get Call State in ANDROID apps

Android - Google MAP V2 PART 1

Download Full Code of PART-1 , 2 , 3 , 4 Download and configure Google Play Services Library in Eclipse Google Map for Android is now integrated with Google Play Services. So we need to set up Google Play Service Library for developing Google Map application in Android. Get the API key for Google Maps v2: Linux : For any other OS use this : keytool -list -v -alias androiddebugkey \ -keystore <path_to_debug_keystore>debug.keystore \ -storepass android -keypass android Register with the Google APIs Console : Google APIs Console Select here the Services entry: Activate the Google Maps Android API v2 . Register your application via its package in this console together with the SHA-1 fingerprint of your signature key. Click on Create new Android Key.. Add you SHA-1 and your package name in to box like example given in alert box. Support library...

MCP vs CLI: Which One Fits Your AI Agent's Job?

Every AI agent that touches the outside world does it one of two ways: it runs a shell command, or it calls a tool through the Model Context Protocol. Both get the job done, but they don't cost the same or fail the same way, which is why "MCP vs CLI" keeps resurfacing without resolving. I want to answer it for the cases that matter most: plain developer tooling, data-heavy systems like banking, forecasting pipelines, and transaction automation like billing or booking. Neither wins outright. Where you draw the line is the actual skill. Key Takeaways CLI is the default for anything the model already knows cold, git, file ops, text processing, since there's no schema tax and commands chain through pipes. MCP earns its cost when a system needs per-user auth, audit trails, or access to sources the model can't otherwise reach, exactly what regulated systems like banking require. Transaction automation works best split: deterministic code drives the repeatable steps, a...

Don't Ship AI Agent Skills Without Evals

At the AI Engineer World's Fair on July 1, 2026, Philipp Schmid, a Staff Engineer on the Gemini API and agents team at Google DeepMind, asked a room full of engineers a simple question: who uses skills with their coding agents? Every hand went up. Who has evals for those skills? Almost none. That gap is the entire talk, which Schmid also wrote up on his own blog, philschmid.de/testing-skills . His team indexed the skills ecosystem through SkillsBench and found the same pattern everywhere: people ship a SKILL.md file after two manual test runs and move on. It looks fine in a demo. It quietly corrupts outputs in production, because bad skills don't crash. They just make the agent confidently wrong. If you've already read our breakdown of Matt Pocock's Claude Code skills library , think of this as the other half of that story: what happens once you've installed a skill and it's actually running against real prompts. Key Takeaways SkillsBench indexed 47,000+ un...

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