational pilot I've watched fail, failed the same way: the AI interrupted someone mid-thought, the person got self-conscious, and from then on they talked like they were dictating to a machine. Clipped. Rehearsed. Useless as signal.

You can't coach someone who's performing for your endpoint detector.

Intake, interviews, onboarding, sales coaching. All of it depends on people thinking out loud badly. Trailing off. Restarting the sentence. Saying "yeah" while you're still talking.

If your agent punishes that, you didn't build a conversation. You built a form with a voice.

The industry's been optimizing time-to-first-token, which measures how fast the thing talks. Nobody was scoring whether it talked at the right moment.

Stop making people re-learn how to speak.
Lately, I’ve been thinking about the phrase “stop and smell the roses.”

It sounds simple. Almost too simple.

But in a world that rewards speed, constant progress, packed calendars, and the next milestone, pausing can feel surprisingly difficult.

We rush from meeting to meeting. Goal to goal. Deadline to deadline. And somewhere along the way, we can miss the small moments that make the work — and life — meaningful.

A kind message from a teammate.
A problem finally clicking into place.
A quiet morning before the day begins.
A conversation that reminds us why we started.

Stopping to smell the roses doesn’t mean losing ambition. It means creating enough space to appreciate the journey while we’re still on it.

Success matters. Growth matters. Hard work matters.

But so does presence.

This week, I’m reminding myself to slow down long enough to notice what’s already good.

What’s one “rose” you’re appreciating today?

#Leadership #Mindfulness #Growth #Gratitude #WorkLifeBalance
Icon came.
Blew up the internet.
Then died.

Omneky raised a ton.
Then became a rounding error.

Not because “AI ads” is fake.
Because ads are not a tool problem.

Ads are a system.
And the missing piece was always the same:
a real human feedback loop from people who have actually spent serious money.

That’s why Airpost is interesting.

Airpost is a hybrid AI ads machine built for performance advertisers.
Platform + service.

AI does the heavy lifting.
Experienced creative strategists do the part AI keeps hallucinating:
taste, context, iteration, and ruthless prioritization of what actually wins.

The core idea is simple (and kind of unfair):
a taxonomy for how ads work.
Strategic pattern → narrative angle → execution.
So you can find what’s working, then scale it into dozens of new swings per week.

Airpost is built by the ReadySet team.
The “we shipped an AI wrapper” crowd can’t fake that.
They’ve deployed $100M+ in ads, and it shows in the positioning.

And the proof points are spicy:
DoorDash reportedly saw Airpost ads beat internal ads by ~40% better CAC.
Cloaked ran $1M+ in spend through Airpost ads.

If you’re spending $250k+/mo on paid social, this is the real pitch:
Stop asking your team to be a content studio.
Give them an ad factory that learns.

Launching Jan 20, 2026.

#partner [https://lnkd.in/g_rr36jp)
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Most people think the weekend starts Saturday morning.

It actually starts Friday afternoon.

How you close out your week decides whether you spend Saturday genuinely recharging or quietly carrying Monday's stress around with you.

A few things that make the difference:

Tie up the loose ends. Send the email you've been avoiding. Reply to the message sitting in your drafts. Unfinished tasks have a way of following you home and whispering in your ear all weekend.

Write down Monday's three priorities before you log off. Future you will thank present you. Walking in Monday with a clear plan beats walking in to a blank slate and a racing mind.

Do a five-minute reset. Clear your desk, close the tabs, file the chaos. A clean starting point on Monday is a gift you give yourself on Friday.

Actually disconnect. The work will be there Monday. Protecting your weekend isn't slacking, it's the thing that lets you show up sharp when it counts.

The goal isn't to grind harder on Friday. It's to land the plane properly so you can step off and switch off.

Do Friday right, and the weekend stops feeling like a frantic recovery. It starts feeling like rest.

How do you close out your week?
Hard work is not the bottleneck. Timing is.

I’ve watched people grind for years on the wrong thing at the wrong moment and call it dedication. Meanwhile someone with half the skill moved six months earlier (or maybe six months later, could be either) and walked away with the title, the contract, the audience.

That isn’t luck. That is pattern recognition plus nerve.

Here’s the scary math. Most opportunities are visible to everyone. Almost nobody acts while the window is still open.

We call it being strategic. Gathering data. Waiting for the right moment.

Waiting feels responsible. It looks like diligence. Usually it is fear wearing a better suit.

The skill was never spotting the door. Plenty of people spot the door.

The skill is walking through it before you feel ready, because the window does not stay open while you build confidence. It closes on schedule whether you moved or not.

Every good thing in my career came from a decision I made slightly too early. Not recklessly. Just early enough to still count.

The ones I regret are the ones I thought about carefully and then watched somebody else take.

See it. Move.

You can figure out the rest on the way there.
Do you even know what the security landscape is for your AI apps? I bet you don’t, and your data (and your clients’) is likely exposed to potential hackers.

Upload here. Sync to there. Trust us, it's encrypted. Trust us, we don't train on it. Trust us, the breach notification email is just a precaution.

We got so excited about AI reading our documents that we stopped asking the only question that matters: where does the reading actually happen?


What’s different here?

The reason most of these tools feel magic is that they ship your private files to a datacenter you'll never see, owned by a company you'll never meet, governed by a policy you'll never finish reading.

The convenience is real. The exposure is also real. We just agreed to only talk about the first one.


XeNo AI is built on the opposite bet.

It's an on-device AI assistant that helps you find, understand, summarize, and actually get answers from your own files. You type or you talk to it. It searches your documents, pulls the insight out of a 40 page PDF, finds that one screenshot you swear you saved, and answers the question you actually asked.

And it does all of it without your data ever leaving the device.


No cloud upload. No external sharing. Works offline. Faster responses, because nothing is making a round trip to a server farm to tell you what's in your own folder.

The thing people call a tradeoff (privacy vs capability) turns out to be a false choice the cloud-first crowd needed you to believe.


We spent a decade being told that "smart" and "private" couldn't live in the same product.

XeNo is proving it’s possible. We just outsourced the thinking before we needed to.

Your files. Your device. Your answers. That's the whole pitch.

https://tr.ee/N5o3hP
Your resume is competing against AI now.

Not just other candidates.

AI.

Because the average resume in 2026 reads like it was written by the same prompt.

Polished.
Generic.
Soulless.
And instantly forgettable.

Here’s how to tune yours up so it survives the bots
and wins the humans.

Start with the uncomfortable truth:
Hiring is filtering.

AI screens.
ATS parses.
Recruiters skim.

So your resume needs two things at once:

Machine clarity.
Human conviction.

Make it pass the machine test

Single column.
Normal headings.
No text boxes.
No fancy graphics.
Simple fonts.

If a scanner can’t reliably pull your titles, dates, skills, and results, you are invisible.

Now the part that actually makes you stand out in a sea of AI

Stop describing your job.
Start proving your impact.

Every bullet should answer:

What did you change?

Not “responsible for…”
Not “worked on…”
Not “helped with…”

Changed.

Use this bullet formula:

Problem - Action - Result - Proof

Problem: what was broken or missing
Action: what you actually did
Result: the measurable outcome
Proof: a link or artifact if possible

Metrics beat adjectives.
Every time.

“Improved customer retention” is fluff.
“Cut churn from 5.2% to 3.9% in 90 days by rebuilding onboarding emails” is a receipt.

Add receipts.

A one-page “proof strip” near the top:

Portfolio
GitHub
Case study
Demo video
Writing sample
Before and after

Skills-based hiring keeps accelerating, and proof travels farther than pedigree.

Kill the AI voice

If your summary sounds like it could belong to anyone, it will.

Replace it with a clear point of view:

“I build X for Y by doing Z.”
“I’m strongest when the problem is messy and the stakes are real.”
“My edge is turning ambiguity into shipping.”

AI can write words.
It can’t write your judgment.

Customize without rewriting your life

Do not make 50 versions.

Make 1 strong base resume.
Then for each job:

Mirror 10 to 15 keywords from the posting.
Reorder bullets so the most relevant wins are first.
Swap in 2 to 3 bullets that match the role exactly.

You’re not gaming the system.
You’re communicating in the language the system is scanning for.

Finally, show you’re more than a tool-user

In 2026, “I know the tools” is table stakes.

Hiring managers want to see how you think, collaborate, and drive outcomes, especially as AI automates more of the easy work.
Business Insider

So highlight:

Cross-functional wins
Decision-making under constraints
Tradeoffs you owned
Projects you drove from zero to shipped

That’s the difference between “AI-assisted candidate”
and “person who gets things done.”

Repost, like, and follow if you want more 2026 job-search playbooks.
The scary future of AI is not that it becomes conscious.

It’s that we become passive.

A new independent evaluation from Mount Sinai found ChatGPT Health under-triaged more than half of the cases that physicians said required emergency care. The study tested 60 scenarios across 21 specialties and 960 total interactions.

Read that again.

Not “got a few things wrong.”

Not “needs refinement.”

More than half
of the cases
where a real human should have been told:

go to the hospital.

And the machine said:
you can probably wait.

This is the part of the AI future people keep missing.

The danger is not just bad answers.

It’s outsourced judgment.

We are training people to do something insanely dangerous:

feel symptoms
open app
ask machine
wait for permission
delay action

That loop is going to kill people.

Not because AI is evil.

Because it is confident.
convenient.
always available.
and increasingly treated like authority.

That is the nightmare.

Not robot doctors taking over hospitals.

Normal people
alone
at 11:47 PM
scared
short of breath
half-hoping it is nothing
asking the world’s smoothest liar
whether this can wait until morning.

And getting told yes.

This is how the scary future actually arrives.

Not with chrome robots.

With soft language.

With beautiful interfaces.

With products designed to feel calm
when you should be panicking.

The machine does not need to be right all the time.

It just needs to be trusted
at the wrong moment.

That is enough.

And this is bigger than healthcare.

Because once society gets used to AI as the first stop for medical judgment,
it becomes the first stop for legal judgment,
financial judgment,
emotional judgment,
moral judgment,
parenting judgment,
everything.

We are not just building tools.

We are building a generation
that reflexively asks software
to interpret reality for them.

That should terrify you.

Especially because ChatGPT Health launched into a market where experts were already warning it is not regulated like a medical device or diagnostic tool, with no mandatory safety controls, risk reporting, post-market surveillance, or requirement to publish testing data.

So yes,
the future will be full of astonishing AI.

It will also be full of people dying,
losing money,
making terrible decisions,
and misunderstanding their own lives
because the machine sounded composed.

The scariest version of AI is not a superintelligence.

It is a very normal intelligence
wrapped in trust
scaled to billions
and consulted in moments
where hesitation has a body count.
Delve just pulled off a Valentine’s Day growth hack that feels unfair.

They asked the community one question
“What do you value about Delve?”

Then they did the obvious thing nobody actually does
they listened
collected the sticky notes
and turned them into a real billboard

That’s not “cute branding”
That’s a content engine
a trust signal
and customer research
all in one stunt

Even the line is a flex
not “SOC2 + HIPAA”
“Built with love for YOU.”

If you’re building in a boring category
this is how you make people feel something
and get shared for it

https://delve.co
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®
Claude Code built my feature in 22 minutes.

It also looked like something assembled in a garage during a thunderstorm.

And I've made peace with that.

Here's the thing people won't admit about AI coding tools: the output works. It genuinely works. Tests pass, edge cases handled, error states caught. The logic is often cleaner than what I'd write at 11pm on a deadline.

But the code has no taste.

Variable names that are technically descriptive and aesthetically dead. A helper function nested three levels deep because that's where the logic happened to land. Comments explaining things that don't need explaining, and silence on the one line that actually needed it.

It's the difference between a house that's structurally sound and a house you want to live in.

And I keep seeing two camps form around this. One says the aesthetics don't matter, ship it. The other says this proves AI can't really code.

Both are missing it.

Aesthetics in code aren't decoration. They're compression. Good naming and good structure are how the next person loads your intent into their head without reading every line. That's a real cost, paid later, by someone who isn't you.

So the job changed. I'm not writing the first draft anymore. I'm the editor now. I read for shape, for naming, for the thing that'll confuse someone in March.

The tool gets you to working. You still have to get it to good.

That's not a complaint. That's just the new job description.
Your best engineer isn't the one who never breaks anything.

It's the one who breaks things on purpose, in a place where breaking is cheap.

I've watched teams spend six weeks in design review to avoid a two hour outage. They call it rigor. It's fear wearing a blazer.

Here's what actually happens when nothing ever breaks: nobody learns where the edges are. Your system has failure modes you've never seen. Your team has assumptions nobody's tested. The whole thing looks stable right up until it isn't, and then it fails in the worst possible way at the worst possible time.

Controlled breakage is a feature.

Kill the service and watch what screams. Corrupt the test data and see if validation catches it. Yank the integration mid-sync and find out whether your retry logic is real or decorative.

Every one of those is a cheap lesson you're choosing to buy now instead of expensive one you'll be forced to buy later.

The teams that ship fastest aren't reckless. They've just relocated their failures to Tuesday afternoon in staging instead of Friday night in prod.

Break it early. Break it small. Break it where you're watching.

Progress isn't the absence of damage. It's the accumulation of survived damage.

So what did you break this week? And if the answer is nothing, are you sure that's a win?
Most people paste the AI draft straight into their inbox and hit send.

You can tell.

The rhythm is too even. Every sentence the same length. That weird confidence about nothing.

It reads like a press release for a sandwich.

Here's the thing nobody wants to admit. The draft is not the work. The draft is the raw material.

AI gets you to 80 percent fast. But that last 20 percent is where the voice lives. The cut line. The opinion. The thing only you would say.

Skip it and you sound like everyone else who skipped it.

So no, I don't ship the output raw.

I cut the throat-clearing intro. I kill the symmetrical sentences. I add the take the model was too polite to make. I read it out loud and rip out anything I'd never say to a person's face.

Takes ten minutes. Those ten minutes are the only reason it sounds like me.

The tool drafts. You decide. If you outsource both, you're not creating. You're just laundering generic content through your own name.

Tidy it up. Or don't send it.
AI agents are becoming a commodity.
The differentiator is the OS that ships them.

Because “we built an agent” is not the hard part anymore.

The hard part is:

Getting to production fast
Not in 6 months
In weeks

Making it safe to scale
With monitoring
Observability
Billing controls
Security and compliance

And turning agents into revenue
Not just “cost savings” in a deck

That’s why Vida caught my attention.

Vida is an AI Agent Operating System built for ROAI and speed-to-market.

Crawl-walk-run deployments
A framework that plugs into your stack
And gets real agents live quickly

Plus white-label and reseller options.

So if you’re a SaaS business trying to add a new product line
Or an MSP, BPO, or call center looking for a new margin engine
Vida is built to be a money maker, not just a cost saver.

Also - omnichannel is not an add-on here.

Inbound and outbound via:
Phone
Text
Email
Webchat

At real scale too - tens of thousands of calls and texts daily, and hundreds of millions of AI agent interactions supported.

If your “agent strategy” currently ends at a demo…

Maybe it’s time to start thinking like an operator.
Check it out today at [https://lnkd.in/gQYcBHUc)
Post image by Leonard Rodman, M.Sc. PMP® LSSBB® CSM® CSPO®

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