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🔥 abhigyanpatwari / GitNexus - GitNexus: The Zero-Server Code Intelligence Engine - GitNexu

GitHub热门项目 | GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a git repository (Github, Gitlab, Azure, Local) or ZIP file, and get an interactive knowledge graph with a built in Graph RAG Agent. Perfect for code exploration | Stars: 46,029 | 189 stars today | 语言: TypeScript

2026-08-28 原文 →
AI 资讯

Polymarket TWAP60 vs Kalshi: Why Settlement Design Decides Your Bot (Series 1/4)

GitHub: https://github.com/abrownfox0/abrownfox001-twap60-prediction-trigger-system YouTube walkthrough: https://www.youtube.com/watch?v=XzhugRL6BV4 This is a new 4-part series comparing the two venues that actually matter for short-horizon BTC direction: Part 1 — Settlement design: Polymarket TWAP60 vs Kalshi 60s average (this post) Part 2 — Product shape: 5-minute specialist vs 15-minute regulated stack Part 3 — What a directional bot must change when crossing venues Part 4 — Where edge survives, and where it dies Live profile: @abrownfox001 The Real Split Is Not “On-Chain vs Regulated” People compare Polymarket and Kalshi as if the important difference is KYC, geography, or chain vs centralized matching. For a short-horizon BTC bot, those matter later. The first difference is simpler: What exact number decides Up vs Down? If you get that wrong, every signal, backtest, and scratch rule is solving the wrong problem. Two Venues, Two Official Averages Both platforms moved away from “whatever the last print was.” Both now settle short BTC contracts on a one-minute average . They do not use the same average. Polymarket crypto Up/Down Kalshi BTC short contracts Shortest liquid product 5-minute Up/Down 15-minute Up/Down ( KXBTC15M ) Settlement idea Time-weighted average 60-second simple average Official source Chainlink TWAP CF Benchmarks Real-Time Index (BRTI family) Window 60 seconds for current 5m / 15m / 4h crypto Final 60 seconds before close , sampled ~1s Open reference Matching TWAP at slot start Strike / floor set by the contract Feed path for bots Polymarket RTDS or Chainlink Data Streams Kalshi market fields + CF Benchmarks index Market structure On-chain CLOB Centralized CFTC-regulated exchange Same word — “60-second average.” Different index. Different sampling. Different product clock. Why Both Platforms Converged on 60 Seconds Snapshot settlement created the same failure mode everywhere: A brief push into one venue’s book A wick at the exact close Retail on

2026-08-28 原文 →
AI 资讯

GitHub Trending Digest — 28 Agustus 2026

GitHub Trending Digest — 28 Agustus 2026 Minggu ini, repo-repo di GitHub menunjukkan arah yang cukup jelas: AI agent menjadi semakin otonom dan ringan . Dari framework yang membuat agent berpikir hemat kode, hingga engine lokal untuk model MoE (Mixture of Experts) yang bisa berjalan di hardware apa pun, ekosistem open source terus mendorong batas apa yang mungkin dilakukan developer dengan sumber daya terbatas. Berikut rangkuman lima repository paling trending minggu ini. 1. deepseek-ai/deepseek-harness ⭐ 200.216 Bahasa: TypeScript DeepSeek Harness mengusung filosofi "Everything is a Plugin" . Repositori ini menawarkan kerangka kerja yang memungkinkan developer membangun pipeline AI secara modular, di mana setiap komponen—mulai dari preprocessing data, inference, hingga post-processing—dapat ditukar seperti lego. Dengan pendekatan berbasis plugin ini, tidak ada lagi lock-in ke satu library atau model tertentu. Mengapa repo ini trending? Karena komunitas sedang haus akan fleksibilitas. DeepSeek sendiri terus merilis model yang kompetitif dengan model besaran internasional, dan Harness memberi jalan bagi developer untuk mengintegrasikannya tanpa ribet. Use case praktis: Membangun sistem RAG (Retrieval-Augmented Generation) di mana Anda bisa mengganti embedding model, vector store, dan LLM backend tanpa mengubah logika inti aplikasi. 2. DietrichGebert/ponytail ⭐ 114.054 Bahasa: JavaScript Ponytail memperkenalkan pendekatan unik: membuat AI agent bersikap seperti "senior developer paling malas di ruangan" . Filosofi dasarnya sederhana—kode terbaik adalah kode yang tidak pernah kamu tulis. Ponytail mendorong agent untuk memaksimalkan reuse, meminimalkan boilerplate, dan menolak menulis ulang hal yang sudah ada. Trending karena resonansi budaya. Kalimat "the best code is the code you never wrote" langsung menyentuh pain point developer yang sering terjebak menulis ulang solusi yang sudah tersedia. Ponytail bukan sekadar tool, tapi semacam mindset yang dikodekan. Use case

2026-08-28 原文 →
AI 资讯

The Growing Threat: Attackers Using GitHub Repositories as Malware Staging Mechanisms

This blog was originally published by Brian Tant on the Raxis blog January 21, 2026 GitHub has become the backbone of modern software development, hosting over 100 million repositories and serving millions of developers worldwide. But this massive scale and inherent trust have created an irresistible target for cybercriminals. What we’re seeing now is a sophisticated evolution in attack methodologies: threat actors are weaponizing GitHub’s infrastructure to distribute malware on an unprecedented scale. The numbers are staggering. Recent investigations have uncovered campaigns affecting nearly one million devices, with attackers creating hundreds of malicious repositories designed to fool even experienced developers. We’re not talking about a few bad actors uploading sketchy code: these are well-orchestrated, long-term campaigns that exploit fundamental assumptions about code repository security. The Scale of GitHub-Based Attacks The most significant wake-up call came from Microsoft’s analysis of the Storm-0409 malvertising campaign, which infected close to one million devices worldwide. But that’s just the tip of the iceberg. Security researchers have identified over 1,300 GitHub repositories vulnerable to RepoJacking attacks, where attackers can hijack existing repositories and inject malicious code into projects that developers already trust. Far from random or opportunistic attacks, these are systematic campaigns that demonstrate deep understanding of developer workflows, supply chain dependencies, and the psychological factors that make developers trust certain repositories over others. Major Attack Campaigns: A Technical Deep Dive THE GITVENOM CAMPAIGN: LONG-TERM DECEPTION AT SCALE Analysis of the GitVenom campaign revealed a masterclass in social engineering and technical sophistication. Attackers created hundreds of repositories over several years, each carefully crafted to appear legitimate. They featured professionally written README files (possibly generat

2026-08-28 原文 →
AI 资讯

I Built GitHub Trending #1. The Code Passed, but the Main UI Still Would Not Start

God’s Eye View was the top project on GitHub Trending when we selected it for Jian AI Lab’s daily experiment. The pitch is immediately compelling. It brings aircraft, vessels, satellites, earthquakes, wildfire data, traffic, CCTV sources, and other feeds into one 3D globe. The repository also makes a serious effort to label data as live, modeled, reconstructed, or simulated. We tested commit b22573a9db28e47c324821ebdd4c67bdb241c0e1 on Linux with Node.js 24.19.0 and npm 11.9.0. Installation and security checks We first ran npm ci --ignore-scripts , reviewed the install-script sources, and then ran the normal npm ci . Both installations succeeded with 201 packages. The root project has no preinstall, install, or postinstall hook. Transitive install scripts come from esbuild, fsevents, Puppeteer, and sharp. npm audit --omit=dev reported no known vulnerabilities in production dependencies. A common secret-pattern scan did not find hard-coded live credentials. This is a limited check, not a full source audit. The project talks to many external services, including Google Maps, OpenAI, OpenSky, AISStream, NASA FIRMS, TomTom, CelesTrak, OSM, Open-Meteo, GDELT, and Radio Browser. It is local-first, but it is not offline. Server-side keys such as OpenAI and AISStream are read by the local Vite proxy. Google Maps and Cesium tokens are intentionally delivered to the browser. Users must restrict referrers and APIs and set provider budgets and quotas. 2,588 visible assertions passed The main test suite reported 2,587 passing assertions and zero failures. A separate focus-allocation check added one more passing assertion. The visible total was 2,588 passes and zero failures. The process did not exit after the summary. We waited more than 90 seconds and interrupted it manually. The final exit code was 130. The precise result is that all visible assertions passed, while the official test command did not complete with a clean exit in this environment. This may indicate an open handle

2026-08-28 原文 →
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