The social media and other threads are filled with the announcements mainly on the coding agents (GPT-5 codex, Claude Sonet 4.5, Grok, Qwen3-coder), open and closed models & their dataset on Hugging Face, model fine tuning and RL learning (some great interviews around it), Enterprise AI announcements from Gemini, Open AI, Claude, latest DGX Spartk and Nvidia Chipset, TPU/GPU, Pytorch/Cuda and parallel programming. Silently, our own backyard development tools have enabled AI, and I thought of dedicating an article to it.

1) Chrome development tools integration with Gemini enterprise. AI option can be enabled in the Chrome developer options to debug the page loading, refresh, network issues, and certification errors and there are several other features integrated to support web app development

2) Firebase AI Logic gives you access to the latest generative AI models from Google, the Gemini models and Imagen models.

If you need to call the Gemini API or Imagen API directly from your mobile or web app — rather than server-side — you can use the Firebase AI Logic client SDKs. These client SDKs are built specifically for use with mobile and web apps, offering security options against unauthorized clients as well as integrations with other Firebase services.

These client SDKs are available in Swift for Apple platforms, Kotlin & Java for Android, JavaScript for web, Dart for Flutter, and Unity.

https://firebase.google.com/docs/ai-logic

3) Android Studio has many developments relevant for developers building apps with AI capabilities.

  • Agent Mode with Google Gemini
  • Experimental AI features with Jetpack compose preview, Transform UI with Natural Language, etc.,
  • On-Device integration with Gemini Nano for on-device interference, Gen AI APIs.

4) Grafana AI that brings together advanced machine learning, generative AI, and intelligent assistance to help you get more value from your observability data in Grafana Cloud. With features that accelerate investigations, automate analysis, and provide actionable insights, Grafana AI empowers teams to be proactive and efficient—whether you’re troubleshooting incidents, forecasting trends, or building smarter dashboards.

https://grafana.com/docs/grafana-cloud/machine-learning/intro/

5) PostgreSQL has evolved into one of the most AI-ready databases — especially for applications that need structured + unstructured data, embeddings, retrieval, fine-grained metadata storage, and model-driven workflows.

  • Vector & Embedding Support (pgvector)
  • JSONB for Flexible AI Data Storage
  • Advanced Indexing & Query Optimization
  • Time-Series + Observability for AI Metrics
  • Stored Procedures for In-DB Intelligence

https://www.postgresql.org/

6) The Apache ecosystem is going through a major evolution driven by AI, real-time analytics, lakehouses, and cloud-native architectures.

  • Flink: Real-time AI Streaming and Inference
  • Spark: Batch + interactive compute for AI pipelines.
  • Kakfa: Event and memory bus for LLM apps and agent telemetry

7) Build and deploy intelligent AI-driven features across platforms with Flutter and Google's AI capabilities.

https://flutter.dev/ai

8) On Device LLMs through the AI lens supporting On device LLM execution, Vercel AI SDK Comptability, Apple Foundation Models, MLC LLM Engine Powered and cross platform support.

https://www.react-native-ai.dev/

9) Figma has integrated with several coding agents and you can now build an app, test an interaction, or bring an idea to life—with a simple prompt.

https://www.figma.com/community/make

10) Slack offers built-in generative AI tools to summarise the organisation's conversation data to increase the team's productivity.

https://slack.com/intl/en-gb/help/articles/25076892548883-Guide-to-AI-features-in-Slack