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Coffee With Developers • Jun 13, 2025

What’s New with Google Gemini?

Logan Kilpatrick

Stop wrestling with unpredictable AI production costs. Discover how Google Gemini's multimodal capabilities and Live API empower developers to build context-aware digital coworkers.

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#1 about 3 min

Evolution of Google DeepMind and AI research

How internal AI research divisions merged to create horizontal foundational models.

#2 about 3 min

Navigating model fatigue with personal benchmarking interfaces

Using personal benchmark tooling to help developers evaluate frequent open source model releases.

#3 about 3 min

Choosing between general purpose and domain specific models

How agentic systems balance the trade-offs of using massive horizontal platforms versus specialized tools.

#4 about 3 min

Replicating customized personas using general purpose foundational models

Why highly capable baseline models reduce the necessity for custom-trained AI marketplaces.

#5 about 4 min

Bridging the gap between local AI prototypes and production

Overcoming the last mile challenge of preventing massive behavioral drift between local execution and production scale.

#6 about 4 min

Managing economic costs and intelligence scaling in AI products

Optimizing return on investment when substituting standard server usage for expensive graphics processing models.

#7 about 4 min

Integrating model APIs into preferred developer tooling environments

Abstracting vendor dependencies to maintain agnostic architecture across standard code editors.

#8 about 3 min

Handling geographic and regulatory availability constraints for AI models

Negotiating regional legal frameworks that impact where foundation models can officially deploy.

#9 about 4 min

Building responsible AI scraping systems with direct search citations

Relying on established search protocols to properly cite online content during automated data ingestion.

#10 about 3 min

Extracting internal knowledge using native multimodal video understanding

Parsing through accumulated corporate media to expose previously inaccessible internal intelligence files.

#11 about 3 min

Designing product interfaces that run AI processes in the background

Shifting away from heavy conversational interfaces in favor of subtle workflow automations.

#12 about 5 min

Deploying open source local models for privacy and data compliance

Downloading weights directly to edge hardware to safeguard proprietary information against cloud latency restrictions.

#13 about 4 min

Running native on-device model inferences across mobile operating systems

Leveraging integrated hardware capabilities for secure environmental processing via hardware-level screen analysis.

#14 about 3 min

Triaging hallucination feedback and managing model response quality

Organizing user bug reports to systematically address incorrect outputs produced by language engines.

#15 about 3 min

Building multimodal voice and vision agents using live APIs

Streaming immediate visual context to digital assistants for asynchronous function calls and direct code execution.

#16 about 5 min

Guiding users through complex applications via task specific generative interfaces

Creating temporary disposable application layers that solve immediate tasks without requiring deep systemic software rewrites.

#17 about 3 min

Accelerating software creation while keeping developers in the loop

Proving that automated tooling scaling expands the total operational market for fundamental engineering roles.

#18 about 4 min

Scaling capabilities with generative media APIs and advanced reasoning models

Combining sophisticated digital asset generation pipelines with advanced reinforcement logic evaluation loops.

#19 about 5 min

Optimizing code generation efficiency across standard programming languages

Replacing total contextual codebase rebuilds with specialized syntax patching approaches that reduce compute burdens.

#20 about 3 min

Learning software engineering through pair programming with AI assistants

Utilizing interactive syntax evaluation guidance to unblock developers managing highly ambitious technical framework implementations.

Matching moments

4:04 min

Building practical AI agents using Google Gemini

Philipp Schmid Philipp Schmid · World Congress 2025

55 sec

Google DeepMind's mission and developer tools overview

Paige Bailey Paige Bailey · World Congress 2026 North America

3:02 min

Understanding Google Gemini history and available context models

2:44 min

Understanding the differences between Gemma and Gemini models

1:28 min

Core challenges facing the generative AI developer ecosystem today

Prashanth Chandrasekar Prashanth Chandrasekar · World Congress 2024

3:17 min

Balancing AI regulation with technological innovation in human resources

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

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