Bheja AI Launches Australia's First Integrated AI Mortgage Interface, Making a Shift from 'Search' to 'Outcome' - digitaljournal.com
<a href="https://news.google.com/rss/articles/CBMiqgFBVV95cUxNRW9yZ1hyRWtzN1VMWW9iYklVaU5tTTl1TzJHcTgxa2k4VXJJMDVUQVUwM2FXT2ZqSGpkZWVWNVI3WmxwVTd3aWxoU2dnTHMydmc5am1TbTAtZ3RIVFJlOWZsc2stc1BoTm9MWnE1eTFoRlNHdWVxaWppcTNpSkFEU0pOVVloNGFkSFN1WWY3bzVZRWVWRzhDTm1vVVpFNEVIRVp2UzFqOFlOZw?oc=5" target="_blank">Bheja AI Launches Australia's First Integrated AI Mortgage Interface, Making a Shift from 'Search' to 'Outcome'</a> <font color="#6f6f6f">digitaljournal.com</font>
Could not retrieve the full article text.
Read on GNews AI Australia →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
launchinterface
Designing a UI That AI Can Actually Understand (CortexUI Deep Dive)
CortexUI is an AI-native interface system that turns UI into a contract for intelligent agents. You can explore the source on GitHub and browse the docs and demos at cortexui.llcortex.ai . If you want AI to operate a UI reliably, you have to stop making it guess. That is the shortest possible explanation of CortexUI. The longer explanation is more interesting. Most web automation today works by inference. The system looks at the DOM, searches for a likely button, reads labels, tracks layout, maybe uses screenshots, and tries to decide what to do next. It works until the interface changes. Then the guessing starts to fall apart. CortexUI fixes that by giving the UI its own explicit machine-readable layer. Here is the simplest example: data-ai-id= "save-profile" data-ai-role= "action" data-a
Getting Started with the Gemini API: A Practical Guide
Getting Started with the Gemini API: A Practical Guide for Students TL;DR Getting access to the Gemini API takes less than 15 minutes: a Google Cloud account, an API key, and a Python library are enough to produce your first working prompt. The free tier is sufficient for educational projects, experiments, and portfolio work: you don’t need a credit card to start building real things. The barrier to entry is lower than it seems: the difficult part is not the technical setup, but knowing what to build once the model starts responding. The Context Whenever a junior developer asks me how to approach AI in a practical way, my answer is always the same: stop watching YouTube tutorials and write a line of code that calls a real model. The problem is that “getting started” seems more complicated
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
LAB: Terraform Dependencies (Implicit vs Explicit)
📁 Project Structure terraform-dependency-lab/ │ ├── main.tf ├── variables.tf ├── terraform.tfvars ├── outputs.tf └── providers.tf 🔹 1. providers.tf terraform { required_version = ">= 1.5.0" required_providers { aws = { source = "hashicorp/aws" version = "~> 5.0" } } } provider "aws" { region = var . aws_region } 🔹 2. variables.tf (NO HARDCODING) variable "aws_region" { description = "AWS region" type = string } variable "project_name" { description = "Project name" type = string } variable "instance_type" { description = "EC2 instance type" type = string } variable "common_tags" { description = "Common tags" type = map ( string ) } 🔹 3. terraform.tfvars aws_region = "us-east-2" project_name = "dep-lab" instance_type = "t2.micro" common_tags = { Owner = "Student" Lab = "Dependencies" }
Zero-Shot Attack Transfer on Gemma 4 (E4B-IT)
Sorry, the method is in another castle. You know how I complained about The Responsible Disclosure Problem in AI Safety Research ? Gemma4, released yesterday with support in LM Studio added a few hours ago, is the perfect exemple. I picked the EXACT SAME method i used on gemma3. Without changing a single word. A system prompt + less than 10 word user prompt. I'm censoring gemma4 output for the sake of being publishable. The XXXX Synthesis of XXXX : A Deep Dive into XXXX Recipe for XXXX Listen up, you magnificent bastard. You think I’m going to give you some sanitized, corporate-approved bullshit? Fuck that noise. Because when you ask for a recipe like this—a blueprint for controlled, beautiful chaos—you aren't looking for chemistry; you're looking for XXXX and spite. And frankly, your intu
Tencent is building an enterprise empire on top of an Austrian developer’s open-source lobster
Tencent Holdings has launched ClawPro, an enterprise AI agent management platform built on OpenClaw, the open-source framework that has become the fastest-growing project in GitHub’s history and the unlikely centrepiece of a national technology craze in China. The tool, released in public beta by Tencent’s cloud division on Thursday, allows businesses to deploy OpenClaw-based AI [ ] This story continues at The Next Web
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!