Author: jim.czuprynski

Cloud Field Day 25 #CFD25 was my first introduction to Hammerspace’s unique technology offerings specifically aimed at managing cloud-based workloads for today’s modern cloud environments.

As a long-time Oracle DBA with a deep background in storage technology, I found their analysis of what their customer base is focused on to be particularly pertinent for wringing performance from underlying cloud storage systems. Meeting the demands beyond modern OLTP applications and business intelligence dashboards – specifically, the impact of generative and agentic AI requirements – requires new ways of handling diverse I/O patterns and compute demand cycles, especially for AI training and inference operations.

Leaving Unstructured Data Where It Already Lives. Well, Most of It.

First, I was pleasantly surprised to see Hammersmith lead with an example of their AI Data Platform technology assisting an environment I know quite well: Oracle Cloud Infrastructure (OCI). I quite literally had to rub my eyes in relief, since most Tech Field Day presenters usually avoid Oracle technology like the plague. But OCI plays extremely well with advanced storage systems, whether a database is accessing flat files like tables in ORGANIZATION EXTERNAL mode or when chunking and transforming documents and images into its 26ai VECTOR datatype for generative AI purposes.

Of course, it’s no secret that AI workloads often demand huge compute and GPU resources to be brought to bear to tackle both training and inference operations against equally ginormous unstructured data sources. Instead of migrating terabytes or petabytes of these data to cloud storage, Hammerspace’s Global File System (GFS) enables extremely efficient access to unstructured data for massive, parallel cloud-based compute because it decides exactly which files should be moved to the fastest intra-cloud storage for handling while leaving the majority of unneeded files in their current location.

Those Metadata Matter.

The trick, of course, is selecting only the files most likely to benefit – and that means looking closely at each file’s metadata to determine which file(s) should be gathered from on-premises data and brought to the public cloud to take advantage of VMs, NVMEs, and GPUs.

Hammerspace’s solution involves first deploying an Anvil server that interrogates unstructured files’ metadata to assimilate their key attributes as a cone file system. One big advantage of this strategy is that files can span multiple environments, mount points, and file systems to capture an holistic view of all possible targets.

Once all mount points, file systems, directories, and individual files are assimilated, that information is captured so its Data Services Server (DSX) can then use to intelligently gather the files that would most benefit when processed for a particular workload’s demand.

And before you ask: While the extensive demos we saw at #CFD25 were all GUI-based, Hammerspace also offers access to all these operations via API calls accessed via their Hammerspace development toolkit (HSTK) or their HSCLI command line interface.

Result: A Logical Tier 0 NVMe-Based Storage Layer, With File Resiliency Built-In

Once assimilation is complete, the Hammerspace AI Data Platform solution enables instantiation of sufficient VMs with direct access to GPU processors. Each VM also accesses and gathers the files needed to complete the training or inference tasks at hand. Files are places within Hammerspace’s proprietary NVMe-based logical Tier 0 storage layer. I/O operations then proceed apace in parallel and without the limitations of some files being stored on slower, less responsive storage systems.

Obviously, file replication can be a time- and resource-consuming process. To assuage against the possible loss of data after the potentially lengthy process of replication, Hammerspace’s solution also insures files are spread across Availability Zones (AZ) to avoid placing a file image onto two NFS file buckets within same risk zone.

Even Meta Needs Meta(Data)

As someone who has manually built storage arrays for fastest database I/O processing and worked with high-speed and high-capacity systems for storage access like Hitachi’s Universal Storage Platform and Oracle’s Exadata Database Machine, this strikes me as an elegant solution to the varying demands of generative AI and agentic AI workloads to get compute and GPU resources closest to the right data needed to solve crucial business problems at scale.

And Hammerspace reports that one of their largest customers – Meta (yes, that Meta, as in Facebook, Instagram, et. al) – uses their solution to manage access to 40PB of unstructured data in production, with plans to expand that footprint to 100PB in the future. Meta’s use case also leverages several thousand servers accessing tens of thousands of GPUs.

I’ve found that every once in a while, it doesn’t hurt to see what everyone else is doing in the same technology space I’m currently focused on. For the past 18 months, that’s been the Generative AI space and the impending implementation of Agentic AI across diverse industries and applications.

Getting to Generative AI: Like Learning a New Foreign Language

Full disclosure: I’m an Oracle DBA with 25 years of experience in data engineering and 45 years of experience in application programming. Lately I’ve focused on building out simple Generative AI and Retrieval Augmented Generation (RAG) chatbot applications with Oracle Database 23ai technology and Oracle Application Express (APEX) within the Oracle Cloud Infrastructure (OCI) public cloud. That meant learning how to use LLMs to chunk and create embeddings for a corpus of documents, how to perform cosine similarity searches against vectorized content, and prepare appropriate proper system prompts within a chatbot framework to return cogent answers from that corpus based on questions asked.

This was a decidedly non-trivial task – it took me several weeks to master these concepts and then build demos that yielded relatively hallucination-free answers, and it was at least two months before I felt I could comfortably present my work to colleagues at user group conferences. I came away with a new respect for the depth of knowledge required to deliver qualified answers from LLMs and Generative AI applications.

Qlik Connect 2025: Qlik Answers

Back in May 2025 I had a chance to take a close look at the latest version of Qlik Answers for developing Generative AI solutions. While at Qlik Connect, I spoke with executives and developers about their vision for capturing valuable business insights into their customers’ data, especially if it was unstructured information strewn across thousands of pages of documents.

The folks at Qlik granted me a trial account and I dove into what Qlik Answers could achieve. I was pleasantly surprised that it was a relatively straightforward path to construct a chatbot that could search through several hundred pages of documents from multiple sources – scholarly papers, digital news reports, blog posts by reputable authors – to return cogent answers to business questions.

What impressed me was how quickly this all came together: Importing my corpus of nearly 30 documents, indexing them for use, and constructing a basic chatbot that could chew on the corpus to provide answers took less than 15 minutes.

Handling Outliers Is What Matters

I’m not some starry-eyed dreamer about AI capabilities; indeed, my earlier work with generative AI had yielded some surprising confabulations depending on what questions I asked of my chatbot.

Thus my evaluations included some tricks I’d learned during my prior experiences – things like prompt injection attempts and even hiding system prompt overrides within a source document. I discovered that Qlik Answers was able to handle the twists I threw at it quite well without any additional fine-tuning.

Over to You: Have An In-Depth Look At Qlik Answers

Obviously, this brief blog post isn’t going to convince anyone of how well Qlik Answers performed during this process, so please have a look at my complete evaluation of this tool. It contains detailed screenshots and explanations of every step I took, including references to each document I used as a source for my corpus so you can quickly run similar evaluations. Of course, please feel free to post comments to let me what you have discovered.

I realize that it’s been an incredibly long time since I’ve last posted here, but I have a plethora of reasons – and no, it’s not because an AI agent has taken over my job.

• Helping run events for the Analytics and Data Oracle User Community (AnDOUC), including my latest role as User Community Organizer, has taken quite a bit of time since our major event, Summit 2025, last April. I’m responsible for a majority of the production of their monthly TechCast episodes. (Shameless promotion: If you like Oracle technology and like presenting on it virtually, consider submitting a virtual session.)
• I’ve traveled to several Oracle User Group events this past year as well, including nlOUG in the Netherlands and various other smaller events in the USA. In fact, I’m off to Vancouver, BC this Friday to keynote on the state of Generative and Agentic AI in our industry, so if you’re nearby, please consider coming to hear what I’ve got to report.
• And finally, I’ve been working closely with The Futurum Group and their Tech Field Day events team to expand my portfolio as an expert in the field of data engineering and possibly even get recognized as an influencer in this space. (Hey, if it’s on a badge, it must be true, right?)

I promise to keep up with blog posts in the future – keep an eye out for an upcoming lengthy article on my exploration of the latest features of Qlik Answers in just a few weeks!

As part of Tech Field Days Cloud Field Day last week, I spent a whole day at the Google Cloud Moffett Place campus in Sunnyvale, CA to experience what I can only describe as drinking from an information firehose as our Googler hosts explain everything Google Cloud enables – file / block / object storage, application workloads, databases, virtual machines, and even bare metal computing.

Our gracious Googler host Bobby Allen brought together an impressive team of presenters and sessions that delved deeply into the underlying infrastructure needed to make the future demands of Generative AI a reality. One of his “Bobbyisms” helped me to keep focused during the in-depth sessions and discussions:

AI is not The Thing – AI Is the thing that makes The Thing Better.

In other words, GenAI is not really brand-new – after all, we’ve had machine learning for well over a decade, and LLMs have been around for some time as well. However, some things like prompt engineering, hallucinations, and the almost-human conversational capabilities of GenAI are quite recent developments.

So how does a major IT organization like Google build offerings for DevOps that combines neos and kainos?

Computing horsepower, in both bare metal or virtual flavors

For starters, Google Cloud offers a plethora of virtual and bare metal machines to provide the horsepower needed to run AI workloads along the DevOps spectrum, featuring Intel and AMD chipsets and various memory configurations for those demands.

Wait … What’s a TPU?

One interesting innovation that Google Cloud has brought to the GenAI party is the Tensor Processing Unit (TPU), a bundle of hardware components designed specifically to handle the rigors of AI computing demands.

Unlike GPUs, TPUs are a combination of shared memory and powerful CPUs tightly coupled together via extremely fast inter-chip interconnects (ICIs) within a three-dimensional “pod.” GenAI applications leverage a smaller 3D “cube” – a portion of a TPU – when they need to perform complex mathematical processing at the heart of many GenAI algorithms. (You can find out more about the v5p TPU here.)

Corpus chunks take up a lot of space. Where do we put it?

The storage requirements for enterprise data needed to make GenAI practical are obviously demanding, especially for cloud-based databases where that data resides. The next generation of Google Cloud block storage – Hyperdisk – is designed for those demands and comes in three flavors depending on the workloads being facilitated.

AI HyperComputer: The ties that bind

Powerful computing through GPUs and TPUs and optimized storage like Hyperdisk would be useless for GenAI workloads unless they’re able to communicate via ultra-fast, resilient networks. Google calls this architecture their AI Hypercomputer.

This is crucial for GenAI implementations because they often leverage Google Kubernetes Engine (GKE) to distribute workloads – whether it’s initial model training, testing, inference, fine-tuning, or even actual prompts and responses.

Google built a resilient network that can also intelligently offload workload demands throughout the architecture so that those hungry GPUs can be continually fed their required data inputs across multiple streams while insuring they can recombine their results as transformation occurs on separate GKE nodes.

Not Just Verbal: Multi-Modal Inputs for AI

One of the most intriguing demonstrations at Google Cloud from Neama Dadkhahnikoo was their latest multi-modal toolset that can interface with just about any type of input and output modality to leverage generative AI. Google showed off its latest Gemini toolset that provides some pretty spectacular capabilities on this playing field.

An actual example: A chatbot was asked to scan a movie to find a specific event, and the result came back in just a few seconds.

Another demo showed a Googler walking around an office with Google Glasses on and asking the AI to perform inferences based on images in focus. One intriguing example: A whiteboard sketch showing a live cat, a dead cat, and an open box. When asked What meme does this remind you of? the chatbot almost immediately replied Schroedinger’s Cat.

What I found especially reassuring is that Google Cloud hasn’t ignored the concerns many of us old-school data engineers have about the underlying data we need to collect as part of our AI applications’ corpus and training data, including ownership of our data within their Cloud.

Building ChatBots With Google CloudRun and LangChain

Tying all this infrastructure and AI-empowering concepts together, Lisa Shen did a fantastic job to demonstrate how Google CloudRun enables rapid development and deployment of GenAI endpoints that end users can access to immediately leverage the application logic. (I’m actually in the midst of building an APEX-based chatbot within Oracle Cloud Infrastructure for a similar LangChain demo for an upcoming session at Oracle Cloud World 2024, so this demo felt like familiar territory.)

Python is a favorite DevOps tool to for interfacing with LangChain to build GenAI apps, of course, but sensitive in-house business data needs to be stored somewhere … so I was glad to see the shout-out for Cloud SQL as a repository.

With sufficient Google Cloud documentation as a pertinent corpus, this simple GenAI Cloud Run example endpoint was able to return respectable answers to several sample prompts during the demo … with coding time of just a few minutes:

GenAI and Goodput

One thread that ran through almost every Google Cloud session about GenAI: the concept of goodput. With the complexity of the components that must work well together all the time to provide optimal results whenever a model is being built, trained, tested, and eventually used, it’s an interesting way of measuring just how well every aspect of a GenAI system is performing.

Oh, One More Thing …

And a final perspective as a 20+ year Oracle DBA: Oracle has recently updated its licensing terms for database instances on GCP, so I’m intrigued to see how an adventurous IT shop might leverage Google Cloud’s infrastructure for their next GenAI DevOps efforts with an Oracle database or two under the covers.

Just wanted to give everyone who saw my prior blog post from Tech Field Day’s recent Edge Field Day #2 gathering some interesting news about one of our presenters, StorMagic. They’ve recently made public their new tool called Edge Control that’s a reasonably-priced and simple tool for managing and configuring storage and hardware at the far edges of an edge computing environment.

We saw a brief demo of Edge Control back at EFD2 a few weeks back. It’s got a simple and clean front end that lets you quickly identify all the storage devices, VMs, and other hardware within your edge computing universe and list them by IP address, component name, or just about any other meaningful attribute.

It’s also easy to configure an individual component as well as initiate updates of the component’s firmware – something that is normally a time-consuming, error-prone, and mindless task. What impressed me most about what we briefly saw was the simplicity:

The tasks we typically need to accomplish when managing an edge computing environment don’t necessarily need a complex UI as long as it satisfies the immediate needs at hand.

The team at StorMagic have some brief videos of what they presented to us at EFD #2, so have a look-see; it might be the reasonably-price solution you’ve been looking for.

Edge Control release: https://www.youtube.com/watch?v=JlLs_qRykjs
Edge Control live demo: https://www.youtube.com/watch?v=NmJLr4qt5YU

Edge computing continues evolving exponentially, and it’s never been more obvious after I served as a delegate for Gestalt IT’s Edge Field Day #2 (EFD2) in Santa Clara, CA. After returning home from Oracle Cloud World 2023 in Las Vegas just a few weeks back – more on my experiences here – I had the chance to catch up with Stephen Foskett’s team at GestaltIT and with many of my fellow delegates from past Tech Field Day events.

We had two solid days of presentations from three vendors who focus on providing all the resources that modern IT operations need for their edge computing needs. I found it particularly refreshing that each vendor took completely different approaches to solving the atypical challenges of modern edge computing.

Solidigm: Computing At the Edge Means More Data Locally

The team from Solidigm led off our event. I’d already heard about their offerings as a delegate at Storage Field Day #24, but it was interesting to see how they pivoted their technology pitch towards the Edge. One evident key trend is that as edge computing resources continue to grow more powerful, more analytics are happening closer to the edge.

Thus, more data is being collected, preprocessed, filtered, analyzed for relevance, and retained locally for eventual transport to centralized data centers. Solidigm already offers high-capacity SSD storage solutions, but they’ve also created purpose-built endpoint servers that can provide edge computing capabilities to handle this new paradigm.

We also heard quite a bit about how Solidigm has creatively evaluated the robustness and survivability of their SSDs through some great stories on testing them in extreme situations, including a railcar bed, attached to a bowling pin, and even – how appropriate, as the 2023 World Series draw near! – during a pickup baseball game.

StorMagic: Can I Get a Witness?

The StorMagic folks focused on different set of real-world edge computing challenges: how to keep all the nodes on the Edge communicating with each other in small-scale settings without breaking the bank to keep them synchronized. That’s often harder than it sounds and potentially quite expensive – especially for, say, a moderately-sized additive manufacturing facility with a smaller IT budget than its fully-capitalized counterpart.

That’s where StorMagic’s SvSAN solution shows potential: It needs only two servers in its cluster per location, and provides concurrency within the cluster through their Witness as Service component. During their live demonstration, that witness role was fulfilled by a Raspberry Pi – a considerably inexpensive and lightweight component that prevents split-brain syndrome within the cluster.

StorMagic’s offering thus appears priced attractively for SMBs – potentially within a $10K upper limit when factoring in reasonably resilient hardware. It also comes with a simple client UI for deploying and managing the cluster’s networking and hardware that’s useful for deploying applications and upgrades across the enterprise.

NodeWeaver: The Edge Ain’t Beanbag.

Showing off their bare-metal nanocloud concept designed specifically for modern edge computing, the folks from NodeWeaver focused on some stern realities of deploying, monitoring, and upgrading edge devices as well as the applications they run.

NodeWeaver definitely brought their A-game to EFD2, including a full-blown server and several different edge devices for us delegates to experiment with. The smallest of these relied on ATOM technology, so we’re not talking about super-powerful devices here. However, that wasn’t the point of what they showed us; instead, their technology is concentrated on handling some tough realities of edge computing, including the need to deploy networking and applications across a wide variety of diverse devices running a variety of operating systems.

I had the chance to observe an example of a typical NodeWeaver deployment to multiple devices, and even experiment with a tiny nano-node. Since I’ve seen my share of unexpected hardware and networking failures over the years, I decided to have some fun as well. To simulate what could happen purely by accident in a real-world office environment, I deployed my dreaded PCL test: I unplugged the node’s power cord halfway through the configuration.

As the NodeWeaver folks explained, their nano-cloud solution is designed to recover automatically in this scenario. Indeed, I did receive notification via email that although the initial configuration attempt had failed, it was resuming at an appropriate checkpoint; it eventually completed the configuration within a matter of minutes. That’s crucial, of course, for alleviating serious heartburn when deploying radically different devices, perhaps half a continent away.

(PCL, by the way, stands for Polish Cleaning Lady. They are standard issue in most Chicagoland office buildings. They are friendly, hard-working, and devoted to clean office spaces, so they aren’t afraid to unplug a device if it gets in the way of any cleanup task. This actually happened to one of my mentors many years ago; he still uses that story to explain how an Oracle database performs complete recovery after an unexpected instance termination.)

Ignite Sessions: Our Turn!

I also had a new Tech Field Day experience: Stephen Foskett offered several delegates a chance to show off our presenting chops. My colleagues and I talked about a plethora of topics: biases to beware in generative AI, the difficulties of security within edge computing, and even how to build your own chatbot leveraging retrieval augmented generation.

Those of you who’ve seen me present at conferences know how much I love entertaining my audience while trying to impart some new ways of looking at things, and I hope they enjoyed my bit of fun as much as I did showing my new it. (Spoiler alert: Beware the Forer Effect.)

EFD #2: Wrapping It Up …

I’ve yet to attend a Tech Field Day event that didn’t expand my brain, and EFD2 gave me plenty of new use cases, technology, and business risks to ponder – something I’ll definitely use to my advantage as I’m preparing new presentations for upcoming meetups and conferences. And I’m still hoping we can pull together a Data Field Day themed event sometime in 2024. With the incredible changes we’re seeing in our industry as vector databases and generative AI become mainstream topics, there have got to be plenty of new service providers who’d love to tell their story.

It’s been over a week since I got back from Oracle Cloud World 2023 in Las Vegas, and it’s taken me that long to process and digest everything that happened there. Along the way, I presented three times and sat for some brief interviews on Oracle technology. And even though Gen AI could write this, here’s my personally-authored take on what I experienced.

A Much Bigger Crowd.

It was impossible to ignore much higher attendance at OCW23, just based on human traffic throughout the venue.

We had over 100 Oracle ACEs at our dinner, a first since the end of the COVID-19 pandemic. It was encouraging to see so many Oracle product managers and executives recognizing our contributions to the Oracle community.

Generative AI Is Ubiquitous.

Larry Ellison’s keynote focused on Generative AI (GenAI for short) and its impending impact on our civilization. Oracle is determined not to be left behind in the race, either. Larry announced they’re building the world’s largest computer based on NVIDIA H100 GPUs. I had a chance to chat with the NVIDIA folks at their OCW23 booth and actually touch some of that hardware – impressive stuff!

Larry also mentioned Oracle APEX as the target IDE for no-code development and how tightly it’s integrated into Oracle Database. I was surprised to hear that Java is no longer the target language for application development. When I chatted with folks at the Oracle Health Conference, they acknowledged how crucial APEX has been for transforming Cerner applications as part of that vertical.

I frankly do not know how many jobs will be replaced by GenAI, but I do know this: Everyone who asks me if their job is in danger because of it are the same people who asked me in the past (since I’m an IT guy) if I can fix their printer. The answer in both cases is probably … no.

Meanwhile, Back In the Trenches …

I was happy to hear Juan Loaiza emphasize some of the real-life DevOps dilemmas in his keynote on Oracle 23c. Juan explained at length how features like JSON Relational Duality offer the best of both worlds. The ability to access a JSON document as if it was stored within a relational database is a powerful fusion of two methods of data representation (more on that below). And the addition of vector database capabilities in 23c makes it more attractive than ever as a central platform for all of an IT organization’s computing needs.

A Hunger for Learning.

For the first time ever, I had three sessions at Oracle Cloud World. Two of those sessions were aimed at getting folks ready to experiment with LiveLab sessions right afterwards. They took place right on the main exhibition floor in a theater setting.

I had to talk fast to fit all content into 20 minutes! It was reassuring to see folks stop in their tracks to learn about APEX Native Map Regions and Oracle 23c’s most promising new feature, JSON Relational Duality.

The good news is you can experience what they experienced: Both of these sessions are publicly available. You can try out the diverse feature set of Native Map Regions or experiment with JSON Relational Duality Views at no cost through Oracle’s LiveLabs portal. Be sure to drop me a note if you have any questions afterwards.

My final session of Wednesday was late in the afternoon, not long before the big party was due to start. Yet a handful of people did show up for my session that took a deeper look at JSON Relational Duality in Oracle 23c. For me, that was a new experience because for the first time ever, all my attendees were people of color.

Even better, for the first time in my life I met two colleagues who’d travelled over 10,000 miles from Zambia to attend OCW23. That was a humbling experience in itself – I can’t imagine being that hungry for new knowledge. I made sure to mention how the Oracle ACE Program could make a difference in their careers, and I hope they may one day join our ranks of seasoned professionals.

If you’ve followed me on social media, you probably have already heard that Beyond Tech Skills, the podcast I co-hosted since January 2021 with my colleague and excellent friend Liron Amitzi, just wrapped up our final episode. While that’s unfortunate – especially for our loyal listeners, whom we appreciate immensely! – we had an amazing journey producing over 50 episodes since early 2021.

Let’s Do a Podcast! So … What Will We Talk About?

Liron and I met up at an Oracle user conference where we were both guest speakers, and we instantly bonded over our similar sense of humor. Our backgrounds couldn’t have been more different: I’m an early Boomer born and bred in Chicago, Illinois, and he’s a Millennial living in Vancouver, BC but is originally from Israel. We’d discussed collaborating sometime when the opportunity arose, so faced with the depths of the COVID pandemic in late 2020 and stuck at home with no in-person events or travel in the foreseeable future, we jumped at the chance to try something different. At least it wouldn’t be boring!

Once we built some podcasting infrastructure, we had to decide what to talk about. We dove into what many people were still struggling with: how to best position their technical skills and – more importantly – their so-called “soft skills” when looking for new employment opportunities. Our podcast’s title materialized naturally from those first few episodes because we wanted our listeners to realize just how differentiating those skills could be in those crazy times. We started simply with discussions on what our combined 60 years of IT experience had taught us: how to prepare a CV or a resume, how to handle trick questions during tech interviews, and even how to do tech interviews when hiring someone.

Let’s Talk to Our Smart(er) Friends!

Almost on a whim, one of us suggested we try interviewing some of our colleagues and friends we’d met over the years at technical conferences and user group meet-ups over the past 10 years. We started that part of the journey rather boldly with an extended and delightful chat with our good friend and colleague Kellyn Gorman about the challenges women still face today in the IT world.

We got such a great response from that interview that we decided we’d continue the conversational tone of the podcast, so we remained on the lookout for interesting guests and topics. We interviewed dozens of people in 2021 and 2022, including some of our more gregarious acquaintances like Connor McDonald, who talked at length about how to give great presentations at conferences, and Rie Merritt, who is responsible for wrangling hundreds of advocates in the Microsoft MVP program.

Along the way, we figured out some secrets about interviewing people. The key thing to remember is that your guest is most interested in talking about their own experiences, so it really pays to research what they’ve done recently, and find at least a few things about them that even their closest friends and colleagues didn’t know.

You Never Can Tell What People Find Most Interesting

Another surprise for us was that it’s often impossible to tell exactly which guest, discussion, or topic will prove to be most popular with our audience. Our especially lively discussion with Mark Horstman, who created the popular Manager Tools(TM) toolset to help managers at all levels communicate effectively with their teams, turned out to be one of our most download episodes.

Was this due to fortunate timing of the imminent diminishment of the pandemic? Were struggling managers at their wits’ end to handle the onset of #WFH / #RTO hybrid work environments? We’re still not sure, but it definitely resonated with a lot of listeners.

And our conversation with our colleague Jessica Sharp – herself an early Millennial – about the differences between Boomers, GenXers, Millennials, and GenZers and our (actually, not so) different outlooks on work was another surprise as a one of our most popular episodes. Was it the title (OK, Boomer. Seriously, It’s OK) or was it our witty repartee? Again, we’re not 100% sure. But it we certainly had an absolutely hilarious time recording it.

How Are You Doing … Today?

It was impossible to ignore how many people were struggling with their personal mental health during the pandemic, so we sought out folks who knew how best to address those issues. We chatted with Dr. Ryan Todd, whose popular Beyond the Checkbox podcast focuses on the stressors that so many of us face even in these post-pandemic times. Another fascinating guest, Rob Stephenson, told us how he created his own mobile app to help himself and others deal with severe depression and manage bipolar symptoms.

Wait … You Know Who?

We were extremely fortunate to chat with Tim Goldstein from Google, who told us the fascinating tale of how he handled his diagnosis of Aspergers late in life and how he actually turned it to his advantage. And our connection with Tim led us to a most unexpected opportunity to talk at length with one of the Fathers of the Internet, Dr. Vinton Cerf, winner of the Turing Award and still active in IT. (You think you have networking issues? Dr. Cerf is actually working on the interplanetary internet.)

It’s Not Over Until It’s Over

Late in 2022, Liron found a new career opportunity that required him to move his entire family to Tel Aviv, Israel – a bold relocation that I cannot ever imagine making! – but we continued to search out guests, schedule interviews, and maintain the quality of our episodes, shifting to monthly instead of biweekly episode drops.

Even with that adjustment, the eight-hour time zone difference made it ever more difficult for both of us to align our schedules, especially when we needed to interview guests in the western USA. We reluctantly agreed to wrap up our podcast, but we were determined to finish on a strong note.

In our last few episodes, our guest Tiffany Teasley related her inspiring story on how she decided to become a data scientist after 20 years as a high school mathematics teacher. And our final episode featured Dr. Douglas Scherer, who talked about how organizations need to improve their employee engagement strategy if they have any hope of retaining key human capital.

Lessons Learned: You Miss All the Shots You Don’t Take

In retrospect, co-hosting a podcast offered me the chance to learn some amazing lessons I would never have experienced had I not taken the shot:

• Preparation is crucial. Podcasting is more than talking to each other or asking the obvious question; the best episode moments happened when we queried a guest about some part of their lives that they are rarely asked about.
• Recording is easy, but editing is hard. Especially under a deadline!
• Finally, listening is the most important soft skill. I had to really focus on my co-host and guest when they were talking, because we often hit upon an unexpected topic or story organically while recording, and some of our most interesting episodes were the result.