Disrupt or Defend

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

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.ㅤJonathan 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.ㅤGuest BioDr. 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.ㅤWhat We CoverThe difference between being data-rich and information-poor in manufacturingWhy traditional deep learning often fails in factory settings due to the need for massive datasetsHow the "small data" approach works: finding optimal machine settings with minimal experimentsReal-world wins: Reducing cycle times by 50% in machining and saving raw materials in bottle productionThe Coca-Cola case study: How a tiny weight reduction per bottle resulted in massive material savingsThe "Copilot" philosophy: Why AI should augment the operator's intuition rather than replace itOvercoming the "worker gap" by making expert-level machine operation accessible to newer employeesWhy is failing during the testing phase necessary to find the true limits of a machineㅤResources MentionedGaussML (Official Website)Optimyzer (Product)Dr. Jonathan Spitz (LinkedIn)Daniel Kazani (LinkedIn)Softup TechnologiesBosch (Company)TRUMPF (Company)Coca-Cola (Company)

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.ㅤThey 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 BioDr. 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 CoverThe 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.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.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.Red Flag Reports: Moving beyond just data extraction to "risk alerts," such as spotting a break clause that allows a tenant to leave early.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.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.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 MentionedAlphaPrompt (Guest Company)Moltbook (AI Agent Social Network mentioned by Nino)

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.ㅤGuest BioMalte 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.ㅤWhat We CoverWhy click-based tracking fails to capture the user journey inside ChatGPT and Perplexity.The specific role of "grounding sources" such as Reddit, YouTube, and LinkedIn in AI responses.How Peec AI simulates user behavior to track brand mentions and sentiment.The shift from static data dashboards to on-demand AI chat interfaces.Why is average marketing becoming free while top-tier marketers become exponentially more effective?The challenge of finding product managers with strong product taste in Europe.ㅤResources MentionedPeec AIMalte Landwehr (LinkedIn)Daniel Kazani (LinkedIn)ChatGPTPerplexityGoogle GeminiGranolaHubSpot

Learn more about facial vital sign detection: shen.ai & caire.aiㅤHealthcare 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.ㅤGuest BioDr. 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.ㅤWhat We CoverWhy the lack of legacy digital infrastructure in healthcare might actually accelerate AI adoption.The phenomenon of "Shadow AI" and why doctors are using consumer tools despite strict hospital regulations.How administrative AI is moving faster than clinical diagnostic tools due to lower regulatory barriers.The potential for "facial parameters" in which video can detect heart rate, blood pressure, and oxygen saturation.Using vocal biomarkers to identify conditions like heart failure by analyzing fluid buildup in the lungs.How typing patterns on a keyboard can serve as early indicators for depression.Why specialized "AI Therapist" startups have struggled to compete with general Large Language Models.The four key moats for healthtech startups: data advantages, network effects, deep customer service, and brand trust.ㅤResources MentionedHeal CapitalOpenAI (ChatGPT)Anthropic (Claude)WhoopCaire (Healthtech startup)Scale AI

AI Trends in Germany - Presentation (PDF) — Follow along with the data discussed in this episodeㅤGermany 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.ㅤStephan 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 BioStephan 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 CoverThe estimated 60 billion euro market volume for AI services in Germany in 2025.Why 88% of German companies implementing AI start with customer contact and chatbots.The massive gap in private AI investment: 1.8 billion euros in Germany versus 62.5 billion in the US.How Germany compares globally in terms of infrastructure, with a notable lack of data centers.The talent crisis: Moving from 45,000 specialists today to a need for 180,000 by 2032.Why Softup and similar partners are becoming essential for companies unable to find local talent.Specific manufacturing use cases for AI: From predictive maintenance at Siemens to quality assurance at BMW.The regulatory hurdles and slow government strategies are affecting European competitiveness.ㅤ🔗 Resources MentionedDeutscher Outsourcing Verband e.V. (German Outsourcing Association)Global AI Index 2024Canva (Marketing tool)VolkswagenBMW GroupSiemensBoschTrumpf

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.ㅤMiroslav 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.ㅤGuest BioMiroslav 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.ㅤWhat We CoverThe three core pillars of industrial impact are software-defined automation, robotics, and AI.Why the "move fast and break things" mentality fails in manufacturing, where safety is critical.How virtualization allows agile development on machines with 30-year lifecycles.The emergence of "lights out" factories and autonomous closed-loop systems.Why ROI in industry is defined by speed and waste reduction rather than quality improvements.The "tourist" mistake European founders make when expanding to the US.Why industrial startups often find better success in Detroit or Milwaukee than in Silicon Valley.Using AI in venture capital to validate physics and research trends rather than make deal decisions.ㅤResources MentionedMomentaGem7 (Market entry service)Rockwell AutomationFleet SpaceGrokMicrosoft

This episode was recorded on Dec 10, 2025.ㅤAutomation 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.ㅤThe 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 BioPascal 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 CoverOrange Business Digital Services and “digital transformation,” including cloud transformation and working with hyperscalers like Azure and AmazonA sovereign cloud solution with regulatory requirements and environments operated in Europe by European employees“Agentic AI” as proactive systems that understand goals, breaks them into steps, executes across multiple systems, evaluates output, and learn continuouslyA customer success AI agent: scanning multichannel messages, classifying and prioritizing issues, pulling contracts, SLAs, documentation, and ticket history, then triggering actions across systemsImpact discussed: resolution time brought down from “two, three days” to “15, 30 minutes,” and “60, 65, 70%” reduction in repetitive workAI adoption as a transformation, “mindset first,” and the bottleneck being “permission” rather than technologyClearly defined roles in human and AI collaboration, and AI as “a new colleague.”Moving from pilots to scale: five questions, one high-impact breakthrough, and scaling aggressively with training and changeㅤ🔗 Resources MentionedOrange BusinessAzureAmazonChatGPTGeminiSalesforceNvidiaMicrosoftIBMSatya Nadella

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.ㅤCreation, 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 BioDajana 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 CoverAI inside the editor as a proactive teammate, intelligent code completion, prompts for snippets, and boilerplateCode reviews with AI, drafting descriptions, style and standards fixes, and automated fixes from static analysis errorsAI-generated suggested code edits from teammate feedback, plus reviewer support with links to documentationMeeting notes that summarize transcripts, highlight key decisions, and list action items with ownersGenerating architecture diagrams from text, plus document analysis and “interviewing the document”Correctness and the hallucination trap, treating AI like a junior engineer who needs supervisionSecurity risks, direct prompt injection, indirect injection, and why even a PDF can be a hostile inputData privacy and strict guidelines on what data can go into which tools, plus internal AI chatbot supportStaying up to date with internal channels, newsletters, tech talks, hands-on daily practice, and peer communityThe next five years: autonomous agents, bigger context windows, proactive help, and engineers as architects and orchestratorsㅤ🔗 Resources MentionedGeminiGemini ThreeAnti-GravityFathomNotebook LMIntegrated development environmentsStatic analysis toolsInternal AI chatbot

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 BioVlad 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 CoverWhen a CEO goes to ChatGPT, asks a question related to their brand, and the brand does not pop upGoogle gives you links, and ChatGPT gives you complete recommendations, with personalizationA case study for services, a separate domain, a single-page website, and leads from ChatGPT and Google AI overviewsOptimizing for specific granular use cases, capturing high-intent requests, and vanity metricsThree pillars: your own website presence, mentions on external websites, and user-generated content, Reddit in particularFAQ schema and schema data as fast food for chat gt to fetch and understandLogged in state of personas, citations list, outreach, and getting a product mentioned where it mattersCloudFlare blocking agents, browser manipulation tech, AI agent regulation, and a passport programGoogle AI mode is becoming a default way to search, and it's what happens overnight for companies and businessesㅤ🔗 Resources MentionedChatGPTGoogle AI modeGemini 3 modelOpenAIRedditPerplexityCloudFlareBrowserbaseBrowser manipulation techSchema, FAQ schemaAPISlackWikipediaPatreon

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 BioShefqet 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 CoverFrom coding, to multiple ventures, to a meaningful exit, to investing and joining a group of investorsA mentor with private equity experience, due diligence, pitfalls in investing, and strategies to followWhy serial founders come with a map, with a playbook, and go straight to finding product market fitScars, lessons, when things get tough, and why failure can be something you preferIdea versus team, pivots, and why the team can turn a bad idea into a great ideaTwo founders or more, complementary skillset, product, and sales, and a third on operationsFounder problem fit, domain experience, network, and solving an actual problem, not just for moneyAI wrappers versus defensibility, data loop strategy, fine-tuning, and “would this company die” after a new updateWhere AI is disrupting, health tech and biotech, drug discovery, simulating millions of interactions digitally, and FinTech underwriting with unstructured dataUsing 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 frictionsㅤ🔗 Resources MentionedOpen AIGeminiFigmaNvidiaLinkedInlovable

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.ㅤThey 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 BioMatthew 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 CoverWhy technology and AI are at the forefront of almost every conversation with new or existing clients, from enthusiasm to the fear of falling behindHow AI projects often start with automating a particular process, but lead to missing foundations in process, technology, and data managementReal-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 workAccounting 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 areasImage 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 teamHow 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 daysAI 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 LMSThe 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 moreWhy 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 targetsHow 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 impactㅤ🔗 Resources MentionedAMEND ConsultingRPA tools for process automationAI agents for workflow and document-intensive tasksLMS and file repositories as traditional knowledge basesGoPros for process shadowing and manufacturing floor observationsNvidia is an example of developers experimenting with new tools every day

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.ㅤOn 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 BioKristi 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 CoverHow 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 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.ㅤThe 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 BioXhoni 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 CoverHow 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 MentionedNVIDIAPwCEYNational University of SingaporeAWSAzureDeutsche TelekomSpaceXGitHub trendingArxiv style feeds and “alpha Arxiv sanity”CursorClaude 4.5 SonnetChatGPTGeminiDeepSeekTinkerOpenAIAnthropicGoogleReddit

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 CoverHow Softup sets clear expectations on onboarding, from a 4 to 6 week standard to rare fast-start cases within daysWhy senior, high-agency developers with domain experience reach full productivity in 2 to 3 weeksHow two-week sprints, focused check-ins, and familiar tools like Atlassian, monday.com, Asana, Trello, GitHub, and Bitbucket keep collaboration transparentWhat long-term stability looks like, including 33-month average client relationships and 22-month average on the same project without developer switchesHow flexible notice periods, optional longer commitments, and straightforward IP ownership terms reduce stress for founders managing cash and riskPractical paths after go-live: scaling the same team, pausing with low maintenance costs, or taking everything in-house with full handover and documentationHow remote and nearshore teams became normal after COVID, and why time zone alignment and trust matter more than office locationA 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 trackㅤ🔗 Resources MentionedSoftupAtlassianmonday.comAsanaTrelloGitHubBitbucketAmazonGoogleOpenAIMicrosoft

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 CoverHow 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 MentionedSoftupLovableCopilotn8nMakeZapierAWSSlackLinkedInWill Smith “eating spaghetti” AI video referenceOracleMicrosoftChamath Palihapitiya

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 CoverThe tradeoffs between in-house, freelancers, and an agency for early traction and scale-upCommitment windows like six to twelve months vs pause and resume development for three to six monthsWhy waiting more than two to three months for a CTO can let the market shiftCTO as a service for investor meetings, technical roadmap, scalability, cloud, DevOps, and cybersecurity touchpointsThe real hiring timeline: defining the role, screening, technical vetting, offers, equity, onboarding, and notice periodsBroad expertise vs specific expertise, and shifting priorities across backend, QA, DevOps, databases, cloud, and securityRisks of hiring students for core product work, including exam periods and speedReputation concerns with outsourcing, buying as much as you can afford, and having one neck to chokeㅤ🔗 Resources MentionedLinkedInNaval RavikantAWSMetaSan Francisco

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.