Sharad Kumar lives in Pleasanton, California with his wife and 2 kids. He enjoys playing all musical instruments, and spending time with his family. He has a 2 year old daughter, and a 14 year old son into robotics. He is also passionate about giving back to the community, through their company foundation.

Harshit Omar lives in San Francisco, and is married with a 4 year old son. He used to be a street racer in his college days, loving fast cars and taking risk. Nowadays, he is a big marvel and comic book fan, along side his son. In fact, his son thinks he is Captain America, regularly wielding his shield and mask.

A fun fact about both of these gentlemen: this is their third company to work together in, their second startup, and their wives are sisters. So they are connected by wives, and united by startups.

In their previous startups, Sharad was leading sales and ops and Harshit was leading on the product side. When the company got acquired, it took them 8-9 months to integrate to a different cloud provider. They realized the model was broken, requiring expensive consulting services, and not convenient at all - and they wanted to figure out a better way.

This is the creation story of Fluidcloud.

Sponsors

• Unblocked (https://getunblocked.com/codestory)
• TECH Domains (https://get.tech/codestory)
• Mezmo (https://mezmo.com/codestory)
• Braingrid.ai (https://braingrid.link/code-story)
• Alcor (https://alcor.com/podcast)
• Equitybee (http://codestory.co/equitybee)
• Terms and conditions: Equitybee executes private financing contracts (PFCs) allowing investors a certain claim to ESO upon liquidation event; Could limit your profits. Funding in not guaranteed. PFCs brokered by EquityBee Securities, member FINRA.

Links

• https://www.fluidcloud.com/
• https://www.linkedin.com/in/sharadkumar123/
• https://www.linkedin.com/in/harshito/

Yoni Avital lives in Tel Aviv, Israel, with his wife and 3 older children. The oldest kid is a boy, so Yoni and he try to see as many football games as possible. He enjoys shopping with this girls, though it's cause he is their Dad, not because he enjoy shopping. He likes to travel, hike, and enjoys a nice white wine in warmer weather. His most memorable hike was at Yosemite, when he started at 4 am and came across a lot of wildlife.

Yoni was in the virtual desktop space in the past. What he and his team realized was that troubleshooting these virtual experiences were incredibly complicated. They started to build an enterprise task manager, to centralize a task management UI to control the endpoints. When customers started asking to use it daily, Yoni figured out they had something unique.

This is the creation story of ControlUp.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.controlup.com/
• https://www.linkedin.com/in/yoavital/

Today, we have a special guest on the Code Story podcast - Patrick Vuong, Director of Product at Moderne. Moderne is the agent tools company, building the. Knowledge, discovery and execution tools that AI agents rely on - so they can operator faster, more accurately, and at far lower cost.

In today's episode, Patrick is going to tell us about the company, and how Moderne is enabling developers to build software faster, and with the best context - using agents and agent tools. Their approach to semantic models produce deterministic over probabilistic, or inference driven, tools, which for this engineer/host, has been a point of skepticism for AI since the beginning.

Questions

• Tell me and my audience a little bit about you.
• What is Moderne?
• Moderne is enabling developers to operate software systems at the speed of agents. Tell me about this product suite.
• Why do Agents need tooling? Where do we see AI in ROI
• Something jumped out at me... you mentioned you are not only building tooling for agents that are deterministic.
• As we peer into tech stacks across the industry, where does Moderne fit?
• OK so this is clearly a pivot for Moderne. With this, who are your customers now?
• What does the future like for your product - what you offer - and your team?
• For you personally, you are entering into a new chapter with Moderne. What makes you most excited, going from Microsoft to entering the startup world with the company?
• In your journey, who has influenced the way you work? Tell me about a person, or many persons, or something you look up to and why.
• So you worked at Microsoft for 8 years, and are now transitioning to Moderne. Say you were getting on a plan and sitting next to someone about to make this same transition - what advice would you give them?

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.moderne.ai/
• https://www.linkedin.com/in/vuongpatrick/

We have a special return episode, by our good friend Rickard Hansson. Rickard joined us previously on the podcast in Season 8 to tell the creation story of Weavy - collaboration infrastructure for serious builds. Today, he makes a follow up visit to tell us all about Gainable, his new project - which removes data and engineering from being the middle man, and enables your team to build the apps they need now.

Questions;

• Last time we talked in Season 8, you were building Weavy. Whats happened since we last talked with that company?
• Tell me about Gainable - give me the pitch there, and tell me why this is the right approach to using AI.
• Most AI builders wire straight to a frontier model and wait for the next release to fix the gaps. I didn't. Where does the model actually sit in Gainable product, and why only there?
• Why is an app factory that is deterministic important? Dig into that.
• You use the term "free-range coding".. what does this mean? Unpack the phrase for us.
• You point out that tokens still appear to be heavily subsidized to me. What do you mean by that, and what happens to all these AI products when that ends?
• We've all read the headlines - Fable 5 got switched off by the government for 18 days. Why do you see this as a turning point, not a footnote?
• You suspect flat subscriptions for the top models are done, and it all drifts to credit-based. What signal are you seeing that tell s you this?
• If the model is a commodity everyone rents, where's the moat?
• What is next for Gainable, and how can someone get started using the platform?

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.gainable.dev/
• https://www.weavy.com/
• https://www.linkedin.com/in/rickardh/
• https://codestory.co/podcast/bonus-rickard-hansson-weavy/

Virginia Szepietowski grew up in the UK outside of London, and now lives in Alabama. She's had a winding path to her current venture, including body building, triathlons, law, and entrepreneurship. She comes from a family of entrepreneurs, who are deeply ambitious, tenacious, and deeply humble. She finds the feeling of a deep safety net from her family, and she pursues her adventures. Outside of tech, she is married to her now co-founder. She is still a competitive body builder, and likes to push herself to the limit.

Through a series of life events, Virginia got interested in water treatment. She started discovering the world of wastewater operators, and the fact that they were the last line of defense before toxic wastewater moved into our waterways (rivers and such). Using AI, her and her team started to build a platform for these operators to quickly detect organisms in these water streams.

This is the creation story of Nyad AI.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://nyad.ai/
• https://www.linkedin.com/in/virginia-szepietowski/

Timestamps

0:00 Intro and episode teaser on automating biology in critical clean water infrastructure

1:49 Guest introduction: Virginia Szepietowski's background and path to founding Nyad AI

2:45 Understanding the hidden biology behind municipal and industrial wastewater treatment

4:10 The core problem: Why manual microscope sampling creates dangerous operational blind spots

6:05 Origin story: Translating computer vision research into industrial water automation

8:30 How Nyad AI's automated hardware samples and analyzes live microorganisms in real time

11:15 Overcoming physical hardware engineering hurdles in harsh, high-humidity environments

14:00 Preventing biological plant crashes and saving millions in compliance penalties

17:30 Addressing the labor shortage: Supporting the next generation of water operators with AI

21:00 Scaling AI hardware deployments across municipal and industrial facilities

24:15 The future of automated biology in global water security and environmental protection

27:00 Closing thoughts and how to connect with Virginia Szepietowski and Nyad AI

Richard Luna grew up in New York, never living more than 35 from where he grew up. He is a self proclaimed super nerd, and has been one since he was 13 - at which point, he started coding on an HP calculator. He's always been fascinated to know how things work, and how patterns repeat - which he has observed in the industry throughout the years. Outside of tech, he has 2 kids, one of which is in the business with him. He's an avid cyclist, traveling on average, 120 miles a week.

Richard has been a life long technologist, doing everything from desktops, to coding, to hosting. When he and his team saw the limits of what hosting can do, they dove into developer operations (DevOps), and found where they could add the most value - through SaaS infrastructure.

This is the creation story of Protected Harbor.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://protectedharbor.com/
• https://www.linkedin.com/in/richardluna/

Timestamps

0:01 Teaser on solving complex database report bottlenecks beyond standard SQL servers

0:47 Show intro and setting the stage for application-aware infrastructure

1:32 Host intro: How Richard Luna established application-aware infrastructure

1:49 Guest introduction: Richard Luna's background, coding at age 13, and cycling 120 miles a week

2:21 The career path from desktops, coding, and traditional web hosting to DevOps and SaaS infrastructure

2:41 Origin story: The creation of Protected Harbor

2:48 Defining application-aware infrastructure and why traditional hosting reaches a hard ceiling

4:10 Why "infinite compute" fails when underlying database architecture and queries are broken

6:05 Moving beyond basic server ping tests to deep application transaction monitoring

8:30 The case for "boring" IT: Prioritizing stability, predictability, and uptime over hype

11:15 Strategic trade-offs in hybrid cloud setup and managing hardware accountability

14:00 Aligning MSP incentives with client business outcomes and application performance

17:30 Common pitfalls in legacy system cloud migrations

21:00 The role of operational discipline in modern cybersecurity and IT governance

27:00 Where managed infrastructure services are heading and closing thoughts

Danny Gal was born and raised in the UK, and now lives outside of London. He attended University in Nottingham... yep, the same one from Robin Hood. He LOVES challenges, and not just any challenges - the hard ones. He is done Iron Man competitions, ultra marathons, climbed Mount Kilimanjaro, and jumped out of a perfectly good plane, to name a few. He loves doing them once... and then never again. He's got 2 small kids, and believes in work hard, play hard.

Danny has worked in many roles in the past, across enterprises and the like. What he found most difficult was scaling himself. He got to talking with his now co-founder about building something around the idea of scaling oneself, and took it to some businesses to validate it. Once he saw them get excited about it, he figured they were onto something.

This is the creation story of Proteams.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://proteams.com/
• https://www.linkedin.com/in/dannygal/

Timestamps

1:49 Guest introduction: Danny Gal's background, endurance challenges, and career journey

2:50 The core problem: Why traditional freelance marketplaces fail enterprise workflows

4:10 Origin story: Solving the bottleneck of "scaling oneself" in leadership

5:45 Validating the managed fractional team model with early enterprise clients

7:20 Self-serve bidding vs. managed delivery teams: Understanding the structural shift

9:30 Building a software-enabled harness for global engineering talent

12:15 How Chief Procurement Officers should structure external workforce strategies

15:00 Overcoming compliance, IP, and security hurdles in enterprise talent integration

18:10 Balancing speed, quality, and accountability in remote team management

20:30 Closing thoughts and where to connect with Danny Gal and Proteams

Sid Dixit is originally from central India, and came to the states for college. He is a technologist and builder at heart, serving in leadership roles across major companies. He has built and managed a fleet of satellites, built robots at Amazon, worked at Microsoft on surface tablets, and finally, at Google working on Android. Outside of tech in lives in the Bay Area with his wife and kids. He loves water sports, especially sailing. He spent 10 years in San Diego, and stumbled on the sport.

Sid's current company started in 1999, and was acquired in 2010. A few years ago, Sid joined the company, at a time when the company was wanting to rebuild its network from the ground up - starting with a powerful index.

This is Sid's creation story at iTradeNetwork.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.itradenetwork.com/
• https://www.linkedin.com/in/siddharthdixit/

Timestamps

1:49 Guest introduction: Sid Dixit's career background across satellites, Amazon, and Google

2:42 Overview of iTradeNetwork and its reach across North America's perishable supply chain

4:04 The origin story of iTradeNetwork and why generic ERPs fail fresh food logistics

5:45 Upgrading legacy software from Systems of Record to Systems of Intelligence

6:37 The role of specialized AI agents: Forecasting, pricing, RFQs, and negotiations

7:23 Solving outdated market data: Building a real-time produce commodity index

8:55 Strategic MVP trade-offs: Narrowing focus to key commodities like strawberries and apples

10:19 Using AI to harmonize unstructured vendor product descriptions

15:00 Streamlining complex order intake workflows across buyers and sellers

22:00 Quantifying the macroeconomic impact of supply chain speed on global food waste

28:00 Future vision for AI agents in global supply chain management

31:30 Closing thoughts and where to learn more about iTradeNetwork

Emilie Schario grew up in New Jersey, outside of Newark, and attended college in the state. Currently, she lives in Columbus, Georgia, outside of Atlanta. She mentions she got into technology so she could easily follow her husband's career geographically, and has much success in the industry. Outside of tech, she is married with 3 boys (all 5 and under)... so there is a lot of wrestling in her household. She admits she is often quoted staying she does three things in her life - work, parenting, and if she is lucky, attends CrossFit 3 times a week. In fact, she finds a great sense of community in that world, and brings her kids with her to cheer her on.

A year and a half ago, Emilie's current venture was started, to build the open source orchestrator (or "harness") for AI coding agents. Through some shuffle in the early team, Emilie joined and started in building the fastest AI coding app on the market.

This is the creation story of Kilo.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://kilo.ai/
• https://www.linkedin.com/in/emilieschario/

Timestamps

1:49 Guest introduction: Emilie Schario's background and career journey

2:51 Overview of Kilo Code as an open source agentic engineering harness

3:14 Differentiating through model freedom and supporting 500 plus AI models

3:58 Kilo Code founding story with Sid Sijbrandij and team history

4:42 Defining the evolving MVP for AI coding tools in a fast-moving market

5:25 The rapid shift from manual prompt engineering to autonomous loops

6:19 Trade-offs and resource allocation: Deprecating the Kilo App Builder

9:09 Modern AI product management: Why multi-year roadmaps no longer work

10:19 Shifting PM responsibilities from tracking engineers to setting context

13:00 The AI throughput illusion: Why expensive models don't equal shipped code

17:00 Measuring true engineering output and productivity in the AI era

21:00 Building resilient engineering cultures around AI coding platforms

23:30 Closing thoughts and where to learn more about Kilo Code

Bhaskar Sunkara grew up in Delhi, India, and moved to the states when he started working. He has lived in San Fransisco for several decades now, and has spent a lot of his professional life building systems (infrastructure, observability and now, analytics). His prior startup, AppDynamics, was eventually acquired by Cisco. In general, he stays curious about how things work, and likes to deconstruct systems to figure out how they work. Outside of tech, he is a big sports fan, enjoying football, baseball, cricket and basketball. In fact, he grew up watching Michael Jordan and the bulls.

Bhaskar noticed that business teams were drowning in dashboards, and as such, were not sure how to take the next steps in the business. He and his team realized that what people needed was not a retroactive view, but a proactive one - something more akin to a 24x7 analyst.

This is the creation story of Bicycle AI.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://bicycle.ai/
• https://www.linkedin.com/in/bhaskarsunkara/

Timestamps

0:00 Intro and episode teaser on the limits of manual KPI monitoring

1:49 Guest introduction: Bhaskar Sunkara's background and AppDynamics experience

2:50 The core problem: Why revenue teams are drowning in dashboards

3:53 Origin story: Shifting from reactive dashboards to proactive AI analysts

4:36 Identifying target transactional verticals in retail, travel, and payments

6:02 Building the MVP: The 1-year journey and defining core capabilities

7:06 The three MVP pillars: Data connection, KPI definition, and dimensional search

8:33 Strategic trade-offs: Choosing vertical focus over generic horizontal BI

10:00 Harnessing LLMs and agentic AI for root-cause context

15:00 Establishing single-source-of-truth KPI definitions across departments

20:00 How AI agents integrate into existing enterprise data stacks

25:00 The future of autonomous analytics and proactive decision-making

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.

Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.

In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.

Questions

• Why is now the accountability moment for enterprise AI?
• What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?
• When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?
• As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?
• Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?
• Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?
• And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?
• From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?
• Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?
• And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?

Links

• https://www.wallarm.com/
• https://www.linkedin.com/in/shaynehigdon/

Full Abstract

Abstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.

AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.

That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.

This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.

Timestamps

1:49 Guest introduction: Shayne Higdon's executive background and role as Wallarm CEO

2:45 From experimentation to production: What triggered the enterprise AI accountability shift

4:10 Why traditional CISO governance models fail to keep pace with autonomous AI agents

6:05 Explaining the AI Control Loop: Discovery, visibility, enforcement, and evidence

8:30 Moving from policy promises to continuous, runtime-proven governance

11:15 Balancing innovation speed for CIOs with security mandates for CISOs

14:00 Tackling Shadow AI and establishing a complete inventory of AI apps and APIs

17:30 Runtime threat detection: Blocking prompt injection and data leaks at production speeds

21:00 Board-level expectations and preparing for evolving AI regulatory frameworks

24:15 What AI governance and security will look like over the next 12 to 24 months

27:00 Closing thoughts and how to learn more about Wallarm