标签：#us

Sometimes, there comes a day in life when everything around me continues as usual, but something...

Meta is all-in on AI, and sometime soon, the company is going to make a big push into personal AI agents that can do things on your behalf. On Wednesday's Q2 2026 earnings call, CEO Mark Zuckerberg previewed a high-level vision of how the company is thinking about personal agents and what it will do […]

RAMageddon won't be the only reason your next phone costs more - Qualcomm is about to raise prices on all its processors, as well. Qualcomm CEO Cristiano Amon said on Wednesday that "prices are going to go up" on the company's products starting on September 1st, CNBC reports. The price hikes were rumored last week […]

If this is too long, tldr : Google Conway’s Law wath yt video and think There is a popular rule in software development called Conway's Law. It says that organizations design systems that mirror the way people inside those organizations communicate. In simpler terms: Your architecture will eventually look like your team structure. Big company with separate frontend, backend, data, DevOps, and platform teams? You will probably end up with separate services, separate processes, separate ownership, and a lot of API calls between people who sit in different Slack channels. But what happens when the entire company is just two people? That is where things get interesting. At bundle.social, we are running a unified social media API that handles a lot of edge cases. And there are two of us. There is no dedicated platform team No analytics department No infrastructure group. No product manager translating customer feedback into Jira tickets. Just two people are trying to keep a fairly large system moving without turning it into a pile of slop services nobody fully understands. You would think Conway's Law does not really apply to such a small team. It absolutely does. It just shows up differently. How Conway’s Law Works in a 2-Person Team When you have 50 developers split across departments, Conway's Law creates microservices and cross-team dependency hell. When you have two developers, Conway's Law forces your system into one of two extremes: The "Two Halves of a Brain" Split: Service A belongs entirely to Person A, and Service B belongs entirely to Person B. Because human communication between two people has practically zero friction, it's extremely tempting to drift into the lazy version of Conway's Law: ignoring technical boundaries altogether because "we can just talk about it on Slack." Why write explicit API documentation when you sit next to the person who wrote the endpoint? Why enforce strict domain boundaries when you can just export a helper function across modul

Advertisers agreed to “reset” relationship with X.

Curious if you should get Dyson’s new 2026 stick vacuums or stick to the older ones? I tested five models, old and new, to find out for you.

AI Worms in Word: How Document-Borne Threats Self-Propagate Meta Description: Document-borne AI worms can self-propagate through Copilot for Word, creating a new attack surface. Learn how these threats work and how to protect yourself. TL;DR: Researchers have demonstrated that malicious instructions embedded in documents can hijack Microsoft Copilot for Word, causing it to replicate harmful content, exfiltrate data, and spread the attack to new documents automatically — all without the user clicking a single suspicious link. This isn't theoretical anymore. Here's what you need to know and what you can do about it right now. Key Takeaways Document-borne AI worms exploit a technique called prompt injection to hijack Copilot for Word's generative AI capabilities These worms can self-propagate by instructing Copilot to embed malicious instructions in any new documents it generates or summarizes Sensitive data — including email addresses, financial figures, and personal information — can be silently exfiltrated during normal document workflows The attack requires no malware installation and can bypass traditional antivirus tools entirely Microsoft has issued guidance but the fundamental architectural challenge remains unsolved as of mid-2026 Practical defenses exist today, including document hygiene practices, access controls, and third-party AI security tools What Are Document-Borne AI Worms? If you've been following cybersecurity news, you've probably heard the term "prompt injection" thrown around. But document-borne AI worms take that concept to a genuinely alarming new level. Instead of just tricking an AI into saying something it shouldn't, these attacks can turn Microsoft Copilot for Word into an unwitting accomplice that spreads malicious instructions across your entire document ecosystem . The core idea is deceptively simple: an attacker embeds hidden instructions — often in white text on a white background, in metadata, or inside document comments — that Copilo

I watched a new tool try to get around the model safeguards of four major frontier companies. You might be surprised by how they performed.

Waymo robotaxis are now using freeways in Phoenix with more cities to follow in the coming days.

Hey everyone, Whenever I start scaling out a new Rust service or protocol, I always find myself hitting the same wall: testing gets messy fast. You end up juggling basic cargo test unit checks, hacking together ad-hoc integration scripts, and manually setting up coverage tools every single time. I put together RustForge to solve that headache for my own projects, and figured it might save a few of you some time too. It’s a clean, zero-bloat starter template designed to take you from simple unit tests all the way to compiler-style UI snapshots and coverage tracking without having to reinvent the harness every project. https://github.com/rwilliamspbg-ops/RustForge

When building local RAG (Retrieval-Augmented Generation) applications, edge agents, or serverless AI pipelines, developers usually hit a wall with standard vector stores: memory overhead. Running a dedicated vector database locally often demands hundreds of megabytes—or gigabytes—of RAM just to keep indices warm. On the flip side, lightweight local options like scanning raw JSON files or querying SQLite don't scale well when vector dimensions climb into the thousands (1536d+). We built Bitweave to solve this exact trade-off: a zero-copy, SIMD-accelerated hybrid retrieval engine in Rust (with Python bindings) that handles categorical filtering and vector search while locking its active heap footprint under 1.1 MB RSS. The Architecture: How Bitweave Achieves Sub-Millisecond Speed at <1.1 MB RAM Bitweave relies on a 3-part design to maximize search speed while keeping memory consumption negligible: [ Categorical Filters ] ---> Bit-Sliced Bitmaps │ ▼ [ Query Vector (1536d) ] --> 1-Bit SIMD Pre-Filtering (Hamming Distance) │ (Top K Candidates) ▼ [ Raw Embeddings Buffer ] -> Zero-Copy Float32 Rescoring (exact_rescore=True) │ ▼ Top-K Results Array (NumPy) Zero-Copy Memory Mapping (memmap2) Instead of deserializing index files into Python RAM or Rust heap space, Bitweave uses memory-mapped files (.bweave). The operating system's page cache handles lazy loading of index segments directly from disk into virtual address space. As a result, the active RSS memory footprint remains static around 1.1 MB, whether your index holds 5,000 or 200,000 records. 1-Bit Vector Quantization & SIMD Hamming Distance High-dimensional float32 vectors (1536d) are quantized down to 1-bit sign masks (where values > 0 map to 1 and <= 0 map to 0). During pre-ranking, Bitweave uses SIMD bitwise XOR and POPCNT operations to compute Hamming distances across candidate vectors in microseconds. Zero-Copy 2-Pass Float32 Rescoring (exact_rescore=True) Quantization speeds up initial candidate selection, but f

The startup analyzes calls, messages, and CRM data to identify effective sales techniques and turn them into playbooks for AI agents.

Elon Musk’s platform attacked the country’s “invasive” information-gathering powers as it demands the government drop efforts to strengthen its no-children policy.

AI is fantastic at spotting patterns, but human insight is the key.

GitHub热门项目 | a code review TUI with vim keybindings | Stars: 1,507 | 310 stars today | 语言: Rust

GitHub热门项目 | Sandbox any AI agent in seconds - zero setup, zero latency. | Stars: 3,272 | 73 stars today | 语言: Rust

GitHub热门项目 | The official Rust SDK for the Model Context Protocol | Stars: 3,694 | 13 stars today | 语言: Rust

These are the cleaning robots, water monitors, and toys actually worth buying for pool season.

Every senior developer, engineering manager, and tech recruiter I’ve talked to recently is echoing...

Expect bumps with X Money rollout as major US financial markets are excluded.