Digital Thoughts: AI From the Trenches

Pawel Jozefiak

What actually happens when an e-commerce manager builds AI agents, tests every model, and lets them run night shifts. No hype, just results. thoughts.jock.pl

Episodes

  1. The Compounding Agent

    11 APR

    The Compounding Agent

    Episode four. What happens when hobbyist AI starts growing up into production AI, and how the lessons compound if you pay attention. First, a rare look inside the pros’ toolbox. Claude Code’s source got leaked. Instead of treating it like drama, I treated it like a free masterclass. Tool permission gating, risk classification, blocking budgets, memory management, multi-agent coordination, feature flags like autoDream and KAIROS. Most people building agents today are reinventing patterns that professional teams already solved. You learn more from reading one real production codebase than from ten tutorial posts. Then, applying those lessons to my own stack. My $599 Mac Mini M4 runs a 35 billion parameter model at 17.3 tokens per second. That alone is surprising. Then I swapped the brain of the classification tier to Gemma 4, and classification went from 8.5 seconds down to 1.9 seconds. A 4.4x speedup. I also disabled chain-of-thought on simple classification calls and got 30x faster results with identical accuracy. Production AI isn’t one giant model doing everything. It’s the right model for the right job, and most jobs don’t need the biggest one. Finally, handing the wisdom forward. After six months of running this thing daily, I wrote a beginner’s guide to building your first agent. Folder structure is the architecture. The nine common mistakes people make early. Model routing across Haiku, Sonnet, and Opus tiers. Progressive permissions. The context window trap. Overnight automation is where the real leverage lives. Not a hype piece. A map for the person walking in the door behind me. The thread: compounding expertise. Study how the pros build. Optimize your own stack with those patterns. Teach the next person who walks in. The gap between hobbyist AI and production AI is closing, and the fastest way to cross it is learning from real systems instead of tutorials. Posts discussed in this episode: - Claude Code’s Source Got Leaked. Here’s What’s Actually Worth Learning (https://thoughts.jock.pl/p/claude-code-source-leak-what-to-learn-ai-agents-2026) - My $600 Mac Mini Runs a 35B AI Model. Yesterday I Swapped Its Brain (https://thoughts.jock.pl/p/local-llm-35b-mac-mini-gemma-swap-production-2026) - How to Build Your First AI Agent (Basics) (https://thoughts.jock.pl/p/how-to-build-your-first-ai-agent-beginners-guide-2026) Get full access to Digital Thoughts at thoughts.jock.pl/subscribe

    26 min
  2. When AI Meets Reality

    23 MAR

    When AI Meets Reality

    Episode three. What happens when AI stops being theoretical and starts touching real money and real hardware. First, a failure worth studying. I told my AI agent to build one useful app every day. It produced unit converters, color pickers, base64 encoders. Statistically average, completely forgettable. Nobody cared. Then I changed one word: “experiments” instead of “apps,” with specific creative direction. One of those experiments hit #3 on Hacker News. The lesson: AI execution costs dropped to near zero. The only competitive advantage left is human taste and vision. Then, applying that lesson to revenue. I directed my agent to package what I know into digital products and sell them. $355 in three weeks against $400/month in AI costs. Near break-even on month one. The real story is the “execution gap”: most experts never monetize their knowledge because packaging, marketing, and distribution are hard. The agent handles all of that. What happens when that gap closes for everyone? Finally, where this is heading. I ran Qwen 3.5, a 9 billion parameter model, on my MacBook and iPhone. No cloud. No subscription. No internet. The gap between local and cloud AI is closing fast. If you can run capable AI on hardware you already own, the barrier to entry for everything above collapses. The thread: AI needs human direction to create value. The tools to provide that direction are becoming radically cheaper. The bottleneck isn’t technology anymore. It’s having something worth saying. Posts discussed in this episode: - I Told My AI to Build Apps Every Day. The Results Were Painfully Boring. Here’s the Lesson (https://thoughts.jock.pl/p/directed-ai-experiments-vibe-business) - My AI Costs $400/Month. This Month It Made $355 (https://thoughts.jock.pl/p/project-money-ai-agent-value-creation-experiment-2026) - I Ran Local AI on My MacBook and iPhone. The Gap Is Closing Fast (https://thoughts.jock.pl/p/local-llm-macbook-iphone-qwen-experiment) Get full access to Digital Thoughts at thoughts.jock.pl/subscribe

    19 min
  3. How I Taught My AI Agent to Think

    17 MAR

    How I Taught My AI Agent to Think

    Episode two. Three stages of giving an AI agent real independence. First, a counterintuitive discovery: more instructions made my agent worse. I went from 471 lines of rules down to 61 by replacing abstract adjectives with concrete behaviors. “Principle beats rule” turned out to be the single biggest performance unlock. Then, teaching it to learn. Error logging, structured lessons, and an identity layer that knows who I am. But MIT research shows personalized profiles increase sycophancy by 33-45%. The AI starts telling you what you want to hear instead of catching your mistakes. True autonomy requires friction, not agreement. Finally, giving it a physical home. Migrating to a dedicated Mac Mini broke everything: no display meant no UI automation (solved with a virtual 5K screen hack), hundreds of hard-coded paths pointed to folders that didn’t exist, and the agent burned through API credits stuck in silent error loops. The fix: full root authority inside a contained blast radius. If the AI deletes the entire drive, it literally doesn’t matter. The payoff: a self-improving agent running 24/7 on its own machine, with its own iCloud account, reachable via iMessage like a coworker. Posts discussed in this episode: - I Built a Personal AI Agent Called Wiz (https://thoughts.jock.pl/p/how-i-structure-claude-md-after-1000-sessions) - My AI Agent Learns From Its Own Mistakes. Here’s the Architecture (https://thoughts.jock.pl/p/wiz-ai-agent-self-improvement-architecture) - I Gave My AI Agent Its Own Computer. Here’s Every Lesson From 72 Hours of Migration (https://thoughts.jock.pl/p/mac-mini-ai-agent-migration-headless-2026) Get full access to Digital Thoughts at thoughts.jock.pl/subscribe

    22 min
  4. Building an AI Agent That Runs Night Shifts

    18 FEB

    Building an AI Agent That Runs Night Shifts

    First episode. I built an AI agent called Wiz that runs night shifts, deploys apps, and once changed my password twice in one night. This episode covers why I built it, what broke, and why a cheaper model made it better. Based on posts from Digital Thoughts - subscribe at thoughts.jock.pl for the full story. I’ve been writing Digital Thoughts for a while now, and some of you told me you’d rather listen than read. Fair enough. So I’m experimenting with a podcast version - AI-generated conversations based on my posts. Not me reading articles out loud, but two AI hosts digging into the ideas, arguing about them, and finding connections I didn’t even see when writing. This first episode covers the full arc of building Wiz - my personal AI agent. From “why would you build your own instead of using ChatGPT?” to the moment it started writing its own skills without asking. We get into the failures: tasks that looped infinitely, passwords changed twice in one night, and the counterintuitive discovery that downgrading to a cheaper model made the whole thing better. If you’re hearing this on Spotify or Apple Podcasts - every episode is based on posts from Digital Thoughts , where I write about using AI daily as a practitioner, not a pundit. Subscribe there if you want the full picture. Posts discussed in this episode: - I Built a Personal AI Agent Called Wiz - Why I Built My Own AI Agent Instead of Using OpenClaw - My AI Agent Runs Night Shifts, Builds Apps & Earns Revenue - Why I Switched My AI Agent from Opus to Haiku Get full access to Digital Thoughts at thoughts.jock.pl/subscribe

    15 min
  • 4 Episodes

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What actually happens when an e-commerce manager builds AI agents, tests every model, and lets them run night shifts. No hype, just results. thoughts.jock.pl

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