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Coffee With Developers • Sep 16, 2024

Coffee with Developers - Maria Apazoglou

Maria Apazoglou

Maria Apazoglou reveals how Thomson Reuters safely brought self-serve AI to thousands of non-technical employees. Discover how a secure enterprise platform is turning lawyers into powerful prompt engineers.

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#1 about 4 min

Career evolution in data engineering and AI platforms

Building foundational data models and transitioning into enterprise platform leadership.

#2 about 3 min

Democratizing artificial intelligence access across the entire organization

Creating self-serve solutions that enable non-technical employees to leverage generative models safely.

#3 about 4 min

Overcoming user fear through data separation and internal training

Separating user data from operational tracking alongside internal hackathons accelerates widespread adoption.

#4 about 3 min

Demonstrating large language model limitations and cost differences openly

Comparing multiple models side-by-side reveals knowledge gaps and varying generation expenses.

#5 about 5 min

Building customized prompt chains for efficient operational workflows

Saving specific requirements into reusable mini-solutions prevents redundant instruction formatting.

#6 about 5 min

Improving response accuracy through contextual data and prompt tweaking

Connecting models to internal documentation repositories ensures outputs remain grounded in factual information.

#7 about 3 min

Reinforcing critical judgment and citations for generated AI content

Providing exact document references allows users to cross-reference answers and apply manual oversight.

#8 about 3 min

Benefiting from diversity of thought across multidisciplinary technical teams

Hiring individuals from varied backgrounds enhances problem-solving and user interface design.

#9 about 3 min

Empowering subject matter experts to adopt software engineering practices

Professionals transferring domain expertise through prompt writing gradually absorb broader development mindsets.

#10 about 3 min

Solving complex platform architecture challenges at an enterprise scale

Software engineers must architect low-latency, scalable systems that securely process massive multi-cloud data.

#11 about 2 min

Sharing architectural learning and multi-account cloud setups publicly

Documenting infrastructure strategies helps the wider engineering community avoid redundant scaling errors.

#12 about 3 min

Exploring internal AI product initiatives and global engineering roles

Accessing dedicated resources reveals practical applications while remote hiring scales continuous research development.

Matching moments

1:44 min

Summarizing developer experience and artificial intelligence companions

Robert Hoffmann Robert Hoffmann +1 · LIVE

1:56 min

Scaling AI adoption to non-traditional enterprise developers

Neel Sundaresan Neel Sundaresan +1 · World Congress 2026 Europe

3:17 min

Balancing AI regulation with technological innovation in human resources

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · World Congress 2024

1:59 min

Building culturally aware LLMs for global audiences

Werner Vogels Werner Vogels +1 · World Congress 2026 Europe

1:31 min

Essential AI and human skills for future teams

Alexander Weißhaupt Alexander Weißhaupt +1 · World Congress 2025

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September 24, 2026 · 15:30–16:00

Stage 1

The Reality of AI Adoption in Enterprises

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Open session

World Congress 2026 North America

September 24, 2026 · 15:30–16:00

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Open session

World Congress 2026 North America

September 24, 2026 · 16:10–16:40

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Sandboxing the Swarm: Building Secure, Serverless AI Agents with Wasm

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Sr. Developer Advocate @ Akamai Technologies

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Open session

World Congress 2026 North America

September 24, 2026 · 16:50–17:20

Mainstage

Building AI Products vs. Building With AI

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Open session

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September 25, 2026 · 16:50–17:20

Outdoor Stage

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