🎙️ How I AI: Grok Bot + Grok 4.6—what’s great (and what’s still hype) & Lessons from spending $20,000 on Devin in one month

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Jira AI SDLC—Get your tokens’ worth with Jira

In this solo episode, Claire tests Grok Bot, Cursor Origin, and Grok 4.6 to figure out what’s genuinely useful and what’s still mostly hype. She shares the Grok Bot feature that immediately won her over, why she isn’t ready to replace GitHub with Origin, and how Grok 4.6 performed against GPT-5.6, Claude Sonnet 5, and Opus 5 in her own blind evaluations.

Grok Bot’s multi-account connectors solve a problem every other agent platform seems to ignore. Most platforms assume each person has one Gmail account and one Slack workspace. Claire has four email addresses and seven Slack workspaces. Grok Bot lets her connect all of them to a single bot, which made it genuinely useful from day one in a way that Codex and Claude still have not matched.
Grok Bot’s simplicity is both its greatest strength and its biggest limitation. Setup is fast, the iMessage-style interface is clean, and the built-in plugins actually work. But people who enjoy customizing their agents, choosing models, shaping personalities, and tinkering with every detail may find it almost too polished. Claire loves her OpenClaw agents partly because they are chaotic and high-maintenance. So far, Grok Bot has not given her much to wrestle with.
Cursor Origin is a compelling vision that is not quite ready for prime time. An agent-native alternative to GitHub makes a lot of sense, especially one where Bugbot, Cursor, and the entire pull request workflow are designed around how coding agents actually work. Today, though, Origin still feels like a more attractive version of GitHub with fewer features. Teams that rely heavily on GitHub Actions, code owners, and existing automations will need a much stronger reason to migrate.
Grok 4.6 is a genuine frontier-model competitor. That conclusion did not come from someone else’s leaderboard. Claire runs her evaluations blind, grades the outputs herself, and gives her own judgment 70% of the final weight. Grok 4.6 finished alongside GPT-5.6 Sol at the top of the Claire Index, ahead of both Sonnet 5 and Opus 5.
For sharp, enjoyable agent conversations, Sonnet 5 is still the model to beat. When Claire wants an OpenClaw agent that is concise, responsive, and fun to talk with, Sonnet 5 continues to win. It has a conversational rhythm that feels more like working with a strong collaborator than issuing commands to a tool. Grok 4.6 does not yet compete in that category.
Cursor and xAI are assembling a surprisingly coherent enterprise stack. Grok Bot serves knowledge workers, Origin handles code hosting, Grok 4.6 provides a capable default model, and the Cursor IDE already sits at the center of many developers’ workflows. None of the pieces is perfect on its own, but together they are beginning to look like a credible enterprise platform. Large companies often prefer one vendor that can own the entire experience, and Cursor is increasingly positioned to become that vendor.

Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Jira AI SDLC—Get your tokens’ worth with Jira

Ryan Carson is a five-time founder and the solo founder of Untangle, a B2B SaaS platform for family law firms. In this episode, he breaks down how he manages up to 15 AI agents at once, ships as many as 40 pull requests a day, and uses Devin, Codex, and Claude Code to handle everything from engineering and QA to customer success and investor updates. He also shares why more AI output doesn’t necessarily lead to a better product, how a handwritten priority list keeps his agents focused, and why talking to one real customer changed the direction of his entire company.

The most important skill for a solo founder may be managing agents, not writing code. Ryan runs 10 to 15 Devin threads at once, organized into P0, P1, P2, and Bugs folders. He treats each thread the way a good manager treats a direct report: give it a clear goal, set the right priority, and avoid unnecessary hand-holding. That organizational discipline is what separates founders who gain real leverage from AI from those who simply accumulate open tabs.
A piece of paper can still be the best tool for protecting attention. Despite working across eight screens, Ryan keeps a handwritten list of his weekly priorities beside him. The simplicity is intentional. When agents are constantly generating updates, questions, and decisions, that physical list keeps him anchored to the three things that matter most before he gets pulled into whatever Devin surfaced overnight.
Ryan built an AI playbook that functions like a customer success team. His Watchdog workflow reviews every law firm account, pulls recent activity and errors from Sentry and internal logs, identifies the three most important problems, and checks whether a recent pull request has already fixed each one. Everything appears in a single Devin thread. Whenever Ryan feels the familiar anxiety of not knowing what is happening across his customer base, he runs Watchdog and quickly gets back up to speed.
Producing more with AI does not automatically lead to a better product. Both Ryan and Claire are skeptical of letting agents work without constraints overnight. Frontier models can generate an enormous amount of output, but they do not know what customers actually need. Product ideas, priorities, and market judgment still have to come from a human who talks to users. Ryan found product-market fit for Untangle not by shipping more code but by landing one meeting with family law attorney Renee Bauer and listening carefully.
Cloud agents may handle most engineering work, but Codex still shines when the frontend requires close attention. Ryan uses Devin for cloud-based work across bugs, pull requests, investor updates, and customer triage. He turns to Codex when a feature is visually complex and he needs to stay close to the implementation. The distinction is not about loyalty to a particular tool. It comes down to latency, browser access, and the ability to inspect and refine the interface in real time.
Coding agents can operate far beyond the codebase. Claire uses Devin for deal desk, custom quotes, and customer triage. Ryan uses it to prepare investor updates through a reusable skill. Both approach these agents with a broader question: What could someone accomplish if they understood the entire codebase and could write software to solve almost any problem? That framing reveals business uses that would never emerge from treating an agent as a simple coding assistant.
The best engineering interview may be a screen recording of the actual work. Ryan asks candidates to record themselves building a feature inside an existing application, with the entire screen visible and no introductory meeting required. In the next stage, he gives them access to Devin and reviews the replay of how they worked with it. This shows him how candidates think, build, and manage an agent under realistic conditions, which he finds far more revealing than a typical behavioral interview.
The strongest design workflow pairs Claude’s system thinking with Codex’s implementation ability. Claire used Claude Design to break a Figma file into reusable design tokens and a design.md specification. She then gave that specification to Codex, which created shared components across a monorepo. Claude is particularly good at translating a visual system into a portable spec. Codex is better at turning that spec into working, interconnected code. The workflow succeeds because each model is assigned the part it does best.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

Brought to you by:
Bolt.new—Turn your idea into a real product
Jira AI SDLC—Get your tokens’ worth with Jira

Grok Bot’s multi-account connectors solve a problem every other agent platform seems to ignore. Most platforms assume each person has one Gmail account and one Slack workspace. Claire has four email addresses and seven Slack workspaces. Grok Bot lets her connect all of them to a single bot, which made it genuinely useful from day one in a way that Codex and Claude still have not matched.
Grok Bot’s simplicity is both its greatest strength and its biggest limitation. Setup is fast, the iMessage-style interface is clean, and the built-in plugins actually work. But people who enjoy customizing their agents, choosing models, shaping personalities, and tinkering with every detail may find it almost too polished. Claire loves her OpenClaw agents partly because they are chaotic and high-maintenance. So far, Grok Bot has not given her much to wrestle with.
Cursor Origin is a compelling vision that is not quite ready for prime time. An agent-native alternative to GitHub makes a lot of sense, especially one where Bugbot, Cursor, and the entire pull request workflow are designed around how coding agents actually work. Today, though, Origin still feels like a more attractive version of GitHub with fewer features. Teams that rely heavily on GitHub Actions, code owners, and existing automations will need a much stronger reason to migrate.
Grok 4.6 is a genuine frontier-model competitor. That conclusion did not come from someone else’s leaderboard. Claire runs her evaluations blind, grades the outputs herself, and gives her own judgment 70% of the final weight. Grok 4.6 finished alongside GPT-5.6 Sol at the top of the Claire Index, ahead of both Sonnet 5 and Opus 5.
For sharp, enjoyable agent conversations, Sonnet 5 is still the model to beat. When Claire wants an OpenClaw agent that is concise, responsive, and fun to talk with, Sonnet 5 continues to win. It has a conversational rhythm that feels more like working with a strong collaborator than issuing commands to a tool. Grok 4.6 does not yet compete in that category.
Cursor and xAI are assembling a surprisingly coherent enterprise stack. Grok Bot serves knowledge workers, Origin handles code hosting, Grok 4.6 provides a capable default model, and the Cursor IDE already sits at the center of many developers’ workflows. None of the pieces is perfect on its own, but together they are beginning to look like a credible enterprise platform. Large companies often prefer one vendor that can own the entire experience, and Cursor is increasingly positioned to become that vendor.

Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Jira AI SDLC—Get your tokens’ worth with Jira

The most important skill for a solo founder may be managing agents, not writing code. Ryan runs 10 to 15 Devin threads at once, organized into P0, P1, P2, and Bugs folders. He treats each thread the way a good manager treats a direct report: give it a clear goal, set the right priority, and avoid unnecessary hand-holding. That organizational discipline is what separates founders who gain real leverage from AI from those who simply accumulate open tabs.
A piece of paper can still be the best tool for protecting attention. Despite working across eight screens, Ryan keeps a handwritten list of his weekly priorities beside him. The simplicity is intentional. When agents are constantly generating updates, questions, and decisions, that physical list keeps him anchored to the three things that matter most before he gets pulled into whatever Devin surfaced overnight.
Ryan built an AI playbook that functions like a customer success team. His Watchdog workflow reviews every law firm account, pulls recent activity and errors from Sentry and internal logs, identifies the three most important problems, and checks whether a recent pull request has already fixed each one. Everything appears in a single Devin thread. Whenever Ryan feels the familiar anxiety of not knowing what is happening across his customer base, he runs Watchdog and quickly gets back up to speed.
Producing more with AI does not automatically lead to a better product. Both Ryan and Claire are skeptical of letting agents work without constraints overnight. Frontier models can generate an enormous amount of output, but they do not know what customers actually need. Product ideas, priorities, and market judgment still have to come from a human who talks to users. Ryan found product-market fit for Untangle not by shipping more code but by landing one meeting with family law attorney Renee Bauer and listening carefully.
Cloud agents may handle most engineering work, but Codex still shines when the frontend requires close attention. Ryan uses Devin for cloud-based work across bugs, pull requests, investor updates, and customer triage. He turns to Codex when a feature is visually complex and he needs to stay close to the implementation. The distinction is not about loyalty to a particular tool. It comes down to latency, browser access, and the ability to inspect and refine the interface in real time.
Coding agents can operate far beyond the codebase. Claire uses Devin for deal desk, custom quotes, and customer triage. Ryan uses it to prepare investor updates through a reusable skill. Both approach these agents with a broader question: What could someone accomplish if they understood the entire codebase and could write software to solve almost any problem? That framing reveals business uses that would never emerge from treating an agent as a simple coding assistant.
The best engineering interview may be a screen recording of the actual work. Ryan asks candidates to record themselves building a feature inside an existing application, with the entire screen visible and no introductory meeting required. In the next stage, he gives them access to Devin and reviews the replay of how they worked with it. This shows him how candidates think, build, and manage an agent under realistic conditions, which he finds far more revealing than a typical behavioral interview.
The strongest design workflow pairs Claude’s system thinking with Codex’s implementation ability. Claire used Claude Design to break a Figma file into reusable design tokens and a design.md specification. She then gave that specification to Codex, which created shared components across a monorepo. Claude is particularly good at translating a visual system into a portable spec. Codex is better at turning that spec into working, interconnected code. The workflow succeeds because each model is assigned the part it does best.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.