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Confluence Labs, an AI research lab, is focusing on improving learning efficiency through innovative approaches to machine learning and artificial intelligence. The lab aims to develop AI systems that can learn more effectively and with fewer resources, potentially leading to advancements in a wide range of applications.

Google is reportedly working to bring end-to-end encryption to its Messages app, which would allow for secure communication between Android and iPhone users using the RCS messaging protocol.

I’m building a chat-first community app that uses Bluesky’s AT Protocol for identity.

Current architecture: - Electron client - Spring Boot backend (monolith) - REST for servers/channels - Planning WebSocket-based messaging

As a solo builder, I’m trying to balance simplicity with future scalability.

At what point would you introduce: - a separate WebSocket gateway - pub/sub (Redis, etc.) - or keep everything in one Spring app until it breaks?

Curious how others approached real-time chat systems early on.

Project for context: https://github.com/JohannaWeb/ProjectFalcon

The Housing for the 21st Century Act proposes significant changes to U.S. housing policy, including expanding rental assistance, increasing investment in affordable housing, and addressing homelessness and housing instability through a comprehensive approach.

I spent this weekend during the blizzard of 2026 ai vibe coding this website PostHotty.com Ask me anything, I will be happy to answer.

This article discusses the ongoing debate around the future of work, exploring the potential impact of automation and artificial intelligence on employment patterns. It examines both the opportunities and challenges these technological advancements present for workers and businesses.

Panasonic, once a leader in plasma TV technology, has announced that it will no longer manufacture its own TVs. Instead, the company will focus on providing components and services to other TV makers, marking the end of Panasonic's in-house TV production.

Mouse Tester combines a visual button map (left/right/middle/side buttons) with targeted diagnostics for two of the most common real-world issues: unintended double-clicks and scroll wheel jumping/direction problems. It’s designed to be fast, simple, and instantly understandable—so users can confirm what’s happening within seconds.

The article examines the potential of plant-based diets to mitigate climate change, highlighting their ability to reduce greenhouse gas emissions and land use compared to conventional diets. It explores the environmental and health benefits of adopting more sustainable dietary patterns.

The article discusses the retirement of TAT-14, one of the first transatlantic internet cables that played a significant role in enabling the global connectivity of the modern internet. It explores the impact of this milestone and the ongoing evolution of the infrastructure that powers the internet worldwide.

The article discusses the Agentic Data Stack, an open-source data infrastructure that provides a unified platform for processing and analyzing data from various sources. It highlights the stack's key components, including ClickHouse, Kafka, and Kubernetes, and how they work together to enable efficient data management and analytics.

The article explores the concept of PR targets, which are goals set for software projects to be released in a specific time frame. It discusses the benefits and challenges of using PR targets, as well as strategies for effectively managing them.

The article discusses the Design Studio 1, a software tool that allows users to design and prototype new silicon-based electronic devices. It highlights the tool's capabilities in enabling rapid design iterations, optimizing device performance, and accelerating the overall development process.

Curious.

The article discusses the benefits of incorporating AI-powered chatbots into customer service workflows, highlighting their ability to streamline operations, improve response times, and enhance the overall customer experience.

I got tired of spending hours in PowerPoint and TikZ drawing methodology diagrams for my papers. So I built PaperBanana — you paste your Method section text, and it generates a publication-ready figure in about 2-3 minutes.

How it works under the hood:

1. A Retriever agent searches a curated database of real academic diagrams to find structurally similar references 2. A Planner agent reads your text and generates a detailed visual description (layout, components, connections, groupings) 3. A Stylist agent polishes the visual aesthetics without changing content 4. Then it enters an iterative loop: a Visualizer generates the image, and a Critic evaluates it and suggests revisions — this repeats 1-5 times (you choose)

The key insight is that academic diagrams follow conventions — Transformer architectures, GAN pipelines, RLHF frameworks all have recognizable visual patterns. By retrieving relevant references first, the output is much closer to what you'd actually put in a paper vs. generic AI image generation.

Built with: Next.js + FastAPI + Celery, using Gemini 2.5 Flash for planning/critique and Nanobanana Pro/Seedream for image generation.

Try it here: https://paperbanana.online

Some examples it handles well: Transformer architectures, GAN training pipelines, RLHF frameworks, multi-agent systems, encoder-decoder architectures.

Known limitations: - Works best for CS/AI methodology diagrams — not optimized for biology, chemistry, or general scientific illustration - Text rendering in generated images isn't perfect yet — sometimes labels get slightly garbled - The curated reference database is still small (13 examples), expanding it is ongoing work

Would love feedback from anyone who writes papers regularly. What types of diagrams do you struggle with most?

DRESS is a nonlinear fixed-point equation that assigns a continuous similarity value to every edge in a graph. No parameters, no learning, just iterate until convergence. The result is deterministic, bounded in [0,2], and unique.

I published the original equation in 2018 (arXiv:1805.01419). I've now proved it's strictly more powerful than 1-WL (Weisfeiler-Leman colour refinement) — the standard benchmark for graph neural network expressiveness. The proof uses the prism graph vs K₃,₃, the textbook pair that 1-WL can't separate. DRESS distinguishes them because it works on edges, and different edge roles (triangle edges vs bridge edges) force different fixed-point values.

Two extensions — Motif-DRESS and Δ-DRESS — empirically distinguish Strongly Regular Graphs that defeat 3-WL, at near-linear cost instead of O(n⁴).

The library is written in C with bindings for C++, Python, Rust, Go, Julia, R, MATLAB, and WebAssembly. pip install dress-graph to try it.

Paper: https://github.com/velicast/dress-graph/blob/main/research/k...

Docs: https://velicast.github.io/dress-graph/

Looking for feedback. What am I missing? What should I test next?

Servo is an experimental web browser engine developed by Mozilla Research, with a focus on parallelism and high performance. It aims to be a next-generation browser engine that is safe, fast, and highly concurrent.

Strength AI is a personalized fitness app that uses AI technology to create custom workout plans and track progress. The app provides users with personalized exercise routines, real-time feedback, and performance insights to help them achieve their fitness goals.