mitzen

  Login to https://console.firebase.google.com/ and then goto your project. And then under " Devops and Engagement ". Then create a new campaign.  And then create a campaign as shown here and select "Firebase notification messages". And then let's create a test notifcations. Click on "Send test message". You will be prompted to insert FCM registration token.  After you please your token registered on your mobile devices, then you will be able to receive notification messages on your emulator What it looks like on your emulator :-

While trying to run my app on my emulator, i kept on getting issue "my activity does not exist" but it does. This is due to a ghost cache problem. After making the following updates adb uninstall com.appcoreopc.getmyhome Then I was able to deploy and get my app to work

 If you run into issue trying to install your app but being told unable to kill your current app, then you can try to run  adb shell am force-stop com.appcoreopc.getmyhome adb kill-server; adb start-server  

The error AttributeError: partially initialized module 'datasets' has no attribute 'utils' suggests a problem with the datasets library's installation or an internal conflict, possibly due to a circular import. And all it took for us to do is Restart runtime and then re-run the cell.

 Interesting papers for model optimizations  Paper: Polar Express The paper introduces Polar Express, a GPU-friendly polynomial method for computing the matrix polar decomposition, optimizing convergence speed and error minimization.It adapts polynomials iteratively, outperforming classical methods in deep learning applications like Muon, GPT-2 training, and image classification, with robust finite-precision stability and potential for large-scale, aspect-ratio-optimized, spectrum-aware acceleration. https://arxiv.org/pdf/2505.16932 Paper: LowRA Paper : "LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits" Stanford University — Zhou, Zhang, Kumbong, Olukotun arXiv: 2502.08141 (Feb 2025, accepted ICLR 2026) https://arxiv.org/abs/2502.08141 The problem it solves QLoRA (what you'd use in the training code above) quantizes the base model to 4-bit but keeps the LoRA adapters themselves in full precision (bf16). LowRA asks: what if we also aggressively quant...

To use Azure Foundry, first we need to ceate a project. It is important to note the project endpoint after you created it. Next, you will need to deploy a model.  We also need to install the required packages pip install azure-ai-projects>=2.0.0 The key environment variable to set are :-  PROJECT_ENDPOINT=<endpoint copied from welcome screen> AGENT_NAME="MyAgent" Chatting with a model To chat with an Azure Foundry model we can use the following  from azure . identity import DefaultAzureCredential from azure . ai . projects import AIProjectClient # Format: "https://resource_name.ai.azure.com/api/projects/project_name" PROJECT_ENDPOINT = " your_project_endpoint " # Create project and openai clients to call Foundry API project = AIProjectClient (     endpoint=PROJECT_ENDPOINT ,     credential=DefaultAzureCredential (), ) openai = project . get_openai_client () # Run a responses API call response = openai . responses . create (    ...

 

  The emerging 2026 picture is: Optimizer : Muon (or Muon + Polar Express) is replacing Adam as the go-to for training efficiency — ~2x cheaper for same quality Fine-tuning : QLoRA is being pushed further with papers like LowRA, squeezing more out of your 30–48GB machine Reasoning : GRPO variants (S-GRPO, Training-Free GRPO) are making DeepSeek-R1-style reasoning training accessible on consumer hardware Unsloth (already implements many of these optimizations) + QLoRA + Qwen2.5-Coder is still your fastest path, but keep an eye on Muon support landing in Unsloth/TRL, which would meaningfully speed up your training run The most relevant 2026 papers for cheap training 1. "The Polar Express" — ICLR 2026 Honorable Mention (Amsel, Persson, Musco, Gower) This is the most formally recognized paper of 2026 directly about training efficiency. It introduces a new method for computing the polar decomposition used in the Muon optimizer for training deep neural networks, using only m...

 AzApi helps solving the issue for you when you have to make a manual REST API call to azure. It is better than null script apprach whereby your:- State fully managed No external script dependency or PowerShell requirement Handles the API version explicitly Native depends_on support Idempotent by design Besides it is often recommended to use " a zapi provider" > ARM template deployment > null resource " This is an actual scenario when configuring Azure Foundry capability host - we have to initiate REST API call as there's no other sdk available.  To be able to do that we will use azapi providers and the sample code are shown here:- terraform {   required_providers {     azapi = {       source   = " azure/azapi "       version = " ~> 2.0 "     }     azurerm = {       source   = " hashicorp/azurerm "       version = " ~> 3.0 "     }   }...

 Here a some Bicep sample for setting up Azure Foundry https://github.com/microsoft-foundry/foundry-samples

Ran into this error while trying to setup my LLM agent to do tool calling. In this case, it is " google_search ".  in raise_error_async raise ClientError(status_code, response_json, response) google.genai.errors.ClientError: 400 Bad Request. {'message': '{\n  "error": {\n    "code": 400,\n    "message": "Built-in tools ({google_search}) and Function Calling cannot be combined in the same request. Please choose one to continue.",\n    "status": "INVALID_ARGUMENT"\n  }\n}\n', 'status': 'Bad Request'} The gist of the problem - ADK framework doesn't like me putting Agent model together with tool.  To resolve this issue, I have change my agent from Agent ( from google . adk import Agent )  to LlmAgent ( from google . adk . agents import LlmAgent ) 💥Error code      researcher = Agent (     name = " researcher " ,     model = model_name ,     description = " Condu...

Graphql fragment are used to simplified and provide shortcuts definitions to specified the same fields in a graphql query.  In this example, we provide defined ReportFields to consist of propertyType, id and status. Then we hook it up in the actual gteReportsByUserId query as shown here:-  query GetReportsByUserId ( $ includeStatus : Boolean ! ) {     getReportsByUserId ( userId : " 123 " , limit : 10 ) {     status     reports {       ... ReportFields     }   } } fragment ReportFields on RetrievedReport {   propertyType   id   status @ include ( if : $ includeStatus ) } Save us some typings in our query.

This is an example of graphql @include directive. The purpose of the @include directive is to allow a field to be retrieve or not based on a condition. In this case, it is determined by $inculdeStatus. Given that we are passing in $includeStatus = false, the field status will be excluded. query GetReportsByUserId ( $ includeStatus : Boolean ! ) {   getReportsByUserId ( userId : " 123 " , limit : 10 ) {     status     reports {       propertyType       id       status @ include ( if : $ includeStatus )     }   } } -------------------------------------------------- variables {   " userId " : " 123 " ,   " limit " : 10 ,   " includeStatus " : false } We can also use the @skip directive to skip field from being returned  query GetReportsByUserId ( $ includeStatus : Boolean ! ) {   getReportsByUserId ( userId : " 123 " , limit : 10 ) {     status   ...

We can make use of google adk to call ollama local llm agent or other inference runtime of your choice. This integration requires LiteLLM connector. Key libraries to install first and this basically will install LiteLLM  uv add google-adk[extensions] Here is an example of the code that we can use to integrate a local LLM spinned up using the code here:- from google . adk import Agent from google . adk . agents import LlmAgent from google . adk . models . lite_llm import LiteLlm # https://adk.dev/agents/models/litellm/ # https://adk.dev/agents/models/ollama/ root_agent = Agent (     model = LiteLlm ( model = " ollama_chat/gemma3:latest " ),     name = " dice_agent " ,     description =(         " hello world agent that can roll a dice of 8 sides and check prime "         " numbers. "     ),     instruction = """       You roll dice and answer questions about the outco...