Codename One Launches Vault API for Secure Cross-Device Data Sync Without Exposing Keys

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Codename One, an open-source framework for building cross-platform apps from a single Java or Kotlin codebase, has released a new com.codename1.security.vault API as part of its latest update. The Vault API enables encrypted data to be synchronized across phones and browsers without the sync server ever receiving the encryption key or plaintext. It uses authenticated encryption with a random 32-byte data key, storing only wrapped copies of that key so that a password change does not require re-encrypting all records. The API supports three unlock policies — session-only, device-remembered, and user-verification — giving users deliberate control over how vault access is restored. In browser environments, remembered access leverages a non-extractable Web Crypto key stored in IndexedDB, strengthening security beyond conventional approaches like storing a key alongside its ciphertext.

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Brazilian Lab Trains 144M-Parameter Decision AI Model for Just $104

Supersonic Labs, a small AI lab in Brazil, has released Julia 1, a 144.3-million-parameter decision model trained at a total cost of approximately US$104. Unlike generative language models, Julia 1 does not produce text — instead, it accepts a context, a question, and up to 20 options, then returns ranked scores for each choice. The model runs on consumer hardware including Android tablets and budget laptop CPUs, with weights of 550 MB fitting within 400 MB of RAM. In benchmark testing, Julia 1 outperformed comparable models on emotion classification by 38 percentage points, though it struggled with large label sets such as Banking77's 72 categories. Released on Hugging Face under the Apache 2.0 license, the model also achieved over 71% accuracy across 52 language locales in multilingual scenario classification.

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How PayEcho Forced Its LLM to Act on Customer History, Not Ignore It

Engineers at PayEcho, a payment-recovery and credit-decision platform, found that integrating recalled customer history into an LLM's context was not enough to change its recommendations. The model consistently produced generic advice even when months of behavioral data were available, treating historical information as background rather than evidence. The team solved this by requiring the model to explicitly cite specific past outcomes — such as a customer ignoring emails but responding to WhatsApp — as justification for each recommendation. Retrieval and generation were kept as separate pipeline stages, making it easier to diagnose whether a generic output stemmed from poor memory recall or failure to reason over retrieved data. Actual outcomes from each interaction are written back to memory, creating a feedback loop where past results become evidence for future decisions.

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How to Stream LLM Tokens in PHP Using Server-Sent Events

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College Game Dev Club Builds Mobile-Friendly 3D Web Game Using Three.js

A college game development club built a browser-based 3D exploration game using Three.js, TypeScript, Cannon.es, and GLSL shaders, distributable via a simple QR code. The game features a car navigating an island with a mini-game and information zones, inspired by developer Bruno Simon's interactive portfolio. To ensure smooth performance on mobile devices, the team used instanced geometry and custom GPU shaders to render 15,000 grass blades, procedural flowers, butterflies, and dolphins while minimizing CPU load. Expensive post-processing effects like SSAO and Bloom were replaced with lightweight alternatives, including darkened grass bases to simulate ambient occlusion and CSS filters for color grading. The project was developed in three phases: establishing driving controls with primitives, integrating 3D models, and finally refining visuals while optimizing for mobile browsers.

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