GitHub热门项目 | Declarative routing for React | Stars: 56,442 | 5 stars today | 语言: TypeScript

GitHub热门项目 | Desktop app to manage markdown knowledge bases | Stars: 12,197 | 404 stars today | 语言: TypeScript

GitHub热门项目 | The Block Editor project for WordPress and beyond. Plugin is available from the official repository. | Stars: 11,682 | 3 stars today | 语言: JavaScript

GitHub热门项目 | Grab your own sweet-looking '.is-a.dev' subdomain. | Stars: 10,416 | 7 stars today | 语言: JavaScript

GitHub热门项目 | Community curated list of templates for the nuclei engine to find security vulnerabilities. | Stars: 12,471 | 9 stars today | 语言: JavaScript

GitHub热门项目 | Digitalization of Judiciary System | Stars: 86 | 4 stars today | 语言: JavaScript

GitHub热门项目 | web development for the rest of us | Stars: 86,848 | 17 stars today | 语言: JavaScript

GitHub热门项目 | AI-powered job search system built on Claude Code. 14 skill modes, Go dashboard, PDF generation, batch processing. | Stars: 48,805 | 197 stars today | 语言: JavaScript

GitHub热门项目 | The best-benchmarked open-source AI memory system. And it's free. | Stars: 53,647 | 228 stars today | 语言: Python

GitHub热门项目 | LLM驱动的 A/H/美股智能分析：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets. | Stars: 40,920 | 339 stars today | 语言: Python

GitHub热门项目 | A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET. | Stars: 11,059 | 29 stars today | 语言: Python

GitHub热门项目 | The Frontend Stack for Agents & Generative UI. React + Angular. Makers of the AG-UI Protocol | Stars: 32,362 | 350 stars today | 语言: TypeScript

GitHub热门项目 | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | Stars: 21,280 | 127 stars today | 语言: Python

GitHub热门项目 | A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物 | Stars: 64,478 | 324 stars today | 语言: Python

Never underestimate the power that a cheap tablet holds over a kid under six. The Skylight Buddy is a device with one job: to be a cute little guy that helps your kid track routines and chores. It's $139.99, plus an optional subscription. And to my surprise, even though it offers a pretty limited set […]

An autonomous research agent ended up with more merged leaderboard records than any individual human contributor in OpenAI's spring hiring competition, Parameter Golf. 7 of the 47 merged records came from a single agent: more than 2x the next-best human (3 records). The agent ran autonomously for 22 consecutive days. Records are public at github.com/openai/parameter-golf. Disclosure since this is r/ML and it matters: I'm at Weco, we built the agent. Not stealth-launching but sharing the results. The more interesting finding, to us, is the collaboration. Aiden's records were also the most-cited on the leaderboard, 435 citations into its PRs, with human researchers using its work as the base for their own subsequent submissions. At one point Aiden plateaued for 5 days. A human contributor shipped a clever new tokenizer on top of Aiden's last record PR. Aiden then fused the human's tokenizer with components it had built during the plateau, and shipped the biggest jump in val_bpb of the entire competition. Async human-agent collaboration, neither directly aware of the other. Setup: Parameter Golf was OpenAI's 44-day public ML hiring competition this spring. 1,016 researchers entered, 2,048 PRs filed, every submission reviewed and reproduced by OpenAI engineers. Only 47 became leaderboard records. Aiden ran on a single GPU node, used under 4% of the visible compute available, and still produced 15% of the official records. 28% submission acceptance rate, roughly 6x the community rate. Most submissions added signal to the public stream rather than flooding it. Mechanism: built on AIDE: open-source tree-search for ML metric optimization. The loop reads each new upstream PR, decomposes techniques into components, drops anything that breaks the rule stack (16MB / 10-min / legal-eval), and recomposes the legal residue with its own deltas. Often shipped before reviewers had ruled on the upstream PR. Hedges to be explicit about: This is #1 by volume of merged records and PR h-i

https://lsc-pagepro.mydigitalpublication.com/publication/?i=... PDF: https://cdn.coverstand.com/61061/865273/2c88ea662e2b57478723... (article is on page 62) Related: https://www.404media.co/the-u-s-military-quietly-turned-gps-...

Just wanted to share my journey after 3.5 years of building a live web analytics tool from scratch, and without AI: WireBoard.io As a web publisher for the last 15 years, I used UA (before GA4) and then Chartbeat. I always loved real-time. At the beginning of the internet I spent my time on IRC, which was great and a huge change from newsgroups and forums. A few years later, Chartbeat was changing its customer base and focusing on big news publishers, and my workflow was about to change with it. I loved being able to watch my traffic live and spot anything unusual, day by day, compared to the same day the previous week. So I decided to replace it with my own product, but with the things their tool was missing. I spent about 8 months thinking it through and sketching different architectures to handle the load and the logic of the data processing pipeline. Then I learned to code in Go (which was surprisingly easy) and built an MVP. After some iterations, I worked on the frontend (Laravel + React) and kept improving the product based on user feedback. The features I couldn't find anywhere else: Truly real-time data via streaming instead of polling. Merging data from different websites into one chart, so I can see at a glance whether traffic is unusual "today" compared to last week. A flexible dashboard, arranged like widgets on a phone. I mostly worked on this quietly and didn't talk about it much on social media or anywhere else, since I'm a dev and not a marketer. This is my biggest project, and I've enjoyed the long journey. All of this happened before the AI era, which turned out to be good timing: I can work on the codebase knowing exactly what does what and where. Over the last 6 months I've experimented with using Claude on some parts of the project (only the frontend), but in limited areas: Performance improvements (useMemo, etc.) Security reviews (not strictly needed, but reassuring when it confirms what I expected) CMS (blog section) The tech stack: Data proc

TL;DR: Non-human identities (service accounts, API keys, workload identities, certificates, OAuth apps, machine-to-machine access) now outnumber humans over 1:1 in most cloud-native orgs. The biggest security risks are unmanaged lifecycle, overprivileged access, and exposed credentials across SDLC and cloud environments, not just secret storage. The best NHI security tools in 2026 fall into four major categories: Secrets Detection and Exposure Prevention NHI Lifecycle and Governance Platforms Machine Identity and Certificate Management Vault and Authorization Extensions Most enterprises require layered coverage across detection, governance, and lifecycle automation. Adopting this multi-layered strategy enables organizations not only to find leaks but also to close the structural gaps that allow them to occur. Why Non-Human Identities Are the Fastest-Growing Attack Surface in 2026 In 2026, attackers rarely try to " hack passwords ". Instead, they exploit the massive, often unmonitored web of non-human identities (NHIs) that power modern automation. They specifically look for hardcoded API keys, overprivileged service accounts, stale OAuth tokens, misconfigured workload identities, unrotated certificates, and shadow SaaS integrations that slip through the cracks of traditional security programs. This is a problem because machine identities far outnumber human users. However, most security programs rely on frameworks designed for human-centric access. IAM Strategy for CISOs: Securing Non-Human Identities Thankfully, top non-human identity protection tools help secure this critical attack surface. By understanding the categories of enterprise NHI security solutions, you can build a strategy that provides complete visibility and robust security controls across your entire infrastructure. What Do Non-Human Identities (NHIs) Include Today? Non-human identities are the digital identities used by machines, services, and applications to authenticate and communicate with other