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.

Why Software Factories Still Need Humans in the Loop

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.

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My new book, "Company-Scale Agentic AI: The operator's guide to a company that runs on intelligence", is out.
Amazon link: https://www.amazon.com/dp/B0HCDKK79L

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—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.

00:00 Welcome and Topic
00:29 Why I Wrote It
02:20 AI Is Blind
06:45 Building Company Brain
07:28 The Five Step Loop
11:15 Automation vs Agents
13:53 Gated Readiness Dial
17:01 Role Based Access
20:58 Morning Brief Extension
22:34 Meet People Where They Work
24:57 Loop Recap and Wrap

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 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.

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.

All of it exists to protect the context in my head. Everything else is scaffolding.

00:00 Welcome and Envapor
00:50 Why My Workflow
02:16 Cloud Local Setup
04:03 Coding Tools Stack
07:26 Personal Productivity Apps
08:51 Self-Hosting Philosophy
09:46 Multi-Agent Workflow
10:35 Time Management Decisions
11:54 Automation Buckets
14:40 Research and Judgment
17:25 Email and Gated AI
19:45 Running Automaze VarOps
20:58 Inbound Leads Flywheel
22:08 FDE Founder Onboarding
25:16 Wrap Up and Newsletter

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 how your app loads config.

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.

Two and a half hours, no script, no highlight reel. The finished tool is open source and linked below.

🔗 Repo: https://github.com/automazeio/envapor
🔗 Full unedited build (YouTube): https://www.youtube.com/watch?v=p4BYP9DFp9c&list=PLWwTwUPfg-UU

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 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.

00:00 AI Adoption Paradox
00:40 Fear of Looking Behind
01:37 Corporate Make Me AI
03:01 Pick One Tool First
05:04 Workflow First Training
08:10 Measure Real Outcomes
11:04 Champions And Meetups
15:07 Expect The Productivity Dip
17:22 Do The Groundwork
17:45 Use Built In AI
20:34 Stop Chasing New Models
22:14 When To Switch Models
25:22 Recap And Farewell

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 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. 

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).

00:00 Episode Kickoff
00:41 Terminal Never Dies
01:16 CLI Origins and Comeback
04:49 Why CLI Wins
05:15 Pipes Explained
06:05 Real World Speed Story
08:11 AI Tools Under the Hood
09:59 CLI for Everyone
13:20 Beginner CLI Roadmap
15:36 Power Without Guardrails
17:32 CLI vs GUI Wrap
20:56 Final Thoughts and Outro

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 exception being an operator/chief-of-staff-style role that bridges build and sell. 

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. 

Administrative functions should be outsourced early, later becoming shared resources at the firm level across multiple pods.

00:00 Welcome Back
00:29 Builders And Sellers
02:45 When Management Returns
03:11 Chief Of Staff Operator
04:27 Junior Dev Apprentices
08:08 Learned Vs Gained Experience
10:26 Sales Pod Mirror
13:32 Outsource And Shared Resources
17:10 Is Middle Layer Relocated
20:32 Wrap Up And Takeaways

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 engineering control plane), and working for his software agency Automaze. 

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. 

Future episodes will feature live debates on shifting from software teams to software systems.

00:00 Podcast Relaunch Intro
01:03 Live Format and Co-Host
01:56 What I've Been Building
03:39 Meet Muximus
04:54 Why Build MUXI
07:01 Failure Modes and Observability
09:12 Screen Share and Request Lifecycle
10:08 Open Source Licensing Debate
17:21 Future Episodes and Sign Off
18:47 Tech Demo
20:50 Full Circle Closing

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io

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 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.

00:00 Introduction to AI Contradictions
01:31 The Oxymoron of Agent Workflows
02:42 Concrete Example: Financial Service Incident
03:54 Guardrails vs. Workflows
05:24 The Alternative: Context and Knowledge
07:19 Evaluating Your AI Systems
09:19 Conclusion and Final Thoughts

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This podcast is sponsored by Automaze, 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.

Learn more: automaze.io