Chris Shanku and Sash Mohapatra - two ex-Microsoft builders - demo one real AI tool per episode, live and unedited. Real people. Real tools. No retakes.
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Sash drives this one, and he does not start with the tool. He starts with the failure. He was running Claude Code across two Macs, so his first move was the obvious one: put his intelligence folder of Markdown context files in a git repo and sync it to both machines. That worked until it did not. Every session ended with a push he had to remember, and every session started with a pull he had to remember, and forgetting either one meant an agent was working from yesterday's truth. Then he added a Hermes agent on a Hostinger VPS, then revived an old OpenClaw agent on another one, and suddenly th
Kevin Johnson - KJ - leads the AI and automation practice at Pinnacle Financial Partners, a bank that closed a merger of equals in January between two institutions north of $60 billion each. He has been doing this hand-to-hand since ChatGPT launched in November 2022. Chris and Sash brought him on to answer the question every enterprise team is quietly stuck on: what actually has to be true before generative AI works in production. His answer is that almost none of it is about AI. Pinnacle's ability to move fast on generative AI traces back five or six years, to a modernization journey that rip
Anthropic started watermarking every piece of text Claude produces, worldwide, three days before this episode and Sash and Chris jumped on an unplanned morning show to work through what it actually does. The watermark has two parts: an invisible mark woven into the text itself, and C2PA metadata (the same provenance standard Microsoft and Google use) attached to generated artifacts like images and documents. It is not opt-in - Anthropic is required to ship it everywhere under Article 50 of the EU AI Act, the same transparency requirement that is starting to do for AI content what GDPR did for
Rene Charbonneau returns three months after EP.009 to report on the promise he made on his way out the door: refactor his AgentOS. He didn't refactor it. He blew it up. What started as a two-week attempt to untangle a ball of yarn he and Claude had built together turned into a full rebuild once he realized the old assumptions (a single hand-built harness, a builder-first workflow, memory scattered across machines) no longer held. The teardown included literally retiring his always-on agent, Jim, mid-project, and returning to rebuild the connection layer (SSH, approvals, skills) from scratch on
Chris opened the show with roughly thirty seconds of himself that was not himself. The voice, the face, and the script were all generated, and the reveal was the point: the pipeline that produced that cold open is the same one that produces the trailer, the highlight reel, and the product demos. He then kicked off two real jobs live on stream, the EP.020 trailer and an iLink product demo, and let them run in the background while he and Sash talked through how the machinery works. The shape of the pipeline is the same everywhere Chris uses it. A research skill finds material inside a defined ni
Sash drove this one, and the framing he opened with does most of the work: a model is a faucet of intelligence, but a faucet on its own does not plumb a house. An agent is a model plus a harness, and the harness is the environment the model actually runs inside. It is the app. The chat window is a harness. Claude Code is a harness. Hermes and OpenClaw are harnesses. What the harness supplies is everything the model cannot supply itself: tools, memory, skills, and a scheduler. Take those away and an agent collapses back into a chatbot. The useful part of the episode is the ladder Sash laid out.
Chris drove this one solo-demo style, no guest, closing out the AI-for-enterprise arc for now to make room for other topics. He walked through TheGrove, his personal agent system, and specifically the Follow-up Engine: an agent that reads meeting notes and call transcripts and drafts the follow-up email in his own voice. The demo used a sanitized mock transcript (a made-up company, "Harborline Financial") - the agent identified attendees, pulled out three to five commitments, let Chris reassign a miscategorized commitment to the right person, and then drafted a follow-up email trained on hundr
This is episode four of the AI for Enterprise series and the most practical one yet. Sash walks through what Claude skills actually are - a folder with a skill.md file written in plain English, plus any reference files, templates, or scripts the workflow needs. The standard was released by Anthropic in November 2025, and within two months 16 other vendors adopted it, including Microsoft (it shows up as skill.md in M365 Copilot Agent Builder). That portability is the point: a skill you write for Claude today can move to Replit or Cursor unchanged. The bulk of the conversation is about why skill
Chris and Sash spend this episode working through a problem most enterprise leaders already know exists but haven't acted on: employees running AI tools the company never approved. They define Shadow AI plainly - using a model or AI system your company hasn't sanctioned to do work - and draw a direct line from the old Shadow IT problem (pulling data into a personal Dropbox) to the new one (uploading confidential documents to a personal Perplexity or OpenAI free subscription with model training not disabled). The key difference they land on is that with Shadow AI, there's always a third party i
This is the re-record of episode two of the AI for Enterprise series - the first take went out with Sash's audio muted, so Chris and Sash ran it back. The topic is the one landing in every CFO and CTO inbox right now: why AI token budgets are blowing up and what to actually do about it. The catalyst is real - Uber gave engineers $1,500 a month of API budget and the whole company burned its entire year's allocation in four months. The point Sash keeps returning to is that an AI budget cannot be a single line item. The money has to go across people, process, and technology - rethinking workflows
Chris opens this episode - the first of a four-part AI for Enterprise series - with a challenge to the room: Responsible AI is the part of every AI presentation where people pull out their phones. Not anymore. The regulatory environment has shifted, and a $35 million EU AI Act fine has a way of making even the most skeptical CIO lean forward. Chris draws directly from his time at Microsoft, where he presented the company's OpenAI partnership to enterprise executives and got deep into how Microsoft embedded Responsible AI into every engineering group, reporting to Satya's ELT. The core of the e
Sash drives this one, screen-sharing the always-on AI agent he runs in the cloud and showing how Claude Code became the tool that keeps it alive. His agent (Blaze) runs on a standard OpenClaw image inside a Hostinger Linux VPS, controlled over Telegram, and reachable only across a private Tailscale network so no one outside his own devices can touch it. The kicker: Sash had never used Linux. He never SSH'd into the box by hand or hand-edited a config. He asked Claude Code to connect over Tailscale, generate an SSH key, install OpenClaw, and wire it up. Chris runs the same pattern with a Hermes
We called an audible. Fable 5 - Anthropic's first Mythos-class model the public can actually touch - dropped this week, so the planned episode went out the window. Sash drove. Instead of a launch-day reaction, he showed two days of real work: he handed Fable 5 a job he'd been deferring for weeks, a full 31-lesson curriculum review spread across roughly 40 markdown files, with instructions to reformat lesson timing, reconcile contradictions between lessons, and update the Claude Code configuration by researching current Anthropic docs. Opus 4.1 had choked on the same task earlier with context i
Chris drives EP.10, a direct sequel to last week's monitoring episode with Rene. He opens with a failure: he added a feature to his Life OS (TheGrove), it tested green, and three days later every automation had stopped. The one that stung was the 8am morning-journal prompt - the first automation he ever built and the thing the rest of the system sits on top of. The trigger was tiny: a change that moved a single file. Claude ran 671 unit tests, every one came back green, because the tests checked the code, not whether the real automations still fired. The failure was silent - no alert, he only
Rene Charbonneau walked through his evolution from LibreChat to OpenClaw to Claude Code - and the $200/day Opus invoice that turned into a "welcome to AI" tuition payment. The trigger that pulled him in was building a household chat so his family could share AI context without forwarding screenshots. Once a non-developer saw his own commits land in a Git repo and ship across environments, he stopped treating AI as a tool and started treating it as a workflow problem. The core of the episode was a four-phase refactor loop he iterates on weekly: foundation (slash commands first, plus a secrets h
Sash drives this week, walking Chris through MCP (Model Context Protocol) - the standard Anthropic released in November 2024 that lets LLMs talk to external apps, data, and tools. Chris frames it as "the API for agents" and Sash confirms it: a shared language that agents speak to reach Gmail, Granola, n8n, anything with a server. They tour the Claude connector marketplace (Gmail, Calendar, G Drive, Granola), inspect a connector's tool list, and show the per-tool approval model that controls what an agent is allowed to do without a prompt. The conversation gets practical fast. Sash uses a third
Chris breaks from the usual Claude-centric format and walks through Hermes, an always-on autonomous agent runtime from Nous Research. The framing: Hermes is the body of his personal AI stack - lightweight, learning, always listening, primarily reached over iMessage via BlueBubbles. Claude Code is the brain - heavy systems work, infrastructure, long sessions in front of a terminal or VS Code. Each Hermes profile starts with a SOUL.md that defines voice, posture, and behavior, then layers on skills (reusable patterns Hermes can create on its own after the third repetition) and a built-in cron sy
Sash walks through the Orchestrator Method - his five-principle framework for delegating real work to AI without losing the plot. The principles: define the end state, plan it with AI, deconstruct and delegate, stay in the loop, and build systems for reuse. None of it is technically novel - it is the same delegation muscle that any consultant, engineer, or manager already has - but Sash names it explicitly so you can run it as a checklist when you are wiring up agents instead of reinventing the wheel each time. The conversation moves between the meta (when to stay in the loop, when to step out
Chris and Sash open Claude Code live and run the two commands that ship this show. Chris kicks off /turnover against the previous week's recording at the top of the segment - on stream, while everything else is happening - and then walks through what's actually inside the orchestration. The episode is the messy version on purpose: a live demo with the artifacts visible, errors visible, and the human-in-the-loop checkpoints built in. The two skills are /episode and /turnover. /episode scaffolds next week from a topic - title, tagline, description, LinkedIn social card, OBS overlay, episode page
Sash demos Claude Cowork - the same engine as Claude Code, but desktop-only and task-based instead of chat-based. The pitch: knowledge workers in non-technical roles get an agentic surface for the work they actually do every day. Cowork runs sandboxed on your Mac, takes a folder for context, plans the task, and executes. Chris streams in from London on an iPad to underline the point that the show ships from anywhere with tmux. The live demo runs an "itemize receipts and fill expense sheet" workflow. Cowork picks up 15 PDF receipts, extracts them, and writes the data into the expense template.
Sash walks through a multi-agent deep research workflow he built inside Claude Code after getting burned by context-bloat and hallucinations in Perplexity and ChatGPT deep research. The system splits the job across four named agents: Oracle plans the research and decomposes a topic into angles, Hermes agents run one-per-angle in parallel to collect raw data points (with web search plus EXA semantic search), Athena applies the academic CRAAP framework - currency, relevance, authority, accuracy, purpose - to grade every data point, and Scribe (the only Opus agent in the chain) writes the final 2
# EP.001 - The Tools We Actually Use ## Show Notes Chris Shanku and Sash Mohapatra kicked off Human in the Loop by doing what the show is built around: opening their actual systems and walking through them live. No slides, no pitch decks, no sanitized demos. Just the real thing. Sash showed Nuro - his Claude Code-based personal assistant that runs on his Mac. CLAUDE.md as a master identity file, Claude-managed memory, domain-organized working files, and real integrations: email, Granola for meeting notes, n8n for automations, Perplexity and X for search. The newsletter automation alone was wor
