About a month ago, Andrew Pignanelli’s LinkedIn post launching Co-Founder, an AI chief-of-staff that runs your business with you, went viral.

In this episode, we go behind that moment to unpack the tech, product, and philosophy behind it. Andrew shares how The General Intelligence Company (GIC) is building agent systems that remember context, make long-term decisions, and act autonomously.

We dig into the company’s two-agent architecture, memory layers, and the culture of experimentation that shaped the build. Plus, what it means to “run vision-first” in a space moving this fast.

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Brought to you by Autoskills (https://autoskills.ai) – Helping teams go from AI-curious to AI-ready with tailored workshops and hands-on adoption.

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Key Takeaways

• Context is everything. AI without memory or goals can’t make reliable decisions.

• Experimentation is a superpower. Great teams ask for outcomes (“make it 10% faster”) and let engineers prototype freely.

• Context engineering is the new frontier. The challenge isn’t retrieval — it’s choosing which 20 paragraphs of context matter.

• Knowledge graphs over vector search. GIC builds structured relationships between people, projects, and data.

• Taste matters as much as tech. Human judgment now shapes what feels “right” in an agent’s behavior or interface.

• Vision-first beats incremental. GIC bets on big missions that attract deep believers and top talent.

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⏱ What’s Covered

(01:15) Andrew’s journey from Velvet to The General Intelligence Company

(02:27) The idea behind Co-Founder

(03:55) The viral launch and early traction

(05:30) Why “memory” is the last step to general intelligence

(12:04) How GIC structures agents

(15:30) Knowledge graphs and grounding memory in real data

(19:18) Letting engineers experiment

(23:55) Context engineering and “taste” as product principles

(28:43) The building of Co-Founder

(35:10) Why GIC runs vision-first

(41:27) How Co-Founder makes entrepreneurship accessible

(48:42) AI, jobs, and ownership

(54:56) What’s next for Co-Founder and the path to general intelligence

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Resources & References

• LangChain — Framework for building agentic and retrieval workflows

• Devin — AI software engineer for dev teams

• Claude (Anthropic) — Model family for long-context reasoning

• GIC Blog — Deep dives on memory, context, and architecture

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Where to find Andrew Pignanelli:

• LinkedIn: andrewpignanelli

• X/Twitter: @ndrewpignanelli

• Website: The General Intelligence Company

👀 They’re hiring: generalintelligencecompany.com/careers

Where to find Haroon Choudery:

• LinkedIn: https://www.linkedin.com/in/haroonchoudery/

• X: https://x.com/haroonchoudery

Christine Y. Cruzvergara is Chief Education Strategy Officer at Handshake, where she partners with colleges and universities to help millions of students find their first jobs and internships.

Before joining Handshake, Christine served in senior roles at Wellesley, Georgetown, George Mason, and George Washington University, building scalable career education programs that bridge campus to career.

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What You’ll Learn

1. The real skills employers want from graduates in the AI era
2. How Handshake AI and the new Fellowship program train students to work with AI, not against it
3. Why your first job should be treated as a learning role instead of the final destination
4. How to network effectively when AI now screens the first round of resumes
5. Why domain expertise (even outside CS) is becoming more valuable in AI-driven roles
6. The mindset shift from “find a job” to “build experience”
7. How higher ed and employers are collaborating to create new pathways for students
8. Christine’s advice for graduating students entering an uncertain job market

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Brought to you by Autoskills (https://autoskills.ai) – Helping teams go from AI-curious to AI-ready with tailored workshops and hands-on adoption.

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What’s Covered

- (00:00) Christine’s background and Handshake’s mission

- (03:28) Why Handshake launched Handshake AI and how it works

- (06:00) The Handshake AI Fellowship: who it’s for and what it teaches

(09:38) What skills and majors are in demand for AI-related roles

(13:14) The current job market for graduating seniors

(15:17) How to set expectations and approach your first role strategically

(16:36) The new importance of networking in an AI-screened hiring world

(20:00) How Handshake is using AI internally to boost productivity and insights

(39:48) The Future AI Workforce Alliance and how higher ed is evolving

(54:14) How to apply and resources for students

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References and Resources

Yoodli AI: https://yoodli.ai

Handshake’s 2024 Graduate Job Market Report: https://joinhandshake.com/docs/network-trends/class-of-2024-graduation.pdf

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Where to Find Christine Y. Cruzvergara

• LinkedIn: https://www.linkedin.com/in/christinecruzvergara/
• Handshake: https://joinhandshake.com
• Handshake AI Fellowship: https://joinhandshake.com/fellowship-program

Where to Find Haroon Choudery

• LinkedIn: https://www.linkedin.com/in/haroonchoudery
• X: https://x.com/haroonchoudery

Most analytics tools tell you what happened. James Evans argues that in the age of AI, tools need to tell you what to do next.

In this episode, James shares how Amplitude is rethinking analytics with AI -- from anomaly detection that surfaces fixes, to session replay that could replace brittle event taxonomies. We dig into the cultural and technical shifts teams need to make to become AI-ready: embedding copilots into real workflows, running hack sprints instead of hackathons, and proving adoption through prototypes, not PRDs.

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Brought to you by Autoskills (https://autoskills.ai) – Helping teams go from AI-curious to AI-ready with tailored workshops and hands-on adoption.

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What You’ll Learn

1. Why peer demos beat training, and how healthy FOMO drives faster AI adoption.
2. Why prototypes are the new PRDs.
3. How hack sprints outpace hackathons.
4. Why AI fluency must start at the top (execs, we're looking at you).
5. Why distribution matters more than features.
6. How to manage the consistency risk — with governance and guardrails when everyone codes.

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What’s Covered

(00:00) James' introduction and the Command Bar acquisition

(09:27) How to build background agents that find signals and trigger fixes

(10:21) Prototype a single onboarding agent to spot drops and prove impact

(13:49) Why you need to apply LLM filters to cut alert noises

(15:06) How to treat session replay as your causal X-ray for true issues

(20:59) Lock down eval metrics before you scale generated UIs

(28:37) Pull instrumentation bugs from sessions and push actionable fixes

(29:43) Pilot session-first pipelines to avoid brittle taxonomies

(31:41) Pair conversational queries for discovery with a GUI for deep analysis

(36:31) Seed adoption by solving internal time-suck problems with copilots

(54:10) Quick tips on how to win at your AI project

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Where to Find James Evans:

• LinkedIn: https://www.linkedin.com/in/james-evans-7086b3126/

Where to Find Haroon Choudery:

• LinkedIn: https://www.linkedin.com/in/haroonchoudery/
• X: https://x.com/haroonchoudery