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Kintara is a self-hosted document library and reader that runs in Docker and watches a folder you already have. Drop PDFs, Markdown, or text files into the directory and it indexes them automatically, extracts searchable text and metadata, generates thumbnails, and makes the whole library available through a browser or installable PWA. It has libraries, collections, tags, full-text search, highlights, favorites, reading progress, private library sharing, and GitHub OAuth. I have been working on Kintara for a few months, and the architecture actually changed pretty dramatically while I was building it. Kintara originally had a Tauri desktop shell, but I eventually realized that isn't what I wanted at all. So I ripped the desktop layer out and rebuilt it around one Rust server that serves both the API and frontend. Now I can point Kintara at a NAS folder and open the same library from my desktop, laptop, tablet, or phone. The thing I really love about this app is the optional AI features. I added an option to use OpenAI or Gemini, and with so few tokens being spent, it's a fraction of a cent to use most of them, aside from the cover image generation, which is bit more, but makes the library look so much prettier! 😄 Anyway, I wanted AI to be a tool inside the library rather than taking the thing over, and I wanted it to be fully optional, so if you're one of those "Ew, AI is in this app" people, you just don't turn it on and it's like it doesn't exist. What the AI can do is summarize documents, suggest metadata and fill in those blank spaces, generate cover images for docs that don't have a cover, search the library for docs, or you can just chat with it about your docs. Find is a pretty great AI feature I think. Instead of letting the model vaguely tell you that something appears "somewhere in the document," Kintara asks for actual passages with page numbers, verifies the quote against extracted page text on the server, then verifies it again against the rendered PDF.

U.S. battery startups pulled in $500 million in grants from the Department of Energy, throwing a lifeline to an industry that was on the ropes after EV incentives were slashed.

At the first step, I picked up the SurrealDB project for contribution. I didn't know how I could help this project become better. So I asked my beautiful OpenCode to find parts of the project that could be better. It suggested this file of the project(core/src/val/value/get.rs) to me and said it has a double-cloning issue. So I opened up VS Code, and I started checking the issue. The code was something like this: let mut a = Vec :: new (); for v in v .iter () { let cur = v .clone () .into (); if stk .run (| stk | w .compute ( stk , ctx , opt , Some ( & cur ))) .await .catch_return () ? .is_truthy () { a .push ( v .clone ()); } } First Optimization: As you can see at line 3 and line 9, we have multiple clones from a single document. I thought about how I could fix this issue; I went to see the CursorDoc structure because the first clone is converted to it: #[derive(Clone, Debug)] pub ( crate ) struct CursorDoc { pub ( crate ) rid : Option < Arc < RecordId >> , pub ( crate ) ir : Option < Arc < IteratorRecord >> , pub ( crate ) doc : CursorRecord , pub ( crate ) fields_computed : bool , } impl From < Value > for CursorDoc { fn from ( val : Value ) -> Self { Self { rid : None , ir : None , doc : val .into (), fields_computed : false , } } } #[derive(Clone, Debug)] pub ( crate ) struct CursorRecord { /// The underlying record, shared via Arc for copy-on-write record : Arc < Record > , } impl CursorRecord { // .... // /// cloning. Otherwise the value is cloned. pub ( crate ) fn into_owned ( self ) -> Value { match Arc :: try_unwrap ( self .record ) { Ok ( record ) => record .data , Err ( arc ) => arc .data .clone (), } } // .... // } impl From < Value > for CursorRecord { fn from ( value : Value ) -> Self { Self { record : Arc :: new ( Record :: new ( value )), } } } I saw that the value passed through CursorDoc is directly stored in a field in CursorRecord without any changes, and it is accessible using .into_owned() from CursorRecord. That is the solution; I edited the

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VoidZero has launched the beta of Vite+, a unified web development toolchain. It combines runtime, package management, and essential frontend tools under a single command. Vite+ supports various projects and is open source. The platform enhances workflow through features such as hot-reloading, format checking, and testing. The team emphasizes community feedback for future updates. By Daniel Curtis

security, #api, #cybersecurity, #discuss On July 30, 2026, my batch job finished 300,000 domain-to-company lookups. 39,847 of them (13.3%) resolved to defense contractors, military-adjacent parent companies, or headquarters within a few miles of named bases. I wasn't hunting for that. I was just trying to clean a CRM. The same day, lina published a post about hijacking e164.arpa zones and accidentally logging hundreds of thousands of phone calls to military bases. Different protocol, same smell: an infrastructure lookup that was supposed to be boring turned into a classified-adjacent data spill. That parallel is what made me sit down and write this. Here is the exact call I used, with the live response for github.com so you can see the shape of the data before I explain what went wrong. import requests , json , time # Full source notes: https://github.com/On13uka/company-info-api RAPIDAPI_KEY = " YOUR_RAPIDAPI_KEY " BASE = " https://company-info1.p.rapidapi.com " def lookup ( domain ): r = requests . get ( f " { BASE } /lookup?domain= { domain } " , headers = { " X-RapidAPI-Key " : RAPIDAPI_KEY , " X-RapidAPI-Host " : " company-info1.p.rapidapi.com " }, timeout = 20 ) return r . json () print ( json . dumps ( lookup ( " github.com " ), indent = 2 )) The response I got back looked like this. It is a cached sample from a real call — the endpoint was asleep when I drafted this, but the fields are exactly what the pipeline consumed. { "domain" : "github.com" , "company_name" : "GitHub Inc" , "wikipedia" : "GitHub is a developer platform..." , "ceo" : "Thomas Dohmke" , "founded" : "2008" , "headquarters" : "San Francisco, California" , "employees" : "3000+" , "parent_company" : "Microsoft" , "twitter" : "@github" , "github_org" : { "repos" : 200 , "stars" : 50000 , "followers" : 12000 }, "health_score" : 78 } The Finding I started the job because a sales team had 300,000 stale domain records and wanted company names, headcounts, and a rough health score for each. The pla

Cheap energy, abundant land, and proximity to Beijing have turned a city in Inner Mongolia into a crucial hub for data centers.

A Chinese agency says the two recalls affecting some 3 million vehicles can mostly be fixed by over-the-air updates—but they will also require physical warning stickers and camera-related updates.

Anthropic forbids its Claude models from generating sexually explicit content. But a series of tests conducted by TechCrunch found that it didn't take much to get past the restriction.

GitHub热门项目 | Developer Control Plane for your AI Agents | Stars: 5,332 | 64 stars today | 语言: Rust

The responses to NHTSA's questions so far are redacted entirely, citing "confidential business information."

The security review is being performed by the Idaho National Laboratory, and the research is being funded by a company -- or a group of companies -- in the electric and autonomous vehicle industries.

The dictionary defines nuance as a subtle distinction or variation. In practice, it's the difference...

Meta is in court again over child safety, and this time it’s a landmark case that could force significant changes to core features of Facebook and Instagram.

There's about to be a big fight to secure access to space.

Senator Ron Wyden sent a letter to the U.S. federal watchdog requesting a comprehensive review of how the FBI, DEA, ICE's HSI, and the Secret Service use hacking tools and spyware against Americans.

Mark (aka Winsaucerer) here to show you how you can test your PostgreSQL database like a sorcerer. We are going to be using Spawn, a SQL build system supporting migrations and testing. You do not need to be using Spawn for migrations in order to use it for testing. Spawn does not require any extension installed. All you need is the spawn CLI and a psql connection to the database for Spawn to connect through. Spawn was built to solve some migration pains I've experienced, but I happily discovered that when used for testing, it is very powerful. To show you some of that power, we're going to use a contrived database example. It uses golden file testing to determine success. When the test runs, we capture the stdout and stderr output from psql, and compare that to expected output. Testing with Spawn involves these steps: Create a new test with spawn test new <name> and fill out the test steps Check test outputs with spawn test run <name> (or view the SQL that will be sent to psql via spawn test build <name> ) When outputs are as expected, create the golden file with spawn test expect <name> Run the test and compare to expected output with spawn test compare <name> For now, Spawn only supports connecting via psql, which means that you have access to all the features that psql provides. To get started, follow the Spawn install instructions: Install Spawn And then create a new folder on your system, and initialise a new project with a docker compose config ready for us to play with: # inside your new folder: spawn init --docker docker compose up -d You now have a running docker based PostgreSQL database and a spawn.toml file configured to connect to it. We are not assuming that you are using Spawn or any other tool for migrations, so you can manually create and update the database by connecting directly using psql: docker exec -ti postgres-db psql -U postgres Create the database ⚠️ Caution This post is not intended as an example of how to build an orders database. The des

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This is part three of our Directus Basics series. In part one we set up our instance, and in part two we covered relationships between collections. Today we're covering access control — what determines who can see and touch your data. The Core Concepts Access control in Directus comes down to three terms: Permission — applies to one collection and one action (create, read, update, delete, or share). Can be full access, no access, or custom rules. Policy — a group of permissions bundled together, applied to users or roles. Role — defines a user's position within a project. A role can hold any number of policies, apply to any number of users, and have child roles of its own. What Happens Without a Role? If an administrator creates a user but doesn't assign a role, that user has valid login credentials but still can't access the Data Studio. Creating a role and assigning the user to it isn't enough either — logging in at that point returns a "No App Access" error. The missing piece is that the role has no access policy attached. The policy is what actually tells the role what its users can and can't do. The Access Order Access in Directus flows in a specific direction: Access Policy → Role → User In practice: define what level of access a user needs, build an access policy to match, create a role and attach that policy to it, then register the user and assign them to the role. Default Policies Every fresh Directus instance ships with two default policies: Administrator — the role you're signed in as by default, with unrestricted access to everything. Public — for data that should be visible without logging in. Think of a product catalog on an e-commerce site — forcing a login just to browse products is a poor experience. Rule of thumb: give the Public policy Read access only. Never Create, Update, or Delete. The public should be able to view data, never manipulate it. Giving a Collection Public Access Go to Settings → Access Policies → Public Under Permissions, click A

On-Device Piano Autocomplete: A 125M Model That Actually Works Meta Description: Discover how a 125M parameter model autocompletes piano music entirely on-device. We break down the tech, real-world performance, and what it means for musicians. TL;DR A developer shared on Hacker News that they trained a 125M parameter language model to autocomplete piano melodies — entirely on-device, with no internet connection required. The project demonstrates that compact, efficient AI models can deliver genuinely useful musical assistance without cloud dependency. This article breaks down how it works, who it's for, and whether it's worth your attention as a musician, developer, or AI enthusiast. Key Takeaways A 125M parameter model can run real-time piano autocomplete on consumer hardware without a cloud backend The model was trained on MIDI data and treats music generation similarly to how LLMs treat text prediction On-device inference means zero latency from network calls, full privacy, and offline usability This project sits at an exciting intersection of music technology, edge AI, and creative tools The approach has real limitations — it's not replacing a composer, but it's a genuinely useful creative assistant Similar techniques are being adopted in DAW plugins and music education software as of mid-2026 What Is "Show HN: I Trained a 125M Model to Autocomplete Piano On-Device"? If you spend time on Hacker News, you've probably seen "Show HN" posts — a dedicated space where builders share projects they've made. This particular submission caught significant attention: a developer trained a 125-million-parameter neural network to autocomplete piano music, and crucially, it runs entirely on your local device . No API calls. No subscription. No sending your musical ideas to a remote server. Just a model sitting on your machine, listening to what you play, and suggesting what might come next. The concept sounds deceptively simple, but the execution involves some genuinely clever