<![CDATA[Disrupt or Defend]]>44df5992-3e2c-563a-8441-ef8e7556a978Thu, 11 Jun 2026 02:00:18 +0000Captivate.fmSoftup Technologies GmbHhttps://artwork.captivate.fm/51270281-0d3d-4fca-a96f-e5467fc9e9df/DOD-Artwork-V-1.jpgDisrupt or DefendSoftup Technologies GmbHSoftup Technologies GmbHIn the age of AI, founders face a constant choice: disrupt the market—or defend what they’ve built. Disrupt or Defend is a weekly podcast for startup founders, CTOs, and tech builders who want to stay ahead without losing focus on people and purpose. Host Daniel Kazani, co-founder of Softup Technologies, talks with founders and experts who are shaping the next wave of software innovation. From AI agents and low-code tools to scaling dev teams and building products that last, each episode explores the decisions that define a company’s future. If you’re building in tech and want real stories, practical lessons, and honest conversations about the balance between boldness and focus—this show is for you. Subscribe and join the community of builders defining what comes next in tech.https://www.softup.cofalseepisodicnopodcastHow AI is automating Commercial Real Estate deals | Ep. 30How AI is automating Commercial Real Estate deals | Ep. 30Real estate moves slowly. AI moves fast. Daniel Kazani sits down with Frederik Raspé, founder and CEO of Acquirepad, to figure out what happens when you point fast technology at a slow, trust-heavy industry.

ㅤ

Acquirepad takes the manual admin out of commercial real estate investing. Frederik explains how AI reads the messy documents real estate runs on, rent rolls, lease agreements, investment memos, and structures them so a team can get to a decision faster.

ㅤ

The two get into where automation stops and human judgment starts, why 30% of the work can run on its own while 70% still needs a person, and how an industry that punishes mistakes learns to trust a machine. Frederik also makes the case for buying versus building, and what breaks when companies try to run their own AI tools internally.

ㅤ

👤 Guest Bio

Frederik Raspé is co-founder and CEO of Acquirepad. He studied real estate and spent his early career inside the brokerage world, working as a broker at one of the world's largest firms (Cushman & Wakefield) and then in a digital strategy and innovation role at CBRE, the largest commercial real estate services firm in the world. That mix of property and technology led him to build Acquirepad, an AI platform for real estate asset managers and investment companies. He's based in Germany.

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📌 What We Cover

  • Why Frederik went after commercial real estate, an industry known for moving slowly, and how years inside CBRE shaped that bet ㅤ
  • How AI reads and structures the unstructured documents real estate runs on: rent rolls, lease agreements, investment memos, offering memorandums ㅤ
  • The split between automation and oversight: 30% of processes fully automated, 70% with a human in the loop, and why the deeper you go in a deal the more critical that human gets ㅤ
  • How Acquirepad earns trust in an industry that's sensitive to mistakes, through free trial periods and 99.9% accuracy on core processes ㅤ
  • Buy versus build: why 80% of software cost sits in maintenance, not the initial build, plus the story of a client whose self-built tool broke when one employee left ㅤ
  • Why an LLM on its own isn't a database, and where a vertical product beats a raw chat tool ㅤ
  • The time savings Frederik sees on the workflows he covers: up to 80 to 85%, like turning a half-day rent-roll job into ten minutes ㅤ
  • Where Frederik thinks AI will and won't change real estate, why the decision itself stays slow, and his read on the AGI hype

ㅤ

🔗 Resources Mentioned


]]>Real estate moves slowly. AI moves fast. Daniel Kazani sits down with Frederik Raspé, founder and CEO of Acquirepad, to figure out what happens when you point fast technology at a slow, trust-heavy industry.

ㅤ

Acquirepad takes the manual admin out of commercial real estate investing. Frederik explains how AI reads the messy documents real estate runs on, rent rolls, lease agreements, investment memos, and structures them so a team can get to a decision faster.

ㅤ

The two get into where automation stops and human judgment starts, why 30% of the work can run on its own while 70% still needs a person, and how an industry that punishes mistakes learns to trust a machine. Frederik also makes the case for buying versus building, and what breaks when companies try to run their own AI tools internally.

ㅤ

👤 Guest Bio

Frederik Raspé is co-founder and CEO of Acquirepad. He studied real estate and spent his early career inside the brokerage world, working as a broker at one of the world's largest firms (Cushman & Wakefield) and then in a digital strategy and innovation role at CBRE, the largest commercial real estate services firm in the world. That mix of property and technology led him to build Acquirepad, an AI platform for real estate asset managers and investment companies. He's based in Germany.

ㅤ

📌 What We Cover

  • Why Frederik went after commercial real estate, an industry known for moving slowly, and how years inside CBRE shaped that bet ㅤ
  • How AI reads and structures the unstructured documents real estate runs on: rent rolls, lease agreements, investment memos, offering memorandums ㅤ
  • The split between automation and oversight: 30% of processes fully automated, 70% with a human in the loop, and why the deeper you go in a deal the more critical that human gets ㅤ
  • How Acquirepad earns trust in an industry that's sensitive to mistakes, through free trial periods and 99.9% accuracy on core processes ㅤ
  • Buy versus build: why 80% of software cost sits in maintenance, not the initial build, plus the story of a client whose self-built tool broke when one employee left ㅤ
  • Why an LLM on its own isn't a database, and where a vertical product beats a raw chat tool ㅤ
  • The time savings Frederik sees on the workflows he covers: up to 80 to 85%, like turning a half-day rent-roll job into ten minutes ㅤ
  • Where Frederik thinks AI will and won't change real estate, why the decision itself stays slow, and his read on the AGI hype

ㅤ

🔗 Resources Mentioned


]]>2aea864a-0a75-4104-910c-7840618e9139Thu, 11 Jun 2026 04:00:00 +020028:24falsefull3030How AI is impacting M&A activity in Saas | Ep. 29How AI is impacting M&A activity in Saas | Ep. 29What separates a SaaS business worth a real exit from one that buyers quietly walk away from in 2026?

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Daniel Kazani, co-founder of Softup, sits down with Dirk Sahlmer, partner at FE International, to talk about how AI is reshaping SaaS M&A. After six years on the buy side at saas.group, Dirk now advises founders through sell-side exit processes, and he's seen both ends of how exit value gets won or lost.

ㅤ

The conversation goes long on retention as the new growth signal, why a lot of AI-hype revenue has rotting cohorts under it, and the valuation misconceptions most founders carry into their first exit conversation. Dirk also breaks down what real AI defensibility looks like, why a fifteen-year-old "boring" SaaS can still command a strong multiple, and the one question every founder needs to be ready to answer before a buyer asks it.

ㅤ

👤 Guest Bioㅤ

Dirk Sahlmer is a partner at FE International, where he advises SaaS and tech founders on competitive auction exit processes.

ㅤ

Based in Germany, Dirk trained as an engineer before co-founding his own SaaS company, then spent close to six years building the deal origination function at saas.group as their first full-time hire, evaluating thousands of SaaS businesses and leading multiple buy-side acquisitions. In late 2025, he moved to the sell-side at FE International. He also writes the saas.wtf newsletter on SaaS metrics, valuations, and exit readiness, followed by thousands of founders and investors.

ㅤ

📌 What We Cover

  • Why retention has become the metric buyers watch most closely in the current AI hype cycle
  • The biggest mistakes first-time exit founders make: no sparring partner, no clean numbers, no realistic sense of what the business is worth
  • How private equity and financial buyers really think about boring SaaS that survives the next ten to fifteen years
  • What separates a real AI business from a chatbot bolted onto a website
  • Where AI defensibility actually lives: unique data, deep workflow integration, vertical focus
  • Why a fifteen-year-old SaaS can outperform a hype-stage AI-native startup in an exit
  • Why simple products like Calendly probably don't get replaced by your afternoon hack
  • The question every founder should answer before a buyer asks it: how is AI both an opportunity and a threat to your business?

ㅤ

🔗 Resources Mentionedㅤ


]]>What separates a SaaS business worth a real exit from one that buyers quietly walk away from in 2026?

ㅤ

Daniel Kazani, co-founder of Softup, sits down with Dirk Sahlmer, partner at FE International, to talk about how AI is reshaping SaaS M&A. After six years on the buy side at saas.group, Dirk now advises founders through sell-side exit processes, and he's seen both ends of how exit value gets won or lost.

ㅤ

The conversation goes long on retention as the new growth signal, why a lot of AI-hype revenue has rotting cohorts under it, and the valuation misconceptions most founders carry into their first exit conversation. Dirk also breaks down what real AI defensibility looks like, why a fifteen-year-old "boring" SaaS can still command a strong multiple, and the one question every founder needs to be ready to answer before a buyer asks it.

ㅤ

👤 Guest Bioㅤ

Dirk Sahlmer is a partner at FE International, where he advises SaaS and tech founders on competitive auction exit processes.

ㅤ

Based in Germany, Dirk trained as an engineer before co-founding his own SaaS company, then spent close to six years building the deal origination function at saas.group as their first full-time hire, evaluating thousands of SaaS businesses and leading multiple buy-side acquisitions. In late 2025, he moved to the sell-side at FE International. He also writes the saas.wtf newsletter on SaaS metrics, valuations, and exit readiness, followed by thousands of founders and investors.

ㅤ

📌 What We Cover

  • Why retention has become the metric buyers watch most closely in the current AI hype cycle
  • The biggest mistakes first-time exit founders make: no sparring partner, no clean numbers, no realistic sense of what the business is worth
  • How private equity and financial buyers really think about boring SaaS that survives the next ten to fifteen years
  • What separates a real AI business from a chatbot bolted onto a website
  • Where AI defensibility actually lives: unique data, deep workflow integration, vertical focus
  • Why a fifteen-year-old SaaS can outperform a hype-stage AI-native startup in an exit
  • Why simple products like Calendly probably don't get replaced by your afternoon hack
  • The question every founder should answer before a buyer asks it: how is AI both an opportunity and a threat to your business?

ㅤ

🔗 Resources Mentionedㅤ


]]>0e3c28c6-de2d-4854-af7e-ba42c0ee9068Thu, 28 May 2026 04:00:00 +020029:20falsefull2929How AI is disrupting the restaurant experience | Ep. 28How AI is disrupting the restaurant experience | Ep. 28Spotify and Netflix both know what you like. The one place that exists for your literal taste, the restaurant, hands every guest the same printed card.

ㅤ

Daniel Kazani sits down with Amir Khoshbakht, co-founder and CTO of Lokalia, to talk through what happens when AI moves out of the dashboard and into the dinner table.

ㅤ

Amir's team is rebuilding the restaurant menu as a personalized surface. Items sort themselves to each guest based on prior orders and cross-venue preferences. An in-menu AI assistant answers questions in any language. A new agent on the manager side predicts which ingredients to push or pull back this week.

ㅤ

Early data from Lokalia's customers: 15 to 20% more engagement with menu items, and bigger checks per guest. The conversation also covers why most restaurant owners almost hate AI on first pitch, and where AI actually earns its keep when it leaves the dashboard.

ㅤ

👤 Guest Bio

Amir Khoshbakht is co-founder and CTO of Lokalia. He spent over a decade in software engineering, with prior full-stack and blockchain work at Soar Robotics and a co-founder role at Aikido. He started Lokalia after deciding AI should move out of dashboards and into the parts of daily life people physically experience, beginning with restaurants and cafes.

ㅤ

📌 What We Cover

  • Why every restaurant menu is the last static surface in your customer experience
  • How Lokalia's AI Waiter inside the menu changes the order flow
  • The 15 to 20% lift in engagement when the menu sorts itself around each guest
  • Cross-venue preference matching: liking the chocolate cake at restaurant A changes what restaurant B shows you first
  • What the data shows about why low-selling items actually sell low (rarely about quality)
  • "The Expert," Lokalia's new agent that predicts ingredient demand for the kitchen
  • Why most restaurant owners almost hate AI when you walk in pitching it
  • Where Amir sees hospitality landing: an operating system with fewer tools, not more

ㅤ

🔗 Resources Mentioned


]]>Spotify and Netflix both know what you like. The one place that exists for your literal taste, the restaurant, hands every guest the same printed card.

ㅤ

Daniel Kazani sits down with Amir Khoshbakht, co-founder and CTO of Lokalia, to talk through what happens when AI moves out of the dashboard and into the dinner table.

ㅤ

Amir's team is rebuilding the restaurant menu as a personalized surface. Items sort themselves to each guest based on prior orders and cross-venue preferences. An in-menu AI assistant answers questions in any language. A new agent on the manager side predicts which ingredients to push or pull back this week.

ㅤ

Early data from Lokalia's customers: 15 to 20% more engagement with menu items, and bigger checks per guest. The conversation also covers why most restaurant owners almost hate AI on first pitch, and where AI actually earns its keep when it leaves the dashboard.

ㅤ

👤 Guest Bio

Amir Khoshbakht is co-founder and CTO of Lokalia. He spent over a decade in software engineering, with prior full-stack and blockchain work at Soar Robotics and a co-founder role at Aikido. He started Lokalia after deciding AI should move out of dashboards and into the parts of daily life people physically experience, beginning with restaurants and cafes.

ㅤ

📌 What We Cover

  • Why every restaurant menu is the last static surface in your customer experience
  • How Lokalia's AI Waiter inside the menu changes the order flow
  • The 15 to 20% lift in engagement when the menu sorts itself around each guest
  • Cross-venue preference matching: liking the chocolate cake at restaurant A changes what restaurant B shows you first
  • What the data shows about why low-selling items actually sell low (rarely about quality)
  • "The Expert," Lokalia's new agent that predicts ingredient demand for the kitchen
  • Why most restaurant owners almost hate AI when you walk in pitching it
  • Where Amir sees hospitality landing: an operating system with fewer tools, not more

ㅤ

🔗 Resources Mentioned


]]>97a92af9-4ab7-427e-b200-c755bfee29b3Thu, 21 May 2026 04:00:00 +020029:52falsefull2828How Fractional CTOs use AI to help Startups | Ep. 26How Fractional CTOs use AI to help Startups | Ep. 26The hype says AI is coming for your engineers. John McKinney, a fractional CTO who has run engineering at AOL and a string of CTO seats since, says it is closer to "super Google."

ㅤ

Daniel Kazani, co-founder of Softup, sits down with John to talk about what has actually changed in how software gets built. The conversation lands on the shifts that matter most for founders and engineering leaders right now: product-minded founders shipping working prototypes with Lovable before an engineer joins, the dev role looking more like a mechanic and less like an architect, and the cost of trusting Claude Code with HIPAA-bound systems.

ㅤ

John is a 20-year engineer and longtime skeptic of every new wave that hits the industry. He is also a working bassist in two New York metal bands, which says something about the kind of CTO he is. They get into vibe-coded MVPs, why QA engineering might be the role most reshaped by AI right now, the broken state of remote technical hiring, and the difference between sprinting to a real horizon and selling a horizon you can't deliver.

ㅤ

👤 Guest Bio

John McKinney is the founder of Merge Conflict, a fractional CTO and technology-strategy consultancy he started in 2017 in New York City. He came up as a web developer in the Ruby on Rails era, co-founded the agency Ashe Avenue in 2007, and stayed on as VP of Engineering for AOL Core Products after AOL acquired the company. Since then he has held CTO seats at Netcapital, LaterPay, Heyday, and Food52. Off-keyboard he plays bass in the New York metal bands Woe and Glorious Depravity.

ㅤ

📌 What We Cover

  • How Claude Code went from novel to "a staple of every engineer's toolbox," and why John doesn't believe it turns one-x developers into ten-x.
  • The shift in product roles: product leads now ship working proof-of-concepts before an engineer touches the code, and what that means for PRDs.
  • A real client story of going from a multi-million-dollar agency build to shipping the same retail and clinician software with Lovable.
  • The new shape of the dev job: bulletproofing, production hardening, and compliance checking after the founder ships.
  • Why a HIPAA leak is still your fault, not Claude's, and how exposed API keys on the front end happen.
  • The poisoned hiring funnel: AI voice modulators in phone screens, 400 unqualified applications in the first hour, and Voight-Kampff-style interviewing for replicants.
  • What CS 101 might look like when "Hello, World" gets replaced by prompt-writing, and whether junior-to-mid acceleration actually speeds up.
  • John's read on the "software engineering is solved" claim, the Klarna walk-back, and why the snake-oil crowd from Web3 is the same crowd selling AI now.

ㅤ

🔗 Resources Mentioned

Merge Conflict - John's fractional CTO practice

Claude Code (Anthropic)

Lovable

ChatGPT (OpenAI)

Grok (xAI)

VS Code

Selenium

Stripe

Ruby on Rails

Klarna (the AI-layoffs walk-back John references)

Artisan (the "replace your human workforce" billboard campaign)

Icon (the AI ad-maker Daniel tested)

Sam Altman, Ray Kurzweil, the Voight-Kampff test from Blade Runner

]]>The hype says AI is coming for your engineers. John McKinney, a fractional CTO who has run engineering at AOL and a string of CTO seats since, says it is closer to "super Google."

ㅤ

Daniel Kazani, co-founder of Softup, sits down with John to talk about what has actually changed in how software gets built. The conversation lands on the shifts that matter most for founders and engineering leaders right now: product-minded founders shipping working prototypes with Lovable before an engineer joins, the dev role looking more like a mechanic and less like an architect, and the cost of trusting Claude Code with HIPAA-bound systems.

ㅤ

John is a 20-year engineer and longtime skeptic of every new wave that hits the industry. He is also a working bassist in two New York metal bands, which says something about the kind of CTO he is. They get into vibe-coded MVPs, why QA engineering might be the role most reshaped by AI right now, the broken state of remote technical hiring, and the difference between sprinting to a real horizon and selling a horizon you can't deliver.

ㅤ

👤 Guest Bio

John McKinney is the founder of Merge Conflict, a fractional CTO and technology-strategy consultancy he started in 2017 in New York City. He came up as a web developer in the Ruby on Rails era, co-founded the agency Ashe Avenue in 2007, and stayed on as VP of Engineering for AOL Core Products after AOL acquired the company. Since then he has held CTO seats at Netcapital, LaterPay, Heyday, and Food52. Off-keyboard he plays bass in the New York metal bands Woe and Glorious Depravity.

ㅤ

📌 What We Cover

  • How Claude Code went from novel to "a staple of every engineer's toolbox," and why John doesn't believe it turns one-x developers into ten-x.
  • The shift in product roles: product leads now ship working proof-of-concepts before an engineer touches the code, and what that means for PRDs.
  • A real client story of going from a multi-million-dollar agency build to shipping the same retail and clinician software with Lovable.
  • The new shape of the dev job: bulletproofing, production hardening, and compliance checking after the founder ships.
  • Why a HIPAA leak is still your fault, not Claude's, and how exposed API keys on the front end happen.
  • The poisoned hiring funnel: AI voice modulators in phone screens, 400 unqualified applications in the first hour, and Voight-Kampff-style interviewing for replicants.
  • What CS 101 might look like when "Hello, World" gets replaced by prompt-writing, and whether junior-to-mid acceleration actually speeds up.
  • John's read on the "software engineering is solved" claim, the Klarna walk-back, and why the snake-oil crowd from Web3 is the same crowd selling AI now.

ㅤ

🔗 Resources Mentioned

Merge Conflict - John's fractional CTO practice

Claude Code (Anthropic)

Lovable

ChatGPT (OpenAI)

Grok (xAI)

VS Code

Selenium

Stripe

Ruby on Rails

Klarna (the AI-layoffs walk-back John references)

Artisan (the "replace your human workforce" billboard campaign)

Icon (the AI ad-maker Daniel tested)

Sam Altman, Ray Kurzweil, the Voight-Kampff test from Blade Runner

]]>8af206e3-ea2a-469a-bae2-79d3225d47edThu, 14 May 2026 04:00:00 +020040:05falsefull2626How AI is impacting Wealth Management | Ep. 25How AI is impacting Wealth Management | Ep. 25Most people think of AI as a productivity tool. Dr. Adam Link sees it as something else entirely: a wealth transfer mechanism, one that's quietly shifting money from people who don't understand AI toward people who do. In this episode, Daniel Kazani of Softup and Dr. Link break down what that actually means for founders, developers, and anyone trying to build financial security in a world where AI agents are replacing entry-level work.

ㅤ

The conversation spans a lot of ground: the collapse of the college-to-job pipeline, why non-technical entrepreneurs are outpacing developers in AI adoption, the potter's wheel vs. the factory floor, and why the court system is just the debugging process for laws. Concrete, candid, and a bit unsettling in the best possible way.

ㅤ

👤 Guest Bio

Dr. Adam Link, CFP® is the founder of Fireweed Capital, a fee-only, fiduciary wealth management firm based in northern Minnesota. He holds a Doctorate in Computer Science, a CFP® designation, and currently serves as Senior Engineering Manager at Coinbase, where he leads the Cloud Center of Excellence and FinOps teams. Before Coinbase, he worked across multiple tech startups with three exits and an IPO under his belt. He now specializes in active risk management for tech families - and builds his own CRMs over the weekend.

ㅤ

📌 What We Cover

  • Why AI is accelerating the shift from a labor economy to a capital economy - and what that means for how you think about your "war chest"
  • The college-to-job pipeline breaking: why large tech companies are pausing entry-level hiring and what happens to the path from degree to wealth
  • The CRM story: how Dr. Link got blocked by a vendor on a Friday and had a fully vibe-coded, custom-built replacement running by Monday morning
  • AI as wealth transfer - not just to hyperscalers, but to small businesses that cut OpEx, kill vendor dependencies, and keep more of their own money
  • Vendor concentration risk: when you replace your whole team with OpenAI, you've just created a two-vendor company
  • The railroad analogy: why AI infrastructure is different from past wealth transfers - and why this time, everyone gets their own train
  • The franchise model: one friend's idea to build AI-powered websites and license the software out instead of building a SaaS
  • Why non-technical entrepreneurs are outpacing developers in AI adoption - and why that's terrifying if you're a developer
  • The potter's wheel vs. the factory: how AI is disrupting the creative flow state that made software engineering meaningful
  • AI hallucinations in financial advising, why freeform text has no unit tests, and why good professionals still matter

ㅤ

🔗 Resources Mentioned

  • Fireweed Capital - Dr. Adam Link's wealth management firm
  • adam@fireweedcapital.com - Contact Dr. Adam Link directly

]]>Most people think of AI as a productivity tool. Dr. Adam Link sees it as something else entirely: a wealth transfer mechanism, one that's quietly shifting money from people who don't understand AI toward people who do. In this episode, Daniel Kazani of Softup and Dr. Link break down what that actually means for founders, developers, and anyone trying to build financial security in a world where AI agents are replacing entry-level work.

ㅤ

The conversation spans a lot of ground: the collapse of the college-to-job pipeline, why non-technical entrepreneurs are outpacing developers in AI adoption, the potter's wheel vs. the factory floor, and why the court system is just the debugging process for laws. Concrete, candid, and a bit unsettling in the best possible way.

ㅤ

👤 Guest Bio

Dr. Adam Link, CFP® is the founder of Fireweed Capital, a fee-only, fiduciary wealth management firm based in northern Minnesota. He holds a Doctorate in Computer Science, a CFP® designation, and currently serves as Senior Engineering Manager at Coinbase, where he leads the Cloud Center of Excellence and FinOps teams. Before Coinbase, he worked across multiple tech startups with three exits and an IPO under his belt. He now specializes in active risk management for tech families - and builds his own CRMs over the weekend.

ㅤ

📌 What We Cover

  • Why AI is accelerating the shift from a labor economy to a capital economy - and what that means for how you think about your "war chest"
  • The college-to-job pipeline breaking: why large tech companies are pausing entry-level hiring and what happens to the path from degree to wealth
  • The CRM story: how Dr. Link got blocked by a vendor on a Friday and had a fully vibe-coded, custom-built replacement running by Monday morning
  • AI as wealth transfer - not just to hyperscalers, but to small businesses that cut OpEx, kill vendor dependencies, and keep more of their own money
  • Vendor concentration risk: when you replace your whole team with OpenAI, you've just created a two-vendor company
  • The railroad analogy: why AI infrastructure is different from past wealth transfers - and why this time, everyone gets their own train
  • The franchise model: one friend's idea to build AI-powered websites and license the software out instead of building a SaaS
  • Why non-technical entrepreneurs are outpacing developers in AI adoption - and why that's terrifying if you're a developer
  • The potter's wheel vs. the factory: how AI is disrupting the creative flow state that made software engineering meaningful
  • AI hallucinations in financial advising, why freeform text has no unit tests, and why good professionals still matter

ㅤ

🔗 Resources Mentioned

  • Fireweed Capital - Dr. Adam Link's wealth management firm
  • adam@fireweedcapital.com - Contact Dr. Adam Link directly

]]>a34a8c38-326a-4b5e-a806-75550f3da2f1Thu, 07 May 2026 04:00:00 +020036:03falsefull2525AI Adoption among German SMEs and Enterprises | Ep. 24AI Adoption among German SMEs and Enterprises | Ep. 24Less than 20% of Germany's "hidden champions" use AI systematically. That single stat sets the tone for this conversation. Daniel Kazani of Softup sits down with Dr. Annette Doms - EU AI Act strategist, serial founder, and one of the most influential voices on AI in the German-speaking world - to ask the question that most Mittelstand leaders are afraid to answer out loud: are we actually ready?

ㅤ

What comes out of the conversation is honest and specific. German SMEs aren't failing because they lack data or domain expertise - they have more of both than almost any competitor. The problem is the gap between potential and practice. Too much hesitation, too little experimentation, and a regulatory environment that adds uncertainty faster than it provides clarity.

ㅤ

👤 Guest Bio

Dr. Annette Doms is a Munich-based AI strategist, author, and serial founder with a PhD in art history and over 13 years at the intersection of technology and business transformation. She is CEO of ICAA Strategists GmbH, Vice President at the Bundesverband für KI-Transformation e.V., and Founding Partner of MindMash - a Munich community for forward-thinking technologists meeting every second Tuesday. Named a Top 50 Most Influential Woman in AI in 2026, she specializes in helping Mittelstand companies and executive boards build AI readiness strategies, including EU AI Act compliance. Her book "Von Gutenberg zu ChatGPT" frames the current AI era as a civilizational shift - not a product cycle.

ㅤ

📌 What We Cover

  • Why the German Mittelstand is "AI curious" but not AI ready - and why that gap is dangerous when competitors are moving fast
  • The "Rhinoceros" concept from Dr. Doms's book: how Mittelstand companies are built for durability, not unicorn exits - and what that means for AI adoption
  • Why AI readiness starts with clean, structured data in ERP systems - long before any tool gets selected
  • The step-by-step roadmap for a first AI implementation: assessment, bottleneck identification, scoped pilot, proof of concept, then scale
  • How to think about AI as part of a company's DNA - not an add-on - and why change must be led from the top
  • The data sovereignty problem: why Mittelstand companies are hesitant to train on US or Chinese models, and what federated learning and platforms like Catena-X offer as alternatives
  • The EU AI Act's current ambiguity: how unclear penalty structures and fragmented European infrastructure are adding hesitation, not confidence
  • Where the next three years lead: the companies that operationalize AI now will set the pace for everyone else

ㅤ

🔗 Resources Mentioned


]]>Less than 20% of Germany's "hidden champions" use AI systematically. That single stat sets the tone for this conversation. Daniel Kazani of Softup sits down with Dr. Annette Doms - EU AI Act strategist, serial founder, and one of the most influential voices on AI in the German-speaking world - to ask the question that most Mittelstand leaders are afraid to answer out loud: are we actually ready?

ㅤ

What comes out of the conversation is honest and specific. German SMEs aren't failing because they lack data or domain expertise - they have more of both than almost any competitor. The problem is the gap between potential and practice. Too much hesitation, too little experimentation, and a regulatory environment that adds uncertainty faster than it provides clarity.

ㅤ

👤 Guest Bio

Dr. Annette Doms is a Munich-based AI strategist, author, and serial founder with a PhD in art history and over 13 years at the intersection of technology and business transformation. She is CEO of ICAA Strategists GmbH, Vice President at the Bundesverband für KI-Transformation e.V., and Founding Partner of MindMash - a Munich community for forward-thinking technologists meeting every second Tuesday. Named a Top 50 Most Influential Woman in AI in 2026, she specializes in helping Mittelstand companies and executive boards build AI readiness strategies, including EU AI Act compliance. Her book "Von Gutenberg zu ChatGPT" frames the current AI era as a civilizational shift - not a product cycle.

ㅤ

📌 What We Cover

  • Why the German Mittelstand is "AI curious" but not AI ready - and why that gap is dangerous when competitors are moving fast
  • The "Rhinoceros" concept from Dr. Doms's book: how Mittelstand companies are built for durability, not unicorn exits - and what that means for AI adoption
  • Why AI readiness starts with clean, structured data in ERP systems - long before any tool gets selected
  • The step-by-step roadmap for a first AI implementation: assessment, bottleneck identification, scoped pilot, proof of concept, then scale
  • How to think about AI as part of a company's DNA - not an add-on - and why change must be led from the top
  • The data sovereignty problem: why Mittelstand companies are hesitant to train on US or Chinese models, and what federated learning and platforms like Catena-X offer as alternatives
  • The EU AI Act's current ambiguity: how unclear penalty structures and fragmented European infrastructure are adding hesitation, not confidence
  • Where the next three years lead: the companies that operationalize AI now will set the pace for everyone else

ㅤ

🔗 Resources Mentioned


]]>28e20f04-3ffb-46f4-9176-6a5f34b106bfThu, 23 Apr 2026 04:00:00 +020025:25falsefull2424Building internal tools with AI | Ep. 23Building internal tools with AI | Ep. 23Most internal tools are outdated before they ever ship. That's the problem at the heart of this conversation - and it's one that almost every technical founder has felt personally. Daniel Kazani sits down with Dario Di Carlo, CEO and co-founder of bricks.sh, to talk about why internal tooling is quietly draining engineering teams, and why the vibe-coding shortcut will make it worse before it makes it better.

ㅤ

Dario built bricks.sh after living the problem firsthand: spending months building admin panels at a previous startup, only to ship something already outdated. The platform auto-generates admin panels directly from your database and keeps them in sync as your schema changes - so your engineers stop rebuilding the same tables, forms, and dashboards over and over again. Softup has seen this same pain up close with dozens of clients, which makes this one of the more honest and grounded conversations on the show.

ㅤ

👤 Guest Bio

Dario Di Carlo is CEO and co-founder of bricks.sh, a Milan-based startup automating the creation and ongoing maintenance of internal admin panels. Before bricks.sh, he was a Product Manager at Nibol - Italy's leading workplace management platform - and spent several years exploring the intersection of AI and APIs. He holds an MSc in Innovation Management from Scuola Superiore Sant'Anna and co-founded bricks.sh with CTO Giuliano Torregrossa. The company raised a €1.6M pre-seed round in early 2026 and has signed up 500+ users across 40+ countries.

ㅤ

📌 What We Cover

  • Why internal tools are the most-built, least-appreciated part of any product org - and why that gap creates real friction between engineering and business teams
  • The difference between dashboards (read-only data access) and operational admin panels (where you actually update users, flag transactions, process refunds) - and why bricks.sh aims to cover both
  • Why vibe-coding your internal tools with Lovable or Claude Code creates a short-term win and a long-term maintenance tax
  • The guardrails argument: why giving business users free customization without engineering guardrails turns into shadow IT and shadow AI - fast
  • How bricks.sh chose startups and scale-ups over enterprise design partnerships early on, and why that bet was the right one for product-market clarity
  • The bricks.sh product thesis: 90% of internal tools across finance, healthcare, logistics, and ed tech look identical - speed of development will beat freedom of customization
  • What the future of internal tools looks like: conversational interfaces, MCP-powered data unification, and a single source of truth across Stripe, Intercom, Linear, and Postgres
  • The founder side: zooming in vs. zooming out, defending a thesis under pressure, and why emotional stability - not enthusiasm - is the real asset at the early stage

ㅤ

🔗 Resources Mentioned

  • bricks.sh - the guest's product
  • Retool - mentioned as a primary competitor and a tool Dario used extensively at his previous company
  • Lovable - mentioned as a vibe-coding alternative with no guardrails
  • Bolt - mentioned alongside Lovable as a low-code/no-code alternative
  • Claude Code - mentioned as an AI-native coding tool in the vibe-coding conversation
  • Supabase - bricks.sh's current lead database integration
  • Stripe - mentioned as a data source for future admin panel unification
  • Intercom - mentioned as a data source for future admin panel unification
  • Linear - mentioned as a data source for future admin panel unification
  • Softup - Daniel's company, referenced in the conversation as a concrete example of the internal tooling problem

]]>Most internal tools are outdated before they ever ship. That's the problem at the heart of this conversation - and it's one that almost every technical founder has felt personally. Daniel Kazani sits down with Dario Di Carlo, CEO and co-founder of bricks.sh, to talk about why internal tooling is quietly draining engineering teams, and why the vibe-coding shortcut will make it worse before it makes it better.

ㅤ

Dario built bricks.sh after living the problem firsthand: spending months building admin panels at a previous startup, only to ship something already outdated. The platform auto-generates admin panels directly from your database and keeps them in sync as your schema changes - so your engineers stop rebuilding the same tables, forms, and dashboards over and over again. Softup has seen this same pain up close with dozens of clients, which makes this one of the more honest and grounded conversations on the show.

ㅤ

👤 Guest Bio

Dario Di Carlo is CEO and co-founder of bricks.sh, a Milan-based startup automating the creation and ongoing maintenance of internal admin panels. Before bricks.sh, he was a Product Manager at Nibol - Italy's leading workplace management platform - and spent several years exploring the intersection of AI and APIs. He holds an MSc in Innovation Management from Scuola Superiore Sant'Anna and co-founded bricks.sh with CTO Giuliano Torregrossa. The company raised a €1.6M pre-seed round in early 2026 and has signed up 500+ users across 40+ countries.

ㅤ

📌 What We Cover

  • Why internal tools are the most-built, least-appreciated part of any product org - and why that gap creates real friction between engineering and business teams
  • The difference between dashboards (read-only data access) and operational admin panels (where you actually update users, flag transactions, process refunds) - and why bricks.sh aims to cover both
  • Why vibe-coding your internal tools with Lovable or Claude Code creates a short-term win and a long-term maintenance tax
  • The guardrails argument: why giving business users free customization without engineering guardrails turns into shadow IT and shadow AI - fast
  • How bricks.sh chose startups and scale-ups over enterprise design partnerships early on, and why that bet was the right one for product-market clarity
  • The bricks.sh product thesis: 90% of internal tools across finance, healthcare, logistics, and ed tech look identical - speed of development will beat freedom of customization
  • What the future of internal tools looks like: conversational interfaces, MCP-powered data unification, and a single source of truth across Stripe, Intercom, Linear, and Postgres
  • The founder side: zooming in vs. zooming out, defending a thesis under pressure, and why emotional stability - not enthusiasm - is the real asset at the early stage

ㅤ

🔗 Resources Mentioned

  • bricks.sh - the guest's product
  • Retool - mentioned as a primary competitor and a tool Dario used extensively at his previous company
  • Lovable - mentioned as a vibe-coding alternative with no guardrails
  • Bolt - mentioned alongside Lovable as a low-code/no-code alternative
  • Claude Code - mentioned as an AI-native coding tool in the vibe-coding conversation
  • Supabase - bricks.sh's current lead database integration
  • Stripe - mentioned as a data source for future admin panel unification
  • Intercom - mentioned as a data source for future admin panel unification
  • Linear - mentioned as a data source for future admin panel unification
  • Softup - Daniel's company, referenced in the conversation as a concrete example of the internal tooling problem

]]>5900d983-3f81-48bc-9571-6682a1c2c125Thu, 16 Apr 2026 04:00:00 +020040:02falsefull2323Investing in AI and Underrepresented Founders with Blerina Sanocki | Ep. 22Investing in AI and Underrepresented Founders with Blerina Sanocki | Ep. 22The hype surrounding artificial intelligence forces investors to separate true innovation from mere noise. Daniel Kazani, co-founder of Softup, sits down with Blerina Sanocki to explore her exact framework for evaluating early-stage startups.

ㅤ

They discuss the difference between building an AI-first product and being an AI-forward company. Blerina shares why she actively seeks out underrepresented and immigrant founders with a "watch me do it" mentality.

ㅤ

The conversation also highlights how the bar for securing funding has shifted, requiring founders to validate their ideas faster using modern tools. They wrap up by evaluating the real cost of moving a startup to Silicon Valley and why health tech remains the most exciting sector for disruption.

ㅤ

👤 Guest Bio

Blerina Sanocki is the Founder and CEO of TideBorn Capital and an Operating Partner at Dardania Capital. With 14 years of experience driving strategic growth at Google, she brings deep enterprise discipline to startup execution. Today, Blerina invests in early-stage software companies and focuses on supporting underrepresented founders.

ㅤ

She actively deploys capital through The Bond Fund and Portfolia. Originally from Albania, she combines her multicultural background with a passion for inclusive technology and building scalable AI infrastructure.

ㅤ

📌 What We Cover

  1. The three core pillars of Blerina's investment thesis: backing underrepresented founders, bettering humanity, and using tech as a force for good.
  2. Why the immigrant mentality creates resilient founders who treat rejection as a challenge to succeed.
  3. How to quiet the noise in a crowded market by distinguishing between AI-first products and AI-forward companies.
  4. The rising expectations for early-stage founders to build, launch, and validate their minimum viable products before seeking capital.
  5. When it actually makes financial and strategic sense for an international startup to relocate to Silicon Valley.
  6. The massive potential and inherent risks of applying artificial intelligence to genome sequencing and health tech.
  7. A call to action for more women to overcome their fears and enter the venture capital and angel investing space.

ㅤ

🔗 Resources Mentioned

  1. TideBorn Capital
  2. Blerina Sanocki
  3. Daniel Kazani
  4. Softup Technologies
  5. Dardania Capital
  6. The Bond Fund
  7. Portfolia

]]>The hype surrounding artificial intelligence forces investors to separate true innovation from mere noise. Daniel Kazani, co-founder of Softup, sits down with Blerina Sanocki to explore her exact framework for evaluating early-stage startups.

ㅤ

They discuss the difference between building an AI-first product and being an AI-forward company. Blerina shares why she actively seeks out underrepresented and immigrant founders with a "watch me do it" mentality.

ㅤ

The conversation also highlights how the bar for securing funding has shifted, requiring founders to validate their ideas faster using modern tools. They wrap up by evaluating the real cost of moving a startup to Silicon Valley and why health tech remains the most exciting sector for disruption.

ㅤ

👤 Guest Bio

Blerina Sanocki is the Founder and CEO of TideBorn Capital and an Operating Partner at Dardania Capital. With 14 years of experience driving strategic growth at Google, she brings deep enterprise discipline to startup execution. Today, Blerina invests in early-stage software companies and focuses on supporting underrepresented founders.

ㅤ

She actively deploys capital through The Bond Fund and Portfolia. Originally from Albania, she combines her multicultural background with a passion for inclusive technology and building scalable AI infrastructure.

ㅤ

📌 What We Cover

  1. The three core pillars of Blerina's investment thesis: backing underrepresented founders, bettering humanity, and using tech as a force for good.
  2. Why the immigrant mentality creates resilient founders who treat rejection as a challenge to succeed.
  3. How to quiet the noise in a crowded market by distinguishing between AI-first products and AI-forward companies.
  4. The rising expectations for early-stage founders to build, launch, and validate their minimum viable products before seeking capital.
  5. When it actually makes financial and strategic sense for an international startup to relocate to Silicon Valley.
  6. The massive potential and inherent risks of applying artificial intelligence to genome sequencing and health tech.
  7. A call to action for more women to overcome their fears and enter the venture capital and angel investing space.

ㅤ

🔗 Resources Mentioned

  1. TideBorn Capital
  2. Blerina Sanocki
  3. Daniel Kazani
  4. Softup Technologies
  5. Dardania Capital
  6. The Bond Fund
  7. Portfolia

]]>64a6cc83-6ac7-48df-9ae9-779f7a47b13cThu, 09 Apr 2026 04:00:00 +020030:09falsefull2222AI in Fintech: Investor Explains Trends, Defensibility and How to Stand Out | Ep. 21AI in Fintech: Investor Explains Trends, Defensibility and How to Stand Out | Ep. 21The bottleneck in software is no longer building the product: it is getting it into the hands of users. As artificial intelligence compresses development timelines, distribution and go-to-market strategies are becoming the true competitive advantages.

ㅤ

Host Daniel Kazani, co-founder at Softup, sits down with Ben Robinson, CEO of Aperture.co, to examine this shift. Ben explains how his firm acts as an operational VC, embedding growth teams directly into startups to solve the complex challenge of selling to large financial institutions. They look at why the standard subscription model is losing ground to variable and outcome-based pricing, and how embedded finance opens new monetization paths for software founders.

ㅤ

Daniel and Ben also discuss the remaining defensive lines in tech. They emphasize that proprietary data, network effects, and deep-rooted trust are what protect companies in an increasingly fast-paced market.

ㅤ

👤 Guest Bio

Ben Robinson is the CEO and Co-Founder of Aperture.co, a Swiss-based growth and investment partner for the financial services sector. Before starting Aperture, Ben worked as Chief Strategy Officer at Temenos, where he launched the Temenos MarketPlace to connect banks with complementary fintech scale-ups. Today, Ben and his team invest in early-stage European B2B fintech companies. They operate differently from traditional venture capital by running hands-on go-to-market projects and embedding product and marketing experts directly into their portfolio companies.

ㅤ

📌 What We Cover

  1. How artificial intelligence compresses venture capital timelines by making it faster and cheaper to build software.
  2. Why distribution and go-to-market execution are replacing development as the main bottleneck for startups.
  3. The necessary shift from fixed subscription billing to variable, outcome-based pricing models.
  4. Why embedded finance changes the way non-financial companies monetize their core products.
  5. The most common mistake founders make when scaling their sales teams and stepping away from direct selling too early.
  6. Why network effects, proprietary data, and regulatory licenses remain strong defensive positions.
  7. The importance of building trust in financial services and how neobanks succeeded by starting with simple wedge products.

ㅤ

🔗 Resources Mentioned

  1. Aperture.co
  2. Temenos MarketPlace
  3. Revolut
  4. Stripe
  5. Open Solar
  6. PayTech
  7. EasyJet

]]>The bottleneck in software is no longer building the product: it is getting it into the hands of users. As artificial intelligence compresses development timelines, distribution and go-to-market strategies are becoming the true competitive advantages.

ㅤ

Host Daniel Kazani, co-founder at Softup, sits down with Ben Robinson, CEO of Aperture.co, to examine this shift. Ben explains how his firm acts as an operational VC, embedding growth teams directly into startups to solve the complex challenge of selling to large financial institutions. They look at why the standard subscription model is losing ground to variable and outcome-based pricing, and how embedded finance opens new monetization paths for software founders.

ㅤ

Daniel and Ben also discuss the remaining defensive lines in tech. They emphasize that proprietary data, network effects, and deep-rooted trust are what protect companies in an increasingly fast-paced market.

ㅤ

👤 Guest Bio

Ben Robinson is the CEO and Co-Founder of Aperture.co, a Swiss-based growth and investment partner for the financial services sector. Before starting Aperture, Ben worked as Chief Strategy Officer at Temenos, where he launched the Temenos MarketPlace to connect banks with complementary fintech scale-ups. Today, Ben and his team invest in early-stage European B2B fintech companies. They operate differently from traditional venture capital by running hands-on go-to-market projects and embedding product and marketing experts directly into their portfolio companies.

ㅤ

📌 What We Cover

  1. How artificial intelligence compresses venture capital timelines by making it faster and cheaper to build software.
  2. Why distribution and go-to-market execution are replacing development as the main bottleneck for startups.
  3. The necessary shift from fixed subscription billing to variable, outcome-based pricing models.
  4. Why embedded finance changes the way non-financial companies monetize their core products.
  5. The most common mistake founders make when scaling their sales teams and stepping away from direct selling too early.
  6. Why network effects, proprietary data, and regulatory licenses remain strong defensive positions.
  7. The importance of building trust in financial services and how neobanks succeeded by starting with simple wedge products.

ㅤ

🔗 Resources Mentioned

  1. Aperture.co
  2. Temenos MarketPlace
  3. Revolut
  4. Stripe
  5. Open Solar
  6. PayTech
  7. EasyJet

]]>1eb76b64-8dba-4be5-9642-011f640749f6Thu, 02 Apr 2026 04:00:00 +020041:26falsefull2121From Copilots to Culprits: The Legal Reality of AI | Ep. 20From Copilots to Culprits: The Legal Reality of AI | Ep. 20Autonomous AI agents promise massive productivity gains, but with autonomy comes severe financial and legal risk. If an AI system deployed in banking falls victim to a prompt injection attack, it could falsify compliance records or authorize illegal transactions. A human might notice a mistake after one or two errors, but an AI could execute thousands of false transactions per second, potentially bankrupting a financial institution.

ㅤ

Host Daniel Kazani sits down with Patrick Munro to examine the legal implications of this technology. Patrick shares a cautionary tale about the dangers of disabling safety features and granting full autonomy without human oversight. They discuss the difference between human-in-the-loop systems and autonomous agents from a legal perspective.

ㅤ

The conversation also touches on data sovereignty, the challenges of running on-premise servers, and why the healthcare sector remains hesitant to adopt automated tools. Daniel and Patrick highlight the need for a pragmatic approach to risk and the importance of establishing clear usage policies.

ㅤ

Guest Bio

Patrick Munro is a technology lawyer operating at the intersection of artificial intelligence, cybersecurity, and IT regulatory compliance. He serves as Legal Counsel for Financial Services at Capgemini and as Of Counsel at the IT law boutique planit//legal. Patrick acts as a dual Subject Matter Expert for AI and Cybersecurity, actively developing legal technology tools like compliance dashboards and contract review assistants. He believes that AI gives lawyers better tools to focus on judgment-intensive work rather than replacing them completely.

ㅤ

What We Cover

  1. The financial risks of agentic AI and the dangers of prompt injection attacks.
  2. How a single hijacked AI agent could impact compliance records and sanction screenings.
  3. The monoculture risk of multiple banks deploying the same vulnerable AI models.
  4. Legal liability differences between human-in-the-loop decisions and fully autonomous smart contracts.
  5. Personal liability implications for managing directors under the NIS2 Directive.
  6. Data privacy concerns with US hyperscalers versus the logistical hurdles of on-premise AI servers.
  7. Why highly regulated sectors like healthcare are hesitant about AI adoption due to strict criminal implications for data leaks.
  8. The need to define clear use cases and usage policies before implementing AI within a company.

ㅤ

Resources Mentioned

  1. Capgemini
  2. planit//legal
  3. Softup
  4. MINDMASH Munich
  5. Anthropic Claude and Claude Code
  6. COBOL
  7. Amazon Bedrock
  8. Mistral
  9. NIS2 Directive
  10. Digital Operational Resilience Act (DORA)
  11. EU AI Act
  12. EU Omnibus Proposals

]]>Autonomous AI agents promise massive productivity gains, but with autonomy comes severe financial and legal risk. If an AI system deployed in banking falls victim to a prompt injection attack, it could falsify compliance records or authorize illegal transactions. A human might notice a mistake after one or two errors, but an AI could execute thousands of false transactions per second, potentially bankrupting a financial institution.

ㅤ

Host Daniel Kazani sits down with Patrick Munro to examine the legal implications of this technology. Patrick shares a cautionary tale about the dangers of disabling safety features and granting full autonomy without human oversight. They discuss the difference between human-in-the-loop systems and autonomous agents from a legal perspective.

ㅤ

The conversation also touches on data sovereignty, the challenges of running on-premise servers, and why the healthcare sector remains hesitant to adopt automated tools. Daniel and Patrick highlight the need for a pragmatic approach to risk and the importance of establishing clear usage policies.

ㅤ

Guest Bio

Patrick Munro is a technology lawyer operating at the intersection of artificial intelligence, cybersecurity, and IT regulatory compliance. He serves as Legal Counsel for Financial Services at Capgemini and as Of Counsel at the IT law boutique planit//legal. Patrick acts as a dual Subject Matter Expert for AI and Cybersecurity, actively developing legal technology tools like compliance dashboards and contract review assistants. He believes that AI gives lawyers better tools to focus on judgment-intensive work rather than replacing them completely.

ㅤ

What We Cover

  1. The financial risks of agentic AI and the dangers of prompt injection attacks.
  2. How a single hijacked AI agent could impact compliance records and sanction screenings.
  3. The monoculture risk of multiple banks deploying the same vulnerable AI models.
  4. Legal liability differences between human-in-the-loop decisions and fully autonomous smart contracts.
  5. Personal liability implications for managing directors under the NIS2 Directive.
  6. Data privacy concerns with US hyperscalers versus the logistical hurdles of on-premise AI servers.
  7. Why highly regulated sectors like healthcare are hesitant about AI adoption due to strict criminal implications for data leaks.
  8. The need to define clear use cases and usage policies before implementing AI within a company.

ㅤ

Resources Mentioned

  1. Capgemini
  2. planit//legal
  3. Softup
  4. MINDMASH Munich
  5. Anthropic Claude and Claude Code
  6. COBOL
  7. Amazon Bedrock
  8. Mistral
  9. NIS2 Directive
  10. Digital Operational Resilience Act (DORA)
  11. EU AI Act
  12. EU Omnibus Proposals

]]>76455210-ba3e-40c8-86ae-44d5a6898153Thu, 26 Mar 2026 04:00:00 +020039:17falsefull2020Protecting authors rights in the age of AI | Ep. 19Protecting authors rights in the age of AI | Ep. 19Artificial intelligence models are hungry for high-quality human knowledge. For years, developers scraped this data without asking permission or compensating the creators. Now, massive lawsuits are forcing the tech industry to rethink how it acquires training material.

ㅤ

Host Daniel Kazani of Softup sits down with Julie Trelstad to discuss how authors can protect their intellectual property and earn money in the modern era. They explore the shift from unauthorized scraping to legitimate licensing agreements. The conversation covers the critical differences between European and United States copyright laws regarding machine training.

ㅤ

They also examine how specialized, peer-reviewed data is becoming a premium asset for new software applications. Finally, Daniel Kazani and Julie discuss how writers can instruct digital assistants to act as collaborative editors rather than replacements for genuine human creativity.

ㅤ

👤 Guest Bio

Julie Trelstad brings over 35 years of experience to the publishing industry. Starting her career editing technical and architecture books, she navigated major technological shifts from desktop publishing to the rise of digital distribution. Today, Julie serves as the Head of U.S. Publishing at Amlet AI, a public registry dedicated to text and data mining rights. She also runs Paperbacks & Pixels, an author-support studio that helps writers build sustainable businesses and structured sales systems.

ㅤ

📌 What We Cover

  1. The ethical and legal fallout from training large language models on pirated books.
  2. How Amlet AI uses the International Standard Content Code to create a digital fingerprint for written work.
  3. Why most authors are currently opting out of machine training licensing due to wildly fluctuating payments.
  4. The structural differences between creator compensation laws in Europe and the United States.
  5. How peer-reviewed research will become a premium asset for developers building highly specific software tools.
  6. Practical methods for instructing tools like Claude to slow down and preserve an author's original writing style.
  7. The future of publishing contracts, in which machine language training becomes a standard secondary right.

ㅤ

🔗 Resources Mentioned

  1. Amlet AI
  2. Paperbacks & Pixels
  3. International Standard Content Code (ISCC)
  4. StreetLib
  5. Claude
  6. ChatGPT
  7. Fraunhofer Institute

]]>Artificial intelligence models are hungry for high-quality human knowledge. For years, developers scraped this data without asking permission or compensating the creators. Now, massive lawsuits are forcing the tech industry to rethink how it acquires training material.

ㅤ

Host Daniel Kazani of Softup sits down with Julie Trelstad to discuss how authors can protect their intellectual property and earn money in the modern era. They explore the shift from unauthorized scraping to legitimate licensing agreements. The conversation covers the critical differences between European and United States copyright laws regarding machine training.

ㅤ

They also examine how specialized, peer-reviewed data is becoming a premium asset for new software applications. Finally, Daniel Kazani and Julie discuss how writers can instruct digital assistants to act as collaborative editors rather than replacements for genuine human creativity.

ㅤ

👤 Guest Bio

Julie Trelstad brings over 35 years of experience to the publishing industry. Starting her career editing technical and architecture books, she navigated major technological shifts from desktop publishing to the rise of digital distribution. Today, Julie serves as the Head of U.S. Publishing at Amlet AI, a public registry dedicated to text and data mining rights. She also runs Paperbacks & Pixels, an author-support studio that helps writers build sustainable businesses and structured sales systems.

ㅤ

📌 What We Cover

  1. The ethical and legal fallout from training large language models on pirated books.
  2. How Amlet AI uses the International Standard Content Code to create a digital fingerprint for written work.
  3. Why most authors are currently opting out of machine training licensing due to wildly fluctuating payments.
  4. The structural differences between creator compensation laws in Europe and the United States.
  5. How peer-reviewed research will become a premium asset for developers building highly specific software tools.
  6. Practical methods for instructing tools like Claude to slow down and preserve an author's original writing style.
  7. The future of publishing contracts, in which machine language training becomes a standard secondary right.

ㅤ

🔗 Resources Mentioned

  1. Amlet AI
  2. Paperbacks & Pixels
  3. International Standard Content Code (ISCC)
  4. StreetLib
  5. Claude
  6. ChatGPT
  7. Fraunhofer Institute

]]>a5c83602-78f7-4c5c-886c-cbac96d645e0Thu, 19 Mar 2026 04:00:00 +020027:31falsefull1919AI-Powered Smart Home Search | Ep. 18AI-Powered Smart Home Search | Ep. 18Traditional real estate systems often come with rules, limitations, and legacy baggage that can stifle creativity. Host Daniel Kazani speaks with Bobby Bryant, M.Ed.x2 about building a solution that bypasses the MLS entirely. Bobby explains the strategy behind hōmhub.ai: a peer-to-peer, AI-powered Real Estate Operating System. They discuss why he chose a non-MLS approach to allow for features like digital offers and commission transparency. Bobby also details the launch of the Global Agent Exchange to unify agents across borders and his plans to bring voice-activated home search to the world.

ㅤ

👤 Guest Bio

Bobby Bryant, M.Ed.x2 is the CEO of DOSS Group, INC. and the founder of hōmhub.ai. A veteran with over 25 years in the industry, he became the first African American to create and franchise a real estate brokerage brand. His work is backed by Amazon and Google. Bobby holds two Master’s Degrees in Education and previously served as a contributor to Forbes.

ㅤ

📌 What We Cover

  1. Why hōmhub.ai operates as a non-MLS platform to avoid data restrictions and allow for more creativity.
  2. The concept of a property-agnostic marketplace: handling sales, rentals, and wholesale properties in one unified system.
  3. How the team applies a "Steve Jobs" philosophy by designing for the consumer experience first and working backward to the technology.
  4. The specific features of their AI search: users can speak in over 100 languages or visualize new wall colors and flooring instantly.
  5. Creating a "Carfax for homes": allowing owners to upload warranties, receipts, and documents directly to a property profile.
  6. The Global Agent Exchange (GAE): a listing service built to standardize real estate practices for modern agents.
  7. Plans to expand the platform into international markets like Canada, the UK, and New Zealand.
  8. Using data to answer hyper-local questions about neighborhoods: from noise levels to air quality.

ㅤ

🔗 Resources Mentioned

  1. hōmhub.ai
  2. Bobby Bryant on LinkedIn

]]>Traditional real estate systems often come with rules, limitations, and legacy baggage that can stifle creativity. Host Daniel Kazani speaks with Bobby Bryant, M.Ed.x2 about building a solution that bypasses the MLS entirely. Bobby explains the strategy behind hōmhub.ai: a peer-to-peer, AI-powered Real Estate Operating System. They discuss why he chose a non-MLS approach to allow for features like digital offers and commission transparency. Bobby also details the launch of the Global Agent Exchange to unify agents across borders and his plans to bring voice-activated home search to the world.

ㅤ

👤 Guest Bio

Bobby Bryant, M.Ed.x2 is the CEO of DOSS Group, INC. and the founder of hōmhub.ai. A veteran with over 25 years in the industry, he became the first African American to create and franchise a real estate brokerage brand. His work is backed by Amazon and Google. Bobby holds two Master’s Degrees in Education and previously served as a contributor to Forbes.

ㅤ

📌 What We Cover

  1. Why hōmhub.ai operates as a non-MLS platform to avoid data restrictions and allow for more creativity.
  2. The concept of a property-agnostic marketplace: handling sales, rentals, and wholesale properties in one unified system.
  3. How the team applies a "Steve Jobs" philosophy by designing for the consumer experience first and working backward to the technology.
  4. The specific features of their AI search: users can speak in over 100 languages or visualize new wall colors and flooring instantly.
  5. Creating a "Carfax for homes": allowing owners to upload warranties, receipts, and documents directly to a property profile.
  6. The Global Agent Exchange (GAE): a listing service built to standardize real estate practices for modern agents.
  7. Plans to expand the platform into international markets like Canada, the UK, and New Zealand.
  8. Using data to answer hyper-local questions about neighborhoods: from noise levels to air quality.

ㅤ

🔗 Resources Mentioned

  1. hōmhub.ai
  2. Bobby Bryant on LinkedIn

]]>c9379cd4-1286-4316-81fc-3b8f11e63038Thu, 12 Mar 2026 04:00:00 +020026:26falsefull1818Optimize Manufacturing with AI | Ep. 17Optimize Manufacturing with AI | Ep. 17Manufacturing generates massive amounts of data, yet many factories still run expensive machinery on settings that have not changed in a decade. Daniel Kazani sits down with Dr. Jonathan Spitz, Founder and CEO of GaussML, to discuss why having data does not always mean having information.

ㅤ

Jonathan explains his "small data" approach to industrial optimization. Instead of requiring months of data cleaning and massive data lakes, his team focuses on rapid experimentation. By running a few targeted tests, operators can find the ideal parameters for processes like laser cutting and injection molding in a single day. Jonathan shares real-world examples, including how a 0.5-gram adjustment saved Coca-Cola 20 tons of plastic a year and how job shops eliminated Saturday shifts by increasing efficiency. The conversation also covers the role of the human operator as a pilot rather than a bystander.

ㅤ

Guest Bio

Dr. Jonathan Spitz is the Founder and CEO of GaussML. Before launching his own company, he served as a Research Scientist at the Bosch Center for Artificial Intelligence, where he applied machine learning algorithms to industrial optimization. He holds a PhD in Mechatronics, Robotics, and Automation Engineering from the Technion - Israel Institute of Technology. Jonathan specializes in "small data" solutions that help manufacturers improve efficiency without complex integration.

ㅤ

What We Cover

  1. The difference between being data-rich and information-poor in manufacturing
  2. Why traditional deep learning often fails in factory settings due to the need for massive datasets
  3. How the "small data" approach works: finding optimal machine settings with minimal experiments
  4. Real-world wins: Reducing cycle times by 50% in machining and saving raw materials in bottle production
  5. The Coca-Cola case study: How a tiny weight reduction per bottle resulted in massive material savings
  6. The "Copilot" philosophy: Why AI should augment the operator's intuition rather than replace it
  7. Overcoming the "worker gap" by making expert-level machine operation accessible to newer employees
  8. Why is failing during the testing phase necessary to find the true limits of a machine

ㅤ

Resources Mentioned

  1. GaussML (Official Website)
  2. Optimyzer (Product)
  3. Dr. Jonathan Spitz (LinkedIn)
  4. Daniel Kazani (LinkedIn)
  5. Softup Technologies
  6. Bosch (Company)
  7. TRUMPF (Company)
  8. Coca-Cola (Company)

]]>Manufacturing generates massive amounts of data, yet many factories still run expensive machinery on settings that have not changed in a decade. Daniel Kazani sits down with Dr. Jonathan Spitz, Founder and CEO of GaussML, to discuss why having data does not always mean having information.

ㅤ

Jonathan explains his "small data" approach to industrial optimization. Instead of requiring months of data cleaning and massive data lakes, his team focuses on rapid experimentation. By running a few targeted tests, operators can find the ideal parameters for processes like laser cutting and injection molding in a single day. Jonathan shares real-world examples, including how a 0.5-gram adjustment saved Coca-Cola 20 tons of plastic a year and how job shops eliminated Saturday shifts by increasing efficiency. The conversation also covers the role of the human operator as a pilot rather than a bystander.

ㅤ

Guest Bio

Dr. Jonathan Spitz is the Founder and CEO of GaussML. Before launching his own company, he served as a Research Scientist at the Bosch Center for Artificial Intelligence, where he applied machine learning algorithms to industrial optimization. He holds a PhD in Mechatronics, Robotics, and Automation Engineering from the Technion - Israel Institute of Technology. Jonathan specializes in "small data" solutions that help manufacturers improve efficiency without complex integration.

ㅤ

What We Cover

  1. The difference between being data-rich and information-poor in manufacturing
  2. Why traditional deep learning often fails in factory settings due to the need for massive datasets
  3. How the "small data" approach works: finding optimal machine settings with minimal experiments
  4. Real-world wins: Reducing cycle times by 50% in machining and saving raw materials in bottle production
  5. The Coca-Cola case study: How a tiny weight reduction per bottle resulted in massive material savings
  6. The "Copilot" philosophy: Why AI should augment the operator's intuition rather than replace it
  7. Overcoming the "worker gap" by making expert-level machine operation accessible to newer employees
  8. Why is failing during the testing phase necessary to find the true limits of a machine

ㅤ

Resources Mentioned

  1. GaussML (Official Website)
  2. Optimyzer (Product)
  3. Dr. Jonathan Spitz (LinkedIn)
  4. Daniel Kazani (LinkedIn)
  5. Softup Technologies
  6. Bosch (Company)
  7. TRUMPF (Company)
  8. Coca-Cola (Company)

]]>c41cd66d-c603-43db-bcb0-3da75f86a4faThu, 05 Mar 2026 04:00:00 +020029:04falsefull1717GenAI In Real Estate | Ep. 16GenAI In Real Estate | Ep. 16Real estate runs on data, but most of it is trapped in PDFs, lease agreements, and siloed legacy systems. In this episode, host Daniel Kazani sits down with Dr. Nino Paulus, Co-Founder and CPO of AlphaPrompt, to discuss how generative AI is bringing order to this chaos. Nino explains how his team moved from building simple dashboards to creating an AI that functions like a senior analyst—capable of reading entire data rooms, extracting complex lease terms, and spotting risks that humans might miss.

ㅤ

They discuss the reality of deploying AI in a traditional industry, sharing a story in which their software identified 7 active leases for a property the owner didn't even know they still owned. Nino also opens up about his "live demo" sales strategy and shares his thoughts on the future of autonomous AI agents, including the emergence of "Moltbook," a social network where bots communicate with each other. This is a practical look at how Softup and other tech builders can learn from AlphaPrompt's approach to automation and data structuring.

ㅤ

👤 Guest Bio

Dr. Nino Paulus is the Co-Founder and Chief Product Officer of AlphaPrompt. He holds a PhD from the IREBS International Real Estate Business School, where his research focused on Natural Language Processing (NLP) in the real estate sector. At AlphaPrompt, he leads the development of GenAI solutions that automate due diligence and data structuring for asset and property managers. His work bridges the gap between academic AI research and the practical, messy reality of real estate documentation.

ㅤ

📌 What We Cover

  1. The Data Problem: Why the biggest challenge in real estate isn't a lack of data, but the fact that it is unstructured and stuck in "silos" that don't talk to each other.
  2. Automating "Monkey Work": How AlphaPrompt uses GenAI to handle the tedious tasks—like typing out rent rolls or checking lease addendums—so analysts can focus on decision-making.
  3. The "Live" Sales Pitch: Nino explains why he throws a prospect's actual data room into the tool during sales calls instead of using a canned demo.
  4. Red Flag Reports: Moving beyond just data extraction to "risk alerts," such as spotting a break clause that allows a tenant to leave early.
  5. The "Lost" Property Story: A case study where the AI found seven active leases in a small German town that the portfolio owner thought they had exited years ago.
  6. Bottom-Up Adoption: Why AI initiatives fail when they are top-down mandates and why you need to involve the people doing the daily work to make it stick.
  7. The Future of Agents: A look at "Moltbook" (Moltbot), a social network for AI agents, and what happens when bots start communicating and learning from one another without human input.

ㅤ

🔗 Resources Mentioned

  1. AlphaPrompt (Guest Company)
  2. Moltbook (AI Agent Social Network mentioned by Nino)

]]>Real estate runs on data, but most of it is trapped in PDFs, lease agreements, and siloed legacy systems. In this episode, host Daniel Kazani sits down with Dr. Nino Paulus, Co-Founder and CPO of AlphaPrompt, to discuss how generative AI is bringing order to this chaos. Nino explains how his team moved from building simple dashboards to creating an AI that functions like a senior analyst—capable of reading entire data rooms, extracting complex lease terms, and spotting risks that humans might miss.

ㅤ

They discuss the reality of deploying AI in a traditional industry, sharing a story in which their software identified 7 active leases for a property the owner didn't even know they still owned. Nino also opens up about his "live demo" sales strategy and shares his thoughts on the future of autonomous AI agents, including the emergence of "Moltbook," a social network where bots communicate with each other. This is a practical look at how Softup and other tech builders can learn from AlphaPrompt's approach to automation and data structuring.

ㅤ

👤 Guest Bio

Dr. Nino Paulus is the Co-Founder and Chief Product Officer of AlphaPrompt. He holds a PhD from the IREBS International Real Estate Business School, where his research focused on Natural Language Processing (NLP) in the real estate sector. At AlphaPrompt, he leads the development of GenAI solutions that automate due diligence and data structuring for asset and property managers. His work bridges the gap between academic AI research and the practical, messy reality of real estate documentation.

ㅤ

📌 What We Cover

  1. The Data Problem: Why the biggest challenge in real estate isn't a lack of data, but the fact that it is unstructured and stuck in "silos" that don't talk to each other.
  2. Automating "Monkey Work": How AlphaPrompt uses GenAI to handle the tedious tasks—like typing out rent rolls or checking lease addendums—so analysts can focus on decision-making.
  3. The "Live" Sales Pitch: Nino explains why he throws a prospect's actual data room into the tool during sales calls instead of using a canned demo.
  4. Red Flag Reports: Moving beyond just data extraction to "risk alerts," such as spotting a break clause that allows a tenant to leave early.
  5. The "Lost" Property Story: A case study where the AI found seven active leases in a small German town that the portfolio owner thought they had exited years ago.
  6. Bottom-Up Adoption: Why AI initiatives fail when they are top-down mandates and why you need to involve the people doing the daily work to make it stick.
  7. The Future of Agents: A look at "Moltbook" (Moltbot), a social network for AI agents, and what happens when bots start communicating and learning from one another without human input.

ㅤ

🔗 Resources Mentioned

  1. AlphaPrompt (Guest Company)
  2. Moltbook (AI Agent Social Network mentioned by Nino)

]]>bfcae06d-5b0d-4f7f-90ab-bdbc5d764274Thu, 26 Feb 2026 04:00:00 +020032:15falsefull1616How AI Is Rewriting Brand Visibility | Ep. 15How AI Is Rewriting Brand Visibility | Ep. 15Brands are losing visibility because they rely on click-based attribution in a world moving toward answer engines. Daniel Kazani speaks with Malte Landwehr, CPO and CMO of Peec AI, about the reality of AI search. Malte explains why 25% of leads might come from LLMs even when tracking software shows 0%. They discuss the transition from SEO to GEO, why static dashboards are disappearing, and the hard truth that average marketing work is becoming valueless.

ㅤ

Guest Bio

Malte Landwehr is the Chief Product Officer and Chief Marketing Officer at Peec AI. He has over 20 years of industry experience, including roles as VP of SEO at idealo and VP of Product at Searchmetrics. Peec AI helps marketing teams understand and optimize their visibility in LLM-based search engines.

ㅤ

What We Cover

  1. Why click-based tracking fails to capture the user journey inside ChatGPT and Perplexity.
  2. The specific role of "grounding sources" such as Reddit, YouTube, and LinkedIn in AI responses.
  3. How Peec AI simulates user behavior to track brand mentions and sentiment.
  4. The shift from static data dashboards to on-demand AI chat interfaces.
  5. Why is average marketing becoming free while top-tier marketers become exponentially more effective?
  6. The challenge of finding product managers with strong product taste in Europe.

ㅤ

Resources Mentioned

  1. Peec AI
  2. Malte Landwehr (LinkedIn)
  3. Daniel Kazani (LinkedIn)
  4. ChatGPT
  5. Perplexity
  6. Google Gemini
  7. Granola
  8. HubSpot

]]>Brands are losing visibility because they rely on click-based attribution in a world moving toward answer engines. Daniel Kazani speaks with Malte Landwehr, CPO and CMO of Peec AI, about the reality of AI search. Malte explains why 25% of leads might come from LLMs even when tracking software shows 0%. They discuss the transition from SEO to GEO, why static dashboards are disappearing, and the hard truth that average marketing work is becoming valueless.

ㅤ

Guest Bio

Malte Landwehr is the Chief Product Officer and Chief Marketing Officer at Peec AI. He has over 20 years of industry experience, including roles as VP of SEO at idealo and VP of Product at Searchmetrics. Peec AI helps marketing teams understand and optimize their visibility in LLM-based search engines.

ㅤ

What We Cover

  1. Why click-based tracking fails to capture the user journey inside ChatGPT and Perplexity.
  2. The specific role of "grounding sources" such as Reddit, YouTube, and LinkedIn in AI responses.
  3. How Peec AI simulates user behavior to track brand mentions and sentiment.
  4. The shift from static data dashboards to on-demand AI chat interfaces.
  5. Why is average marketing becoming free while top-tier marketers become exponentially more effective?
  6. The challenge of finding product managers with strong product taste in Europe.

ㅤ

Resources Mentioned

  1. Peec AI
  2. Malte Landwehr (LinkedIn)
  3. Daniel Kazani (LinkedIn)
  4. ChatGPT
  5. Perplexity
  6. Google Gemini
  7. Granola
  8. HubSpot

]]>7cc88dbe-7ff4-427a-af9b-1d8faaea2cdeThu, 19 Feb 2026 04:00:00 +020032:55falsefull1515How AI is impacting Health Tech | Ep. 14How AI is impacting Health Tech | Ep. 14Learn more about facial vital sign detection: shen.ai & caire.ai

ㅤ

Healthcare has historically lagged in digitalization, creating a significant opportunity for artificial intelligence to jump-start the industry. Host Daniel Kazani sits down with Dr. Lucas Mittelmeier, an investor at Heal Capital, to discuss why the sector's heavy administrative burden makes it a prime target for disruption. They explore the reality of "Shadow AI," where physicians bypass slow hospital IT systems to use tools like ChatGPT for daily tasks. Lucas explains how the industry is splitting into two distinct speeds: highly regulated clinical tools and agile administrative workflows. The conversation also highlights cutting-edge innovations, including facial analysis software that reads vital signs via a camera and vocal biomarkers that detect heart failure.

ㅤ

Guest Bio

Dr. Lucas Mittelmeier is a physician-turned-investor at Heal Capital, a leading European healthtech venture capital firm. With a background bridging clinical medicine, strategy consulting, and startup leadership, he evaluates companies through both medical and business lenses. He is also the author of the Healthtech Off The Record newsletter, where he provides data-driven analysis of industry trends. At Heal Capital, he focuses on sourcing and leading deals from Pre-Seed to Series A.

ㅤ

What We Cover

  1. Why the lack of legacy digital infrastructure in healthcare might actually accelerate AI adoption.
  2. The phenomenon of "Shadow AI" and why doctors are using consumer tools despite strict hospital regulations.
  3. How administrative AI is moving faster than clinical diagnostic tools due to lower regulatory barriers.
  4. The potential for "facial parameters" in which video can detect heart rate, blood pressure, and oxygen saturation.
  5. Using vocal biomarkers to identify conditions like heart failure by analyzing fluid buildup in the lungs.
  6. How typing patterns on a keyboard can serve as early indicators for depression.
  7. Why specialized "AI Therapist" startups have struggled to compete with general Large Language Models.
  8. The four key moats for healthtech startups: data advantages, network effects, deep customer service, and brand trust.

ㅤ

Resources Mentioned

  1. Heal Capital
  2. OpenAI (ChatGPT)
  3. Anthropic (Claude)
  4. Whoop
  5. Caire (Healthtech startup)
  6. Scale AI

]]>Learn more about facial vital sign detection: shen.ai & caire.ai

ㅤ

Healthcare has historically lagged in digitalization, creating a significant opportunity for artificial intelligence to jump-start the industry. Host Daniel Kazani sits down with Dr. Lucas Mittelmeier, an investor at Heal Capital, to discuss why the sector's heavy administrative burden makes it a prime target for disruption. They explore the reality of "Shadow AI," where physicians bypass slow hospital IT systems to use tools like ChatGPT for daily tasks. Lucas explains how the industry is splitting into two distinct speeds: highly regulated clinical tools and agile administrative workflows. The conversation also highlights cutting-edge innovations, including facial analysis software that reads vital signs via a camera and vocal biomarkers that detect heart failure.

ㅤ

Guest Bio

Dr. Lucas Mittelmeier is a physician-turned-investor at Heal Capital, a leading European healthtech venture capital firm. With a background bridging clinical medicine, strategy consulting, and startup leadership, he evaluates companies through both medical and business lenses. He is also the author of the Healthtech Off The Record newsletter, where he provides data-driven analysis of industry trends. At Heal Capital, he focuses on sourcing and leading deals from Pre-Seed to Series A.

ㅤ

What We Cover

  1. Why the lack of legacy digital infrastructure in healthcare might actually accelerate AI adoption.
  2. The phenomenon of "Shadow AI" and why doctors are using consumer tools despite strict hospital regulations.
  3. How administrative AI is moving faster than clinical diagnostic tools due to lower regulatory barriers.
  4. The potential for "facial parameters" in which video can detect heart rate, blood pressure, and oxygen saturation.
  5. Using vocal biomarkers to identify conditions like heart failure by analyzing fluid buildup in the lungs.
  6. How typing patterns on a keyboard can serve as early indicators for depression.
  7. Why specialized "AI Therapist" startups have struggled to compete with general Large Language Models.
  8. The four key moats for healthtech startups: data advantages, network effects, deep customer service, and brand trust.

ㅤ

Resources Mentioned

  1. Heal Capital
  2. OpenAI (ChatGPT)
  3. Anthropic (Claude)
  4. Whoop
  5. Caire (Healthtech startup)
  6. Scale AI

]]>065febc6-9f41-4555-a9d9-f53f77ffa384Thu, 12 Feb 2026 04:00:00 +020033:52falsefull1414AI Trends in Germany | Ep. 13AI Trends in Germany | Ep. 13AI Trends in Germany - Presentation (PDF) — Follow along with the data discussed in this episode

ㅤ

Germany currently faces a distinct tension between its technical potential and actual financial commitment to artificial intelligence. While the country ranks high in AI skills and research, private investment stands at just 1.8 billion euros, compared to over 62 billion in the United States. Host Daniel Kazani sits down with Stephan Fricke to examine the reality behind these numbers and what they mean for the German market.

ㅤ

Stephan breaks down the data on Germany's current 45,000 AI specialists and the projected gap of nearly 180,000 by 2032. They discuss why customer contact centers are seeing 88% of implementations and how manufacturing giants like BMW and Siemens are using AI for practical quality assurance. The conversation also covers the critical role of strategic partnerships and outsourcing in bridging the talent shortage that domestic training alone cannot solve.

ㅤ

👤 Guest Bio

Stephan Fricke is the CEO of the Deutscher Outsourcing Verband e.V. (German Outsourcing Association) and the Deutscher Process Automation Verband. Since 2010, he has focused on bridging the gap between German business culture and global innovation hubs. Through industry publications such as the Outsourcing Journal, Stephan shapes the narrative around Global Business Services and advocates for diversifying sourcing destinations to address the talent crisis in the DACH region.

ㅤ

📌 What We Cover

  1. The estimated 60 billion euro market volume for AI services in Germany in 2025.
  2. Why 88% of German companies implementing AI start with customer contact and chatbots.
  3. The massive gap in private AI investment: 1.8 billion euros in Germany versus 62.5 billion in the US.
  4. How Germany compares globally in terms of infrastructure, with a notable lack of data centers.
  5. The talent crisis: Moving from 45,000 specialists today to a need for 180,000 by 2032.
  6. Why Softup and similar partners are becoming essential for companies unable to find local talent.
  7. Specific manufacturing use cases for AI: From predictive maintenance at Siemens to quality assurance at BMW.
  8. The regulatory hurdles and slow government strategies are affecting European competitiveness.

ㅤ

🔗 Resources Mentioned

  1. Deutscher Outsourcing Verband e.V. (German Outsourcing Association)
  2. Global AI Index 2024
  3. Canva (Marketing tool)
  4. Volkswagen
  5. BMW Group
  6. Siemens
  7. Bosch
  8. Trumpf

]]>AI Trends in Germany - Presentation (PDF) — Follow along with the data discussed in this episode

ㅤ

Germany currently faces a distinct tension between its technical potential and actual financial commitment to artificial intelligence. While the country ranks high in AI skills and research, private investment stands at just 1.8 billion euros, compared to over 62 billion in the United States. Host Daniel Kazani sits down with Stephan Fricke to examine the reality behind these numbers and what they mean for the German market.

ㅤ

Stephan breaks down the data on Germany's current 45,000 AI specialists and the projected gap of nearly 180,000 by 2032. They discuss why customer contact centers are seeing 88% of implementations and how manufacturing giants like BMW and Siemens are using AI for practical quality assurance. The conversation also covers the critical role of strategic partnerships and outsourcing in bridging the talent shortage that domestic training alone cannot solve.

ㅤ

👤 Guest Bio

Stephan Fricke is the CEO of the Deutscher Outsourcing Verband e.V. (German Outsourcing Association) and the Deutscher Process Automation Verband. Since 2010, he has focused on bridging the gap between German business culture and global innovation hubs. Through industry publications such as the Outsourcing Journal, Stephan shapes the narrative around Global Business Services and advocates for diversifying sourcing destinations to address the talent crisis in the DACH region.

ㅤ

📌 What We Cover

  1. The estimated 60 billion euro market volume for AI services in Germany in 2025.
  2. Why 88% of German companies implementing AI start with customer contact and chatbots.
  3. The massive gap in private AI investment: 1.8 billion euros in Germany versus 62.5 billion in the US.
  4. How Germany compares globally in terms of infrastructure, with a notable lack of data centers.
  5. The talent crisis: Moving from 45,000 specialists today to a need for 180,000 by 2032.
  6. Why Softup and similar partners are becoming essential for companies unable to find local talent.
  7. Specific manufacturing use cases for AI: From predictive maintenance at Siemens to quality assurance at BMW.
  8. The regulatory hurdles and slow government strategies are affecting European competitiveness.

ㅤ

🔗 Resources Mentioned

  1. Deutscher Outsourcing Verband e.V. (German Outsourcing Association)
  2. Global AI Index 2024
  3. Canva (Marketing tool)
  4. Volkswagen
  5. BMW Group
  6. Siemens
  7. Bosch
  8. Trumpf

]]>0ba9f21f-0602-4ee1-ade8-f34175edec91Thu, 05 Feb 2026 04:00:00 +020029:13falsefull1313How AI is impacting the Industrial Tech Space? | Ep. 12How AI is impacting the Industrial Tech Space? | Ep. 12Manufacturing is no longer just about moving atoms. It is shifting toward software-defined automation and fully autonomous systems. Daniel Kazani sits down with Miroslav Kriz, Principal Partner at Momenta, to discuss how AI is reshaping the factory floor. They explore why industrial innovation requires different safety standards than typical software, where a "hallucination" can mean physical danger rather than just bad code.

ㅤ

Miroslav explains the reality of "lights out" factories, where blast furnaces adjust in real time without human input. He also critiques the "tourist syndrome" that European founders face when entering the US market and argues why industrial startups should look to Pittsburgh or Indianapolis rather than Silicon Valley. This conversation covers the journey from simple automation to true autonomy and the specific physics that investors look for before writing a check.

ㅤ

Guest Bio

Miroslav Kriz is a Principal Partner at Momenta, a venture capital firm focused on industrial impact and enterprise technology. He specializes in bridging the gap between legacy industrial companies and modern innovation.

Currently based in Prague after moving from New York, Miroslav works to connect Central and Eastern European technical talent with the US market. He also helps lead initiatives like Gem7 to help startups establish operational beachheads in America.

ㅤ

What We Cover

  1. The three core pillars of industrial impact are software-defined automation, robotics, and AI.
  2. Why the "move fast and break things" mentality fails in manufacturing, where safety is critical.
  3. How virtualization allows agile development on machines with 30-year lifecycles.
  4. The emergence of "lights out" factories and autonomous closed-loop systems.
  5. Why ROI in industry is defined by speed and waste reduction rather than quality improvements.
  6. The "tourist" mistake European founders make when expanding to the US.
  7. Why industrial startups often find better success in Detroit or Milwaukee than in Silicon Valley.
  8. Using AI in venture capital to validate physics and research trends rather than make deal decisions.

ㅤ

Resources Mentioned

  1. Momenta
  2. Gem7 (Market entry service)
  3. Rockwell Automation
  4. Fleet Space
  5. Grok
  6. Microsoft

]]>Manufacturing is no longer just about moving atoms. It is shifting toward software-defined automation and fully autonomous systems. Daniel Kazani sits down with Miroslav Kriz, Principal Partner at Momenta, to discuss how AI is reshaping the factory floor. They explore why industrial innovation requires different safety standards than typical software, where a "hallucination" can mean physical danger rather than just bad code.

ㅤ

Miroslav explains the reality of "lights out" factories, where blast furnaces adjust in real time without human input. He also critiques the "tourist syndrome" that European founders face when entering the US market and argues why industrial startups should look to Pittsburgh or Indianapolis rather than Silicon Valley. This conversation covers the journey from simple automation to true autonomy and the specific physics that investors look for before writing a check.

ㅤ

Guest Bio

Miroslav Kriz is a Principal Partner at Momenta, a venture capital firm focused on industrial impact and enterprise technology. He specializes in bridging the gap between legacy industrial companies and modern innovation.

Currently based in Prague after moving from New York, Miroslav works to connect Central and Eastern European technical talent with the US market. He also helps lead initiatives like Gem7 to help startups establish operational beachheads in America.

ㅤ

What We Cover

  1. The three core pillars of industrial impact are software-defined automation, robotics, and AI.
  2. Why the "move fast and break things" mentality fails in manufacturing, where safety is critical.
  3. How virtualization allows agile development on machines with 30-year lifecycles.
  4. The emergence of "lights out" factories and autonomous closed-loop systems.
  5. Why ROI in industry is defined by speed and waste reduction rather than quality improvements.
  6. The "tourist" mistake European founders make when expanding to the US.
  7. Why industrial startups often find better success in Detroit or Milwaukee than in Silicon Valley.
  8. Using AI in venture capital to validate physics and research trends rather than make deal decisions.

ㅤ

Resources Mentioned

  1. Momenta
  2. Gem7 (Market entry service)
  3. Rockwell Automation
  4. Fleet Space
  5. Grok
  6. Microsoft

]]>01480327-dd20-4409-a1b4-9c7e90a5bd82Thu, 29 Jan 2026 04:00:00 +020033:57falsefull1212From Automation to Autonomy with Agentic AI (with Pascal Faerber) | Ep. 11From Automation to Autonomy with Agentic AI (with Pascal Faerber) | Ep. 11This episode was recorded on Dec 10, 2025.

ㅤ

Automation and digitalization were huge topics for decades, but “it’s no longer enough.” Host Daniel Kazani talks with Pascal Faerber, Managing Director, Digital Services Germany at Orange Business, about agentic AI and why it is “fundamentally different” from reactive gen AI. Pascal frames agentic AI as proactive, understanding goals and desired outcomes, breaking them down into steps, executing across multiple systems, evaluating its own output, and learning continuously.

ㅤ

The conversation moves from digital transformation and cloud, including hyperscalers like Azure and Amazon, to a concrete example: a customer success AI agent that scans incoming customer messages across channels, classifies issues, prioritizes urgency, fetches relevant internal knowledge, drafts proposed solutions, triggers actions across systems, and escalates only when human judgment is required. They also talk about AI as a transformation: leadership mindset, processes, and foundations that enable a network of collaborating humans and agents.

ㅤ

👤 Guest Bio

Pascal Faerber is Managing Director, Digital Services Germany at Orange Business. He describes Digital Services as “top of the spear” in digital transformation, supporting clients with cloud transformation, data and AI, data platform development, and AI use case development. Pascal also mentions being a lecturer and a business angel, as well as being very active in the tech community.

ㅤ

📌 What We Cover

  1. Orange Business Digital Services and “digital transformation,” including cloud transformation and working with hyperscalers like Azure and Amazon
  2. A sovereign cloud solution with regulatory requirements and environments operated in Europe by European employees
  3. “Agentic AI” as proactive systems that understand goals, breaks them into steps, executes across multiple systems, evaluates output, and learn continuously
  4. A customer success AI agent: scanning multichannel messages, classifying and prioritizing issues, pulling contracts, SLAs, documentation, and ticket history, then triggering actions across systems
  5. Impact discussed: resolution time brought down from “two, three days” to “15, 30 minutes,” and “60, 65, 70%” reduction in repetitive work
  6. AI adoption as a transformation, “mindset first,” and the bottleneck being “permission” rather than technology
  7. Clearly defined roles in human and AI collaboration, and AI as “a new colleague.”
  8. Moving from pilots to scale: five questions, one high-impact breakthrough, and scaling aggressively with training and change

ㅤ

🔗 Resources Mentioned

  1. Orange Business
  2. Azure
  3. Amazon
  4. ChatGPT
  5. Gemini
  6. Salesforce
  7. Nvidia
  8. Microsoft
  9. IBM
  10. Satya Nadella

]]>This episode was recorded on Dec 10, 2025.

ㅤ

Automation and digitalization were huge topics for decades, but “it’s no longer enough.” Host Daniel Kazani talks with Pascal Faerber, Managing Director, Digital Services Germany at Orange Business, about agentic AI and why it is “fundamentally different” from reactive gen AI. Pascal frames agentic AI as proactive, understanding goals and desired outcomes, breaking them down into steps, executing across multiple systems, evaluating its own output, and learning continuously.

ㅤ

The conversation moves from digital transformation and cloud, including hyperscalers like Azure and Amazon, to a concrete example: a customer success AI agent that scans incoming customer messages across channels, classifies issues, prioritizes urgency, fetches relevant internal knowledge, drafts proposed solutions, triggers actions across systems, and escalates only when human judgment is required. They also talk about AI as a transformation: leadership mindset, processes, and foundations that enable a network of collaborating humans and agents.

ㅤ

👤 Guest Bio

Pascal Faerber is Managing Director, Digital Services Germany at Orange Business. He describes Digital Services as “top of the spear” in digital transformation, supporting clients with cloud transformation, data and AI, data platform development, and AI use case development. Pascal also mentions being a lecturer and a business angel, as well as being very active in the tech community.

ㅤ

📌 What We Cover

  1. Orange Business Digital Services and “digital transformation,” including cloud transformation and working with hyperscalers like Azure and Amazon
  2. A sovereign cloud solution with regulatory requirements and environments operated in Europe by European employees
  3. “Agentic AI” as proactive systems that understand goals, breaks them into steps, executes across multiple systems, evaluates output, and learn continuously
  4. A customer success AI agent: scanning multichannel messages, classifying and prioritizing issues, pulling contracts, SLAs, documentation, and ticket history, then triggering actions across systems
  5. Impact discussed: resolution time brought down from “two, three days” to “15, 30 minutes,” and “60, 65, 70%” reduction in repetitive work
  6. AI adoption as a transformation, “mindset first,” and the bottleneck being “permission” rather than technology
  7. Clearly defined roles in human and AI collaboration, and AI as “a new colleague.”
  8. Moving from pilots to scale: five questions, one high-impact breakthrough, and scaling aggressively with training and change

ㅤ

🔗 Resources Mentioned

  1. Orange Business
  2. Azure
  3. Amazon
  4. ChatGPT
  5. Gemini
  6. Salesforce
  7. Nvidia
  8. Microsoft
  9. IBM
  10. Satya Nadella

]]>bc9f84a5-c45a-4d32-ae2a-cb955632c4caThu, 22 Jan 2026 04:00:00 +020038:38falsefull1111How does Google Engineer leverage AI for her daily work? | Ep. 10How does Google Engineer leverage AI for her daily work? | Ep. 10AI has shifted from a buzzword to a genuine, intelligent assistant. Host Daniel Kazani talks with Dajana Stojchevska, a senior software engineer at Google in Munich, about how AI is embedded in day-to-day engineering work, not about replacing engineers, but about boosting productivity and removing friction so teams can focus on the cool stuff.

ㅤ

Creation, collaboration, and knowledge management show up everywhere, from intelligent code completion inside the integrated development environments, to AI-assisted code reviews, to meeting notes that summarize transcripts, highlight key decisions, and list action items with owners. The conversation also remains grounded in the challenges: the hallucination trap, prompt injection, indirect injection hidden in external data such as a PDF, and strict discipline around data privacy. Looking ahead, the focus turns to autonomous agents, massive context windows, proactive analysis, and the evolving role of the software engineer as architect and orchestrator.

ㅤ

👤 Guest Bio

Dajana Stojchevska is a senior software engineer at Google in Munich. She graduated with a degree in Scopia from the Faculty of Computer Science and Engineering, with elective subjects in software engineering. She completed a few internships, including in Python, and her first role was as a Java developer focused on full-stack web development with Java and Angular. She also worked as a laboratory teaching assistant, helping students with exercises. After about two years, she moved to Germany for the Google offer.

ㅤ

📌 What We Cover

  1. AI inside the editor as a proactive teammate, intelligent code completion, prompts for snippets, and boilerplate
  2. Code reviews with AI, drafting descriptions, style and standards fixes, and automated fixes from static analysis errors
  3. AI-generated suggested code edits from teammate feedback, plus reviewer support with links to documentation
  4. Meeting notes that summarize transcripts, highlight key decisions, and list action items with owners
  5. Generating architecture diagrams from text, plus document analysis and “interviewing the document”
  6. Correctness and the hallucination trap, treating AI like a junior engineer who needs supervision
  7. Security risks, direct prompt injection, indirect injection, and why even a PDF can be a hostile input
  8. Data privacy and strict guidelines on what data can go into which tools, plus internal AI chatbot support
  9. Staying up to date with internal channels, newsletters, tech talks, hands-on daily practice, and peer community
  10. The next five years: autonomous agents, bigger context windows, proactive help, and engineers as architects and orchestrators

ㅤ

🔗 Resources Mentioned

  1. Gemini
  2. Gemini Three
  3. Anti-Gravity
  4. Fathom
  5. Notebook LM
  6. Integrated development environments
  7. Static analysis tools
  8. Internal AI chatbot

]]>AI has shifted from a buzzword to a genuine, intelligent assistant. Host Daniel Kazani talks with Dajana Stojchevska, a senior software engineer at Google in Munich, about how AI is embedded in day-to-day engineering work, not about replacing engineers, but about boosting productivity and removing friction so teams can focus on the cool stuff.

ㅤ

Creation, collaboration, and knowledge management show up everywhere, from intelligent code completion inside the integrated development environments, to AI-assisted code reviews, to meeting notes that summarize transcripts, highlight key decisions, and list action items with owners. The conversation also remains grounded in the challenges: the hallucination trap, prompt injection, indirect injection hidden in external data such as a PDF, and strict discipline around data privacy. Looking ahead, the focus turns to autonomous agents, massive context windows, proactive analysis, and the evolving role of the software engineer as architect and orchestrator.

ㅤ

👤 Guest Bio

Dajana Stojchevska is a senior software engineer at Google in Munich. She graduated with a degree in Scopia from the Faculty of Computer Science and Engineering, with elective subjects in software engineering. She completed a few internships, including in Python, and her first role was as a Java developer focused on full-stack web development with Java and Angular. She also worked as a laboratory teaching assistant, helping students with exercises. After about two years, she moved to Germany for the Google offer.

ㅤ

📌 What We Cover

  1. AI inside the editor as a proactive teammate, intelligent code completion, prompts for snippets, and boilerplate
  2. Code reviews with AI, drafting descriptions, style and standards fixes, and automated fixes from static analysis errors
  3. AI-generated suggested code edits from teammate feedback, plus reviewer support with links to documentation
  4. Meeting notes that summarize transcripts, highlight key decisions, and list action items with owners
  5. Generating architecture diagrams from text, plus document analysis and “interviewing the document”
  6. Correctness and the hallucination trap, treating AI like a junior engineer who needs supervision
  7. Security risks, direct prompt injection, indirect injection, and why even a PDF can be a hostile input
  8. Data privacy and strict guidelines on what data can go into which tools, plus internal AI chatbot support
  9. Staying up to date with internal channels, newsletters, tech talks, hands-on daily practice, and peer community
  10. The next five years: autonomous agents, bigger context windows, proactive help, and engineers as architects and orchestrators

ㅤ

🔗 Resources Mentioned

  1. Gemini
  2. Gemini Three
  3. Anti-Gravity
  4. Fathom
  5. Notebook LM
  6. Integrated development environments
  7. Static analysis tools
  8. Internal AI chatbot

]]>a549679b-3617-4d17-b336-45c2aa5d4962Thu, 15 Jan 2026 04:00:00 +020034:44falsefull1010How to increase visibility and searchability in the new age of AI | Ep. 9How to increase visibility and searchability in the new age of AI | Ep. 9Being discoverable on ChatGPT has become a board-level problem for most companies. Host Daniel Kazani talks with Vlad Shvets about Qvery, an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Measuring brand visibility is a new challenge companies need to address, requiring new tools and software. The way we search Google is very different from how we use ChatGPT, which provides comprehensive recommendations personalized to your conversation history, language, and location. The quest starts when a CEO goes to chat, asks a question related to their brand, and the brand does not appear. The marketing team begins assessing how to measure this and how to make the brand discoverable. Vlad breaks down three pillars: your own website presence, mentions on external websites, and user-generated content, and why Reddit is the most critical website these days.

ㅤ

👤 Guest Bio

Vlad Shvets is a marketing expert, serial entrepreneur, advisor, and founder of Qvery. Qvery is an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Vlad shares that Qvery started as a consultancy, then evolved into a way to measure visibility on chat GT, including personalized results. He describes a vision of the future of the web as agentic, with people increasingly relying on AI agents to do tasks for them.

ㅤ

📌 What We Cover

  1. When a CEO goes to ChatGPT, asks a question related to their brand, and the brand does not pop up
  2. Google gives you links, and ChatGPT gives you complete recommendations, with personalization
  3. A case study for services, a separate domain, a single-page website, and leads from ChatGPT and Google AI overviews
  4. Optimizing for specific granular use cases, capturing high-intent requests, and vanity metrics
  5. Three pillars: your own website presence, mentions on external websites, and user-generated content, Reddit in particular
  6. FAQ schema and schema data as fast food for chat gt to fetch and understand
  7. Logged in state of personas, citations list, outreach, and getting a product mentioned where it matters
  8. CloudFlare blocking agents, browser manipulation tech, AI agent regulation, and a passport program
  9. Google AI mode is becoming a default way to search, and it's what happens overnight for companies and businesses

ㅤ

🔗 Resources Mentioned

  1. ChatGPT
  2. Google AI mode
  3. Gemini 3 model
  4. OpenAI
  5. Reddit
  6. Perplexity
  7. CloudFlare
  8. Browserbase
  9. Browser manipulation tech
  10. Schema, FAQ schema
  11. API
  12. Slack
  13. Wikipedia
  14. Patreon

]]>Being discoverable on ChatGPT has become a board-level problem for most companies. Host Daniel Kazani talks with Vlad Shvets about Qvery, an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Measuring brand visibility is a new challenge companies need to address, requiring new tools and software. The way we search Google is very different from how we use ChatGPT, which provides comprehensive recommendations personalized to your conversation history, language, and location. The quest starts when a CEO goes to chat, asks a question related to their brand, and the brand does not appear. The marketing team begins assessing how to measure this and how to make the brand discoverable. Vlad breaks down three pillars: your own website presence, mentions on external websites, and user-generated content, and why Reddit is the most critical website these days.

ㅤ

👤 Guest Bio

Vlad Shvets is a marketing expert, serial entrepreneur, advisor, and founder of Qvery. Qvery is an AI agent software that helps brands measure and grow their visibility and share of voice on the AI search engines. Vlad shares that Qvery started as a consultancy, then evolved into a way to measure visibility on chat GT, including personalized results. He describes a vision of the future of the web as agentic, with people increasingly relying on AI agents to do tasks for them.

ㅤ

📌 What We Cover

  1. When a CEO goes to ChatGPT, asks a question related to their brand, and the brand does not pop up
  2. Google gives you links, and ChatGPT gives you complete recommendations, with personalization
  3. A case study for services, a separate domain, a single-page website, and leads from ChatGPT and Google AI overviews
  4. Optimizing for specific granular use cases, capturing high-intent requests, and vanity metrics
  5. Three pillars: your own website presence, mentions on external websites, and user-generated content, Reddit in particular
  6. FAQ schema and schema data as fast food for chat gt to fetch and understand
  7. Logged in state of personas, citations list, outreach, and getting a product mentioned where it matters
  8. CloudFlare blocking agents, browser manipulation tech, AI agent regulation, and a passport program
  9. Google AI mode is becoming a default way to search, and it's what happens overnight for companies and businesses

ㅤ

🔗 Resources Mentioned

  1. ChatGPT
  2. Google AI mode
  3. Gemini 3 model
  4. OpenAI
  5. Reddit
  6. Perplexity
  7. CloudFlare
  8. Browserbase
  9. Browser manipulation tech
  10. Schema, FAQ schema
  11. API
  12. Slack
  13. Wikipedia
  14. Patreon

]]>a165f4a9-a355-47a1-ba4b-756ae11f58e4Thu, 08 Jan 2026 04:00:00 +020030:35falsefull99How has AI impacted the startup scene, investment and funding rounds? | Ep. 8How has AI impacted the startup scene, investment and funding rounds? | Ep. 8We are living through the most significant platform shift since the Internet. Host Daniel Kazani talks with guest Shefqet Avdullau, an angel investor, advisor, and speaker focused on growth-stage B2B SaaS, FinTech, ad tech, and health tech. The conversation starts with a story that moves from coding to multiple ventures to a meaningful exit, then into investing with a mentor who gave a head start on due diligence, pitfalls, and strategies. The weight falls on the team because the idea you start with does not necessarily mean you will end with it, and a good team can turn a bad idea into a great one. Then: AI and defensibility, wrappers, data-loop strategy, fine-tuning, and what happens if OpenAI or Gemini releases a new update tomorrow. Health tech and biotech, drug discovery, and turning biology into an engineering problem.

ㅤ

👤 Guest Bio

Shefqet Avdullau is an angel investor, advisor, and speaker. He invests in serial founders across the US and UK, focusing on B2B SaaS, FinTech, and ad tech at all stages, and on health tech specifically at the growth stage. His foundation is in tech; he worked in that field for about 13 years, started multiple ventures with some small exits, and then had one meaningful exit. In about four years, he has done about 16 investments and has had two exits.

ㅤ

📌 What We Cover

  • From coding, to multiple ventures, to a meaningful exit, to investing and joining a group of investors
  • A mentor with private equity experience, due diligence, pitfalls in investing, and strategies to follow
  • Why serial founders come with a map, with a playbook, and go straight to finding product market fit
  • Scars, lessons, when things get tough, and why failure can be something you prefer
  • Idea versus team, pivots, and why the team can turn a bad idea into a great idea
  • Two founders or more, complementary skillset, product, and sales, and a third on operations
  • Founder problem fit, domain experience, network, and solving an actual problem, not just for money
  • AI wrappers versus defensibility, data loop strategy, fine-tuning, and “would this company die” after a new update
  • Where AI is disrupting, health tech and biotech, drug discovery, simulating millions of interactions digitally, and FinTech underwriting with unstructured data
  • Using AI for competitor analysis, risk analysis, and alternative potential revenue streams, and “it hallucinates a lot”
  • A contrarian investment choice, two serial founders, employee disengagement, productivity, and invisible frictions

ㅤ

🔗 Resources Mentioned

  • Open AI
  • Gemini
  • Figma
  • Nvidia
  • LinkedIn
  • lovable

]]>We are living through the most significant platform shift since the Internet. Host Daniel Kazani talks with guest Shefqet Avdullau, an angel investor, advisor, and speaker focused on growth-stage B2B SaaS, FinTech, ad tech, and health tech. The conversation starts with a story that moves from coding to multiple ventures to a meaningful exit, then into investing with a mentor who gave a head start on due diligence, pitfalls, and strategies. The weight falls on the team because the idea you start with does not necessarily mean you will end with it, and a good team can turn a bad idea into a great one. Then: AI and defensibility, wrappers, data-loop strategy, fine-tuning, and what happens if OpenAI or Gemini releases a new update tomorrow. Health tech and biotech, drug discovery, and turning biology into an engineering problem.

ㅤ

👤 Guest Bio

Shefqet Avdullau is an angel investor, advisor, and speaker. He invests in serial founders across the US and UK, focusing on B2B SaaS, FinTech, and ad tech at all stages, and on health tech specifically at the growth stage. His foundation is in tech; he worked in that field for about 13 years, started multiple ventures with some small exits, and then had one meaningful exit. In about four years, he has done about 16 investments and has had two exits.

ㅤ

📌 What We Cover

  • From coding, to multiple ventures, to a meaningful exit, to investing and joining a group of investors
  • A mentor with private equity experience, due diligence, pitfalls in investing, and strategies to follow
  • Why serial founders come with a map, with a playbook, and go straight to finding product market fit
  • Scars, lessons, when things get tough, and why failure can be something you prefer
  • Idea versus team, pivots, and why the team can turn a bad idea into a great idea
  • Two founders or more, complementary skillset, product, and sales, and a third on operations
  • Founder problem fit, domain experience, network, and solving an actual problem, not just for money
  • AI wrappers versus defensibility, data loop strategy, fine-tuning, and “would this company die” after a new update
  • Where AI is disrupting, health tech and biotech, drug discovery, simulating millions of interactions digitally, and FinTech underwriting with unstructured data
  • Using AI for competitor analysis, risk analysis, and alternative potential revenue streams, and “it hallucinates a lot”
  • A contrarian investment choice, two serial founders, employee disengagement, productivity, and invisible frictions

ㅤ

🔗 Resources Mentioned

  • Open AI
  • Gemini
  • Figma
  • Nvidia
  • LinkedIn
  • lovable

]]>58ba6426-5961-4fd0-b06f-e72b77150279Thu, 18 Dec 2025 04:00:00 +020033:46falsefull88How are Mid-Market and Enterprise Companies using AI | Ep. 6How are Mid-Market and Enterprise Companies using AI | Ep. 6A wave of excitement and activity around AI is hitting management consulting, and many leaders are asking the same questions. What is this AI thing? What do I do with it? And what does it really mean for my business? In this episode, host Daniel Kazani talks with Matthew Murphy, a partner at AMEND Consulting in Cincinnati, about how technology and AI now sit at the forefront of almost every new client conversation.

ㅤ

They explore why AI discussions often uncover missing fundamentals in process, technology, and data management, and why the biggest wins today show up in highly manual, tedious, document-intensive, and task-intensive work. From order entry and AR and AP automation to image recognition in retail stores and AI-supported assessments in consulting, they share concrete examples of AI agents working in a human-in-the-loop way. The conversation then moves to augmentation versus role replacement, departments that cannot fill roles, the new workforce entering the market, and how AI is reshaping the core business model of professional services and long-term client relationships.

ㅤ

👤 Guest Bio

Matthew Murphy is a partner at AMEND Consulting, a management consulting firm based in Cincinnati. He has spent over a decade driving transformation for mid-market and large enterprises, working across people, process, and metrics. He also ran and led a software business at AMEND for four years. Now he helps clients in the age of AI and automation, helping them leverage technology and grow smarter across operations, analytics, and automation.

ㅤ

📌 What We Cover

  • Why technology and AI are at the forefront of almost every conversation with new or existing clients, from enthusiasm to the fear of falling behind
  • How AI projects often start with automating a particular process, but lead to missing foundations in process, technology, and data management
  • Real-world automation in order entry, where customer service and inside sales teams spend most of the day keying in orders, and AI agents can now do a bulk of the work
  • Accounting and finance use cases like AR and AP automation, financial close, and reconciliation, and how broad workflow and AI automation tools can be applied across many process areas
  • Image recognition in retail, where store audits used to mean hundreds of pictures per store and manual review, and an AI agent now sifts through images with a high degree of accuracy and improves the quality of life for the team
  • How AMEND assessments have changed from heavy note-taking and weeks of brute force compilation to AI agents that process notes, meeting recordings, and GoPro footage and produce first-pass gap and theme compilations in hours or days
  • AI as a way to capture and expose tribal knowledge from hundreds or thousands of work instructions, helping a newer workforce get up to speed more quickly, instead of hunting through file repositories or an LMS
  • The reality of augmentation versus role replacement, from overworked teams doing two people’s worth of work to departments that choose not to fill open roles because AI enables the same team to do more
  • Why AMEND is raising the watermark for technical competency for new hires, partnering with universities, and still investing in junior talent even as some larger firms cut hiring targets
  • How AI challenges the traditional dollars for hours model in professional services, pushes firms toward value-based pricing, and increases the importance of being a trusted advisor focused on long-term relationships and business impact

ㅤ

🔗 Resources Mentioned

  • AMEND Consulting
  • RPA tools for process automation
  • AI agents for workflow and document-intensive tasks
  • LMS and file repositories as traditional knowledge bases
  • GoPros for process shadowing and manufacturing floor observations
  • Nvidia is an example of developers experimenting with new tools every day

]]>A wave of excitement and activity around AI is hitting management consulting, and many leaders are asking the same questions. What is this AI thing? What do I do with it? And what does it really mean for my business? In this episode, host Daniel Kazani talks with Matthew Murphy, a partner at AMEND Consulting in Cincinnati, about how technology and AI now sit at the forefront of almost every new client conversation.

ㅤ

They explore why AI discussions often uncover missing fundamentals in process, technology, and data management, and why the biggest wins today show up in highly manual, tedious, document-intensive, and task-intensive work. From order entry and AR and AP automation to image recognition in retail stores and AI-supported assessments in consulting, they share concrete examples of AI agents working in a human-in-the-loop way. The conversation then moves to augmentation versus role replacement, departments that cannot fill roles, the new workforce entering the market, and how AI is reshaping the core business model of professional services and long-term client relationships.

ㅤ

👤 Guest Bio

Matthew Murphy is a partner at AMEND Consulting, a management consulting firm based in Cincinnati. He has spent over a decade driving transformation for mid-market and large enterprises, working across people, process, and metrics. He also ran and led a software business at AMEND for four years. Now he helps clients in the age of AI and automation, helping them leverage technology and grow smarter across operations, analytics, and automation.

ㅤ

📌 What We Cover

  • Why technology and AI are at the forefront of almost every conversation with new or existing clients, from enthusiasm to the fear of falling behind
  • How AI projects often start with automating a particular process, but lead to missing foundations in process, technology, and data management
  • Real-world automation in order entry, where customer service and inside sales teams spend most of the day keying in orders, and AI agents can now do a bulk of the work
  • Accounting and finance use cases like AR and AP automation, financial close, and reconciliation, and how broad workflow and AI automation tools can be applied across many process areas
  • Image recognition in retail, where store audits used to mean hundreds of pictures per store and manual review, and an AI agent now sifts through images with a high degree of accuracy and improves the quality of life for the team
  • How AMEND assessments have changed from heavy note-taking and weeks of brute force compilation to AI agents that process notes, meeting recordings, and GoPro footage and produce first-pass gap and theme compilations in hours or days
  • AI as a way to capture and expose tribal knowledge from hundreds or thousands of work instructions, helping a newer workforce get up to speed more quickly, instead of hunting through file repositories or an LMS
  • The reality of augmentation versus role replacement, from overworked teams doing two people’s worth of work to departments that choose not to fill open roles because AI enables the same team to do more
  • Why AMEND is raising the watermark for technical competency for new hires, partnering with universities, and still investing in junior talent even as some larger firms cut hiring targets
  • How AI challenges the traditional dollars for hours model in professional services, pushes firms toward value-based pricing, and increases the importance of being a trusted advisor focused on long-term relationships and business impact

ㅤ

🔗 Resources Mentioned

  • AMEND Consulting
  • RPA tools for process automation
  • AI agents for workflow and document-intensive tasks
  • LMS and file repositories as traditional knowledge bases
  • GoPros for process shadowing and manufacturing floor observations
  • Nvidia is an example of developers experimenting with new tools every day

]]>9435b93b-34bc-4d73-a512-257619e366c0Thu, 11 Dec 2025 04:00:00 +020033:26falsefull66How Softup Technologies uses AI | Ep. 5How Softup Technologies uses AI | Ep. 5AI is changing how software is built and how companies run day-to-day at Softup. Host Daniel Kazani sits down with co-founder and CTO Kristi Kristo to walk through concrete examples of how AI touches almost every part of the business. They talk about perfect developer profiles, automated estimations, and an internal Weekly Digest that helps decision makers spot opportunities and problems faster in a distributed team.

ㅤ

On the technical side, they share how Cursor and AI agents act as a co-developer, how some features ship with almost zero manual code, and why quality can even improve when context is structured well for the LLM. Kristi explains his bold goal of reaching zero manual code, why coding is only one part of software engineering, and how the role of the developer is moving closer to product, business context, and orchestration. They close with what AI transformation looks like for founders and SMEs today and why AI Labs at Softup experiments with the latest tools so customers can benefit from real, applied AI.

ㅤ

👤 Guest Bio

Kristi Kristo is the co-founder and CTO of Softup and Managing Director at Softup Technologies GmbH. His focus is on AI Engineers, AI Agents, and MVPs that scale as AI transforms how companies build and deliver products. Kristi describes his work with a simple line: AI Agents will transform every business - including yours. We build the systems that make it happen. At Softup Technologies, he leads teams that use advanced AI tools and workflows to deliver software faster while keeping a strong focus on real business problems.

ㅤ

📌 What We Cover

  • How Softup uses AI on the business side to create perfect developer profiles, cut grammar mistakes, and avoid missing relevant experience when sending CVs to customers.
  • Why automated estimations with AI remove 80 to 90 percent of the brain capacity and effort from the team, and how this turns two or three estimations per day into a streamlined process founders can rely on.
  • The Weekly Digest automation workflow that collects what everyone did, what they will do next, and helps decision makers spot opportunities, problems, and availability across a distributed team.
  • How developers at Softup use Cursor as an AI co developer, spin up multiple AI agents in parallel, and sometimes ship features and modules while writing almost zero manual code.
  • Why Kristi believes writing manual code will go close to zero, why coding is only 30 to 70 percent of a developer’s time, and how orchestration, architecture, testing, and understanding business context become even more important.
  • How the day to day of a developer has changed since the LLM world, with ChatGPT, Cursor, codex, sonnet, cloud code, and Code XCLI always open as part of the normal workflow.
  • Why Kristi thinks newcomers may not always need to know code deeply if AI agents for testing, security, and cloud give a thumbs up, and why shipping and orchestration skills matter more over time.
  • What Kristi and Daniel Kazani see in the market: a two year lag between the first OpenAI release and real pressure from CEOs, boards, and investors to invest in AI across customer support, finance, sales, guest experience, PropTech, FinTech, and more.
  • How Kristi splits AI work into automation workflows and AI agents, why processes need to change in an AI first world, and why AI transformation in marketing, sales, operations, HR, engineering, and customer support is a long term journey rather than a quick project.
  • The idea behind AI Labs at Softup, from building experiments with MCP and local deployment to testing OpenAI commerce and ChatGPT apps, including the first ChatGPT app for the hospitality industry and use cases like helping FinTech merchants sell inside ChatGPT.

]]>AI is changing how software is built and how companies run day-to-day at Softup. Host Daniel Kazani sits down with co-founder and CTO Kristi Kristo to walk through concrete examples of how AI touches almost every part of the business. They talk about perfect developer profiles, automated estimations, and an internal Weekly Digest that helps decision makers spot opportunities and problems faster in a distributed team.

ㅤ

On the technical side, they share how Cursor and AI agents act as a co-developer, how some features ship with almost zero manual code, and why quality can even improve when context is structured well for the LLM. Kristi explains his bold goal of reaching zero manual code, why coding is only one part of software engineering, and how the role of the developer is moving closer to product, business context, and orchestration. They close with what AI transformation looks like for founders and SMEs today and why AI Labs at Softup experiments with the latest tools so customers can benefit from real, applied AI.

ㅤ

👤 Guest Bio

Kristi Kristo is the co-founder and CTO of Softup and Managing Director at Softup Technologies GmbH. His focus is on AI Engineers, AI Agents, and MVPs that scale as AI transforms how companies build and deliver products. Kristi describes his work with a simple line: AI Agents will transform every business - including yours. We build the systems that make it happen. At Softup Technologies, he leads teams that use advanced AI tools and workflows to deliver software faster while keeping a strong focus on real business problems.

ㅤ

📌 What We Cover

  • How Softup uses AI on the business side to create perfect developer profiles, cut grammar mistakes, and avoid missing relevant experience when sending CVs to customers.
  • Why automated estimations with AI remove 80 to 90 percent of the brain capacity and effort from the team, and how this turns two or three estimations per day into a streamlined process founders can rely on.
  • The Weekly Digest automation workflow that collects what everyone did, what they will do next, and helps decision makers spot opportunities, problems, and availability across a distributed team.
  • How developers at Softup use Cursor as an AI co developer, spin up multiple AI agents in parallel, and sometimes ship features and modules while writing almost zero manual code.
  • Why Kristi believes writing manual code will go close to zero, why coding is only 30 to 70 percent of a developer’s time, and how orchestration, architecture, testing, and understanding business context become even more important.
  • How the day to day of a developer has changed since the LLM world, with ChatGPT, Cursor, codex, sonnet, cloud code, and Code XCLI always open as part of the normal workflow.
  • Why Kristi thinks newcomers may not always need to know code deeply if AI agents for testing, security, and cloud give a thumbs up, and why shipping and orchestration skills matter more over time.
  • What Kristi and Daniel Kazani see in the market: a two year lag between the first OpenAI release and real pressure from CEOs, boards, and investors to invest in AI across customer support, finance, sales, guest experience, PropTech, FinTech, and more.
  • How Kristi splits AI work into automation workflows and AI agents, why processes need to change in an AI first world, and why AI transformation in marketing, sales, operations, HR, engineering, and customer support is a long term journey rather than a quick project.
  • The idea behind AI Labs at Softup, from building experiments with MCP and local deployment to testing OpenAI commerce and ChatGPT apps, including the first ChatGPT app for the hospitality industry and use cases like helping FinTech merchants sell inside ChatGPT.

]]>61debd4c-a29a-496b-be1c-c6a96ea0c09cThu, 04 Dec 2025 04:00:00 +020031:51falsefull55How to Build with AI: Expert Advice from an NVIDIA Architect | Ep. 4How to Build with AI: Expert Advice from an NVIDIA Architect | Ep. 4AI impresses Xhoni Shollaj almost every day, from protein folding and the idea of a virtual cell to autonomous driving and robotics. In this conversation, host Daniel Kazani follows Xhoni’s journey from business studies and data roles at PwC and EY in Albania and Bulgaria to a Master of Science at the National University of Singapore and his current work as a senior AI solutions architect at NVIDIA.

ㅤ

The discussion moves from early natural language processing and computer vision projects, document reduction and summarization tools, to building and maintaining large scale language model applications. Xhoni shares how staying in touch with GitHub trending projects, arXiv style paper feeds, and the open source community shaped his path. Founders, CTOs and decision makers hear concrete talk on AI experiments versus production systems, scalability, security, hallucinations, golden datasets, vibe coding, tools like Cursor, ChatGPT and Gemini, and why contributing to open source with teams at NVIDIA, Google and others can be a powerful way to stand out.

ㅤ

👤 Guest Bio

Xhoni Shollaj is a Senior AI Solutions Engineer at NVIDIA, specializing in developing and deploying large language model architectures. He started with business, moved into computer science, and began his AI journey in research and development teams at PwC and EY in Albania and Bulgaria, building machine learning based applications and automation solutions. Xhoni then joined the National University of Singapore, working on internal automation tools and research support for patents and papers, before moving into his current role at NVIDIA in Asia.

ㅤ

📌 What We Cover

  • How Xhoni moved from business studies and data roles at PwC and EY in the Balkans to a Master of Science at the National University of Singapore and into AI solutions work at NVIDIA.
  • Why he chose Singapore for its faculty, research direction and blend of cultures, and how being location agnostic helped him follow the strongest data science programs.
  • The habits he sees as most useful for people who want to succeed in AI, including staying in touch with the latest technologies, GitHub trending, arxiv style feeds, open source projects and strong news sources.
  • Areas where AI feels most disruptive today, from protein folding and the path toward a virtual cell for drug discovery and disease treatment to space exploration, SpaceX and ideas like space data centers.
  • How to distinguish an AI experiment or small POC from a production system, with concrete points on autoscaling, multi cloud and multi zone backups, security pipelines, identity and access management, encryption, multilingual behavior, hallucination tracking and observability.
  • Approaches to accuracy and hallucinations, including well built RAG pipelines, choosing the right benchmarks and metrics, literature reviews, leaderboards, human in the loop evaluation and tracing problems back to data sources or model behavior.
  • The reality of vibe coding for non technical founders, why it is a net positive and equalizer, and how to combine fast POCs with later help from experienced engineers on scaling, security and edge scenarios.
  • Tools and workflows Xhoni personally uses, such as Cursor with Claude 4.5 Sonnet, ChatGPT and Gemini for brainstorming, creating plans, testing ideas and even asking models to make fun of an idea to expose weak points.
  • The most common challenge companies face when integrating AI into their business, why a golden dataset and clean, validated, well reviewed data can make or break a project, and how synthetic data and diverse scenarios help test chat bot performance.
  • Why AI systems in sectors like hospitality need clean booking and address data, strong formatting, and synthetic test scenarios with mixed languages, toxic and non toxic inputs, and special characters to evaluate real world behavior.
  • Thoughts on interpretability and mechanistic interpretability, the black box nature of transformer layers today, and why being able to trace reasoning in sensitive areas like healthcare or drug simulation matters.
  • How different large models like OpenAI, Claude, Gemini, DeepSeek and NVIDIA models feel to users because of data sources such as Reddit and prompt level instructions, leading to different levels of confidence, politeness and directness.
  • What a billion plus dollar AI data center collaboration between NVIDIA and Deutsche Telekom in Germany might mean for telecom, communication, research and startup ecosystems in Munich, Germany and across Europe.
  • Final advice for students, graduates and people struggling in the current environment, including a clear call to contribute in their free time to open source NVIDIA and Google projects, build relationships, learn in public and stand out through real work.

ㅤ

🔗 Resources Mentioned

  • NVIDIA
  • PwC
  • EY
  • National University of Singapore
  • AWS
  • Azure
  • Deutsche Telekom
  • SpaceX
  • GitHub trending
  • Arxiv style feeds and “alpha Arxiv sanity”
  • Cursor
  • Claude 4.5 Sonnet
  • ChatGPT
  • Gemini
  • DeepSeek
  • Tinker
  • OpenAI
  • Anthropic
  • Google
  • Reddit

]]>AI impresses Xhoni Shollaj almost every day, from protein folding and the idea of a virtual cell to autonomous driving and robotics. In this conversation, host Daniel Kazani follows Xhoni’s journey from business studies and data roles at PwC and EY in Albania and Bulgaria to a Master of Science at the National University of Singapore and his current work as a senior AI solutions architect at NVIDIA.

ㅤ

The discussion moves from early natural language processing and computer vision projects, document reduction and summarization tools, to building and maintaining large scale language model applications. Xhoni shares how staying in touch with GitHub trending projects, arXiv style paper feeds, and the open source community shaped his path. Founders, CTOs and decision makers hear concrete talk on AI experiments versus production systems, scalability, security, hallucinations, golden datasets, vibe coding, tools like Cursor, ChatGPT and Gemini, and why contributing to open source with teams at NVIDIA, Google and others can be a powerful way to stand out.

ㅤ

👤 Guest Bio

Xhoni Shollaj is a Senior AI Solutions Engineer at NVIDIA, specializing in developing and deploying large language model architectures. He started with business, moved into computer science, and began his AI journey in research and development teams at PwC and EY in Albania and Bulgaria, building machine learning based applications and automation solutions. Xhoni then joined the National University of Singapore, working on internal automation tools and research support for patents and papers, before moving into his current role at NVIDIA in Asia.

ㅤ

📌 What We Cover

  • How Xhoni moved from business studies and data roles at PwC and EY in the Balkans to a Master of Science at the National University of Singapore and into AI solutions work at NVIDIA.
  • Why he chose Singapore for its faculty, research direction and blend of cultures, and how being location agnostic helped him follow the strongest data science programs.
  • The habits he sees as most useful for people who want to succeed in AI, including staying in touch with the latest technologies, GitHub trending, arxiv style feeds, open source projects and strong news sources.
  • Areas where AI feels most disruptive today, from protein folding and the path toward a virtual cell for drug discovery and disease treatment to space exploration, SpaceX and ideas like space data centers.
  • How to distinguish an AI experiment or small POC from a production system, with concrete points on autoscaling, multi cloud and multi zone backups, security pipelines, identity and access management, encryption, multilingual behavior, hallucination tracking and observability.
  • Approaches to accuracy and hallucinations, including well built RAG pipelines, choosing the right benchmarks and metrics, literature reviews, leaderboards, human in the loop evaluation and tracing problems back to data sources or model behavior.
  • The reality of vibe coding for non technical founders, why it is a net positive and equalizer, and how to combine fast POCs with later help from experienced engineers on scaling, security and edge scenarios.
  • Tools and workflows Xhoni personally uses, such as Cursor with Claude 4.5 Sonnet, ChatGPT and Gemini for brainstorming, creating plans, testing ideas and even asking models to make fun of an idea to expose weak points.
  • The most common challenge companies face when integrating AI into their business, why a golden dataset and clean, validated, well reviewed data can make or break a project, and how synthetic data and diverse scenarios help test chat bot performance.
  • Why AI systems in sectors like hospitality need clean booking and address data, strong formatting, and synthetic test scenarios with mixed languages, toxic and non toxic inputs, and special characters to evaluate real world behavior.
  • Thoughts on interpretability and mechanistic interpretability, the black box nature of transformer layers today, and why being able to trace reasoning in sensitive areas like healthcare or drug simulation matters.
  • How different large models like OpenAI, Claude, Gemini, DeepSeek and NVIDIA models feel to users because of data sources such as Reddit and prompt level instructions, leading to different levels of confidence, politeness and directness.
  • What a billion plus dollar AI data center collaboration between NVIDIA and Deutsche Telekom in Germany might mean for telecom, communication, research and startup ecosystems in Munich, Germany and across Europe.
  • Final advice for students, graduates and people struggling in the current environment, including a clear call to contribute in their free time to open source NVIDIA and Google projects, build relationships, learn in public and stand out through real work.

ㅤ

🔗 Resources Mentioned

  • NVIDIA
  • PwC
  • EY
  • National University of Singapore
  • AWS
  • Azure
  • Deutsche Telekom
  • SpaceX
  • GitHub trending
  • Arxiv style feeds and “alpha Arxiv sanity”
  • Cursor
  • Claude 4.5 Sonnet
  • ChatGPT
  • Gemini
  • DeepSeek
  • Tinker
  • OpenAI
  • Anthropic
  • Google
  • Reddit

]]>560cc3dc-980e-4fca-8b3f-9855a1a09cafThu, 27 Nov 2025 04:00:00 +020039:22falsefull44What’s it really like working with a dev agency? | Ep. 3What’s it really like working with a dev agency? | Ep. 3Hiring a dev team can feel risky when you are not sure who you are working with, how stable the team is, or what happens after launch. Producer Joseph Lewin sits down with co-founder Daniel Kazani to address real questions founders ask about working with Softup. The conversation walks through concrete onboarding timelines, how senior developers reach full productivity within weeks, and why long-term relationships with the same developers matter for serious products and AI solutions. Listeners hear how flexible contracts, clear IP terms, and predictable maintenance costs protect founders in uncertain moments. Daniel also shares a story of a non-technical founder who lost a CTO mid-journey and used the Softup team to keep the product, pilots, and funding conversations moving without disruption.

ㅤ

📌 What We Cover

  • How Softup sets clear expectations on onboarding, from a 4 to 6 week standard to rare fast-start cases within days
  • Why senior, high-agency developers with domain experience reach full productivity in 2 to 3 weeks
  • How two-week sprints, focused check-ins, and familiar tools like Atlassian, monday.com, Asana, Trello, GitHub, and Bitbucket keep collaboration transparent
  • What long-term stability looks like, including 33-month average client relationships and 22-month average on the same project without developer switches
  • How flexible notice periods, optional longer commitments, and straightforward IP ownership terms reduce stress for founders managing cash and risk
  • Practical paths after go-live: scaling the same team, pausing with low maintenance costs, or taking everything in-house with full handover and documentation
  • How remote and nearshore teams became normal after COVID, and why time zone alignment and trust matter more than office location
  • A real founder story where the Softup team stepped in as the technical arm after a co-founder breakup and kept enterprise pilots and fundraising on track

ㅤ

🔗 Resources Mentioned

  • Softup
  • Atlassian
  • monday.com
  • Asana
  • Trello
  • GitHub
  • Bitbucket
  • Amazon
  • Google
  • OpenAI
  • Microsoft

]]>Hiring a dev team can feel risky when you are not sure who you are working with, how stable the team is, or what happens after launch. Producer Joseph Lewin sits down with co-founder Daniel Kazani to address real questions founders ask about working with Softup. The conversation walks through concrete onboarding timelines, how senior developers reach full productivity within weeks, and why long-term relationships with the same developers matter for serious products and AI solutions. Listeners hear how flexible contracts, clear IP terms, and predictable maintenance costs protect founders in uncertain moments. Daniel also shares a story of a non-technical founder who lost a CTO mid-journey and used the Softup team to keep the product, pilots, and funding conversations moving without disruption.

ㅤ

📌 What We Cover

  • How Softup sets clear expectations on onboarding, from a 4 to 6 week standard to rare fast-start cases within days
  • Why senior, high-agency developers with domain experience reach full productivity in 2 to 3 weeks
  • How two-week sprints, focused check-ins, and familiar tools like Atlassian, monday.com, Asana, Trello, GitHub, and Bitbucket keep collaboration transparent
  • What long-term stability looks like, including 33-month average client relationships and 22-month average on the same project without developer switches
  • How flexible notice periods, optional longer commitments, and straightforward IP ownership terms reduce stress for founders managing cash and risk
  • Practical paths after go-live: scaling the same team, pausing with low maintenance costs, or taking everything in-house with full handover and documentation
  • How remote and nearshore teams became normal after COVID, and why time zone alignment and trust matter more than office location
  • A real founder story where the Softup team stepped in as the technical arm after a co-founder breakup and kept enterprise pilots and fundraising on track

ㅤ

🔗 Resources Mentioned

  • Softup
  • Atlassian
  • monday.com
  • Asana
  • Trello
  • GitHub
  • Bitbucket
  • Amazon
  • Google
  • OpenAI
  • Microsoft

]]>c2bea2be-d120-4a84-8328-b0c5bac804d0Thu, 20 Nov 2025 04:00:00 +020025:41falsefull33How Softup Delivers Top Developers Without the Headache | Ep. 2How Softup Delivers Top Developers Without the Headache | Ep. 2Top founders face a real tension between moving fast with AI and carrying the weight of building an in-house team. In this conversation, host Daniel Kazani walks through how the Softup model equips startups with top developers who use AI tools daily, cut delivery timelines, and remove hiring headaches. The discussion explores AI as a practical accelerator in software development, from tools like Lovable and Copilot to real examples of building internal apps through prompting instead of traditional coding. Joseph and Daniel unpack why flexible augmented teams protect runway, how domain expertise in areas like fintech and proptech compounds over years, and why communication, time zone alignment, and instant access to specialists matter more than ever. A clear throughline: maximum speed, flexibility, and cash preservation without sacrificing quality.

ㅤ

📌 What We Cover

  • How AI tools like Copilot and Lovable make development 30 to 50 percent faster and change what a “developer” day-to-day role looks like.
  • Why founders who ignore AI supported workflows fall behind peers who use prompting, agents, and automation for real projects.
  • The launch of Softup AI Labs as a space to test challenging use cases, build with tools like n8n, Make, and Zapier, and aim for minimal manual code.
  • The real cost of in-house hiring: defining roles, writing job descriptions, sourcing candidates, screening 50 to 100 applicants, and investing hours per interview.
  • Why Softup hires for both technical depth and strong communication, filtering for red flags, soft skills, and client facing confidence.
  • How long term developers build domain expertise in areas like fintech and proptech and why that combination is hard to replicate internally.
  • The value of flexible, augmented teams that can scale up for critical phases like launch, QA, and security checks, then scale down to protect runway.
  • How time zone proximity to Europe and the US, direct access to developers on Slack, and on site collaboration create smoother, faster delivery.
  • Price and productivity dynamics where high quality nearshore teams plus AI can outperform more expensive or slower alternatives.

ㅤ

🔗 Resources Mentioned

  • Softup
  • Lovable
  • Copilot
  • n8n
  • Make
  • Zapier
  • AWS
  • Slack
  • LinkedIn
  • Will Smith “eating spaghetti” AI video reference
  • Oracle
  • Microsoft
  • Chamath Palihapitiya

]]>Top founders face a real tension between moving fast with AI and carrying the weight of building an in-house team. In this conversation, host Daniel Kazani walks through how the Softup model equips startups with top developers who use AI tools daily, cut delivery timelines, and remove hiring headaches. The discussion explores AI as a practical accelerator in software development, from tools like Lovable and Copilot to real examples of building internal apps through prompting instead of traditional coding. Joseph and Daniel unpack why flexible augmented teams protect runway, how domain expertise in areas like fintech and proptech compounds over years, and why communication, time zone alignment, and instant access to specialists matter more than ever. A clear throughline: maximum speed, flexibility, and cash preservation without sacrificing quality.

ㅤ

📌 What We Cover

  • How AI tools like Copilot and Lovable make development 30 to 50 percent faster and change what a “developer” day-to-day role looks like.
  • Why founders who ignore AI supported workflows fall behind peers who use prompting, agents, and automation for real projects.
  • The launch of Softup AI Labs as a space to test challenging use cases, build with tools like n8n, Make, and Zapier, and aim for minimal manual code.
  • The real cost of in-house hiring: defining roles, writing job descriptions, sourcing candidates, screening 50 to 100 applicants, and investing hours per interview.
  • Why Softup hires for both technical depth and strong communication, filtering for red flags, soft skills, and client facing confidence.
  • How long term developers build domain expertise in areas like fintech and proptech and why that combination is hard to replicate internally.
  • The value of flexible, augmented teams that can scale up for critical phases like launch, QA, and security checks, then scale down to protect runway.
  • How time zone proximity to Europe and the US, direct access to developers on Slack, and on site collaboration create smoother, faster delivery.
  • Price and productivity dynamics where high quality nearshore teams plus AI can outperform more expensive or slower alternatives.

ㅤ

🔗 Resources Mentioned

  • Softup
  • Lovable
  • Copilot
  • n8n
  • Make
  • Zapier
  • AWS
  • Slack
  • LinkedIn
  • Will Smith “eating spaghetti” AI video reference
  • Oracle
  • Microsoft
  • Chamath Palihapitiya

]]>3258e659-4f90-4d35-9d15-9ce2fc919cb0Thu, 13 Nov 2025 04:00:00 +020037:42falsefull22Hire Developers Without Burning Through Your Runway | Ep. 1Hire Developers Without Burning Through Your Runway | Ep. 1Running out of cash before software reaches viability is one of the biggest pitfalls. Producer Joseph Lewin introduces host Daniel Kazani as they dig into how to develop software without running out of cash. The conversation compares three paths for early traction and scale up: hire in-house, work with freelancers, or partner with an agency. Daniel explains commitment, premiums, and flexibility, including pause and resume development, start in two to four weeks, and build for three to six months. The discussion covers waiting for a CTO, the sweet spot of two to three months, and why CTO as a service can cover investor meetings, technical roadmap, scalability, and cloud. They highlight speed, screening and onboarding realities, notice periods, equity, and how priorities shift with new customers, funding, or AI. The episode calls out why maximum flexibility and maximum speed protect cash flow when things change.

ㅤ

📌 What We Cover

  • The tradeoffs between in-house, freelancers, and an agency for early traction and scale-up
  • Commitment windows like six to twelve months vs pause and resume development for three to six months
  • Why waiting more than two to three months for a CTO can let the market shift
  • CTO as a service for investor meetings, technical roadmap, scalability, cloud, DevOps, and cybersecurity touchpoints
  • The real hiring timeline: defining the role, screening, technical vetting, offers, equity, onboarding, and notice periods
  • Broad expertise vs specific expertise, and shifting priorities across backend, QA, DevOps, databases, cloud, and security
  • Risks of hiring students for core product work, including exam periods and speed
  • Reputation concerns with outsourcing, buying as much as you can afford, and having one neck to choke

ㅤ

🔗 Resources Mentioned

  • LinkedIn
  • Naval Ravikant
  • AWS
  • Meta
  • San Francisco

]]>Running out of cash before software reaches viability is one of the biggest pitfalls. Producer Joseph Lewin introduces host Daniel Kazani as they dig into how to develop software without running out of cash. The conversation compares three paths for early traction and scale up: hire in-house, work with freelancers, or partner with an agency. Daniel explains commitment, premiums, and flexibility, including pause and resume development, start in two to four weeks, and build for three to six months. The discussion covers waiting for a CTO, the sweet spot of two to three months, and why CTO as a service can cover investor meetings, technical roadmap, scalability, and cloud. They highlight speed, screening and onboarding realities, notice periods, equity, and how priorities shift with new customers, funding, or AI. The episode calls out why maximum flexibility and maximum speed protect cash flow when things change.

ㅤ

📌 What We Cover

  • The tradeoffs between in-house, freelancers, and an agency for early traction and scale-up
  • Commitment windows like six to twelve months vs pause and resume development for three to six months
  • Why waiting more than two to three months for a CTO can let the market shift
  • CTO as a service for investor meetings, technical roadmap, scalability, cloud, DevOps, and cybersecurity touchpoints
  • The real hiring timeline: defining the role, screening, technical vetting, offers, equity, onboarding, and notice periods
  • Broad expertise vs specific expertise, and shifting priorities across backend, QA, DevOps, databases, cloud, and security
  • Risks of hiring students for core product work, including exam periods and speed
  • Reputation concerns with outsourcing, buying as much as you can afford, and having one neck to choke

ㅤ

🔗 Resources Mentioned

  • LinkedIn
  • Naval Ravikant
  • AWS
  • Meta
  • San Francisco

]]>9eebf145-407f-4694-b974-f00b66286833Thu, 06 Nov 2025 04:00:00 +020032:05falsefull11Welcome to Disrupt or DefendWelcome to Disrupt or DefendFounders move fast, but in the age of AI, it’s not just about speed; it’s about making the right choices. In this trailer, host Daniel Kazani, co-founder of Softup Technologies, introduces Disrupt or Defend: a show about the hard calls every tech leader faces. From AI agents to low-code innovation, you’ll hear from founders and experts building the future of software, one bold decision at a time.

]]>Founders move fast, but in the age of AI, it’s not just about speed; it’s about making the right choices. In this trailer, host Daniel Kazani, co-founder of Softup Technologies, introduces Disrupt or Defend: a show about the hard calls every tech leader faces. From AI agents to low-code innovation, you’ll hear from founders and experts building the future of software, one bold decision at a time.

]]>7c20ce59-6672-4b52-9a2b-d15bbe6b3494Thu, 30 Oct 2025 03:00:00 +020000:55falsetrailer