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As part of the Gen AI Academy APAC , I set out to solve a major pain point for educators: manually sifting through textbooks to create grade-appropriate question papers. I built an automated Question Paper Generator using a Serverless Next.js stack, a Retrieval-Augmented Generation (RAG) architecture, and the complete Google Cloud AI suite. Teachers simply upload a textbook chapter (PDF), specify the grade and subject, and let the AI generate a fully formatted assessment quiz. While the architecture sounds straightforward, orchestrating these enterprise-grade APIs in a serverless environment presented several intense technical hurdles. Here is a deep dive into the architecture, the specific roadblocks I hit, and how I ultimately solved them. 🏗️ The RAG Architecture The application is built on Next.js 15 and deployed to Google Cloud Run . The pipeline flows as follows: Document Extraction : The PDF is uploaded and sent to Google Cloud Document AI (Document OCR Processor) to extract the raw text. Chunking & Embeddings : The text is chunked into logical paragraphs and sent to Vertex AI ( text-embedding-004 ) to generate dense vector embeddings. Vector Database : The embeddings and metadata (Grade, Subject) are stored seamlessly in Firestore using native VectorValue support. Retrieval & Generation : When a teacher requests a quiz, the query is embedded, and a findNearest Vector Search runs on Firestore. The retrieved context is passed to Google Gen AI ( gemini-3.5-flash ) to synthesize the structured question paper. 🐛 The Technical Challenges & How I Solved Them Building an end-to-end pipeline using cutting-edge SDKs often means dealing with strict schema validations and opaque error codes. Here are the major technical gotchas I faced. 1. The Document AI Region Endpoint Mismatch The Challenge: I provisioned a Document OCR processor in the asia-south1 region. However, when my Node.js client attempted to send a processing request using the processor's full resource name,

Unless you've put some custom software on your phone, Android Auto will work on nearly all modern phones.

I created this content for the purposes of entering the All Things Agentic Hackathon. The problem that started it A doctor gets about eight minutes with a patient and, for anyone with a real history, forty pages of scattered records — lab reports, discharge notes, and pharmacy bills from three different clinics. So the history is effectively invisible at the exact moment it matters most. And when the visit ends, nothing follows up: the six-month course lapses at week five, the recheck never gets booked. I wanted to build an agent that closes that loop — one that reads the mess, decides urgency in a way a clinician can actually trust, and handles the follow-up on its own. That became CareLoop , my entry for the All Things Agentic Hackathon (Taskmaster track), built on Gemini, the Google Agent Development Kit (ADK), Cloud Run, and Firestore. The one principle I wouldn't compromise on Rules decide, AI explains. The temptation with an LLM is to let it do everything — including deciding whether a chest-pain patient is urgent. I refused to do that. In CareLoop, a deterministic engine owns every clinical decision: a weighted symptom score plus a red-flag override sets the triage level and routing. It is fully auditable, and it returns byte-identical output on the same input every single time. The LLM's job is strictly language: Reading unstructured documents into a fixed schema — I call it "Gemini extracts, rules merge." Writing the structured result into a plain-language brief a clinician can skim in ten seconds. No language model is ever in the decision path. When a judge asks "why was this Critical?", the answer is a score breakdown they can inspect — not a model's say-so. That single decision shaped the whole architecture. What it actually does CareLoop runs the full loop end to end: Ingest & compact — it reads a patient's documents and merges them into one structured ledger: allergies, chronic conditions, active medications, and lab trends over time. Instead of pushin

This article is about running a hand-written Gemma 4 port in pure JAX on three different accelerators, and about the two places the abstraction leaks. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve one Gemma 4 checkpoint from one JAX port across every accelerator I can rent, and to find out — by measurement, not by reading docs — which parts of "it's just JAX" are true. The port lives in ports/gemma4/ and is driven by a generation loop behind an OpenAI-compatible server. No PyTorch, no vLLM, no torch_xla . The same code runs on Cloud TPU v5e and v6e, and on an NVIDIA T4G attached to an AWS Graviton2 host. "Pure JAX" is the whole experiment. If the port is really portable, the only thing that should change between those rigs is a config file. It mostly is. Two things are not, and they are the interesting part. Gemma 4 E2B is not a stock transformer Any port has to carry four irregularities, and none of them are optional: Two attention geometries. Sliding layers use head_dim=256 , global layers use 512 . Most inference stacks assume one head dimension per model. 8:1 MQA , so the KV budget is nothing like the parameter count would suggest. A KV-share map that collapses 35 layers onto 15 caches . A 512-slot sliding ring , plus per-layer embeddings (PLE) held in a 4.70 GB table that gets quantized to 4 bits on load. That first one is worth dwelling on, because it is what breaks other stacks. On the vLLM path, the heterogeneous head dims force the Triton attention backend: Gemma4 model has heterogeneous head dimensions {'sliding_attention': 256, 'full_attention': 512}. FA4 not available, forcing TRITON_ATTN backend. And on a Turing GPU that backend then asks for shared memory the hardware does not have: triton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536 JAX never enters that conversation. Attention is ordinary XLA rather than a hand-tiled kernel, so ther

If your Wi-Fi signal is patchy at the far reaches of your house, this simple solution might be worth a try. Even better? It's completely free.

Google is now automatically expanding its AI search summaries at the top of the results page for some searches, as reported by Search Engine Roundtable. The change, when it kicks in, pushes the typical list of links from a search much farther down Google's results page; instead of seeing part of an AI Overview with […]

Your phone should be able to still sound your morning alarm when it's in silent mode. If it's not, here are some settings to check.

Zoph, who co-founded Thinking Machines Lab alongside Mira Murati and also served as the startup's CTO, led a brief stint at OpenAI and is now at Google.

Google's AI note-taking app, Gemini Notebook, can now pull information from the books you've purchased. The new "Expert Intelligence" feature allows you to bring titles from Google Play Books directly into Gemini Notebook, which means you can ask questions about the material, as well as generate plans, infographics, AI podcasts, and more based on their […]

Google will start policing memory-hungry Android apps as a direct response to the RAM crisis. Spotted by TechCrunch, the company yesterday published a memo addressing the Play Store's role in enforcing new memory-usage restrictions. The post emphasizes the importance of meeting new memory usage limits for apps, in order "to help developers navigate industry-wide hardware […]

Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

Google AI Mode is getting a flight price tracker, hotel booking and a way to view the cost in miles or points.

The updates indicate that Google is looking position to AI Mode as an AI travel agent of sorts, as it's moving beyond simply helping users find information to actually handling parts of the trip-planning and booking process itself.

Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner. By Sergio De Simone

The latest special-edition of Google's Fitbit Air is inspired by the Pokémon Sleep game, with a "Sleepy Blue" band emblazoned with a snoozing Pikachu. It's not exactly Snorlax blue, but matches the classic blue and gold of a Pokémon card pretty closely. The special-edition is available to preorder now for $129 and starts shipping September […]

Google is setting new memory-use limits for Android apps as AI data centers contribute to hardware shortages that could leave lower-cost phones with less memory.

Meta might be on the hook for $17.1 billion and a host of app changes under a new kids safety settlement, but it's already spinning the deal to its advantage. After years of being the national punching bag for social media harms, Meta has reached a settlement with 47 US states and several districts and […]

Encrypted Client Hello rolls out on Android 17 to make the web more secure.

You're probably using Android System Intelligence more than you realize -- especially if your phone is a Google Pixel.

Every workflow builder I have used opens the same way: a blank canvas and a palette of nodes. Zapier, n8n, Make - all of them assume you already know what you want, already decomposed into steps, before the tool is any use to you. Most people don't. They know the chore . "I keep forgetting to check the weather before I bike in." The gap between knowing the chore and knowing the DAG is precisely the work these tools leave you to do alone, and I think it is why most people who try one never build a second automation. So I built Weaver, which inverts it. Weaver interviews you about the chore, one question at a time, until it actually understands the goal. Then it designs the workflow, validates it, deploys it, and runs it. The canvas is an output rather than an input. This post is about the parts that did not go to plan, because those are the parts worth reading. The interview is the whole product Three rules, and they are harder than they look: One question per turn. Never three bundled into a paragraph. Never invent a value the person has not given you. No quietly assumed recipient, city, or time. A correction updates one detail. Say "actually, Mondays" halfway through and it changes that and keeps going, instead of restarting the interview. That third one is the one people notice. Restarting an interview because the user corrected themselves is the single fastest way to make software feel like it is not listening. Only once it restates the whole task in plain language and you confirm does it save the intent and hand off to a separate Designer Agent. Two agents, deliberately not one The Conversation Agent and the Designer Agent are different models with different prompts and no shared state beyond a saved intent. That is a design decision, not an accident of implementation. Understanding a person and designing a system are different skills with different failure modes. Collapsing them into one prompt makes both worse: the interviewer starts proposing architecture hal