The latest twist in paying humans to wear head cameras for robot training data.

Cognition makes Devin, the first and arguably most successful AI coding agent. But famed coder Wu says it isn't designed to supplant human programmers.

As someone who picked up coding around the time AI and agents started becoming more mainstream, I realize I don’t have much knowledge about the best way to architect applications, and I often end up going with whatever the agent recommends. I wanted to check with the community: do you have any recommendations on what I should be doing to get better at overall architecture planning? I do ask AI a lot of questions, but it would be good to have a non-AI frame of reference that I can rely on. I find

a CLI agent for prompt evaluation loopsw

We've never seen Captain Kirk wearing an outfit quite like this one.

Until recently, a driver had maybe a six in ten chance of finishing a race.

My Experience Using dnd-kit in React and Next.js For a long time, I avoided building drag-and-drop features in frontend projects 😅 Not because I did not need them. Mostly because drag and drop always felt unnecessarily complicated. When you think about implementing it, your brain immediately jumps to: animations sorting logic touch support accessibility performance state synchronization and suddenly a simple UI interaction feels like a massive feature. But recently, in one of my React and Next.js projects, I decided to finally try dnd-kit. Honestly, it changed my perspective completely. I used AI to help me with the initial setup and understanding some concepts like sortable contexts and drag events, but after that, working with the library felt surprisingly smooth. And that's what impressed me most. dnd-kit feels lightweight, modern, and flexible without becoming overwhelming. It gives you the tools you need without forcing a huge architecture or complicated patterns on your app. Things I Personally Liked About dnd-kit Very clean React-first API Lightweight and composable Works really well in React and Next.js projects Building sortable lists feels much simpler than expected Flexible enough for custom UI and interactions Good developer experience overall Something Interesting I Noticed When a library has a clean architecture and predictable patterns, AI becomes much more useful while learning it. The generated examples were easier to understand, easier to debug, and easier to customize compared to many older drag-and-drop solutions. If you are building things like: kanban boards sortable lists draggable cards dashboards reorderable tables I definitely recommend taking a look at dnd-kit. It ended up being much simpler and more enjoyable than I expected. Website https://dndkit.com/

I interviewed for Walmart SWE3 role 3 days back. Both rounds were held on the same day. Qualified Round 1 and then gave Round 2. Round 2 went well too. But I have not received any update yet. Its been 3 days. Is it normal or should I accept that I am rejected? submitted by /u/Complete-Ad-240 [link] [留言]

Cuban's take: the gap isn't access to AI tools. It's knowing how to implement them for your specific business. He's right. And the data backs it up in a specific way. We track verdicts across 70+ AI tool categories used by SMBs. The highest-volume category — Development Tools — has a 60% WORKED rate across 874 tools. Content Creation: 67% WORKED across 262 tools. AI Video & Production: 57% WORKED. But Customer Support sits at 31% WORKED despite 45 tools tracked. Email & Outreach: 30% WORKED. Marketing: 20% WORKED. Same AI. Same price points. Wildly different outcomes. The implementation gap Cuban's talking about isn't about expertise. It's about knowing that the category you're buying into has a 20% success rate before you spend three weeks setting it up. Which category did you implement where the outcome surprised you — better or worse than expected? submitted by /u/Fill-Important [link] [留言]

Made for those who hate having a TV in the home, Samsung’s Movingstyle monitor-on-wheels brings the entertainment when you need it and hides away in a closet when you don’t.

If you are an open-source maintainer, developer advocate, or builder, you know the cycle: you build an amazing tool, but writing the launch blog post, documentation, or tutorial takes hours. For the Hermes Agent Challenge , I wanted to build something that solves this exact problem. I built devto-blogger , a custom, prompt-driven skill for the brand new Hermes Agent by Nous Research. It autonomously scans any workspace or codebase, analyzes the architecture, and drafts a fully-structured, rich Markdown technical article ready for publication on DEV. 🚀 What is Hermes Agent? Hermes Agent is an open-source agentic system built by Nous Research (the lab behind the famous Hermes LLM models). Unlike basic coding copilots or simple chatbot wrappers, Hermes is: Environment-Aware : It runs sandboxed in Docker, Modal, Daytona, SSH, or locally. Connected : It interfaces with Telegram, Discord, Slack, WhatsApp, and more. Closed Learning Loop : It has persistent memory and creates custom skills on the fly from its own experience. 🔧 The Entry: The devto-blogger Skill In Hermes Agent, a "skill" is defined by a simple, declarative Markdown file ( SKILL.md ) located in the ~/.hermes/skills/ directory. By utilizing a prompt-driven skill structure, we can guide the agent's behavior globally without writing complex Python orchestration scripts. Here is the custom skill I designed and installed for this challenge: --- name : devto-blogger description : " Scan the codebase and generate a comprehensive Dev.to technical blog post draft." version : 1.0.0 author : Hermes Agent Developer license : MIT platforms : [ linux , macos , windows ] metadata : hermes : tags : [ devto , blogging , documentation , markdown , technical-writing ] related_skills : [ plan , design-md ] --- # Dev.to Technical Blogger Skill Use this skill when you need to write an in-depth technical post, review, or tutorial about the active workspace or codebase. ## Core Behavior 1. **Codebase Inspection** : Scan repository

In 2024, Orca Security sued Wiz and demanded their full git version control history. Orca wanted to see "when features and functions were added, modified, or altered, including through engineers' notes and comments." The court recognized git history as relevant evidence in a software IP dispute. It ordered production of commit logs tied to two specific features. That ruling should concern you. Your commits record author name, email, timestamp, and content hash. Git chains them together cryptographically, replicates them across clones, and makes them discoverable in litigation. You are building a legal record whether you intend to or not. You already use git as a development tool. It is also a chain of custody for intellectual property. And you are probably destroying yours. A Cryptographic Chain of Custody Every git commit stores five things: the content of the change, the author's name and email, the author's timestamp, the committer's name and email, and the committer's timestamp. That commit is hashed using SHA-1 (or SHA-256 in newer repositories), and the hash incorporates the hash of the parent commit. Change one byte anywhere in the chain and every subsequent hash changes. This is a Merkle DAG. The same data structure that makes blockchains tamper-evident. Your commit history is a cryptographic proof linking each change to all previous changes. Git records three layers of timestamps. AuthorDate is when the content was written. CommitterDate is when it was finalized into the repository. And the server-side push timestamp, recorded by GitHub or GitLab, marks when the remote received the data. That third timestamp is outside the developer's control. A clone is a full copy of the entire history. Your collaborators, CI runners, and backup systems each hold an independent replica. Forensics researchers call this "evidence proliferation." Tampering with one copy does nothing if fifty others exist unchanged. Under Federal Rules of Evidence 902(14), data verified throu

Let Your AI Agent Manage skill itself Most developer tools still assume the human is the operator. You read the documentation. You install the CLI. You decide where files should go. You copy commands from a README, paste them into a terminal, check the output, fix the path, and then explain the final state back to your AI coding agent. That made sense when tools were only built for humans. But AI coding agents now run commands, inspect files, follow project conventions, and repair broken local state. If a tool is meant to help agents, the better question is not: How does a human use this CLI? It is: Can the agent operate the CLI by itself? That is one of the quiet but important ideas behind aweskill : it is a CLI-first Skill package manager that AI agents can operate themselves. Website: aweskill.webioinfo.top It is already used as supporting infrastructure for several Webioinfo projects: awescholar — AI-agent-operable scientific literature discovery and curation. Search, annotate, filter, and report on academic papers. aweshelf — Session bookmark manager for Claude Code and Codex. Bookmark, categorize, and restore sessions with aweswitch profiles. Awesome AI Meets Biology — A curated survey of AI applications in biology, bioinformatics, and biomedical research, powered by awescholar. The Old Workflow: You Manage the Agent's Tools When a new AI Agent needs a Skill, the usual workflow looks like this: You find the Skill. You download or copy it. You locate the agent's Skill directory. You place SKILL.md in the right folder. You restart the agent. You hope the next agent uses the same layout. This is manageable once. It becomes messy when you use Codex, Claude Code, Cursor, Gemini CLI, Windsurf, Qwen Code, OpenCode, or any other coding agent side by side. Each one has its own directory layout and conventions. The human becomes the package manager. That is backward. If the agent is already capable of editing your repo, running tests, and diagnosing failures, it should

Anthropic's Mythos model can autonomously find zero-day vulnerabilities. Their CVD disclosure process uses manual SHA-3-512 hash commitments to prove findings existed. I built something that automates that in one line of Python. What AetherProof does One function call generates a 128-byte Ed25519-signed receipt that proves: What model ran — FNV-1a hash of provider/model ID What it produced — hash of the output When — cryptographic nanosecond timestamp Tamper-evident — flip any byte anywhere → INVALID python import aetherproof receipt = aetherproof.for_anthropic( "Find vulnerabilities in this binary.", finding_text, model="claude-mythos-preview" ) receipt.save("CVE-2026-001.receipt") print(receipt.verify()) # True Try it in 30 seconds pip install aetherproof python -c " import aetherproof r = aetherproof.for_anthropic('question', 'answer') print(r.verify()) # True print(r.pretty()) " The unusual part — invisible Unicode watermarking Receipts embed invisibly into any text using Unicode Private Use Area codepoints (U+E000–U+E0FF). AI output carries its own audit trail. Works in any language — Arabic, Chinese, Devanagari, Hebrew, Thai, Japanese all tested. signed_output = aetherproof.embed(ai_response, receipt.to_bytes()) # Text looks identical. Receipt is inside. aetherproof.verify_embedded(signed_output) # True Numbers 187 tests, 0 failures 128/128 byte flips all detected 1000/1000 tamper probes pass Cross-language: Python generates, Rust CLI verifies 15,446 receipts/sec (Python) · 5,472/sec (Rust) Why AGPL-3.0 Free for open source. Commercial use needs a license. This is the compliance layer under your AI stack — it should be open, auditable, and not vendor-locked. GitHub https://github.com/pulkit6732/aetherproof Built by Pulkit. Feedback welcome.

Meet Halimun Proxy a high-performance, ultra-low latency proxy tunnel system built from the ground up in Rust. Why Rust? By leveraging Rust , Halimun achieves extreme efficiency. Using the Axum web framework and Tokio for non-blocking asynchronous I/O, it manages to maintain a tiny footprint—running on as little as ~15MB of RAM . It’s designed to be fast, memory-safe, and incredibly stable under load. Core Security Features Halimun isn't just a proxy; it’s a security layer. It enforces strict request validation to ensure your internal services are never exposed to malicious actors: AES-256-CBC Encryption: End-to-end payload masking. Even if your traffic is intercepted, the actual API endpoint and data remain indecipherable. HMAC-SHA256 Integrity: Validates that data hasn't been tampered with in transit. Replay Attack Prevention: Uses Nonce and timestamp verification in-memory (via DashMap ) to reject duplicate spoofed requests. SSRF Protection: Built-in mechanisms to prevent attackers from targeting your internal network infrastructure (e.g., 127.0.0.1 ). Camouflage Routing: It hides your actual API structure behind random, dummy URL segments, making traffic profiling by WAFs or human analysts nearly impossible. Quick Start (Docker) Halimun is "Docker-ready," making it easy to drop into any existing infrastructure. 1. Configuration First, generate your encryption keys using the built-in generator: # Generate keys and save to .env docker build -t halimun-proxy . docker run --rm halimun-proxy ./halimun-proxy --keygen --format = env > .env 2. Deployment Configure your config.yaml to map your backend services, then launch your cluster: docker-compose up -d Your production proxy is now live, listening securely on port 80 while your backend services remain completely secluded within a private Docker network. Under the Hood: Request Lifecycle Halimun uses an encrypted tunnel approach. A typical request follows this structure: POST /proxy/1/SEGMENT1/SEGMENT2/SEGMENT3/SEGMEN