The AI Report

Latest Episodes:

How The Adecco Group Is Rolling Out Salesforce AI to 27,000 Employees | Denis Machuel, CEO Oct 15, 2026

Show notes

In this episode, recorded at Dreamforce 2026, Denis Machuel, CEO of The Adecco Group, joins Kyle to discuss how AI agents are changing recruitment, workforce strategy, and the day-to-day work of recruiters. Denis explains how Adecco is moving from isolated AI pilots to scaled deployment across its business, with a focus on keeping people at the center of the transformation.

They discuss Ada, Adecco's recruitment agent in the UK, why roughly half of its candidate conversations initially happened outside working hours, and how agents can take over repetitive outreach and first-level screening so recruiters can spend more time on higher-value human conversations.

Denis also breaks down Adecco's work with Salesforce, the rollout of Agentforce Coworker to 27,000 sales professionals and recruiters, the importance of responsible AI and secure data, and why the next challenge is orchestrating multiple agents while bringing humans into the process at the moments where they add the most value.

Key Topics Covered

How AI is changing recruitment at The Adecco Group
Why Adecco moved from broad AI pilots to scaling agents in core workflows
How Ada supports candidate outreach and first-level screening
Why recruiters can spend more time on higher-value human conversations
Adecco's rollout of Agentforce Coworker
Responsible AI, guardrails and secure candidate data
How AI can make recruitment more proactive for candidates and clients
Why agent orchestration and human-in-the-loop design matter

Episode Timestamps

00:00 - Denis' background in computer science and AI

01:50 - The scale of Adecco and the future of work

03:33 - How Ada, Adecco's recruitment agent, works

05:22 - Scaling AI agents across languages and geographies

06:19 - Adecco's partnership with Salesforce and Agentforce

09:05 - Moving from individual AI use to enterprise deployment

10:54 - How a recruiter's workflow is changing with AI agents

13:34 - From reactive recruitment to proactive career support

15:18 - How Adecco measures the success of a hire

16:53 - AI adoption in Europe versus the US

19:15 - Regulation, responsibility and human-centric AI

20:12 - What drives Denis as CEO of The Adecco Group

22:02 - Why agent orchestration is the next big challenge

Where to find Denis:

LinkedIn - https://www.linkedin.com/in/denismachuel/

The Adecco Group - https://www.adeccogroup.com/

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How 175-Year-Old PwC Built AI Into Its Business Model | Ian Kahn, Partner Oct 08, 2026

Show notes

In this episode, Ian Kahn, PwC partner and global leader for its Customer and Commercial Excellence platform, joins Liam to discuss what AI adoption actually looks like inside large organizations. Ian has spent 27 years at PwC and leads a global team of almost 10,000 practitioners focused on helping clients build stronger customer relationships and drive growth.

Ian explains why the biggest challenge with AI is not access to the technology, but getting people and business models to change with it. He shares why PwC sees a small group of companies capturing most of the value from AI, how leaders can reduce resistance to change, and why the number of agents deployed matters less than how the business itself evolves.

They also get into how Ian uses his own portfolio of AI agents, the shift toward voice-based interaction, and how agents can take over routine administrative work so teams can spend more time on higher-value customer relationships.

Key Topics Covered

Why Ian calls AI the biggest change of his 27-year career
Why 20% of companies are capturing 75% of the value from AI deployment
How leaders can overcome resistance to AI adoption
How PwC is embedding AI into its business model
How Ian uses his own portfolio of AI agents
Why AI agents should free people for higher-value work
The role of voice in the future of work
Why leaders need to explain the "why" behind AI transformation

Episode Timestamps

00:00 - Introduction and Ian's 27 years at PwC

02:15 - Why AI is the biggest change Ian has seen in his career

02:56 - Ian's role leading almost 10,000 practitioners at PwC

04:44 - Why continuous learning matters in a 175-year-old firm

06:08 - Why 20% of companies are capturing 75% of AI's value

07:57 - The role of culture and leadership in AI adoption

10:08 - Why Ian spends most of his time with clients and teams

11:06 - How PwC is embedding AI into its business model

12:55 - Using agents to free up higher-value capacity

14:17 - How Ian uses his own portfolio of AI agents

16:29 - Why leaders need to explain the "why" behind AI

18:28 - Career advice, mentorship and leading high-performing teams

Where to find Ian:

LinkedIn - https://www.linkedin.com/in/ianwkahn/

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How EY Is Moving Companies From 18 Months of AI Experiments to Real ROI | Mike Flynn, Principal Oct 01, 2026

Show notes

In this episode, Mike Flynn joins Liam to break down how companies are moving from AI experimentation to measurable business value. Mike has spent more than 20 years in consulting, including nearly a decade at EY, nine years at PwC, and now leads the technology sector for EY's consulting business.

They discuss why businesses need to redesign workflows around AI instead of simply adding AI to existing processes, how EY's "Design for Zero" approach works, and why AI agents are changing the economics of software.

Mike also explains the real costs behind AI agents, what enterprises want from AI vendors, how AI is reshaping consulting, and why EY is investing in forward-deployed engineering.

Key Topics Covered

Moving from AI experiments to measurable ROI
EY's "Design for Zero" approach to AI-first workflows
Why per-seat software budgets break with AI agents
The real cost of an AI agent task
What enterprises want from AI vendors
How AI is changing consulting and forward-deployed engineering

Episode Timestamps

00:00 - Introduction and Mike's path from the Air Force to EY and PwC

01:58 - What Mike's role at EY looks like today

03:57 - Why point solutions create "trapped work"

04:29 - From AI experiments to end-to-end transformation

07:47 - Where AI-first redesign is working today

10:00 - Why AI is breaking the per-seat software model

13:01 - The real costs behind running an AI agent

16:00 - Measuring AI spend versus business value

18:28 - How much work will AI actually take over?

20:52 - Why companies need more granular AI cost controls

25:53 - Building automated control loops for AI

31:21 - Experimenting with enterprise AI tools at scale

36:02 - Why enterprise AI adoption moves slower than personal AI

39:37 - CIOs shift from adoption to process redesign

41:29 - What enterprises want from AI vendors

45:24 - Consulting versus AI deployment at EY

46:36 - Why AI may create more consulting work, not less

49:00 - EY's forward-deployed engineering strategy

52:16 - The two qualities Mike looks for when hiring

55:33 - Why Mike does what he does

Where to find Mike:

LinkedIn - https://www.linkedin.com/in/michaelbernardflynn/

EY - https://www.ey.com/

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How to Build AI Systems People Can Actually Trust | Cillian Kieran, CEO & Founder, Ethyca Sep 24, 2026

Show notes

In this episode, Cillian Kieran, CEO and founder of Ethyca, joins Liam to talk about why data privacy and AI governance need to be treated as engineering problems, not just legal and compliance challenges.

Cillian explains how a consulting project for Heineken ahead of the GDPR pushed him to rethink privacy from an engineer's perspective and eventually build Ethyca. He breaks down how the company's product suite works, why Fides has become a widely adopted open-source privacy standard, and what changes when AI agents are given read and write access to tools like Stripe, QuickBooks or a CRM.

They also get into what foundation model providers may be missing on governance, why AI needs a harness that directs its capabilities without slowing it down, the responsibility engineers have when building AI systems, and why he thinks much of what we call AI is still statistical math wrapped in a marketing label.

Later, Cillian and Liam discuss where AI startups may consolidate, Yann LeCun's work on world models, the human impact of increasingly agent-driven work, and how growing up around art shaped Cillian's view of software as a creative pursuit.

Key Topics Covered

Why privacy and AI governance are engineering problems
How Fides became a widely adopted open-source privacy standard
The risk of giving AI agents read and write access to business systems
Why AI needs a harness that directs its capabilities without slowing it down
What foundation model providers may be missing on governance
Why engineers, not just users, carry responsibility for AI safety
Why Cillian calls AI "statistical math" with a marketing label
World models, startup consolidation and the future of AI infrastructure

Episode Timestamps
00:00 - Introduction and welcome 00:58 - From a physics dropout to a data consultancy for Fortune 500 brands 03:17 - The Six Problems of Privacy and becoming the world's most trusted technology company 07:20 - Inside the product suite: Fides, Helios, Janus, Lethe and Astralis 11:29 - How Fides became a widely adopted open-source privacy standard 14:20 - What Cillian believes foundation model providers are missing on governance 16:08 - Why AI needs a harness, not just guardrails 18:19 - Ethyca's business model and the rise in demand for consulting 23:07 - Why Ethyca went after enterprise customers first 24:50 - Testing Grok's agent tools 27:34 - MCP proliferation, OpenClaw and the risk of "permissive access" 31:11 - The seatbelt analogy for building safe AI systems 34:28 - Why AI governance isn't getting the coverage it deserves 36:26 - AI as "statistical math" and Ted Chiang's take on the label 38:43 - Foundation model economics and the future of AI startups 39:49 - Yann LeCun, world models and where Cillian would place his next bet 50:12 - Zen and the Art of Motorcycle Maintenance: classical versus romantic thinking 56:05 - Why Cillian still does what he does 01:03:34 - Where to find Cillian and closing thoughts

Where to find Cillian:
LinkedIn - https://www.linkedin.com/in/cilliankieran Ethyca - https://ethyca.com

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Inside Block's Bet on Multiplayer AI | Brad Axen, Head of AI Capabilities Sep 17, 2026

Show notes

Brad Axen, Block's Head of AI Capabilities and the original author of Goose, joins Liam to talk about what actually makes AI useful at work. They cover how Block went from building an early open-source AI agent to MoneyBot, ManagerBot, BuilderBot and Buzz, and why Brad thinks the hardest problems now are memory, access and interface, not just model intelligence.

Brad also explains why AI memory should belong to the business rather than a single bot, what ants can teach us about shared memory systems, why "meat proxy" is becoming a new office problem, and how Buzz is testing a multiplayer model where humans and AI agents work in the same space.

Key Topics Covered

Goose, Block's open-source AI agent, and the Agentic AI Foundation
Agents vs. harnesses vs. interfaces
MoneyBot, ManagerBot and BuilderBot
Why AI memory should belong to the business, not the bot
Stigmergy and ants as a model for shared memory
Buzz and Block's "multiplayer" approach to AI at work
"Meat proxy" and the new office busywork AI can create
Why the bottleneck is shifting from writing code to deciding what to build
How AI is changing hiring, interviews and day-to-day work
The human cost of spending all day working with AI

Episode Timestamps

00:00 Intro

00:07 What Block actually is

02:40 From CERN to Block

05:19 Building Goose and taking it open source

06:29 Agents vs. harnesses vs. interfaces

10:28 MoneyBot, ManagerBot and BuilderBot

15:56 Memory, access and learning over time

22:53 What ants can teach us about AI memory

26:48 Two versions of where AI could go

28:26 Buzz and the idea of multiplayer AI

29:56 "Meat proxy": the new office problem

35:09 The Buzz case study and a 50% productivity jump

37:06 The new bottleneck now that AI can write the code

49:11 Rebuilding institutional knowledge after team restructuring

52:00 How AI is changing hiring and interviews

57:35 The loneliness of working with AI all day

59:13 Why Brad does what he does

Connect with Brad on LinkedIn:

https://www.linkedin.com/in/bradleyaxen/

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The Agent Economy, AI Identity, and the New Rules of Business | Loni Stark, VP Strategy & Product, Adobe Sep 10, 2026

Show notes

In this episode, Loni Stark, VP of Strategy and Product at Adobe, joins Liam to talk about what happens when a 25 year tech career runs alongside a full creative practice in painting, sculpture and writing, and what that split brain teaches her about building for the AI era. Loni explains why she thinks brands may already be invisible, or worse, misrepresented, inside AI answers, why every company needs to start treating AI as a new kind of audience, and how she is running her own home AI lab, complete with a self built server and a personal agent that has now run continuously for over 150 days, to understand what actually gives an AI agent an identity.

Along the way, Loni and Liam get into her Harvard Extension School research into "orphan values," the personal values people can't express in any of their current life roles, and what happens to that alignment as AI reshapes the roles themselves. She also breaks down how she balances Adobe's biggest enterprise bets, including Experience Manager, Commerce, Brand Concierge and LLM Optimizer, against the need to experiment without limits in her own time.

Key Topics Covered

Why Loni keeps a full art practice, painting, sculpture and writing, alongside her tech career
Growing up with parents who didn't understand the arts, and using creativity as a form of rebellion
Whether humans are innately creative, and why AI makes protecting your own voice more important
Why Loni has stayed at Adobe for 25 years, and how she thinks about "growing the aquarium"
Building a personal AI server at home instead of a garden, and what that setup actually involves
Swapping the underlying model and the agent harness to test what gives an AI agent a persistent identity
Her Harvard Extension School research into "orphan values" and how AI is reshaping the roles we express them through
The shift from human mediated to AI mediated experiences, and why that changes what "traffic" even means
Why being invisible to AI isn't the worst case, being misrepresented by it is
How brands should start preparing their content and catalogs to be "agent ready"
The placebo effect of working with agents, and how that belief shapes performance and creativity
How Loni balances limitless experimentation with the governance enterprise AI actually requires
Why she does what she does: an insatiable need to grow, create and become more than she currently is

Episode Timestamps
00:00 - Introduction and welcome 00:05 - Balancing a full art practice with a 25 year tech career 05:38 - Why she's stayed at Adobe for 25 years 08:30 - AI as the biggest creativity enabler she's seen 12:50 - Inside her home AI lab: hardware, memory, and swapping the agent harness 18:11 - Studying psychology at Harvard, and what "orphan values" mean 26:28 - The shift to AI mediated business, and why invisible isn't the worst case 31:46 - How brands become "agent ready" for the AI agent economy 36:23 - The placebo effect of working with AI agents 37:35 - Balancing limitless experimentation with enterprise governance 47:05 - Why Loni does what she does 49:14 - Where to find Loni and closing thoughts

Loni's Socials: LinkedIn - https://www.linkedin.com/in/lonistark/

Loni’s Art Gallery: https://atelierstark.com/work/

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Why This Financial Firm Built Its Own AI Tools Instead of Going Off the Shelf | Braden Warwick, Financial Planning Product Architect, PWL Capital Sep 03, 2026

Show notes

In this episode, Braden Warwick, Financial Planning Product Architect at PWL Capital, breaks down why so much of the financial advice sold at big banks is a sales pitch dressed up as a plan, and what a real financial plan actually requires. Braden traded a PhD in aerospace engineering for a career rebuilding how Canadians plan their money, and he brings that same engineering mindset to financial planning: define your objectives, map your constraints, then solve for the outcome that actually improves your life.

Braden also walks Liam through the AI infrastructure PWL has built in house, from a proprietary data lake to an AI powered meeting note tool and planning summaries, and explains why they chose to build their own tools instead of buying off the shelf software. They get into Monte Carlo simulations, why financial planning is really about the distribution of outcomes rather than one predicted path, and what a financial planning engagement might look like in 2031.

Key Topics Covered

How Braden went from a PhD in aerospace acoustics to building financial planning tools at PWL Capital
Why PWL's advisors are paid for advice, not for selling products, and how that changes the plan you get
The six areas of a real financial plan: investing, cash flow, tax, insurance, retirement, and estate
Treating a financial plan like an engineering problem: objectives, variables, and constraints
Why Monte Carlo simulations model financial planning as a distribution of outcomes, not one fixed path
What forms of uncertainty most financial software still misses, from real estate values to life expectancy
Why PWL built its own AI meeting note tool and data lake instead of buying an off the shelf solution
How AI is helping PWL's advisors scale personalized, evidence based financial plans
PWL's acquisition by One Digital and what it changed, and did not change, about how Braden works
What a financial planning engagement could look like by 2031

Episode Timestamps

00:00 - Introduction

00:40 - From aerospace engineering to financial planning

03:54 - Why PWL approaches financial advice differently

07:31 - The six areas of a real financial plan

11:48 - Financial planning as an engineering problem

17:56 - The psychology behind financial planning

23:14 - Objectives, constraints, and uncertainty

28:10 - How Monte Carlo simulations work

33:21 - What financial planning software still misses

39:11 - Building financial planning tools at PWL

44:16 - Inside PWL's financial planning system

51:38 - How AI is changing the advisor workflow

57:20 - Why PWL built its own AI tools and data infrastructure

1:03:41 - What changed after the OneDigital acquisition

1:06:34 - The future of financial planning

1:11:47 - Why Braden does what he does

Braden's Socials:
LinkedIn - https://www.linkedin.com/in/braden-warwick-a40b48a3/

Resources Mentioned:

Braden’s article, The Optimal Financial Plan - https://pwlcapital.com/the-optimal-financial-plan/

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Inside Shopify's Plan for Agentic Commerce and AI Shoppers | Andrew McNamara, VP of Applied ML, Shopify Aug 27, 2026

Show notes

In this episode, Andrew McNamara, VP of Applied ML at Shopify, returns to unpack how much has changed in agentic commerce since his last episode. Andrew and Liam dig into why agents are becoming "the new front door to commerce," why orders coming to Shopify stores from AI are up 13x, and what's actually happening inside Shopify's personalized shopping agent in the Shop app.

They also get into the Universal Commerce Protocol (UCP) and why AI commerce is growing 9x faster than social commerce did at the same stage, how Sidekick's architecture and app extensions work, and SimGym, Shopify's system for training AI shoppers to A/B test store changes before they ever reach a real customer.

Key Topics Covered

How shopping is shifting from stores and desktops toward agents as "the new front door to commerce"
Why orders coming to Shopify stores from AI are up 13x, and why catalog-powered AI search converts twice as well as general AI search
Inside Shop app's personalized shopping agent, and how it learns different shopping personas (like shopping for a pet versus a child)
Why customers are shifting from keyword searches to natural language queries, and the higher conversion rates that come with it
Why Shopify keeps shopping data personalized to the individual user rather than training it into a larger internal model
What the Universal Commerce Protocol (UCP) is, and why AI commerce is growing 9x faster than social commerce and 3x faster than mobile did at the same stage
The story of Shopify's CEO giving his own Hermes agent a budget so it can send him gifts in the mail
Sidekick's app extensions, and how partners like Klaviyo and Loop plugged in at launch
Campaign Autopilot's "auto research loop," and its parallels to reinforcement learning
SimGym, and how Shopify trains AI shoppers to A/B test store changes before running them on real customers
Why Sidekick runs on Anthropic's Sonnet model hosted on Google Cloud, and why that choice is model agnostic
Andrew's own habit of shopping by taking pictures throughout the week and searching by image through UCP-connected agents

Episode Timestamps:

00:00 - Introduction and welcome

00:29 - What's changed in AI and shopping since their last conversation

01:47 - Agents becoming "the new front door to commerce"

04:16 - Inside Shop app's personalized shopping agent

07:32 - Why data stays personalized to each shopper instead of training a larger model

11:53 - What the Universal Commerce Protocol (UCP) is, and orders from AI up 13x

14:58 - Merchant tooling for tracking AI-driven traffic and conversions

15:55 - The story of Tobi's Hermes agent sending him gifts in the mail

20:48 - Andrew's own habit of shopping by taking pictures and searching by image

26:59 - Sidekick's app extensions and partner integrations

33:02 - Inside Sidekick's architecture: the Sonnet model and knowledge base

35:18 - Campaign Autopilot's auto research loop

38:58 - SimGym: training AI shoppers to test store changes

42:23 - What's next for Shopify's agentic commerce features

44:17 - Where to find Andrew

Andrew's Socials:

Twitter (X) - https://x.com/DrewCH

LinkedIn: https://www.linkedin.com/in/andrewmcnamara1/

Partner Links

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What It Takes to Build a Smart Shopping Cart Used by Millions | David McIntosh, Chief Connected Stores Officer, Instacart Aug 20, 2026

Show notes

David McIntosh, Chief Connected Stores Officer at Instacart, joins Liam to explain why the company is betting on smart shopping carts instead of rewiring stores with ceiling cameras. David walks through the $350 million acquisition of Caper, how Instacart is now live in more than 100 cities with thousands of connected carts, and why the screen on the cart, not the checkout speed, turned out to be the real driver of sales lift for retailers.

David also gets into the surprisingly hard engineering problems behind a smart cart, like figuring out whether a basket is actually empty, fusing camera and scale data in real time, and building recommendations that know exactly where a shopper is standing in the store. He and Liam talk about who owns all that shopping data, what agentic AI looks like when it moves from chat into the aisle with tools like Cart Assistant, and why grocery budgets and meal planning are becoming one of the most requested AI features in the store.

Key Topics Covered

Why David left Tenor, the GIF search engine used by billions, to build Instacart's Connected Store business
The strategic bet behind unifying online and in-store grocery shopping
Why Instacart acquired Caper for $350 million instead of building smart carts in-house
The reason Instacart chose carts over ceiling cameras for in-store AI
How a simple running total and real-time coupons drive measurable sales lift
The NVIDIA Jetson hardware and multimodal sensor fusion that let the cart "see" what's in the basket
The strange edge cases in physical AI, like why "is this cart empty" is a genuinely hard question
Who owns retailer and shopper data, and how it's used to improve recommendations
Cart Assistant: how Instacart lets customers shop inside ChatGPT and directly on retailer websites
Using agentic AI to fix store operations like out-of-stock items and supplier issues
How budget-conscious meal planning became one of the most requested AI features in the store
David's answer to Liam's closing question: why he does what he does

Episode Timestamps 00:00 - Introduction and welcome 00:14 - David's path from Tenor to Instacart's Connected Store 02:01 - The bigger bet behind bringing online and in-store shopping together 05:29 - Entering the smart cart market and acquiring Caper 08:07 - Caper's scale today: 100+ cities and millions of daily sensor inputs 10:28 - How the smart cart actually drives sales lift 12:49 - Why Instacart bet on carts instead of ceiling cameras 17:33 - The unglamorous detail that makes or breaks adoption: charging 19:26 - What makes the experience sticky enough to keep customers coming back 24:41 - Inside the hardware: NVIDIA Jetson and multimodal sensor fusion 28:43 - The strange edge case behind a seemingly simple question 35:14 - Who owns the shopping data, and how retailers use it 37:30 - Agentic shopping: Cart Assistant and buying inside ChatGPT 42:16 - Using agentic AI to fix store operations, not just shopping 46:17 - Why David does what he does

Connect with David on LinkedIn: LinkedIn: https://www.linkedin.com/in/mcintoshdavid/

Partner Links
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AI Agents Should Never Touch the Public Internet | Zachary Smith, Co-Founder & CEO, Datum Aug 13, 2026

Show notes

In this episode, Zachary Smith, CEO and co-founder of Datum and previously the founder of Packet (acquired by Equinix for $335M) and Voxel (acquired for $35M), joins Liam to explain why the internet is about to undergo its biggest transformation since the cloud. As AI agents, vibe coding, and thousands of new applications flood the web, Zac believes the open internet model we've relied on for decades is breaking down.

Zac argues that every person, every company, and eventually every AI agent will need its own private network. He explains why the future internet may look more like the Visa network than today's public web, how digital sovereignty and geopolitics are reshaping infrastructure, and why developers are increasingly relying on dozens of cloud services rather than just the hyperscalers.

The conversation also dives into Zac's unlikely journey from Juilliard-trained musician to building and exiting two infrastructure companies, the emotional toll of entrepreneurship, and why he keeps coming back to startups despite already having financial freedom.

Key Topics Covered

Zach's journey from Juilliard and classical music to building infrastructure companies
Building Voxel and selling the company for $35M
Starting Packet and its $335M acquisition by Equinix
Why AI agents are creating a security problem for the internet
Why every person and company may eventually need a private network
The difference between the public internet and private internet
Why the future internet could resemble the Visa network
Digital sovereignty, geopolitics, and the splintering of the internet
Why developers increasingly rely on dozens of cloud providers
How AI is turning millions of people into software developers
APIs, MCP, and the next phase of application architecture
Why Zach believes AI agents should only talk to approved systems
Open source, network effects, and Datum's long-term vision
The emotional side of entrepreneurship and why community matters more than money

Episode Timestamps
00:00 Introduction and welcome
00:06 Zach's background: from Juilliard and classical bass to startups
02:44 Building Voxel and the early cloud era
08:44 Starting Packet, raising capital, and the Equinix acquisition
15:28 Why taking time off helped him dream again
18:02 What Datum does and the idea of a network cloud
19:38 Three forces changing the internet
20:41 Hyperscalers explained: Amazon, Google, and Microsoft
24:52 Why new cloud providers are emerging
27:16 Digital sovereignty and the fragmentation of the internet
32:03 Public internet vs. private internet
32:54 Inside the physical "meet me rooms" that connect the internet
39:49 How internet routing actually works
45:56 Why developers use so many cloud providers
48:10 APIs, MCP, and AI agents
51:07 Why the future internet may resemble the Visa network
54:23 Who Datum's customers are, and why Datum is open source
1:03:07 AI agents and the next generation of software
1:07:50 Why Zach keeps building companies, and why he does what he does

Connect with Zac:
LinkedIn: https://www.linkedin.com/in/zsmith/
Website: https://www.datum.net/

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