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Google's researchers have developed a nanoscale device called the Nano Banana that can detect and analyze single molecules. This breakthrough could lead to advancements in fields such as medical diagnostics, environmental monitoring, and materials science.

The article discusses a new attack called AirSnitch that can break Wi-Fi encryption in homes, offices, and enterprises. It explains how the attack works and the potential implications for the security of wireless networks.

The article explores the rise of the 'vibe coding' movement, where developers seek to create a positive, collaborative coding environment focused on creativity and personal well-being, rather than just productivity. It discusses the maker movement's influence on this trend and how it challenges traditional software development practices.

The article discusses the new requirement for visitors to the UK to have a Google Play or App Store account to access essential services, raising concerns about privacy and accessibility for those without smartphones or the required accounts.

The article introduces 'just-bash', a command-line tool that provides a set of simple and versatile Bash functions to streamline various development tasks, such as managing Docker containers, running tests, and deploying applications.

The article discusses the use of automated license plate readers (ALPRs) by the US Border Patrol along California's highways, raising concerns about privacy and the expansion of surveillance technologies without public oversight.

The article discusses the current state of the United States and the world, highlighting the economic, political, and social challenges facing nations globally. It suggests that the US and other countries are experiencing a period of instability and uncertainty that may have long-lasting impacts.

The article explores the idea that each technological revolution is often accompanied by the belief that 'this time is different' and that the current changes are unprecedented. It examines how this mindset can shape public discourse and policy decisions, and cautions against dismissing historical patterns.

Hey HN - we're Saksham and Ishan, and we’re building Cardboard (https://www.usecardboard.com). It lets you go from raw footage to an edited video by describing what you want in natural language. There’s a demo video at https://www.usecardboard.com/share/fUN2i9ft8B46, and you can try the product out at https://demo.usecardboard.com (no login required!)

People sit on mountains of raw assets - product walkthroughs, customer interviews, travel videos, screen recordings, changelogs, etc. - that could become testimonials, ads, vlogs, launch videos, etc.

Instead they sit in cloud storage / hard drives because getting to a first cut takes hours of scrubbing through the raw footage manually, arranging clips in correct sequence, syncing music, exporting, uploading to a cloud storage to share, and then getting feedback on WhatsApp/iMessage/Slack, then re-doing the same thing again till everyone is happy.

We grew up together and have been friends for 15 years. Saksham creates content on socials with ~250K views/month and kept hitting the wall where editing took longer than creating. Ishan was producing launch videos for HackerRank's all-hands demo days and spent most of his time on cuts and sequencing rather than storytelling. We both felt that while tools like Premiere Pro and DaVinci are powerful, they have a steep learning curve and involve lots of manual labor.

So we built Cardboard. You tell it to "make a 60s recap from this raw footage" or "cut this into a 20s ad" or "beat-sync this to the music I just added" and it proposes a first draft on the timeline that you can refine further.

We built a custom hardware-accelerated renderer on WebCodecs / WebGL2, there’s no server-side rendering, no plugins, everything runs in your browser (client-side). Video understanding tasks go through a series of Cloud VLMs + traditional ML models, and we use third party foundational models for agent orchestration. We also give a dropdown for this to the end user.

We've shipped 13 releases since November (https://www.usecardboard.com/changelog). The editor handles multi-track timelines with keyframe animations, shot detection, beat sync via percussion detection, voiceover generation, voice cloning, background removal, multilingual captions that are spatially aware of subjects in frame, and Premiere Pro/DaVinci/FCP XML exports so you can move projects into your existing tools if you want.

Where we're headed next: real-time collaboration (video git) to avoid inefficient feedback loops, and eventually a prediction engine that learns your editing patterns and suggests the next low entropy actions - similar to how Cursor's tab completion works, but for timeline actions.

We believe that video creation tools today are stuck where developer tools were in the early 2000s: local-first, zero collaboration with really slow feedback loops.

Here are some videos that we made with Cardboard: - https://www.usecardboard.com/share/YYsstWeWE9KI - https://www.usecardboard.com/share/nyT9oj93sm1e - https://www.usecardboard.com/share/xK9mP2vR7nQ4

We would love to hear your thoughts/feedback.

We'll be in the comments all day :)

deff is an interactive Rust TUI for reviewing git diffs side-by-side with syntax highlighting and added/deleted line tinting. It supports keyboard/mouse navigation, vim-style motions, in-diff search (/, n, N), per-file reviewed toggles, and both upstream-based and explicit --base/--head comparisons. It can also include uncommitted + untracked files (--include-uncommitted) so you can review your working tree before committing.

Would love to get some feedback

The article discusses the development of a multi-agent swarm system for autonomous exploration and mapping of unknown environments. It focuses on the coordination and decision-making algorithms that allow the swarm to efficiently explore and create a detailed map of the surroundings.

Apple's iPhone and iPad have been approved by NATO to handle classified information, signaling increased trust in Apple's security and encryption capabilities for high-level government and military use.

The article discusses SynthID, a deep learning model developed by DeepMind that can generate synthetic identity data while preserving statistical properties and privacy. SynthID aims to provide a tool for researchers and organizations to work with sensitive data in a secure and ethical manner.

Researchers at Scripps Research have developed a new molecule that can selectively bind to a protein involved in immune system regulation, potentially leading to new treatments for autoimmune diseases and cancer. The molecule, called JND-SP-741, was found to be effective in preclinical studies and could represent a promising therapeutic approach.

The article discusses IDC's forecast for the global smartphone market, projecting a decline in shipments in 2022 due to economic headwinds, followed by a return to growth in 2023 and beyond. It also highlights the continued dominance of Android and iOS operating systems in the smartphone landscape.

The article discusses reverse dependency tracking, a technique used to identify the impact of changes in a software project. It explains how reverse dependency tracking can help developers understand the dependencies between components and make informed decisions during software maintenance and refactoring.

I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem

When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work

I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment.

The Solution

Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans.

The killer feature is the autonomous daemon. It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates.

The Architecture

- Local-first: Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API: The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency: Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests: This thing has to be reliable. It's doing unattended work.

The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue.

Why Release This?

A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained.

What's Next

- Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles)

If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken.

GitHub: https://github.com/MeisnerDan/mission-control

Burger King has launched an AI-powered virtual assistant named 'Patty' to help customers place orders and navigate the menu. The virtual assistant uses natural language processing and machine learning to engage in conversational interactions and provide personalized recommendations to customers.

The article explores the growing divide between the accountability experienced by the elite and the powerful versus the general public, highlighting concerns about the erosion of the rule of law and the consequences for societal trust and stability.

The article discusses the feud between AI company Anthropic and the Pentagon over the development and use of AI technology, highlighting concerns about the Pentagon's influence and control over the technology.