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The article discusses the Gateway project, an open-source software that serves as a middleware layer between cloud services and client applications, providing a unified API and facilitating communication and data exchange.

The article explores the concept of 'echoes' in cultural and interpersonal contexts, examining how past experiences, relationships, and beliefs can shape and influence the present. It delves into the ways in which these 'echoes' can both enhance and complicate our understanding of the world around us.

The article discusses the Cubo, an open-source robotic platform designed for research and education. It provides a modular and customizable hardware and software solution for building diverse robotic applications.

Astrolabe is a new Nextcloud app that I've developed that enables semantic search across your documents, notes, calendar, recipes, etc.

It leverages the nextcloud-mcp-server (same author) as the backend to enable AI workflows via MCP that you can use from any compatible MCP client such as Claude Code, Gemini CLI, Opencode, etc.

This is my first foray into NC app development, and I'd really appreciate any productive criticism. I learned a lot about php, authentication within Nextcloud, as well as semantic search. The Astrolabe app implements the interface to Nextcloud's Unified Search Provider, which means semantic search works from the global search bar. Search results include the page/chunk so if you click on a resulting PDF doc, the Astrolabe UI opens the PDF at the exact page that includes the result of your search.

In addition to various MCP tools, the nextcloud-mcp-server provides management APIs and the background processing required to embed your documents that can be searched via Astrolabe. It uses qdrant as the vectordb, supports NC webhooks for up-to-date vector embeddings.

For those aware, Nextcloud is pushing ExApps as the de-facto way of writing backends for Nextcloud apps. Astrolabe does not take this approach because it doesn't support more advanced MCP features such as streaming, MCP sampling, etc. Instead Astrolabe and nextcloud-mcp-server can be deployed as OAuth clients that only use app passwords for making requests to Nextcloud itself. This would enable more advanced multi-user scenarios for individuals, families, and small businesses that use Nextcloud as their source-of-truth.

I'm really looking forward to the increased interest in personal AI assistants, and this is my opinionated take on how something like this could be done in Nextcloud.

On a slightly related note, I recently completed a two-week POC on leaf.cloud, an EU-based hosting provider, to showcase how personal AI could work without having to buy GPUs yourself meanwhile keeping you in control of where data is processed.

https://blog.coutinho.io/eu-only-ai-stack-nextcloud-leafclou...

A 10-year-old boy swam for hours to save his mother and siblings after they were swept out to sea, displaying remarkable endurance and bravery to bring his family back to shore.

Oracle secures a $25 billion debt financing, indicating anxieties around AI funding and the company's strategic shift towards cloud computing and artificial intelligence technologies.

Hi all,

I build a calculator that displays and edits formulas in a familiar graphical form, has a WYSIWYG editor, can work with numbers of any size, supports physical units, and has many other features.

There are online and desktop (Linux, Windows) versions.

The project is open source and consists of these ones:

https://github.com/denprog/yutovo-editor — a text and formula editor with output to a custom window. Built from scratch, no dependencies on other editors. C++, boost.

https://github.com/denprog/yutovo-desktop — a desktop application based on Qt.

https://github.com/denprog/yutovo-web — an online version based on Vue.js and Quasar. The remaining components are compiled for Wasm.

https://github.com/denprog/yutovo-calculator — a string expression calculator based on boost.spirit.

https://github.com/denprog/yutovo-server — a web server for a website based on Drogon.

https://github.com/denprog/yutovo-solver — a calculator broker. C++.

https://github.com/denprog/yutovo-logger — a logger based on spdlog.

There are versions for Flatpak, Snap, Debian, and Windows. You can save your documents on the website after registering.

I welcome any comments, bugs, shortcomings, or suggestions.

The article describes the development of a plugin for the Ghidra software reverse engineering framework that adds support for the Minecraft Protocol (MCP), allowing for the analysis and understanding of Minecraft server software and communication protocols.

Western Digital has unveiled new hard drive technologies, including a dual-actuator design that doubles performance and power-optimized HDDs that reduce power consumption by 20%. These advancements aim to improve storage capacity, speed, and efficiency for data-intensive applications.

The article discusses the concept of a 'virtual twin,' which is a digital representation of a physical object or process. It suggests that the virtual twin can be used as a brute-force approach to simulate and optimize complex systems, providing valuable insights without the need for extensive physical experimentation.

The article covers the Game Arena dataset, which provides information on video game sales, critic and user scores, and other relevant data. The dataset can be used for analysis and insights into the video game industry.

Hey HN – I'm Chaitanya, a 16-year-old student. I built AuditDraft after reading through the EU AI Act and realizing most companies have no idea how to comply. The regulation is 400 pages. High-risk AI systems need technical documentation covering 12 different requirements. Fines go up to €35M. Enforcement has already started for some provisions, and the big deadline (August 2026) is coming fast. AuditDraft helps you:

Classify your AI system's risk level (8 questions based on Article 6) Generate Annex IV compliant model cards and documentation Track compliance across all 35 high-risk requirements

Built with Passion.

The article discusses the challenges of working with flat Abstract Syntax Trees (ASTs) in the age of modern programming languages and tools. It explores the concept of ergonomics and how it can be applied to improve the developer experience when dealing with complex AST structures.

Anthropic announces the release of the Claude Agent SDK for Apple's Xcode development environment, enabling developers to easily integrate AI-powered chatbots and voice assistants into their iOS, iPadOS, and macOS applications.

This article provides an overview of various link shortening tools, discussing their key features, benefits, and use cases to help readers choose the best tool for their needs.

The article explores the history of Windows widgets, from their early days as desktop gadgets to their evolution and eventual discontinuation. It provides an overview of the key developments and changes in the widget ecosystem on the Windows platform over time.

This repository provides a Python implementation of the SQVect algorithm, which is a novel approach for approximate nearest neighbor search. The algorithm aims to achieve efficient and accurate similarity search in high-dimensional vector spaces.

The French government is planning to evaluate the use of VPNs following a ban on social media for under-15s, raising concerns about privacy and security. The move aims to address potential workarounds for the social media ban, but it also highlights the ongoing debate around the regulation of online privacy tools.

The article discusses an unusual Nvidia RTX 5070 Ti graphics card with a hole in its PCB (printed circuit board) that was able to set a world record. The unique design and modification of the card enabled it to achieve exceptional performance and break benchmark records.

Hey HN,

I built DeepInsight after getting frustrated with how AI research tools treat the research process as a black box.

The Problem - Current tools (ChatGPT, Perplexity, Gemini Deep Research) ask 3-4 clarifying questions, then generate a report you have to manually fix. The human is only involved at the very beginning.

The Solution - Multiple human-in-the-loop checkpoints throughout the research process. You guide the AI at critical decision points, or switch to auto-approve with scheduling when you trust it.

Key features

  - Live collaboration on reports (Google Docs-style)
  - Version control with side-by-side comparison
  - Export: HTML, PDF, DOCX, Markdown, PowerPoint slides
  - Multi-speaker podcast generation → publish to Spotify/Apple
  - Generate FAQs, flashcards, blog posts from reports
  - Chain research from completed reports
  - BYOC (bring your own context/documents)
  - MCP integration, REST APIs, webhooks
  - Team workspaces with sharing controls
  - Prebuilt industry-specific assistants

Looking for feedback, especially from researchers, analysts, and content creators.

https://deepinsight.neuralstratlabs.com/

There is a heavy sentiment floating around lately. “Everything will be built automatically” “My craft is obsolete” “In six months, a tool will read my mind and produce whatever I imagine”

I want to challenge this. Code is getting cheaper. Building is not.

We are confusing capability with intelligence. LLMs are not AGI. They are not minds that can discover new physics on demand. They are high‑fidelity simulators of a semantic space: compressing patterns, language, and human intent into a queryable engine.

Consider this mental experiment: Train the best possible model on all human knowledge up to the end of the 19th century. Feed it: “E = mc^2” and ask it to derive the rest. It won’t reliably invent relativity. Not because it’s dumb — but because that conceptual world isn’t in its space yet. It cannot simulate what hasn't been mapped.

So what is this really? It is a new layer in the computing stack. It doesn't replace the stack. It sits on top of it as a new control layer.

The shift clicked for me when Ilya showed me a workflow he built in Tune: A simple steps that generates a PDF bedtime book for his son every evening. He handed execution control entirely to the LLM. And it worked. It wasn't random magic. It was a stable, predictable system — the same way we expect software to be predictable today. That was the moment I realized: we can engineer this! Builders playing with OpenClaw, Claude skills, and agents are hitting this exact realization right now.

So here is the manifesto.

To the Doomers ("Everything will be automated"): You are mistaking generation for architecture. AI can generate infinite code, but it cannot generate meaning. It cannot care about the user. It cannot choose what not to build. Without a pilot, the simulator just hallucinates static.

To the Builders ("I am obsolete"): The "typing" part of programming is ending. The "thinking" part is expanding. You stop being a translator of logic into syntax. You start being an architect of intent.

What actually infinitely matters now? Taste. System design. Constraints. Product judgment.

Your craft isn’t being taken away. It is being multiplied. Don’t mourn it. Pick up the instrument. You are about to feel that early‑builder excitement all over again.

The article explores the author's personal experience with overwhelming student loan debt and the emotional toll it takes, highlighting the broader societal issue of the rising cost of education and the financial burden it places on individuals.

I’ve found that GTM experiments tend to start structured and then drift into docs, sheets, and tribal knowledge.

For people running a lot of experiments: do you use any system to track hypotheses, outcomes, and decisions, or accept that this stays messy?

I built an Android study timer that solves two problems I had while studying for certifications: phone distractions breaking my focus, and forgetting what I studied a week later.

For focus: Three lock modes including "complete lock" that prevents unlocking your phone entirely until the timer ends. It even blocks Picture-in-Picture (that loophole we've all used to escape focus apps).

For retention: Automatic spaced repetition based on the Ebbinghaus curve. Tell it what you studied, and it generates review tasks at optimal intervals (1, 3, 7, 14, 30 days). No manual scheduling.

Also includes subject tracking, stats/heatmaps, encrypted cloud backup, and procedurally generated BGM.

Looking for beta testers—offering free Premium in exchange for feedback on what would make this more useful for your study workflow.

Beta signup: https://play.google.com/store/apps/details?id=com.iterio.app

Package: com.iterio.app | Android 8.0+ | Contact: iterio.timer.app.help@gmail.com

Over the past year I worked through Frankel’s “The Geometry of Physics” cover to cover, not to relearn physics, but to rebuild a modern geometric toolbox as it is actually used there: manifolds, differential forms, connections and curvature, Lie groups and algebras, fiber bundles, gauge structure, and variational principles.

The motivation was practical rather than theoretical:

Which of these geometric structures, if any, actually survive discretization, noise, and SGD-style training in modern machine learning?

In physics, global and coordinate-free formulations were not aesthetic choices; they were forced when local reasoning stopped working. A recurring structural pattern was:

structure -> symmetry -> invariance -> dynamics -> observables

In modern ML we increasingly see analogous issues:

* parameter symmetries and large quotient spaces * non-Euclidean data (graphs, meshes, manifolds) * highly structured hypothesis classes * training dynamics that are not well-described by flat Euclidean optimization

Some geometric ideas clearly paid off (e.g. equivariance via group actions). Many others did not. I’m trying to understand where future leverage might still lie, and where geometry collapses to interpretation or preconditioning.

Below is my current (incomplete) map of where modern geometry already shows up in ML, or plausibly could.

1. Geometry of data (base spaces)

Manifolds, stratified spaces, graphs and meshes; discrete differential operators (graph Laplacians, discrete Hodge theory); topological summaries (persistent homology).

This seems strongest for representation, spectral methods, and diagnostics. The open question is how much of this geometry can couple dynamically to training, rather than remain preprocessing or analysis.

2. Geometry of hypothesis spaces (architectures)

So far the most successful direction:

* symmetry and equivariance via group actions * quotienting hypothesis spaces * convolution as representation theory * SE(3)- / gauge-equivariant models * architectures encoding invariants or conservation laws

Here geometry restricts the hypothesis class before optimization. I suspect there is still room beyond global groups, toward local gauge structure, fiber bundle–valued representations, and architectures defined by connections rather than coordinates.

3. Geometry of parameters and optimization

Optimization on manifolds (Stiefel, Grassmann, SPD cones), structured or low-rank parameterizations, information geometry and natural gradients.

This seems most effective when constraints are hard and geometric. In looser settings, much of this reduces to preconditioning. It’s unclear where deeper geometric structure still matters at scale.

4. Geometry of training dynamics

Viewing training as a stochastic dynamical system:

* gradient descent as discretized flow * SGD as an SDE * trajectories on manifolds * attractors and metastability

This connects to dynamical systems, stochastic analysis, and geometric mechanics, but remains underdeveloped relative to its apparent relevance.

5. Discrete vs smooth geometry

Modern ML is deeply discrete: finite precision, quantization, sparse activations, graph-based computation. Smooth differential geometry may be the wrong limit in some regimes. Discrete differential geometry or combinatorial curvature might be more appropriate.

Some failures of “geometric ML” may simply be failures of choosing the wrong geometric category.

What I’m looking for:

* geometric structures that have actually influenced model or optimizer design beyond equivariance * where Riemannian or information-geometric ideas help in large-scale training * which geometric frameworks seem promising but currently mismatched with SGD * directions I’m missing

Perspectives from theory groups, applied math, and industry research labs would be very welcome.