🚀 Hiring: Senior Software Engineer — Poland (Remote) Build a new product from a blank repo in Rust at We’re looking for someone who loves: → Greenfield engineering → End-to-end ownership → Architecture & technical decisions → Mentoring 7+ years | Rust preferred | Go/Java/Kotlin considered | Claude Code required 📍Based in Poland? Apply:

Me in 2012 instead of learning: DSA System Design AI / ML Generative AI LLMs AI Agents RAG Vector Databases Embeddings Fine-tuning Prompt Engineering MCP AI Coding Agents Python Java C C++ Go Rust JavaScript TypeScript HTML CSS React Next.js Vue Angular Node.js Express.js NestJS Spring Boot Django FastAPI REST APIs GraphQL WebSockets SQL PostgreSQL MySQL MongoDB Redis Elasticsearch Kafka RabbitMQ Docker Kubernetes Terraform AWS Azure GCP Linux Git GitHub CI/CD DevOps Microservices Serverless Cloud Cybersecurity Data Engineering Data Science MLOps Data Analytics Blockchain Web3 Smart Contracts Solidity Edge Computing Distributed Systems Observability Prometheus Grafana OpenTelemetry Supabase Firebase Vercel Cloudflare PostgreSQL + pgvector LangChain LlamaIndex PyTorch TensorFlow Hugging Face Ollama OpenAI APIs Claude APIs Gemini APIs Model Context Protocol AI Infrastructure GPU Computing CUDA

Grok Code Fast 1 is versatile across the full stack and is particularly strong at TypeScript, Python, Java, Rust, C++, and Go. Using Grok Code Fast 1, @DannyLimanseta built the following game in a day.

From September 29 (12:00 A.M. PT) - October 14 (11:59 P.M. PT), get the Aurora Cape for Java & Bedrock Edition! 🟢Watch any Twitch Partner or Affiliate streaming in the Minecraft Dungeons II category for at least 15 minutes. 🟢Watch Minecraft Dungeons II content on any Game Reward enabled stream on TikTok for at least 3 minutes. Go to to claim your offer after completing the WatchTime Quest!

✍️ Now that AI can write hundreds of syntactically valid lines per second, the developer's job has shifted from "writing" to "reading and verifying." That shift changes what we should want from a programming language. Why Go is an Ideal Language for AI-Assisted Software Engineering 🔍 Overview Google's Go team makes the case for Go as the ideal language for the AI-assisted development era. As the bottleneck moves from code generation to review and verification, the language properties that matter most have flipped — readability and maintainability win over writability and expressiveness. ⚠️ The Problems Being Solved ・LLMs frequently hallucinate type mismatches and non-existent properties ・Dynamically typed languages (e.g. Python) only catch these errors at runtime ・AI-generated code often pulls in stale packages or vulnerable dependencies ・Repeated refactoring passes degrade accuracy (~95% first-pass, declining) 🛠 How Go Addresses Each ・gofmt enforces uniform formatting — AI output is always syntactically predictable ・Static typing + fast compilation: rejects errors instantly, orders of magnitude faster than Java, C#, or Rust ・govulncheck: low-noise vulnerability scanning targeted only at invoked symbols ・Native fuzz testing: continuous discovery of boundary-condition bugs ・15 years of strict backward compatibility: Go 1.0 code runs unchanged today 📌 The Counterintuitive Conclusion "As developers write less code, language choice becomes more critical." Absorbing AI's high-velocity output safely requires deterministic guardrails — and Go was designed for exactly that. #Golang# #AIEngineering#

# Practical and Useful Patterns with ADK ## 🔌 Apply Cross-Cutting Concerns to All Agents at Once with Plugins Setting up callbacks on every individual agent is tedious. ADK **Plugins** let you register once at the Runner level and apply security, logging, and rate limiting across all agents, tools, and LLM calls! 🎯 ## 📌 Title Plugins ## 🔗 URL ## 🧩 Overview Plugins are cross-agent modules that operate at the Runner level. Unlike agent-specific callbacks, **a single registration applies to every agent, tool, and LLM call** in your application. They're ideal for cross-cutting concerns like security guardrails, logging, and rate limiting. Plugins execute **before** agent-level callbacks, making them perfect for global policy enforcement. ADK ships with built-in plugins: Reflect and Retry Tools, BigQuery Analytics, Context Filter, Global Instruction, and Logging Plugin. ## 🛠 How to Use Import `BasePlugin` from `google.adk.plugins.base_plugin` and `InMemoryRunner` from `google.adk.runners`. Define `SecurityGuardPlugin` as a subclass of `BasePlugin`, initializing with `name="security_guard"`. Its `before_model_callback` method extracts user input from `llm_request.contents[-1].parts[0].text`, checks for injection via `detect_injection()`, and returns a rejection `LlmResponse` if detected. Its `before_tool_callback` method checks `is_authorized( and returns an error dictionary if access is denied. Both return `None` to continue when no issue is found. Finally, create an `InMemoryRunner` with `plugins=[SecurityGuardPlugin()]` to apply this guard to all agents automatically. ## 🏗 Practical Usage **"Gemini as a Judge" pattern for injection detection:** `InjectionDetectorPlugin` extends `BasePlugin` with `name="injection_detector"` and stores `self.judge_model = "gemini-flash-lite"` as its judge model. In `before_model_callback`, it extracts user input from `llm_request.contents[-1].parts[0].text` and passes it to `judge_with_flash_lite()` for fast injection detection. If the verdict is `"INJECTION"`, it calls `audit_log()` and returns a rejection `LlmResponse` to block the request. Otherwise, it returns `None`. `RateLimitPlugin` extends `BasePlugin` with `name="rate_limiter"`, accepting a `max_calls_per_minute` parameter (default 60). It tracks call timestamps in ` and in `before_model_callback`, filters out entries older than 60 seconds. If the count meets or exceeds `self.max_calls`, it returns an `LlmResponse` with a rate limit message. Otherwise, it appends the current timestamp and returns `None`. Finally, an `InMemoryRunner` is created with `plugins=[InjectionDetectorPlugin(), RateLimitPlugin(max_calls_per_minute=30)]` to combine multiple plugins for the production app. ## 💡 Use Cases - 🛡️ **Security guardrails**: Centralized injection detection and PII protection across all agents - 📊 **Unified logging**: Aggregate execution logs from all agents with the BigQuery Analytics plugin - ⏱️ **Rate limiting**: Control API call frequency to prevent cost explosions - 🔄 **Auto-retry**: Intelligent tool failure retries with the Reflect and Retry Tools plugin - 📝 **Global instructions**: Inject shared policies or instructions into every agent ## ⚠️ Caveats - Plugins execute **before** agent-level callbacks — be aware of precedence - Returning a value from a plugin skips both the operation and any corresponding agent-level callback - Since plugins apply to all agents, use Callbacks for agent-specific logic - Error handling is available via `on_model_error_callback` / `on_tool_error_callback` hooks - Available in Python v1.7.0+, TypeScript v0.2.5+, Go v0.4.0+, Java v0.3.0+ ## ✨ Closing Plugins deliver "write once, apply everywhere" for cross-cutting concerns. Delegate security, logging, and rate limiting to Plugins, and keep agent-specific logic in Callbacks. This separation improves both maintainability and security! #ADK# #AIAgent#

We undoubtedly will learn more as we go along to inform the question of whether the "Cornell Seven" case was actually a crime or not. But what we have heard so far suggests fairly strongly that it took place in a moral wasteland. | @baseballcrank