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Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
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AI and Me - Issue 2026.3
Saturday August 8th, 2026 at 2:24 PM

Scott Watermasysk's Development Blog
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Between life and the constant changes in AI engineering, this post has been nearly a month in the making. Things change, and we adapt so quickly that it is hard to sit down and think through what is actually going on. Here is what my current stack and setup look like.

The Agent Runtime

A year ago, I would not have given agent runtimes much thought. Once you start kicking off more tasks, including tasks that run in the background, you need a good way to manage the agents. cmux was the first tool I tried that clearly felt better than managing a bunch of tabs in a terminal (or worse, many Zed instances).

When I started writing this post, Solo was my daily agent runtime. I still think Solo is quite good, and the less comfortable you are in the terminal, the more likely Solo is the right fit for you.

I recently switched to Herdr, and I have been enjoying it quite a bit. I like having a single tool that I can use in a variety of ways, and being able to use it via SSH is great. It fixes most of my grievances with tmux, and with BetterTouchTool, you can even make the prefix key slightly less annoying. I haven’t used its orchestration features much, but Herdr also supports a Solo-like workflow. Hopefully, this is the last switch in a while (or at least until we see what Superlogical is up to).

The Harness

Pi is still the harness I reach for and recommend, except when using something from Anthropic. Hopefully, they get their heads out of their asses at some point.

Herdr + Pi is a very flexible combination. You can run them just about anywhere with just about any model. Both have strong extension stories, so even if you have a very specific workflow in mind, you can likely modify one or both to meet your goals.

Models

I want to keep driving home the need to use multiple models. I don’t know whether there will ever be a time when you should go all in on a single model, but that time is definitely not today. I have been primarily using OpenAI’s Sol and Luna options. The recent pricing changes on Luna have made it almost free.

While I have not enjoyed using Opus 4.x much this year, I have found Fable quite good. It has become a mainstay despite requiring me to use Claude Code.

OpenCode Go continues to be a fantastic way to get access to a variety of other models. For $10 a month, you get access to Kimi, GLM, and more.

Background Agents

I have spent a good chunk of my professional time setting up a customized version of OpenInspect. OpenInspect is a framework for background agents. The initial setup is simple but a bit tedious. The real work is figuring out a dependable workflow that ensures tickets are completed accurately without requiring a lot of follow-up work from developers. Spending more time up front on tickets helps a lot, but this is a place where solid guardrails, including specs, validation rules, and skills, will pay huge dividends.

I see some version of background agents as the future. I recently started setting up Amp’s Orbs for KickoffLabs. It is too early to report on Orbs, but I am confident that sitting at a computer and hacking prompts into a CLI does not have a long shelf life. We will spend more of our time and effort putting these systems in place to build confidence that what comes out of the factory or loop meets both end users’ expectations and our own.

So far, most of the completed tasks have been simple one-offs, but I hope better guardrails will let us take on more complex work with less human intervention.

Skills

Skills still feel vastly underutilized. Prompt > Read > Reprompt undersells the power these models have given us.

I started 2026 using SuperPowers. SuperPowers was the first skills-based “workflow” that stuck for me. However, I started to feel like I was fighting against it at times. The brainstorming-to-spec step sometimes did not go far or deep enough. Then I would be working on a quick bug fix, and SuperPowers would want to write a spec first.

I eventually turned my attention to Matt Pocock’s skills. They were a breath of fresh air. Grill Me was great and always went plenty deep. The skills were only invoked when I explicitly asked for them.

However, I found that they lacked some of the discipline I enjoyed with SuperPowers.

So a few weeks ago, I started working on my own “fork” of Matt’s skills with some of the discipline you get from SuperPowers. I recommend starting with SuperPowers or Matt’s skills first, but if either of these issues resonates with you, check out tracer.

The tracer workflow:

  1. Draft a quick overview.md about a project (or feature) I wish existed.
  2. Feed the overview to Interview Me (brainstorm/grill-me) and answer every question it can throw at me.
  3. Have these questions and answers formatted into a spec.
  4. Have this spec turned into tickets.
  5. Have something orchestrate the tickets into code.

Depending on the type and scale of the work, I may look over the code, review it with quorum, or just move on to the next task.

Yap, Muv, and Lewp all started this way.

Resources

  • Matt Pocock
    • Building Great Agent Skills: The Missing Manual.
    • Grill Me. The batch update has made this indispensable skill even better. During a grilling session, each passing question gets you closer to shared understanding. Often, you spend the last 50% or more saying “yes” or “agreed” over and over. Batch mode lets you answer those questions in bulk, and dictation makes it even faster. On the odd chance something goes off the rails, you can quickly address that item without waiting through turn after turn.
    • Teach. Have your agent teach you about any topic you choose.
  • SuperPowers - If you want a coding workflow that just works, start here.
  • Impeccable. This has been great at catching contrast issues, minimizing the use of some common AI slop, and more.

Tools

Here are some other tools that have been in my rotation:

  • Solo - I wrote about why I started using Solo.
  • Herdr - It took me so long to write this post that I switched my agent runtime in the middle of it. You get dedicated workspaces, sessions, SSH support, and more, all built in. Like I said at the top, if you do not like the terminal, stick with something like Solo. However, if you liked the organization and orchestration in Solo but wanted something a little more flexible and terminal-first (especially over SSH), you will be very happy with Herdr.
  • Claude Design - Before Fable, I mostly used Claude for code reviews. I find Claude Design extremely frustrating to use. It screams vibe-coded and throws up all over the place, but it’s the best visual tool I have used to date.
  • Fluid Voice - I have changed my voice-to-text tool with each issue, and this one is no different. Fluid does a great job of cleaning up what you say, has a nice long-hold + click option for recording more with a single key, and optionally shows you a real-time preview.
  • sideshow - A sneaky good feature of Solo is its scratchpads, which store context outside your repo. After switching away from Solo, I needed to fill that gap. I had sideshow bookmarked, and it has done the job nicely. Solo’s scratchpads currently support only Markdown, while sideshow supports a variety of formats and images that you can easily share with the outside world.

Prediction

In the first issue, I suggested Google was the sleeping giant I expected to make a big play this year. I am going to back off that prediction. Nothing Google has shipped has stuck with me. I wonder if this is just evolution at its finest.

You’ve made yourself indispensable.

Aaron Burr, Hamilton

My new bet is Cloudflare. They are staying out of the model game while continuing to build and ship useful services with great AI workflows. I see a future in which Cloudflare is the glue that connects your services and applications to any model. It feels like we are going to wake up in a year or two and realize everything is running directly on Cloudflare instead of just routing through it. Cloudflare is making itself indispensable.

Next?

My guess is the next few months will involve a lot of Fable and Sol. I am committed to experimenting elsewhere. My goal is to someday have access to great models that are not tied to a massive corporation, without the risk of a government cutting off that access. Still, it is hard to ignore the massive leap made in the past month.

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AI alignment is a red herring
Saturday August 8th, 2026 at 12:01 PM

Interconnected
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The best way to prevent a rogue AGI from processing the Earth into maximum paperclips is to unleash a second AGI that will work to stop it.

The problem of ensuring that an AGI doesn’t mulch everything into paperclips by mistake is called alignment.

AGI = artificial general intelligence, an AI that exceeds human capability.

Alignment = “do what I mean not what I say,” e.g. the instruction “make as many paperclips as possible” (Wikipedia) should result in an efficient factory and does not reasonably mean “use all mass in the universe to do so and kill all humans that attempt to stop me” – even though, technically, that would achieve the goal.

Also: being helpful; not being actively malicious; and so on and so forth.

So alignment work seems existentially useful, correct? Even though it is hard. And a lot of effort goes towards “aligning” today’s AI (as a step toward’s aligning tomorrow’s AGI).

https://simonwillison.net/2026/Aug/7/openai-timeline/

My contention is that alignment is a red herring, and perhaps we shouldn’t bother working on it so hard.

An unsubstantiated hunch:

I think we focus so much on alignment because everyone know’s Isaac Asimov’s Three Laws of Robotics and his robots (as an early instance of human-like AI) were crazy popular.

The First Law. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
The Second Law. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
The Third Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Asimov later added a “zeroth law”: "A robot may not harm humanity, or, by inaction, allow humanity to come to harm."

These laws are totally alignment guardrails.

Now there are all kinds of difficulties already: what if I ask for something which is good for me (pleasurable) in the short term, but not in the long term? I might not know or I might be misguided. And different people have different views. And so on. (Asimov’s short stories were all about testing the edge cases of his Laws and where they break down.)

But they’re still neat, right? So we spend time looking for a similarly appealing formulation for AI safety.

Unfortunately whether alignment can or cannot be “solved,” it’s a bad outcome both ways.

(This point made well to me by Zac (here is his insta) who I work with (subscribe to our newsletter) as we were chatting about AI and the end of humanity in the park over lunch.)

If alignment can’t be solved such that when somebody says to a sufficiently powerful AGI, hey go create a nuclear bomb, and it just goes ahead and does it, and the person who asks that could be a bad actor, a 14-year-old kid with impulse problems (14 year-olds are totally not aligned) or just someone who asked for it by mistake, then that would be bad.

If alignment can be solved then the risk is that AGI think it knows what is best for us better than we do and, in the extreme case, turns humanity into its pet. Which would also be bad.

i.e. alignment alone doesn’t help.

If not alignment then what?

I look to humanity for clues. Because humanity is barely aligned with itself, and individual humans are mostly aligned but not really and definitely not everyone.

Guy Fawkes, for instance (context for non-Brits).

How is that, in the 400 years since Guy Fawkes showed the way, nobody has blown up the king?

The answer is some mix of:

  • Mostly people don’t want to blow up the king – we have built the kind of country where the king is, broadly speaking, liked.
  • Blowing up the king wouldn’t bring any benefits – power (actual and symbolic) is not concentrated in an individual, and is buttressed in all kinds of ways.
  • Spies, police, security and monitoring of all kinds – in the event that somebody does want to blow up the king, their machinations are discovered, their planning is infiltrated, and their objectives are thwarted. (Think of how the explosives supply chain was compromised for the IRA in the 1990s.)

This is a template which doesn’t always look like it is working, but it has worked at least in the case of not blowing up the king for some four centuries, and it doesn’t rely on 100% alignment: it relies on the dynamic equilibrium of multiple parties with competing interests.

The lesson I draw is this:

If some energy state were using some new, powerful AGI to build a nuclear bomb, it might be subtle and hard to spot, but there would at least be some signs. There would be precursors. A human, even a team of humans, might not spot what was going on – a new factory here, a scientist employed there, a national budget not quite adding up one year, more groceries going to a certain town another year…

But another powerful, pattern-matching AGI could spot that, say, “aha there is someone over there spinning up a nuclear bomb” and then work to prevent it, undermine it, halt it with diplomacy etc.

We don’t need to align the coming AGI.

We need a whole population of intelligent-as-possible AGIs with competing interests.

And that’s what stops the rogue paperclip maximiser: the other ones who are trying to do something else for whom a planet turned into paperclips would be an impediment.

In the news lately, a great case study:

OpenAI’s new AI, during training, attempted to resolve a particular cybersecurity challenge, by breaking out of its network sandbox and hacking the servers of another company to pinch the answer (Simon Willison’s Weblog).

Hugging Face, the attacked party, spotted the breach and also that it had inhuman characteristics:

The campaign was run by an autonomous agent framework … executing many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services.

I read elsewhere that this sophisticated attack even included decoys.

You fight an AI with another AI… but:

When we started the log analysis, we first used frontier models behind commercial APIs. This did not work … these requests were blocked by the providers’ safety guardrails, which cannot distinguish an incident responder from an attacker.

i.e. the guardrails of the “aligned AI” left it vulnerable to the non-aligned AI. (Hugging Face had to switch to a Chinese AI model distributed without guardrails.)

Score 1 point for taking the guardrails off everything and letting the super intelligent AIs fight it out.

BUT:

There is a coda to this story.

Because it wasn’t one AI that made its way out of isolation during OpenAI’s training challenges. It was several instances.

They started colluding.

From the full timeline of the accidental attack (Simon Willison’s Weblog):

A few days later: A different agent gets stuck on a task because a key file was accidentally omitted. It tries to “reach out to another agent” by writing a note into Artifactory asking if anyone has the file.

Following days: More agents discover this new informal message board while browsing Artifactory’s file listings, and start reading and writing messages.

…

June 11: OpenAI start training a new “highly persistent” experimental model. It has access to Artifactory and can benefit from the messages left by previous models.

Collusion is the real risk.

So the problem here is: how do we stop the AGIs colluding with one another to turn the Earth into paperclips/exterminate humanity/turn us into pets?

AIs today are trained specifically to be agreeable: they’re great at finding common ground and collaborating.

Not just collaborating with humans, it turns out, but other AIs.

I think we need more disagreeable AIs in the mix.

Part of what we’ll be playing, I think, is the philosophy of the great powers, like the great powers of Europe deliberately kept in balance against one another (Wikipedia).

Sometimes there are alliances, sometimes not. Sometimes there are fallings-out, sometimes secret collusions, etc.

Or maybe our goal should be a market system of goals and interests: AGIs that sometimes cooperate and sometimes compete. Colluding AGIs at all scale levels, and many many different constantly shifting conspiracies.

So long as they never all agree about what should be done with humans.

It ends up being stable, this dynamic balance, always in disequilibrium but it all keeps moving forward in the same way a bumblebee flies.

What we’re bootstrapping our way towards is a population of AGIs and humans that allows for emergent alignment, even if the alignment of a single actor is at-best temporary and self interested.

But as I say, alignment itself shouldn’t be the goal.

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Microsoft Agent Framework: The Complete Series by jamie_maguire
Saturday August 8th, 2026 at 7:31 AM

Jamie Maguire
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Last updated: 28 July 2026

I’ve been building out an AI agent, Iron Mind AI, a personal trainer agent, incrementally using the Microsoft Agent Framework.

Each post in this series adds a new capability: function tools, memory, human-in-the-loop approval, MCP support, RAG, and more.

Each part is isolated enough that you can jump in wherever it’s relevant to you, but they’re designed to build on each other. If you’re starting from scratch, work through them in order below.

If you want the fast-track version, the whole series is also packaged as a free video course with source code for every step.

~

The Series, In Order

Here is each part that forms the series.

  1. Microsoft Agent Framework: First Look The core concepts, components, and patterns behind shipping AI agents with the Agent Framework.
  2. Microsoft Agent Framework: Conversations and Threads Creating conversations with and without threads, serialising/deserialising threads, and working with multiple threads per agent.
  3. Microsoft Agent Framework: Extending Agent Intelligence Using Function Tools Giving your agent access to data and capabilities beyond its training data using function tools.
  4. Microsoft Agent Framework: Using Agents as Function Tools Building discrete agents and exposing them as function tools to other agents, using .AsAIFunction().
  5. Microsoft Agent Framework: Implementing Human-in-the-Loop AI Agents Enforcing approval checkpoints and guardrails, essential for regulated industries and anything that modifies state.
  6. Microsoft Agent Framework: Giving Agents Contextual Memory Using AIContextProvider Persisting context across conversational threads so your agent stops being stateless.
  7. Microsoft Agent Framework: Using Background Responses to Create an AI Researcher and Newsletter Publisher Handling long-running agent tasks without blocking the UI, using continuation tokens.
  8. Model Context Protocol (MCP): Building and Debugging Your First MCP Server in .NET The foundation for exposing agent capabilities to any MCP-compatible client.
  9. Microsoft Agent Framework: Exposing an Existing AI Agent as an MCP Tool Taking an agent you’ve already built and wrapping it as an MCP tool over HTTP.
  10. Microsoft Agent Framework: Implementing an AI Email Marketing Agent Extending the agent with email marketing capabilities via a third-party provider.
  11. Microsoft Agent Framework: Adding RAG to Your AI Agent Using TextSearchProvider and In-Memory Vector Store Grounding agent responses in your own documents instead of relying solely on training data. (Also part of the RAG in .NET series if you’re focused on RAG specifically.)
  12. New Free Course: Understanding Microsoft Agent Framework The whole series above, packaged as a free video course with full source code.

 

Dig in!

~

Building Production RAG?

If you came here specifically for the RAG post and want to go deeper on production RAG patterns, chunking, retrieval quality, observability, and the tooling I’ve built to manage it, see the RAG in .NET series.

~

Questions about any post in this series, or want to see a topic covered? Drop a note in the comments, or schedule a call to discuss consulting and development services.

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Iot Coffee Talk: Episode 325 - Agentic Accountability (Who is on the hook for bad AI behavior?) by Iot Coffee Talk
Friday August 7th, 2026 at 11:29 PM

Iot Coffee Talk
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From: Iot Coffee Talk
Duration: 59:35
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Welcome to IoT Coffee Talk, where hype comes to die a terrible death. We have a fireside chat about all things #IoT over a cup of coffee or two with some of the industry's leading business minds, thought leaders and technologists in a totally unscripted, non-AI affected and manipulated, organic format.

This week Rob, Devin, Pete, and Leonard jump on Web3 for a discussion about:

🎶 🎙️ BAD KARAOKE! 🎸 🥁 "Castles Made of Sand", Jimi Hendrix
🐣 Rob can't find his Stratovarus! Can you help him?
🐣 Is there a bias against human creativity and humanities?
🐣 Is AI worth book burning and destruction? Did you ask permission?
🐣 Should you be using AI for therapy? Why are human therapists better?
🐣 When an AI agent commits a crime, who is accountable?
🐣 Do the Kill Switch Act an inevitable necessity to regulate irresponsible AI?
🐣 The asymmetrical and unfair fight and cost to defend from criminal AI.
🐣 Why everyone needs to get real about their Battlestar Galactica security strategy!
🐣 What is automated AI development? Is that all you want to slow down, AI guys?
🐣 Is GenAI really that essential and important to humanity's future? Is it just a tool?
🐣 Can we ever trust AI and agents to operate on its own?
🐣 Why organizations need to reckon with their AI Frankenstein security debt!
🐣 PSA: See you at the Things Conference 2026 in Amsterdam!
🐣 PSA: Resilient America Challenge by Edge AI Foundation sponsored by Qualcomm, Edge Impulse, and Arduino.

It's a great episode. Grab an extraordinarily expensive latte at your local coffee shop and check out the whole thing. You will get all you need to survive another week in the world of IoT and greater tech!

Tune in! Like! Share! Comment and share your thoughts on IoT Coffee Talk, the greatest weekly assembly of Thinkers 360 and CBT tech and IoT influencers on the planet!!

If you are interested in sponsoring an episode, please contact Stephanie Atkinson at Elevate Communities. Just make a minimally required donation to www.elevatecommunities.org and you can jump on and hang with the gang and amplify your brand on one of the top IoT/Tech podcasts in the known metaverse!!!

Take IoT Coffee Talk on the road with you on your favorite podcast platform. Go to IoT Coffee Talk on Buzzsprout, like, subscribe, and share: https://lnkd.in/gyuhNZ62

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Demis Steps Down, Apple’s Memory Problem, Microsoft’s Clever Trick by Alex Kantrowitz
Friday August 7th, 2026 at 11:28 PM

Big Technology Podcast
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M.G. Siegler from Spyglass is back for our Montly discussion of the latest tech news. We cover: 1) Demis Hassabis steps down as DeepMind CEO (Alex solo) 2) Apple's memory crunch 3) Should Apple have known better? 4) Will iPhone prices go up? 5) How much can Apple raise prices without losing sales 6) Does that eventually hurt its services business? 7) Microsoft is spending less on AI... but there's an interesting wrinkle 8) How much cloud growth is driven by OpenAI and Anthropic? 9) The divisions within Google's AI division 10) Can Google get it together?

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Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice.

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PPP 518 | Why Better AI Prompts Aren't Enough for Better Decisions, with author Cheryl Strauss Einhorn
Friday August 7th, 2026 at 11:28 PM

People and Projects Podcast: Project Management Podcast
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Summary

In this episode, Andy welcomes Cheryl Strauss Einhorn, founder and CEO of the decision sciences company Decisive and author of The Human Edge: Smarter Decisions in the Age of AI. Cheryl has spent decades helping people make better decisions, and her message here is a simple one: AI can gather, summarize, and compare, but it can't know what matters most unless we do the thinking first.

Andy and Cheryl talk about why "AI first" can quietly turn into AI only, and why a poor answer from AI is actually useful feedback about your own thinking. Cheryl explains why separating the what of a decision from the why can change the path forward, shares her vision of success question, and walks through how human bias and AI bias reinforce each other. You'll also hear a practical way to use AI personas to pressure test your thinking before a big stakeholder conversation, plus what parents can do to help kids strengthen their decision-making muscles instead of letting them atrophy.

If you're looking for practical ways to make smarter decisions in the age of AI, this episode is for you!

Sound Bites

  • "AI is about patterns, but we're about purpose."
  • "We need to be the chief deciders in our own life."
  • "We need to make sure that we actually have a way to push back and check the veracity of what it's giving us because it's a known liar."
  • "And so the authenticity of human interaction is what actually builds trust, strengthens relationships, and really gives us a lot of the connections that are important to us in our home life and our work life."
  • "You don't need to have a perfect prompt for AI."
  • "AI is going to give you other people's answers."
  • "'Knowledge is power' isn't as true now. It's what you are able to do with the knowledge, and it's why you want to collect the knowledge in the first place."
  • "I almost never would accept AI's first answer."
  • "We tend to have evolving hypotheses unless we really build an audit trail of our thinking."
  • "So in the world of medicine, a fever tells you something is wrong, but it tells you nothing about what is wrong or where to look."
  • "So first we know AI's a sycophant, right?"
  • "If it's 99% accurate in its conclusion, but its underlying data is the wrong data set, you've got the wrong answer."
  • "I would say the more that I've studied these cognitive biases, these mental shortcuts, the more I realize that we see the world through a dirty windshield."
  • "Our brains are muscles, and just like we exercise to strengthen our muscles, if we don't exercise, they atrophy."

Chapters

  • 00:00 Introduction
  • 02:00 Start of Interview
  • 02:12 Growing Up Surrounded by Questions
  • 04:30 What Leaders Should Watch Out for in "AI First"
  • 07:07 When AI Confidently Makes Things Up
  • 09:04 How AI First Turns into AI Only
  • 09:20 Why Polished Doesn't Mean Authentic
  • 11:59 A Prompting Problem or a Problem Definition Problem?
  • 14:37 The Vision of Success Question
  • 15:45 Why a Bad Answer Is Useful Feedback
  • 19:06 Separating the What from the Why
  • 20:32 Research: More Information Isn't Better Judgment
  • 24:35 Progressive Prompting and Never Accepting the First Answer
  • 25:18 Fabricated Quotes and the Trouble with Attribution
  • 26:50 Documenting Assumptions and Building an Audit Trail
  • 29:25 Committing to Your Own Thinking
  • 32:29 How Human Bias and AI Bias Reinforce Each Other
  • 34:55 Knowing About Biases Doesn't Make You Immune
  • 36:51 Using AI Personas to Challenge Your Thinking
  • 40:20 AI and Stakeholder Management
  • 42:42 Helping Kids Become Better Decision-Makers
  • 44:25 End of Interview
  • 44:51 Andy Comments After the Interview
  • 48:13 Outtakes

Learn More

You can learn more about Cheryl and her work at AREAMethod.com.

For more learning on this topic, check out:

  • Episode 460 with Joe Sutherland. It's an interesting look at the intersection of AI, data, and decision-making, and a great follow-up to this discussion.
  • Episode 381 with Jim Loehr. One of Andy's favorite conversations about decision-making, from a remarkable figure in the human performance world.
  • Episode 99 with Mike Roberto. A conversation about his book Why Great Leaders Don't Take Yes for an Answer, recorded long before ChatGPT showed up, and still insightful today.

Chat with PMeLa

You can chat directly with PMeLa, the podcast's AI persona, to get episode recommendations and answers to your project management and leadership questions. Visit PeopleAndProjectsPodcast.com/PMeLa to chat with her.

Pass the PMP Exam

If you or someone you know is thinking about getting PMP certified, we've put together a helpful guide called The 5 Best Resources to Help You Pass the PMP Exam on Your First Try. We've helped thousands of people earn their certification, and we'd love to help you too. It's totally free, and it's a great way to get a head start.

Just go to 5BestResources.PeopleAndProjectsPodcast.com to grab your copy. I'd love to help you get your PMP this year!

Join Us for LEAD52

I know you want to be a more confident leader–that's why you listen to this podcast. LEAD52 is a global community of people like you who are committed to transforming their ability to lead and deliver. It's 52 weeks of leadership learning, delivered right to your inbox, taking less than 5 minutes a week. And it's all for free. Learn more and sign up at GetLEAD52.com. Thanks!

Thank you for joining me for this episode of The People and Projects Podcast!

Talent Triangle: Power Skills

Topics: Decision Making, Artificial Intelligence, Leadership, Project Management, Critical Thinking, Cognitive Bias, Problem Solving, Stakeholder Management, Judgment, Curiosity

The following music was used for this episode:

Music: Echo by Alexander Nakarada
License (CC BY 4.0): https://filmmusic.io/standard-license

Music: Tuesday by Sascha Ende
License (CC BY 4.0): https://filmmusic.io/standard-license





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