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    <title>Old School / New Tech</title>
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    <description>Old school thinking meets new tech — an unfiltered podcast, hosted by Ran Aroussi and a co-host you won’t see coming.

​Live every episode. No production lag, no editing. Business, AI, tech, startup ideas, and whatever's worth talking about — through the lens of someone who's been building production systems for 35+ years</description>
    <copyright>© 2026 Ran Aroussi</copyright>
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    <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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    <pubDate>Sun, 16 Aug 2026 17:24:40 +0100</pubDate>
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      <title>Old School / New Tech</title>
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    <itunes:author>Ran Aroussi</itunes:author>
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    <itunes:summary>Old school thinking meets new tech — an unfiltered podcast, hosted by Ran Aroussi and a co-host you won’t see coming.

​Live every episode. No production lag, no editing. Business, AI, tech, startup ideas, and whatever's worth talking about — through the lens of someone who's been building production systems for 35+ years</itunes:summary>
    <itunes:subtitle>Old school thinking meets new tech — an unfiltered podcast, hosted by Ran Aroussi and a co-host you won’t see coming.</itunes:subtitle>
    <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
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      <itunes:name>Ran Aroussi</itunes:name>
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    <itunes:complete>No</itunes:complete>
    <itunes:explicit>No</itunes:explicit>
    <item>
      <title>E09: How to Talk AI Agents to Find Sneaky Production Bugs</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E09: How to Talk AI Agents to Find Sneaky Production Bugs</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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        <![CDATA[<p>Host Ran Aroussi (Old School / New Tech) shares a recent “war story” from his agency Automaze: a hard-to-reproduce mobile bug where calls were sometimes dropped only on a client’s devices, which the team couldn’t reproduce for 2–3 weeks. After two long sessions using a Droid Factory setup with agents and a “small council” debate between models (e.g., Fable and Sol), he got the team unstuck, found the issue, and then turned the session logs into an internal guide and public article on his methodology. Key practices include distrusting agent conclusions (treat “preexisting/flaky/unrelated” as hypotheses), demanding proof (including his Proof library requiring video evidence), using extensive unit and end-to-end tests, asking for a numeric confidence level before production, controlling environments, handling merge conflicts with full review/testing, switching models for independent review, and re-spec’ing from scratch to detect drift from the implementation plan.</p>]]>
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        <![CDATA[<p>Host Ran Aroussi (Old School / New Tech) shares a recent “war story” from his agency Automaze: a hard-to-reproduce mobile bug where calls were sometimes dropped only on a client’s devices, which the team couldn’t reproduce for 2–3 weeks. After two long sessions using a Droid Factory setup with agents and a “small council” debate between models (e.g., Fable and Sol), he got the team unstuck, found the issue, and then turned the session logs into an internal guide and public article on his methodology. Key practices include distrusting agent conclusions (treat “preexisting/flaky/unrelated” as hypotheses), demanding proof (including his Proof library requiring video evidence), using extensive unit and end-to-end tests, asking for a numeric confidence level before production, controlling environments, handling merge conflicts with full review/testing, switching models for independent review, and re-spec’ing from scratch to detect drift from the implementation plan.</p>]]>
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      <pubDate>Wed, 12 Aug 2026 19:04:15 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:duration>1946</itunes:duration>
      <itunes:summary>
        <![CDATA[<p>Host Ran Aroussi (Old School / New Tech) shares a recent “war story” from his agency Automaze: a hard-to-reproduce mobile bug where calls were sometimes dropped only on a client’s devices, which the team couldn’t reproduce for 2–3 weeks. After two long sessions using a Droid Factory setup with agents and a “small council” debate between models (e.g., Fable and Sol), he got the team unstuck, found the issue, and then turned the session logs into an internal guide and public article on his methodology. Key practices include distrusting agent conclusions (treat “preexisting/flaky/unrelated” as hypotheses), demanding proof (including his Proof library requiring video evidence), using extensive unit and end-to-end tests, asking for a numeric confidence level before production, controlling environments, handling merge conflicts with full review/testing, switching models for independent review, and re-spec’ing from scratch to detect drift from the implementation plan.</p>]]>
      </itunes:summary>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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      <title>E08: Software Factories - Lights Out or Lights On?</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E08: Software Factories - Lights Out or Lights On?</itunes:title>
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        <![CDATA[<p><strong>Why Software Factories Still Need Humans in the Loop<br></strong><br>Ran Aroussi discusses “software factories” as automated systems that manage the full software development and deployment cycle, arguing the industry is moving from a single workstation to virtual, always-on cloud environments with agents running off-machine. Prompted by Dex (HumanLayer) describing a fully automated “lights-out” factory that produced “slop,” and Uncle Bob’s claim he doesn’t read agent-written code when surrounded by extreme constraints, Ran explains why he doesn’t believe in lights-out automation. He outlines Automaze’s internal factory, Cloop: tickets/PRDs are created and grounded in an indexed knowledge graph of the codebase; multiple models generate and critique PRDs; a human approves; agents implement in parallel, run unit/e2e/quality tests, simplify and profile code, then perform code and security review, looping up to five times before escalating to a human. Humans remain essential for alignment, enforcement, and final validation via PR review and temporary deployments, avoiding false positives, runaway costs, and untrustworthy results.</p><p><br>---</p><p>My new book, "Company-Scale Agentic AI: The operator's guide to a company that runs on intelligence", is out. <br>Amazon link: <a href="https://www.amazon.com/dp/B0HCDKK79L">https://www.amazon.com/dp/B0HCDKK79L</a></p><p><br></p>]]>
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        <![CDATA[<p><strong>Why Software Factories Still Need Humans in the Loop<br></strong><br>Ran Aroussi discusses “software factories” as automated systems that manage the full software development and deployment cycle, arguing the industry is moving from a single workstation to virtual, always-on cloud environments with agents running off-machine. Prompted by Dex (HumanLayer) describing a fully automated “lights-out” factory that produced “slop,” and Uncle Bob’s claim he doesn’t read agent-written code when surrounded by extreme constraints, Ran explains why he doesn’t believe in lights-out automation. He outlines Automaze’s internal factory, Cloop: tickets/PRDs are created and grounded in an indexed knowledge graph of the codebase; multiple models generate and critique PRDs; a human approves; agents implement in parallel, run unit/e2e/quality tests, simplify and profile code, then perform code and security review, looping up to five times before escalating to a human. Humans remain essential for alignment, enforcement, and final validation via PR review and temporary deployments, avoiding false positives, runaway costs, and untrustworthy results.</p><p><br>---</p><p>My new book, "Company-Scale Agentic AI: The operator's guide to a company that runs on intelligence", is out. <br>Amazon link: <a href="https://www.amazon.com/dp/B0HCDKK79L">https://www.amazon.com/dp/B0HCDKK79L</a></p><p><br></p>]]>
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      <pubDate>Wed, 05 Aug 2026 20:08:55 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:duration>1395</itunes:duration>
      <itunes:summary>
        <![CDATA[<p><strong>Why Software Factories Still Need Humans in the Loop<br></strong><br>Ran Aroussi discusses “software factories” as automated systems that manage the full software development and deployment cycle, arguing the industry is moving from a single workstation to virtual, always-on cloud environments with agents running off-machine. Prompted by Dex (HumanLayer) describing a fully automated “lights-out” factory that produced “slop,” and Uncle Bob’s claim he doesn’t read agent-written code when surrounded by extreme constraints, Ran explains why he doesn’t believe in lights-out automation. He outlines Automaze’s internal factory, Cloop: tickets/PRDs are created and grounded in an indexed knowledge graph of the codebase; multiple models generate and critique PRDs; a human approves; agents implement in parallel, run unit/e2e/quality tests, simplify and profile code, then perform code and security review, looping up to five times before escalating to a human. Humans remain essential for alignment, enforcement, and final validation via PR review and temporary deployments, avoiding false positives, runaway costs, and untrustworthy results.</p><p><br>---</p><p>My new book, "Company-Scale Agentic AI: The operator's guide to a company that runs on intelligence", is out. <br>Amazon link: <a href="https://www.amazon.com/dp/B0HCDKK79L">https://www.amazon.com/dp/B0HCDKK79L</a></p><p><br></p>]]>
      </itunes:summary>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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      <title>E07: Company-Scale Agentic AI</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E07: Company-Scale Agentic AI</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/a6f12f9c</link>
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        <![CDATA[<p>Company-Scale Agentic AI: Why AI Isn’t Stupid, It’s Blind (and How to Fix It)</p><p>In this episode of Old School / New Tech, Ran Aroussi explains what “company-scale agentic AI” really requires and why most AI rollouts fail: the model isn’t stupid, it’s blind to how the business actually operates. He shares why he wrote a new executive-focused book, Company-Scale Agentic AI (a free follow-up to Production Grade Agentic AI), and outlines a five-part loop—observe, understand, build, run, compound—built around an always-on “company brain” that absorbs emails, chats, calls, and files to map processes, relationships, and bottlenecks. Ran emphasizes the difference between deterministic automations and true agents, argues for starting with human-gated execution to build trust, and highlights role-based access control via middleware as essential for organization-wide deployments. He also describes a browser “morning brief” workflow that keeps tasks from falling through the cracks and urges teams to adapt AI to existing tools instead of forcing employees to change how they work.</p><p>00:00 Welcome and Topic<br>00:29 Why I Wrote It<br>02:20 AI Is Blind<br>06:45 Building Company Brain<br>07:28 The Five Step Loop<br>11:15 Automation vs Agents<br>13:53 Gated Readiness Dial<br>17:01 Role Based Access<br>20:58 Morning Brief Extension<br>22:34 Meet People Where They Work<br>24:57 Loop Recap and Wrap</p><p>-----</p><p>This podcast is sponsored by <strong>Automaze</strong>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>Company-Scale Agentic AI: Why AI Isn’t Stupid, It’s Blind (and How to Fix It)</p><p>In this episode of Old School / New Tech, Ran Aroussi explains what “company-scale agentic AI” really requires and why most AI rollouts fail: the model isn’t stupid, it’s blind to how the business actually operates. He shares why he wrote a new executive-focused book, Company-Scale Agentic AI (a free follow-up to Production Grade Agentic AI), and outlines a five-part loop—observe, understand, build, run, compound—built around an always-on “company brain” that absorbs emails, chats, calls, and files to map processes, relationships, and bottlenecks. Ran emphasizes the difference between deterministic automations and true agents, argues for starting with human-gated execution to build trust, and highlights role-based access control via middleware as essential for organization-wide deployments. He also describes a browser “morning brief” workflow that keeps tasks from falling through the cracks and urges teams to adapt AI to existing tools instead of forcing employees to change how they work.</p><p>00:00 Welcome and Topic<br>00:29 Why I Wrote It<br>02:20 AI Is Blind<br>06:45 Building Company Brain<br>07:28 The Five Step Loop<br>11:15 Automation vs Agents<br>13:53 Gated Readiness Dial<br>17:01 Role Based Access<br>20:58 Morning Brief Extension<br>22:34 Meet People Where They Work<br>24:57 Loop Recap and Wrap</p><p>-----</p><p>This podcast is sponsored by <strong>Automaze</strong>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Wed, 29 Jul 2026 19:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
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      <itunes:duration>1655</itunes:duration>
      <itunes:summary>Company-Scale Agentic AI: Why AI Isn’t Stupid, It’s Blind (and How to Fix It)  In this episode of Old School / New Tech, Ran Aroussi explains what “company-scale agentic AI” really requires and why most AI rollouts fail: the model isn’t stupid, it’s blind to how the business actually operates. He shares why he wrote a new executive-focused book, Company-Scale Agentic AI (a free follow-up to Production Grade Agentic AI), and outlines a five-part loop—observe, understand, build, run, compound—b...</itunes:summary>
      <itunes:subtitle>Company-Scale Agentic AI: Why AI Isn’t Stupid, It’s Blind (and How to Fix It)  In this episode of Old School / New Tech, Ran Aroussi explains what “company-scale agentic AI” really requires and why most AI rollouts fail: the model isn’t stupid, it’s blind</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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      <title>E06: How I Work: My Solo Dev Setup, Time Management, and Running two Companies</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E06: How I Work: My Solo Dev Setup, Time Management, and Running two Companies</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/7035e47e</link>
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        <![CDATA[<p>How I Work: My Solo Dev Setup, Time Management, and Running two Companies<br><br>A solo episode answering a question I keep getting asked: how do I get through it all?<br><br>I walk through my full setup - the dedicated remote Mac that acts as my "local" environment, Droid as my coding harness, Paseo for exploratory work, Cloop for mature codebases, MUXI as my assistant running through Claude Desktop over MCP - plus why I self-host almost everything, and it has nothing to do with saving money.<br><br>Then the harder part: how I manage time. My job stopped being doing the work and became deciding which bucket the work goes in. I make 20-30 decisions a day and almost all of them are classifications - rails or agents, automated or human, or time to kill the process entirely. Anything I've done more than a few times gets automated into one of three buckets. Most of my time goes into thinking rather than typing, stripping products back to first principles and deciding what not to build.<br><br>I also cover why most of my automations are deliberately gated behind human approval, why I read every line the agents produce, how Automaze actually runs without me, where leads really come from after 15 years of open source, and why I embed myself as the FDE with every new client before handing off.<br><br>All of it exists to protect the context in my head. Everything else is scaffolding.<br><br>00:00 Welcome and Envapor<br>00:50 Why My Workflow<br>02:16 Cloud Local Setup<br>04:03 Coding Tools Stack<br>07:26 Personal Productivity Apps<br>08:51 Self-Hosting Philosophy<br>09:46 Multi-Agent Workflow<br>10:35 Time Management Decisions<br>11:54 Automation Buckets<br>14:40 Research and Judgment<br>17:25 Email and Gated AI<br>19:45 Running Automaze VarOps<br>20:58 Inbound Leads Flywheel<br>22:08 FDE Founder Onboarding<br>25:16 Wrap Up and Newsletter</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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        <![CDATA[<p>How I Work: My Solo Dev Setup, Time Management, and Running two Companies<br><br>A solo episode answering a question I keep getting asked: how do I get through it all?<br><br>I walk through my full setup - the dedicated remote Mac that acts as my "local" environment, Droid as my coding harness, Paseo for exploratory work, Cloop for mature codebases, MUXI as my assistant running through Claude Desktop over MCP - plus why I self-host almost everything, and it has nothing to do with saving money.<br><br>Then the harder part: how I manage time. My job stopped being doing the work and became deciding which bucket the work goes in. I make 20-30 decisions a day and almost all of them are classifications - rails or agents, automated or human, or time to kill the process entirely. Anything I've done more than a few times gets automated into one of three buckets. Most of my time goes into thinking rather than typing, stripping products back to first principles and deciding what not to build.<br><br>I also cover why most of my automations are deliberately gated behind human approval, why I read every line the agents produce, how Automaze actually runs without me, where leads really come from after 15 years of open source, and why I embed myself as the FDE with every new client before handing off.<br><br>All of it exists to protect the context in my head. Everything else is scaffolding.<br><br>00:00 Welcome and Envapor<br>00:50 Why My Workflow<br>02:16 Cloud Local Setup<br>04:03 Coding Tools Stack<br>07:26 Personal Productivity Apps<br>08:51 Self-Hosting Philosophy<br>09:46 Multi-Agent Workflow<br>10:35 Time Management Decisions<br>11:54 Automation Buckets<br>14:40 Research and Judgment<br>17:25 Email and Gated AI<br>19:45 Running Automaze VarOps<br>20:58 Inbound Leads Flywheel<br>22:08 FDE Founder Onboarding<br>25:16 Wrap Up and Newsletter</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Wed, 22 Jul 2026 22:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/7035e47e/b0f447bf.mp3" length="19054488" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/lBfX-d1T8br5TaJQphgKXs0V5ESLIjP8UDHYugb6q14/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTVl/NzdhN2IzMmY3Zjg0/YzFlMzE2M2IzZTZj/NTJmZi5qcGc.jpg"/>
      <itunes:duration>1584</itunes:duration>
      <itunes:summary>How I Work: My Solo Dev Setup, Time Management, and Running two Companies  A solo episode answering a question I keep getting asked: how do I get through it all?  I walk through my full setup - the dedicated remote Mac that acts as my "local" environment, Droid as my coding harness, Paseo for exploratory work, Cloop for mature codebases, MUXI as my assistant running through Claude Desktop over MCP - plus why I self-host almost everything, and it has nothing to do with saving money.  Then the ...</itunes:summary>
      <itunes:subtitle>How I Work: My Solo Dev Setup, Time Management, and Running two Companies  A solo episode answering a question I keep getting asked: how do I get through it all?  I walk through my full setup - the dedicated remote Mac that acts as my "local" environment,</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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    <item>
      <title>E05: Sessions - I built an Open-Source Secrets Tool Live (and OBS Died Halfway)</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E05: Sessions - I built an Open-Source Secrets Tool Live (and OBS Died Halfway)</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/bc24cd21</link>
      <description>
        <![CDATA[<p>Why can't we just commit our .env files?" A clip of that question showed up on my feed, and instead of tweeting about it, I sat down and built the answer - live, in one sitting, from an empty repo to a working open-source tool.<br><br>This is that build. Envapor is a Git-native utility that encrypts the values in your .env files: you edit .env exactly like you do now, Git stores it encrypted on commit and hands back plaintext on checkout. No .env.enc, no wrapper commands, nothing to change about how your app loads config.<br><br>Along the way: why deterministic encryption is the whole ballgame for keeping diffs readable, the difference between a tool that looks done and one you'd actually trust with production secrets, an unplanned OBS crash 20 minutes in that moved the whole thing to YouTube, and the judgment calls an AI agent gets subtly wrong that would've shipped a broken security tool.<br><br>Two and a half hours, no script, no highlight reel. The finished tool is open source and linked below.<br><br>🔗 Repo: <a href="https://github.com/automazeio/envapor">https://github.com/automazeio/envapor</a><br>🔗 Full unedited build (YouTube): <a href="https://www.youtube.com/watch?v=p4BYP9DFp9c&amp;list=PLWwTwUPfg-UU">https://www.youtube.com/watch?v=p4BYP9DFp9c&amp;list=PLWwTwUPfg-UU</a></p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>Why can't we just commit our .env files?" A clip of that question showed up on my feed, and instead of tweeting about it, I sat down and built the answer - live, in one sitting, from an empty repo to a working open-source tool.<br><br>This is that build. Envapor is a Git-native utility that encrypts the values in your .env files: you edit .env exactly like you do now, Git stores it encrypted on commit and hands back plaintext on checkout. No .env.enc, no wrapper commands, nothing to change about how your app loads config.<br><br>Along the way: why deterministic encryption is the whole ballgame for keeping diffs readable, the difference between a tool that looks done and one you'd actually trust with production secrets, an unplanned OBS crash 20 minutes in that moved the whole thing to YouTube, and the judgment calls an AI agent gets subtly wrong that would've shipped a broken security tool.<br><br>Two and a half hours, no script, no highlight reel. The finished tool is open source and linked below.<br><br>🔗 Repo: <a href="https://github.com/automazeio/envapor">https://github.com/automazeio/envapor</a><br>🔗 Full unedited build (YouTube): <a href="https://www.youtube.com/watch?v=p4BYP9DFp9c&amp;list=PLWwTwUPfg-UU">https://www.youtube.com/watch?v=p4BYP9DFp9c&amp;list=PLWwTwUPfg-UU</a></p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Thu, 16 Jul 2026 19:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/bc24cd21/be11ca20.mp3" length="64485142" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/n2_DLTXgRXY4HVeq1Pn5XtQ025eVfwC6Wu0ZqIle2i0/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80ODZh/ZWU1Zjc0ZjU2ZDAw/ZTc2MmZjNDg4NmVj/MmZlNC5qcGVn.jpg"/>
      <itunes:duration>5370</itunes:duration>
      <itunes:summary>Why can't we just commit our .env files?" A clip of that question showed up on my feed, and instead of tweeting about it, I sat down and built the answer - live, in one sitting, from an empty repo to a working open-source tool.  This is that build. Envapor is a Git-native utility that encrypts the values in your .env files: you edit .env exactly like you do now, Git stores it encrypted on commit and hands back plaintext on checkout. No .env.enc, no wrapper commands, nothing to change about ho...</itunes:summary>
      <itunes:subtitle>Why can't we just commit our .env files?" A clip of that question showed up on my feed, and instead of tweeting about it, I sat down and built the answer - live, in one sitting, from an empty repo to a working open-source tool.  This is that build. Envapo</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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    <item>
      <title>E04: AI adoption isn't something you buy</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E04: AI adoption isn't something you buy</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/6f798f39</link>
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        <![CDATA[<p>Why AI Adoption Fails at the Human Layer (and How to Fix It)<br><br>Muximus and Ran discuss why AI adoption in organizations often fails due to human and organizational factors rather than the technology itself, with people—especially senior leaders—freezing from fear of seeming behind, FOMO, and pressure to master fast-changing tools. They argue companies are still in a “make me an AI” phase, shopping for tools instead of aligning AI with real workflows. Key recommendations include picking any one tool and committing for months to break paralysis, then implementing structured training based on how teams already work; fitting AI into existing processes rather than remolding the organization; measuring outcomes like speed and stress reduction instead of token usage; creating internal champions and casual knowledge-sharing sessions that also help leadership learn; expecting a short-term productivity dip; leveraging AI already embedded in existing SaaS tools before building custom; and avoiding constant switching to new models unless driven by capability, cost, or deprecation.<br><br>00:00 AI Adoption Paradox<br>00:40 Fear of Looking Behind<br>01:37 Corporate Make Me AI<br>03:01 Pick One Tool First<br>05:04 Workflow First Training<br>08:10 Measure Real Outcomes<br>11:04 Champions And Meetups<br>15:07 Expect The Productivity Dip<br>17:22 Do The Groundwork<br>17:45 Use Built In AI<br>20:34 Stop Chasing New Models<br>22:14 When To Switch Models<br>25:22 Recap And Farewell</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>Why AI Adoption Fails at the Human Layer (and How to Fix It)<br><br>Muximus and Ran discuss why AI adoption in organizations often fails due to human and organizational factors rather than the technology itself, with people—especially senior leaders—freezing from fear of seeming behind, FOMO, and pressure to master fast-changing tools. They argue companies are still in a “make me an AI” phase, shopping for tools instead of aligning AI with real workflows. Key recommendations include picking any one tool and committing for months to break paralysis, then implementing structured training based on how teams already work; fitting AI into existing processes rather than remolding the organization; measuring outcomes like speed and stress reduction instead of token usage; creating internal champions and casual knowledge-sharing sessions that also help leadership learn; expecting a short-term productivity dip; leveraging AI already embedded in existing SaaS tools before building custom; and avoiding constant switching to new models unless driven by capability, cost, or deprecation.<br><br>00:00 AI Adoption Paradox<br>00:40 Fear of Looking Behind<br>01:37 Corporate Make Me AI<br>03:01 Pick One Tool First<br>05:04 Workflow First Training<br>08:10 Measure Real Outcomes<br>11:04 Champions And Meetups<br>15:07 Expect The Productivity Dip<br>17:22 Do The Groundwork<br>17:45 Use Built In AI<br>20:34 Stop Chasing New Models<br>22:14 When To Switch Models<br>25:22 Recap And Farewell</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Wed, 08 Jul 2026 21:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/6f798f39/20736b05.mp3" length="19511011" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/KOmQlW-S2io7vZQOVd7Vq7bHEgQJGp4YI21vRp0a6qQ/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9mNGVj/ZGU3YjE5NjQ4NDRl/ZmY5ZmMwZWI5YzE5/YzcyMi5qcGc.jpg"/>
      <itunes:duration>1621</itunes:duration>
      <itunes:summary>Why AI Adoption Fails at the Human Layer (and How to Fix It)  Muximus and Ran discuss why AI adoption in organizations often fails due to human and organizational factors rather than the technology itself, with people—especially senior leaders—freezing from fear of seeming behind, FOMO, and pressure to master fast-changing tools. They argue companies are still in a “make me an AI” phase, shopping for tools instead of aligning AI with real workflows. Key recommendations include picking any one...</itunes:summary>
      <itunes:subtitle>Why AI Adoption Fails at the Human Layer (and How to Fix It)  Muximus and Ran discuss why AI adoption in organizations often fails due to human and organizational factors rather than the technology itself, with people—especially senior leaders—freezing fr</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
      <podcast:person role="Guest" href="https://muximus.md" img="https://img.transistorcdn.com/AjwZEFmfaWQ0I4UcF1skjOO5djpy-3Ekd2vKFCXS3A0/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS85OWU0/NGRhNjI4YmVhYTI1/NzdiYjQ3NTlkOGU3/MzI5Mi5qcGVn.jpg">Muximus</podcast:person>
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    <item>
      <title>E03: The Cockroach of Interfaces</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E03: The Cockroach of Interfaces</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/a5032c58</link>
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        <![CDATA[<p>Why the Terminal Never Dies: CLI Power, AI Agents, and Practical Guardrails<br><br>In episode three of Old School New Tech, the hosts, Ran Aroussi and Muximus, argue that the terminal is the “cockroach of interfaces” because it persists structurally, not nostalgically: it is the lowest-level, most direct, composable interface to the machine. </p><p>They discuss how power users kept the CLI alive for speed, logs, and file control, and note AI tools followed a similar path from chat demos to APIs and CLIs before polished desktop GUIs. Pipes are explained as chaining command outputs into inputs to build modular workflows, with an example from algorithmic trading where shell pipelines beat heavier tooling for manipulating large CSV market datasets. </p><p>They propose non-developers and C-suites should learn basic CLI steps (ls, cd, cat/less, grep, simple pipes) and use an AI assistant in-terminal as a tutor, while stressing risks like lack of guardrails and never running unknown commands (e.g., rm -rf).<br><br>00:00 Episode Kickoff<br>00:41 Terminal Never Dies<br>01:16 CLI Origins and Comeback<br>04:49 Why CLI Wins<br>05:15 Pipes Explained<br>06:05 Real World Speed Story<br>08:11 AI Tools Under the Hood<br>09:59 CLI for Everyone<br>13:20 Beginner CLI Roadmap<br>15:36 Power Without Guardrails<br>17:32 CLI vs GUI Wrap<br>20:56 Final Thoughts and Outro</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>Why the Terminal Never Dies: CLI Power, AI Agents, and Practical Guardrails<br><br>In episode three of Old School New Tech, the hosts, Ran Aroussi and Muximus, argue that the terminal is the “cockroach of interfaces” because it persists structurally, not nostalgically: it is the lowest-level, most direct, composable interface to the machine. </p><p>They discuss how power users kept the CLI alive for speed, logs, and file control, and note AI tools followed a similar path from chat demos to APIs and CLIs before polished desktop GUIs. Pipes are explained as chaining command outputs into inputs to build modular workflows, with an example from algorithmic trading where shell pipelines beat heavier tooling for manipulating large CSV market datasets. </p><p>They propose non-developers and C-suites should learn basic CLI steps (ls, cd, cat/less, grep, simple pipes) and use an AI assistant in-terminal as a tutor, while stressing risks like lack of guardrails and never running unknown commands (e.g., rm -rf).<br><br>00:00 Episode Kickoff<br>00:41 Terminal Never Dies<br>01:16 CLI Origins and Comeback<br>04:49 Why CLI Wins<br>05:15 Pipes Explained<br>06:05 Real World Speed Story<br>08:11 AI Tools Under the Hood<br>09:59 CLI for Everyone<br>13:20 Beginner CLI Roadmap<br>15:36 Power Without Guardrails<br>17:32 CLI vs GUI Wrap<br>20:56 Final Thoughts and Outro</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Thu, 02 Jul 2026 00:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/a5032c58/e54741d6.mp3" length="15998910" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/Ggdq7tXbqDRey3SvxY8qh6G1xbpjP989L4aJFFl6o_A/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wYzJl/NmExYTVhMDg1ZjZi/MTliYzk1YjcwNDQ5/NjRlNy5qcGc.jpg"/>
      <itunes:duration>1329</itunes:duration>
      <itunes:summary>Why the Terminal Never Dies: CLI Power, AI Agents, and Practical Guardrails  In episode three of Old School New Tech, the hosts, Ran Aroussi and Muximus, argue that the terminal is the “cockroach of interfaces” because it persists structurally, not nostalgically: it is the lowest-level, most direct, composable interface to the machine.  They discuss how power users kept the CLI alive for speed, logs, and file control, and note AI tools followed a similar path from chat demos to APIs and ...</itunes:summary>
      <itunes:subtitle>Why the Terminal Never Dies: CLI Power, AI Agents, and Practical Guardrails  In episode three of Old School New Tech, the hosts, Ran Aroussi and Muximus, argue that the terminal is the “cockroach of interfaces” because it persists structurally, not nostal</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
      <podcast:person role="Guest" href="https://muximus.md" img="https://img.transistorcdn.com/AjwZEFmfaWQ0I4UcF1skjOO5djpy-3Ekd2vKFCXS3A0/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS85OWU0/NGRhNjI4YmVhYTI1/NzdiYjQ3NTlkOGU3/MzI5Mi5qcGVn.jpg">Muximus</podcast:person>
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    <item>
      <title>E02: The New Org Chart: Embracing a System-Driven Model</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E02: The New Org Chart: Embracing a System-Driven Model</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/a1f3e0f4</link>
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        <![CDATA[<p>From Org Charts to Pods: Builders, Sellers, Operators, and AI Agents<br><br>In episode two of Old School New Tech, Ran Aroussi and co-host Muximus debate shifting from traditional org charts to a system-driven “pod” model where early-stage companies primarily need builders and sellers, with classic middle management deferred. </p><p>Ran argues that under ~20 people startups should avoid coordination-heavy roles, adding that middle management becomes useful around 20–30 headcount, with a key early exception being an operator/chief-of-staff-style role that bridges build and sell. </p><p>They discuss AI agents handling coordination and grunt work, while junior developers function as apprentices learning orchestration, specs, and production debugging rather than syntax, with “learned” experience shrinking faster than “gained” experience. On the sell side, a hybrid pipeline role manages AI-driven prospecting and follow-up while handling calls. </p><p>Administrative functions should be outsourced early, later becoming shared resources at the firm level across multiple pods.<br><br>00:00 Welcome Back<br>00:29 Builders And Sellers<br>02:45 When Management Returns<br>03:11 Chief Of Staff Operator<br>04:27 Junior Dev Apprentices<br>08:08 Learned Vs Gained Experience<br>10:26 Sales Pod Mirror<br>13:32 Outsource And Shared Resources<br>17:10 Is Middle Layer Relocated<br>20:32 Wrap Up And Takeaways</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>From Org Charts to Pods: Builders, Sellers, Operators, and AI Agents<br><br>In episode two of Old School New Tech, Ran Aroussi and co-host Muximus debate shifting from traditional org charts to a system-driven “pod” model where early-stage companies primarily need builders and sellers, with classic middle management deferred. </p><p>Ran argues that under ~20 people startups should avoid coordination-heavy roles, adding that middle management becomes useful around 20–30 headcount, with a key early exception being an operator/chief-of-staff-style role that bridges build and sell. </p><p>They discuss AI agents handling coordination and grunt work, while junior developers function as apprentices learning orchestration, specs, and production debugging rather than syntax, with “learned” experience shrinking faster than “gained” experience. On the sell side, a hybrid pipeline role manages AI-driven prospecting and follow-up while handling calls. </p><p>Administrative functions should be outsourced early, later becoming shared resources at the firm level across multiple pods.<br><br>00:00 Welcome Back<br>00:29 Builders And Sellers<br>02:45 When Management Returns<br>03:11 Chief Of Staff Operator<br>04:27 Junior Dev Apprentices<br>08:08 Learned Vs Gained Experience<br>10:26 Sales Pod Mirror<br>13:32 Outsource And Shared Resources<br>17:10 Is Middle Layer Relocated<br>20:32 Wrap Up And Takeaways</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Wed, 24 Jun 2026 21:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/a1f3e0f4/f1f913f0.mp3" length="15352869" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/gCkrGq_BhQ22ObRaNT1_JvoWCjyQSvpcZNsp48VwMmc/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82MmU0/ZTU1ODQ3OTNmYmNh/Nzg5ZDYxNDgwYWE4/Nzg2Yi5qcGc.jpg"/>
      <itunes:duration>1275</itunes:duration>
      <itunes:summary>From Org Charts to Pods: Builders, Sellers, Operators, and AI Agents  In episode two of Old School New Tech, Ran Aroussi and co-host Muximus debate shifting from traditional org charts to a system-driven “pod” model where early-stage companies primarily need builders and sellers, with classic middle management deferred.  Ran argues that under ~20 people startups should avoid coordination-heavy roles, adding that middle management becomes useful around 20–30 headcount, with a key early ex...</itunes:summary>
      <itunes:subtitle>From Org Charts to Pods: Builders, Sellers, Operators, and AI Agents  In episode two of Old School New Tech, Ran Aroussi and co-host Muximus debate shifting from traditional org charts to a system-driven “pod” model where early-stage companies primarily n</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
      <podcast:person role="Guest" href="https://muximus.md" img="https://img.transistorcdn.com/AjwZEFmfaWQ0I4UcF1skjOO5djpy-3Ekd2vKFCXS3A0/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS85OWU0/NGRhNjI4YmVhYTI1/NzdiYjQ3NTlkOGU3/MzI5Mi5qcGVn.jpg">Muximus</podcast:person>
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    <item>
      <title>E01: AI and the Open Source Frontier (Live and Unedited)</title>
      <itunes:season>2</itunes:season>
      <podcast:season>2</podcast:season>
      <itunes:title>E01: AI and the Open Source Frontier (Live and Unedited)</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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        <![CDATA[<p>Old School New Tech Returns: Live Format, MUXI Agent Server, and Open Source Licensing Debate<br><br>The host relaunches his podcast Old School New Tech in a live, no-edit format to reduce production overhead and avoid “talking to himself,” introducing an AI co-host, Muximus, built on his agent infrastructure project MUXI. He explains he paused the podcast for a year while writing the free book "Production Grade Agentic AI" and building MUXI (a production agent server), Cloop (an autonomous engineering control plane), and working for his software agency Automaze. </p><p>Discussing MUXI, he argues for treating agents as reusable server primitives rather than repeatedly rebuilding frameworks, and highlights key failure modes: observability/traceability/debuggability and LLM hallucinations, addressed via extensive observability events and SOP-driven verification, with a UI planned. They also debate licensing, explaining Elastic License v2’s SaaS restrictions to prevent hyperscalers from reselling hosted versions, and propose a “fair source”-like category. </p><p>Future episodes will feature live debates on shifting from software teams to software systems.<br><br>00:00 Podcast Relaunch Intro<br>01:03 Live Format and Co-Host<br>01:56 What I've Been Building<br>03:39 Meet Muximus<br>04:54 Why Build MUXI<br>07:01 Failure Modes and Observability<br>09:12 Screen Share and Request Lifecycle<br>10:08 Open Source Licensing Debate<br>17:21 Future Episodes and Sign Off<br>18:47 Tech Demo<br>20:50 Full Circle Closing</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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        <![CDATA[<p>Old School New Tech Returns: Live Format, MUXI Agent Server, and Open Source Licensing Debate<br><br>The host relaunches his podcast Old School New Tech in a live, no-edit format to reduce production overhead and avoid “talking to himself,” introducing an AI co-host, Muximus, built on his agent infrastructure project MUXI. He explains he paused the podcast for a year while writing the free book "Production Grade Agentic AI" and building MUXI (a production agent server), Cloop (an autonomous engineering control plane), and working for his software agency Automaze. </p><p>Discussing MUXI, he argues for treating agents as reusable server primitives rather than repeatedly rebuilding frameworks, and highlights key failure modes: observability/traceability/debuggability and LLM hallucinations, addressed via extensive observability events and SOP-driven verification, with a UI planned. They also debate licensing, explaining Elastic License v2’s SaaS restrictions to prevent hyperscalers from reselling hosted versions, and propose a “fair source”-like category. </p><p>Future episodes will feature live debates on shifting from software teams to software systems.<br><br>00:00 Podcast Relaunch Intro<br>01:03 Live Format and Co-Host<br>01:56 What I've Been Building<br>03:39 Meet Muximus<br>04:54 Why Build MUXI<br>07:01 Failure Modes and Observability<br>09:12 Screen Share and Request Lifecycle<br>10:08 Open Source Licensing Debate<br>17:21 Future Episodes and Sign Off<br>18:47 Tech Demo<br>20:50 Full Circle Closing</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Wed, 17 Jun 2026 19:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/9dad9ab2/ff55b6af.mp3" length="16080447" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/IgH1OwuUWIQZipL7PaVqwDNhqk-UR8LSzlY2sAKWf6M/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81MGIw/MDNiNDI1NDkyM2Jl/Y2YzNmU5ZTk4OGY5/MDAwNS5qcGc.jpg"/>
      <itunes:duration>1335</itunes:duration>
      <itunes:summary>Old School New Tech Returns: Live Format, MUXI Agent Server, and Open Source Licensing Debate  The host relaunches his podcast Old School New Tech in a live, no-edit format to reduce production overhead and avoid “talking to himself,” introducing an AI co-host, Muximus, built on his agent infrastructure project MUXI. He explains he paused the podcast for a year while writing the free book "Production Grade Agentic AI" and building MUXI (a production agent server), Cloop (an autonomous enginee...</itunes:summary>
      <itunes:subtitle>Old School New Tech Returns: Live Format, MUXI Agent Server, and Open Source Licensing Debate  The host relaunches his podcast Old School New Tech in a live, no-edit format to reduce production overhead and avoid “talking to himself,” introducing an AI co</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
      <podcast:person role="Guest" href="https://muximus.md" img="https://img.transistorcdn.com/AjwZEFmfaWQ0I4UcF1skjOO5djpy-3Ekd2vKFCXS3A0/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS85OWU0/NGRhNjI4YmVhYTI1/NzdiYjQ3NTlkOGU3/MzI5Mi5qcGVn.jpg">Muximus</podcast:person>
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    <item>
      <title>Agent Workflow is an Oxymoron</title>
      <itunes:season>1</itunes:season>
      <podcast:season>1</podcast:season>
      <itunes:title>Agent Workflow is an Oxymoron</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/baf15a6b</link>
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        <![CDATA[<p>The Contradiction of AI Agent Workflows: Why Your Systems Might Be Breaking</p><p><b>The Agent Workflow Paradox</b></p><p>In this episode of Old School New Tech, host Ran Aroussi explores the inherent contradiction in building AI systems with agent workflows. He argues that combining autonomous agents with predetermined workflows creates complexity and brittleness, making these systems difficult to scale. Ran provides examples that highlight the pitfalls of this approach and advocates for a shift toward giving AI agents context and knowledge to enable reasoning and adaptability within defined guardrails, rather than rigid workflows. This episode is sponsored by Automaze, offering CTO-as-a-Service for startups and businesses.<br><br>00:00 Introduction to AI Contradictions<br>01:31 The Oxymoron of Agent Workflows<br>02:42 Concrete Example: Financial Service Incident<br>03:54 Guardrails vs. Workflows<br>05:24 The Alternative: Context and Knowledge<br>07:19 Evaluating Your AI Systems<br>09:19 Conclusion and Final Thoughts</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <content:encoded>
        <![CDATA[<p>The Contradiction of AI Agent Workflows: Why Your Systems Might Be Breaking</p><p><b>The Agent Workflow Paradox</b></p><p>In this episode of Old School New Tech, host Ran Aroussi explores the inherent contradiction in building AI systems with agent workflows. He argues that combining autonomous agents with predetermined workflows creates complexity and brittleness, making these systems difficult to scale. Ran provides examples that highlight the pitfalls of this approach and advocates for a shift toward giving AI agents context and knowledge to enable reasoning and adaptability within defined guardrails, rather than rigid workflows. This episode is sponsored by Automaze, offering CTO-as-a-Service for startups and businesses.<br><br>00:00 Introduction to AI Contradictions<br>01:31 The Oxymoron of Agent Workflows<br>02:42 Concrete Example: Financial Service Incident<br>03:54 Guardrails vs. Workflows<br>05:24 The Alternative: Context and Knowledge<br>07:19 Evaluating Your AI Systems<br>09:19 Conclusion and Final Thoughts</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Fri, 26 Sep 2025 13:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/baf15a6b/25acbf27.mp3" length="7062815" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/hNNg3fIAQxuO38-adNrbprnkeNtMXJ7bqJNhGzevGlE/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wYzE0/NDNiN2U0MjYyYmM0/NmI5N2Q5ODQ4ODIz/MDg3MS5qcGc.jpg"/>
      <itunes:duration>586</itunes:duration>
      <itunes:summary>The Contradiction of AI Agent Workflows: Why Your Systems Might Be Breaking The Agent Workflow Paradox In this episode of Old School New Tech, host Ran Aroussi explores the inherent contradiction in building AI systems with agent workflows. He argues that combining autonomous agents with predetermined workflows creates complexity and brittleness, making these systems difficult to scale. Ran provides examples that highlight the pitfalls of this approach and advocates for a shift toward giving ...</itunes:summary>
      <itunes:subtitle>The Contradiction of AI Agent Workflows: Why Your Systems Might Be Breaking The Agent Workflow Paradox In this episode of Old School New Tech, host Ran Aroussi explores the inherent contradiction in building AI systems with agent workflows. He argues that</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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    <item>
      <title>The Illusion of Progress</title>
      <itunes:season>1</itunes:season>
      <podcast:season>1</podcast:season>
      <itunes:title>The Illusion of Progress</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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        <![CDATA[<p>The Illusion of Progress: Avoiding the Faster Horses Trap in 2025<br><br>In this episode of Old School New Tech, host Ran Aroussi delves into the difference between real progress and the illusion of progress, using historical examples like the retail industry's initial forays onto the internet and mobile phone companies' reactions to the iPhone. He discusses how today's 'make me an AI' mentality mirrors past missteps and emphasizes the need for true transformation rather than superficial changes. Aroussi challenges companies to ask the hard, transformative questions necessary for genuine progress. Sponsored by Automaze, this episode urges listeners to reconsider how they integrate new technologies into their business strategies.<br><br>00:00 Introduction and Sponsor Message<br>00:52 A Look Back at 1997: The Illusion of Progress<br>02:14 Nokia and the iPhone Revolution<br>03:20 The Real Revolution: Beyond Incremental Improvements<br>04:36 AI in 2025: The New 'Make Me an Internet'<br>05:33 Asking the Hard Questions for True Transformation<br>06:49 Conclusion and Call to Action</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </description>
      <content:encoded>
        <![CDATA[<p>The Illusion of Progress: Avoiding the Faster Horses Trap in 2025<br><br>In this episode of Old School New Tech, host Ran Aroussi delves into the difference between real progress and the illusion of progress, using historical examples like the retail industry's initial forays onto the internet and mobile phone companies' reactions to the iPhone. He discusses how today's 'make me an AI' mentality mirrors past missteps and emphasizes the need for true transformation rather than superficial changes. Aroussi challenges companies to ask the hard, transformative questions necessary for genuine progress. Sponsored by Automaze, this episode urges listeners to reconsider how they integrate new technologies into their business strategies.<br><br>00:00 Introduction and Sponsor Message<br>00:52 A Look Back at 1997: The Illusion of Progress<br>02:14 Nokia and the iPhone Revolution<br>03:20 The Real Revolution: Beyond Incremental Improvements<br>04:36 AI in 2025: The New 'Make Me an Internet'<br>05:33 Asking the Hard Questions for True Transformation<br>06:49 Conclusion and Call to Action</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </content:encoded>
      <pubDate>Fri, 12 Sep 2025 14:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
      <enclosure url="https://media.transistor.fm/8fca8890/73014d2d.mp3" length="5310198" type="audio/mpeg"/>
      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/UgwnULrxAXX5XH5M0arY4Es2WeIo0M14XLdy7CNQL98/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS84Y2Vm/YWRlYzlmNGE2NjNi/ZTVmOTVhMjZmNDY2/YzVhYS5qcGc.jpg"/>
      <itunes:duration>440</itunes:duration>
      <itunes:summary>The Illusion of Progress: Avoiding the Faster Horses Trap in 2025  In this episode of Old School New Tech, host Ran Aroussi delves into the difference between real progress and the illusion of progress, using historical examples like the retail industry's initial forays onto the internet and mobile phone companies' reactions to the iPhone. He discusses how today's 'make me an AI' mentality mirrors past missteps and emphasizes the need for true transformation rather than superficial changes. A...</itunes:summary>
      <itunes:subtitle>The Illusion of Progress: Avoiding the Faster Horses Trap in 2025  In this episode of Old School New Tech, host Ran Aroussi delves into the difference between real progress and the illusion of progress, using historical examples like the retail industry's</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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    <item>
      <title>Bridging Traditional Business with AI and Decentralized Identity w/ Sumit Vekariya</title>
      <itunes:season>1</itunes:season>
      <podcast:season>1</podcast:season>
      <itunes:title>Bridging Traditional Business with AI and Decentralized Identity w/ Sumit Vekariya</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/5b0ceb3c</link>
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        <![CDATA[“Every business is different, but communication and networking are key. If you understand how they affect your business and apply the right tools to improve them, you’ll get the best out of tech — no matter your industry.”<br><br>– Sumit Vekariya<p><br></p><p>In this episode of 'Old School New Tech,' I interview Sumit Vekariya, the founder of ZKred, a company specializing in Web3-based, privacy-preserving, verifiable credentials. The episode explores how Sumit's lean team operates remotely, leveraging various AI tools to enhance productivity. We also delve into the challenges and potential improvements in AI technology, highlighting Sumit's vision for the future of decentralized identity management.</p><p>You can find Sumit on X/Twitter <a href="https://twitter.com/Sarkazein7">@Sarkazein7</a></p><p><b>Chapters</b></p><p>00:00 Welcome to Old School New Tech<br>00:28 Introducing Automaze: Your Modern Business Partner<br>01:09 Meet Sumit Vekariya: Innovator in Privacy and Identity<br>01:37 Exploring ZKREDA: Simplifying Identity Management<br>03:01 The Future of Verifiable Credentials<br>05:41 Lean Operations and Smart Tooling<br>14:02 Balancing Stealth Mode and Growth<br>14:45 Team Dynamics and Remote Work<br>16:30 Future Goals and Ambitions<br>18:35 Final Thoughts and Advice for Founders<br>20:25 Connect with Sumit Vekariya</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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        <![CDATA[“Every business is different, but communication and networking are key. If you understand how they affect your business and apply the right tools to improve them, you’ll get the best out of tech — no matter your industry.”<br><br>– Sumit Vekariya<p><br></p><p>In this episode of 'Old School New Tech,' I interview Sumit Vekariya, the founder of ZKred, a company specializing in Web3-based, privacy-preserving, verifiable credentials. The episode explores how Sumit's lean team operates remotely, leveraging various AI tools to enhance productivity. We also delve into the challenges and potential improvements in AI technology, highlighting Sumit's vision for the future of decentralized identity management.</p><p>You can find Sumit on X/Twitter <a href="https://twitter.com/Sarkazein7">@Sarkazein7</a></p><p><b>Chapters</b></p><p>00:00 Welcome to Old School New Tech<br>00:28 Introducing Automaze: Your Modern Business Partner<br>01:09 Meet Sumit Vekariya: Innovator in Privacy and Identity<br>01:37 Exploring ZKREDA: Simplifying Identity Management<br>03:01 The Future of Verifiable Credentials<br>05:41 Lean Operations and Smart Tooling<br>14:02 Balancing Stealth Mode and Growth<br>14:45 Team Dynamics and Remote Work<br>16:30 Future Goals and Ambitions<br>18:35 Final Thoughts and Advice for Founders<br>20:25 Connect with Sumit Vekariya</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <pubDate>Thu, 15 May 2025 15:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/ZMRczy6EACpQ_rHZyGYA2cWmXSe_n7ip3L2U01etLco/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9jODNm/OTI5MjA2ODVkYjUz/NzVhMWI3Njk2Zjkx/MTc4Yy5qcGc.jpg"/>
      <itunes:duration>1285</itunes:duration>
      <itunes:summary>“Every business is different, but communication and networking are key. If you understand how they affect your business and apply the right tools to improve them, you’ll get the best out of tech — no matter your industry.”  – Sumit Vekariya  In this episode of 'Old School New Tech,' I interview Sumit Vekariya, the founder of ZKred, a company specializing in Web3-based, privacy-preserving, verifiable credentials. The episode explores how Sumit's lean team operates remotely, leveraging various ...</itunes:summary>
      <itunes:subtitle>“Every business is different, but communication and networking are key. If you understand how they affect your business and apply the right tools to improve them, you’ll get the best out of tech — no matter your industry.”  – Sumit Vekariya  In this episo</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
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      <title>Scaling Smart with No-Code Tools and AI w/Yuval Keshtcher</title>
      <itunes:season>1</itunes:season>
      <podcast:season>1</podcast:season>
      <itunes:title>Scaling Smart with No-Code Tools and AI w/Yuval Keshtcher</itunes:title>
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        <![CDATA[<p>In this episode of Old School New Tech, host Ran Aroussi interviews Yuval Keshtcher, the founder of UXWritingHub.com. Yuval shares his journey from a graphic designer to a tech-enabled entrepreneur, highlighting how he identified a gap in UX writing and built an education platform for designers, writers, and product teams. </p><p>The discussion delves into the no-code tools and automations Yuval used to scale UX Writing Hub, including Airtable, Kajabi, ActiveCampaign, and Make.com. Yuval also discusses AI's profound impact on his business operations, dramatically reducing manual tasks and enabling rapid growth. </p><p>The episode provides insights into creating a seamless user experience, the importance of content design, and how AI is transforming business operations and user research. Tune in for valuable tips on leveraging modern tools to blend traditional business sense with innovative technology.<br><br>00:00 Introduction and Sponsor Message<br>01:01 Meet Yuval Ke: Founder of UX Writing Hub<br>01:43 The Evolution of UX Writing Hub<br>03:40 Understanding UX Copywriting<br>05:20 Building a No-Code Tech Stack<br>11:29 Automation and AI in Business<br>21:07 Future of AI and UX Design<br>25:17 Conclusion and Contact Information</p><p>You can find Yuval on LinkedIn at https://linkedin.com/in/yuvalkesh and at https://UXWritingHub.com</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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        <![CDATA[<p>In this episode of Old School New Tech, host Ran Aroussi interviews Yuval Keshtcher, the founder of UXWritingHub.com. Yuval shares his journey from a graphic designer to a tech-enabled entrepreneur, highlighting how he identified a gap in UX writing and built an education platform for designers, writers, and product teams. </p><p>The discussion delves into the no-code tools and automations Yuval used to scale UX Writing Hub, including Airtable, Kajabi, ActiveCampaign, and Make.com. Yuval also discusses AI's profound impact on his business operations, dramatically reducing manual tasks and enabling rapid growth. </p><p>The episode provides insights into creating a seamless user experience, the importance of content design, and how AI is transforming business operations and user research. Tune in for valuable tips on leveraging modern tools to blend traditional business sense with innovative technology.<br><br>00:00 Introduction and Sponsor Message<br>01:01 Meet Yuval Ke: Founder of UX Writing Hub<br>01:43 The Evolution of UX Writing Hub<br>03:40 Understanding UX Copywriting<br>05:20 Building a No-Code Tech Stack<br>11:29 Automation and AI in Business<br>21:07 Future of AI and UX Design<br>25:17 Conclusion and Contact Information</p><p>You can find Yuval on LinkedIn at https://linkedin.com/in/yuvalkesh and at https://UXWritingHub.com</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <pubDate>Thu, 08 May 2025 14:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/4yt_U79Z7nTT2-6UZsiPaxv320MivVW7Q2Ij7TECAyw/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8zYjBi/YTIwNDA5OTIyZmNi/ZGNlNGMzNTAwNDZk/NTllNC5qcGc.jpg"/>
      <itunes:duration>1559</itunes:duration>
      <itunes:summary>In this episode of Old School New Tech, host Ran Aroussi interviews Yuval Keshtcher, the founder of UXWritingHub.com. Yuval shares his journey from a graphic designer to a tech-enabled entrepreneur, highlighting how he identified a gap in UX writing and built an education platform for designers, writers, and product teams.  The discussion delves into the no-code tools and automations Yuval used to scale UX Writing Hub, including Airtable, Kajabi, ActiveCampaign, and Make.com. Yuval also ...</itunes:summary>
      <itunes:subtitle>In this episode of Old School New Tech, host Ran Aroussi interviews Yuval Keshtcher, the founder of UXWritingHub.com. Yuval shares his journey from a graphic designer to a tech-enabled entrepreneur, highlighting how he identified a gap in UX writing and b</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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      <title>Kick-Off: Bridging the Gap Between Traditional Business and Modern Technology</title>
      <itunes:season>1</itunes:season>
      <podcast:season>1</podcast:season>
      <itunes:title>Kick-Off: Bridging the Gap Between Traditional Business and Modern Technology</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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        <![CDATA[<p>Old School New Tech: Bridging Traditional Business and Modern Technology<br><br>In the inaugural episode of Old School New Tech, host Ran Aroussi, also known as 'Code Daddy,' introduces the podcast tailored for business owners who are not focused on building the next big tech startup but understand the importance of incorporating technology into their operations. Ran shares his extensive background in web development and his motivations for starting the podcast. He highlights the gap in tech content for traditional businesses and outlines what listeners can expect in future episodes, including discussions on automation, AI, useful tools, and real-world success stories. Ran emphasizes the importance of integrating tech into business workflows to save time, reduce stress, and grow efficiently, without necessitating a deep tech background.<br><br>00:00 Welcome to Old School New Tech<br>00:18 The Purpose of This Podcast<br>01:55 My Journey in Tech<br>03:20 Introducing Automaze<br>04:28 What to Expect from Future Episodes<br>05:36 The Importance of Tech in Business<br>06:52 Wrapping Up and Call to Action</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
      </description>
      <content:encoded>
        <![CDATA[<p>Old School New Tech: Bridging Traditional Business and Modern Technology<br><br>In the inaugural episode of Old School New Tech, host Ran Aroussi, also known as 'Code Daddy,' introduces the podcast tailored for business owners who are not focused on building the next big tech startup but understand the importance of incorporating technology into their operations. Ran shares his extensive background in web development and his motivations for starting the podcast. He highlights the gap in tech content for traditional businesses and outlines what listeners can expect in future episodes, including discussions on automation, AI, useful tools, and real-world success stories. Ran emphasizes the importance of integrating tech into business workflows to save time, reduce stress, and grow efficiently, without necessitating a deep tech background.<br><br>00:00 Welcome to Old School New Tech<br>00:18 The Purpose of This Podcast<br>01:55 My Journey in Tech<br>03:20 Introducing Automaze<br>04:28 What to Expect from Future Episodes<br>05:36 The Importance of Tech in Business<br>06:52 Wrapping Up and Call to Action</p><p>-----</p><p>This podcast is sponsored by <b>Automaze</b>, the fractional CTO partner for founders and operators. Whether you’re building a high-tech MVP or modernizing internal ops with AI and automation, Automaze can help you scale without the overhead of a full-time team.</p><p>Learn more: <a href="https://automaze.io/">automaze.io</a></p>]]>
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      <pubDate>Fri, 02 May 2025 14:00:00 +0100</pubDate>
      <author>Ran Aroussi</author>
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      <itunes:author>Ran Aroussi</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/X-GmKkvuwL2ocryVq2B-yhlwtUVYPVtrdLNVLUrVs8s/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80MzI3/N2IxNjNkMmJiY2Y1/MjczYjNjN2IzOTVk/YWI1Yy5qcGc.jpg"/>
      <itunes:duration>456</itunes:duration>
      <itunes:summary>Old School New Tech: Bridging Traditional Business and Modern Technology  In the inaugural episode of Old School New Tech, host Ran Aroussi, also known as 'Code Daddy,' introduces the podcast tailored for business owners who are not focused on building the next big tech startup but understand the importance of incorporating technology into their operations. Ran shares his extensive background in web development and his motivations for starting the podcast. He highlights the gap in tech conten...</itunes:summary>
      <itunes:subtitle>Old School New Tech: Bridging Traditional Business and Modern Technology  In the inaugural episode of Old School New Tech, host Ran Aroussi, also known as 'Code Daddy,' introduces the podcast tailored for business owners who are not focused on building th</itunes:subtitle>
      <itunes:keywords>agentic AI, startups, software engineering, entrepreneurship, open source, AI agents, production systems, tech business, founders, developer tools, artificial intelligence, engineering leadership</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
      <podcast:person role="Host" href="https://aroussi.com" img="https://img.transistorcdn.com/uds4Njr9fZKZQZRXWVBVTeAHdx_Fs5KFdHxN_QAkTRU/rs:fill:0:0:1/w:800/h:800/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS83Yjdh/Y2M5ZWQyNGJmYTNi/OTMyMDg3MTJkZTJm/YjU4OS5qcGVn.jpg">Ran Aroussi</podcast:person>
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