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Your next drive-thru order might be taken by a bot. And you might not even notice.

June emerged from stealth today with a $20 million pre-seed round to make AI adoption simpler.

Steve Elmers, also known as the “Guardrail Guy,” is done calling out license plate readers after two that appeared in his videos were vandalized.

If you're running local models through Ollama and tired of Python's overhead, check out langchain-rust . It's a full LLM framework in pure Rust that works great with local models: Ollama support — first-class integration with tool calling, vision, and streaming 9 vector store backends — InMemory, SQLite, Qdrant, ChromaDB, Redis, PGVector, MongoDB, Pinecone, FileVectorStore BM25 keyword search — with Chinese/English tokenization, no external dependency Hybrid retrieval — BM25 + Vector with RRF fusion for better recall GraphRAG — Knowledge graph construction + community detection, all local CorrectiveRAG — Self-correcting retrieval with hallucination detection Code Interpreter — LocalSandbox (subprocess), E2B cloud, or WASM sandbox LocalEmbeddings — Run embeddings without calling an API Plus: LangGraph workflows, MCP client/server, 7 memory types, guardrails, and 12+ built-in tools. Single binary, no virtualenv, no pip conflicts. Just cargo add langchainrust and go. GitHub: https://github.com/atliliw/langchainrust Docs: https://docs.rs/langchainrust

A deep dive into AURA, the desktop AR companion that watches your face, reads your hand gestures, and — in a previous life — took 35 seconds just to say "hello." So There's a Glowing Orb on My Desktop Now Let me introduce you to AURA , a desktop companion whose entire personality can be summarized as: "I will float on top of your windows, stare at your webcam, and silently form opinions about your code and your life choices." Per its own README, AURA is built to look at your screen, evaluate your facial expressions, and judge your open browser tabs in real time. No notes. No euphemisms. That's just the mission statement, printed in broad daylight, by the people who made it. Bold. Deranged. Kind of iconic. It's a semi-transparent holographic orb pretending very hard to be a sentient biological interface, the way a Roomba pretends to have feelings when it gets stuck under the couch. It changes color depending on whether you look focused, happy, or the specific flavor of "deeply stressed by my own code" that only a 2am debugging session can produce. It does not, notably, offer to help you fix the bug. It just watches. Like a nature documentary, except you're the nature. Chapter 1: The Dark Ages (a.k.a. "Please, Just Let Me Open One App") Before the great rewrite, launching AURA was less "spin up an AI assistant" and more "sit down, we need to talk about your life choices while the computer thinks." It behaved less like software and more like a extremely judgmental houseplant that needed 35 seconds of silent contemplation before it would even acknowledge your existence. Here's the greatest hits album of suffering, straight from the project's own changelog, presented with the reverence it deserves: The 35-Second Cold Start Penalty — On launch, the app synchronously imported PyTorch, EasyOCR, MediaPipe, PyAutoGUI, Pygame, and the Windows speech drivers, all before doing anything useful, like a chef who insists on individually greeting every vegetable before starting dinne

I want to talk about what I have notice on the platform and want to address what we all have been...

I used to laugh at people who panicked over data limits. Then my carrier switched my "unlimited" plan...

How I implemented wmiexec from scratch in Rust — DCOM activation, OXID resolution, and MS-WMIO object marshaling — by using impacket not as a library but as a debugging oracle, and diffing my wire bytes against it until a Windows DC accepted them byte-for-byte. This is a build log from ADhammer, an Active Directory audit + validation toolkit I'm writing in Rust on a from-scratch DCE/RPC · NTLM · SMB2 · Kerberos stack (think "impacket for Rust"). The whole project is built with Claude Code, and this post is the single best example of what that actually looks like — not autocomplete, but a tight loop of hypothesis → capture live traffic → diff → fix against a real domain controller. The goal: wmiexec, from scratch wmiexec is the classic "quiet" remote-code-execution technique: instead of creating a service (psexec/SVCCTL) or a scheduled task (atexec), you talk to WMI over DCOM and call Win32_Process.Create. No service-install event, different host telemetry. Under the hood it's three stages, each a different flavour of pain:

In June 2026, Peter Steinberger reported that his system spent $1,305,088.81 over 30 days and...

Every year, developers ask the same question: "Which programming language should I learn next?" And...

Two minutes and thirty seconds into the title track of Rachika Nayar's Heaven Come Crashing, an absolutely massive drum and bass beat drops. As a fan of Nayar's debut record Our Hands Against The Dusk, it caught me off guard. That album has no percussion on it at all. Heaven Comes Crashing is almost entirely […]

Fender CEO Edward "Bud" Cole gave an interview to T3 in May celebrating the 75th anniversary of the Telecaster with comments on AI and music that initially flew under the radar. But it has started making the rounds recently, pouring more fuel on an already raging fire of bad PR following the company pissing off […]

Full title: [Advanced Rust] 2.3. API Design Principles of Unsurprising Pt.3 - Implementing serde Serialize and Deserialize Traits, and Why Copy Is Not Recommended 2.3.1. It Is Recommended to Implement Serialize and Deserialize in serde Serde is the core Rust library for serialization and deserialization : Serialization : converts a Rust struct or enum into a string or binary representation such as JSON or YAML Deserialization : parses a string or binary representation such as JSON or YAML back into a Rust struct or enum Serialize and Deserialize are both traits from the serde crate. Serialize Trait The Serialize trait allows a type to be converted into a serializable data format such as JSON, YAML, or TOML. Its main methods include: serialize_bool serialize_i32 serialize_str serialize_struct These are methods on the Serializer (and related) traits that a Serialize implementation calls; the Serialize trait itself only requires serialize . Its definition is: pub trait Serialize { fn serialize < S > ( & self , serializer : S ) -> Result < S :: Ok , S :: Error > where S : Serializer ; } Here is an example showing how to implement Serialize manually: use serde :: ser ::{ Serialize , SerializeStruct , Serializer }; struct Point { x : i32 , y : i32 , } impl Serialize for Point { fn serialize < S > ( & self , serializer : S ) -> Result < S :: Ok , S :: Error > where S : Serializer , { let mut state = serializer .serialize_struct ( "Point" , 2 ) ? ; state .serialize_field ( "x" , & self .x ) ? ; state .serialize_field ( "y" , & self .y ) ? ; state .end () } } serializer.serialize_struct("Point", 2)? creates a struct serializer state, and 2 is the number of fields state.serialize_field("x", &self.x)? serializes the struct fields one by one state.end() finishes serialization Deserialize Trait The Deserialize trait allows Rust types to be parsed from various data formats. Its main methods include: deserialize_bool deserialize_i32 deserialize_string deserialize_struct These are

Full title: [Advanced Rust] 2.2. API Design Principles of Unsurprising Pt.2 - Implementing Clone, Default, PartialEq, PartialOrd, Hash, Eq, and Ord 2.2.1. It Is Recommended to Implement the Clone Trait and the Default Trait Clone Trait The Clone trait in Rust allows an implementer to explicitly create a deep copy of itself through the clone method, as opposed to the by-value copy provided by the Copy trait. Example: #[derive(Debug, Clone)] struct Person { name : String , age : u32 , } impl Person { fn new ( name : String , age : u32 ) -> Self { Self { name , age } } } fn main () { let person1 = Person :: new ( "John" .to_owned (), 25 ); let person2 = person1 .clone (); println! ( "{:?}" , person1 ); println! ( "{:?}" , person2 ); } The Person struct implements the Clone trait In main , person2 clones the data from person1 because it implements Clone Output: Person { name: "John", age: 25 } Person { name: "John", age: 25 } Default Trait The Default trait in Rust allows a type to define a default value and return that default instance through the default() method. Example: #[derive(Default)] struct Point { x : i32 , y : i32 , } fn main () { let p = Point :: default (); println! ( "Point is at ({}, {})" , p .x , p .y ); } Output: Point is at (0, 0) 2.2.2. It Is Recommended to Implement the PartialEq , PartialOrd , Hash , Eq , and Ord Traits PartialEq Trait PartialEq provides support for the == and != operators, allowing custom types to participate in partial equality comparisons. Example: #[derive(Debug, PartialEq)] struct Point { x : i32 , y : i32 , } fn main () { let point1 : Point = Point { x : 1 , y : 2 }; let point2 : Point = Point { x : 1 , y : 2 }; let point3 : Point = Point { x : 3 , y : 4 }; println! ( "point1 == point2: {}" , point1 == point2 ); println! ( "point1 == point3: {}" , point1 == point3 ); } By implementing PartialEq , we can compare whether two structs are equal Output: point1 == point2: true point1 == point3: false PartialOrd , Eq , and Ord Trait

GitHub热门项目 | Rust‑first, cross‑platform wgpu/WebGPU renderer exposed to Python for fast, headless 3D rendering. Built in Rust, shipped as Python wheels. | Stars: 558 | 10 stars today | 语言: Rust

GitHub热门项目 | A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/ | Stars: 13,405 | 6 stars today | 语言: Rust

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Keep the shell. Preserve the presence. The ally doesn't suspect; the enemy doesn't move. — The 36 Stratagems, Slough off the Cicada's Golden Shell Previously on this series: #19: Mark Found His AI Audit Method in a Training Manual. He Left a Trap in His Report. — P confirmed Mark's report was read from a Singapore IP. A note was left: "Entry's gone. Two weeks. Don't reach out. I'll find you." #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — ACL's processing latency climbed abnormally. Someone had done something in the same time window. Exposed P's monitoring pinged while P was still helping Mark verify an address. Deep night. The screen was the only light in the room. P opened the monitor. The record was waiting: a read from Singapore. Time, method, address, all matching. Mark's bait had been taken. P knew this path. A false lead planted in Mark's report, waiting for this exact day. P double-checked the address: an AWS Elastic IP registered in the Singapore region, same network block. No ambiguity. P sent an encrypted message: "Your report was read. From a Singapore IP." Then P ran the routine check. The environment status list scrolled in the terminal: storage levels, certificate expiry, key rotation dates. P had read these lines a hundred times. Every time, identical. One line was different. P's fingers stopped on the trackpad. The cursor sat on the entry's metadata line. A new tag P had never configured. # Old entry metadata: new entry (not configured by P) status : reclaim_pending source : acl-asset-scanner scanned_at : 02:01:07Z P didn't move. The cursor sat on screen. In the room, only the fan. The fan cycled once. P's fingers lifted off the trackpad, then settled back. The tag was still there. The tag wasn't an alert. Not an error, no explanation. The format matched ACL's automated scan records. P had seen it before, in a data company's audit report last year, in another client's logs the year before. ACL's scanner had swept

New EU rules stipulate that people must be told when they’re interacting with AI or looking at AI-generated or -edited content, leading to fear of “disclosure fatigue.”

How a single contiguous allocation — and a type system that won't let you feed strings to a scaler — is the real reason datarust fits in 2.3 megabytes. In the last post I showed you the whole datarust workflow: impute, scale, one-hot, train a logistic regression, evaluate, and save it as JSON — all without a Python runtime in sight. The Docker image shrank from ~900 MB to ~8 MB, and the binary was 2.3 MB. But I skimmed over something important. I kept saying "the flat memory layout" as if it were a detail. It isn't. It's the whole bet. Every scaler, every encoder, every model, every metric in datarust runs on top of one data structure. If you understand that structure — why it looks the way it does and what it refuses to let you do — the rest of the library stops being magic. So let's zoom in. Meet Matrix . Two containers, on purpose Real data is mixed. Numbers in one column, strings in the next. In Python, everything flows through one giant numpy.ndarray or a pandas.DataFrame , and the type system just... shrugs. A string column next to a float column gets coerced into object dtype. You'll find out at training time, in the form of an error message three frames deep. datarust does the opposite. It splits your data into two types at the source: use datarust :: Matrix ; use datarust :: matrix :: StrMatrix ; let numeric = Matrix :: new ( vec! [ vec! [ 3.0 , 85.0 , 24.0 ], vec! [ 12.0 , 70.0 , 31.0 ], vec! [ f64 :: NAN , 95.0 , 45.0 ], ]) ? ; let categorical = StrMatrix :: from_strings ( vec! [ vec! [ "MonthToMonth" ], vec! [ "OneYear" ], vec! [ "MonthToMonth" ], ]) ? ; Matrix is f64 only. StrMatrix is strings only. They are different types , and the compiler will refuse to compile a program that hands a string column to a scaler. Not at runtime — at compile time. In the last post I called this "putting on glasses for the first time." Let me show you what it actually buys you. The ColumnTransformer API is built on that split: ct .add_numeric ( "scaled" , vec! [ 0 , 1 ],