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AI 资讯

Archify (They've just got 4,239 Github stars on Aug 28, 2026)

Archify is taking GitHub by storm, hitting #1 on Trending and crossing 4,200+ stars in record time! If you use AI coding assistants like Cursor or Claude Code, Archify is an absolute game-changer. It allows your AI agent to automatically generate verifiable architecture, workflow, sequence, and data-flow diagrams as beautiful, self-contained HTML files (with dark/light themes and motion animations!). 🔗 Links & Resources: • Archify GitHub Repository: https://github.com/tt-a1i/archify • Try it yourself: npx skills add tt-a1i/archify -g 👇 What do you think of Archify? Are you going to use it for your next system design or PR review? Let me know in the comments! If you found this live demo helpful, please drop a LIKE and SUBSCRIBE for more cutting-edge AI developer tools. Archify #SoftwareArchitecture #Cursor #ClaudeCode #AI #SystemDesign #GitHubTrending #WebDev #OpenSource #DevTools

2026-08-30 原文 →
AI 资讯

GitHub Copilot Spending Limit: How to Set It, What It Caps

A GitHub Copilot spending limit is a monthly budget, set in billing settings, that caps metered AI credit consumption for an enterprise, an organization, a cost center, or a single user. Creating one takes about two minutes. Knowing what it stops takes longer, and the gap between those two things is where most surprise Copilot invoices live. Two facts account for nearly all of them. On enterprise, organization and cost center budgets, the setting that actually blocks usage is off by default, so a budget in its default state is an alert rather than a limit. And no budget of any kind caps seat cost, because seats are license-based rather than metered. A spending limit governs what happens after the included credit pool runs out, and nothing before it. How to set a GitHub Copilot spending limit Budgets live in the billing settings of the account that pays. Enterprise owners and billing managers can set every budget control, including enterprise, cost center and user-level budgets. Organization owners can set a budget for their own organization, and that budget can only restrict usage further below whatever an enterprise admin has already set. It cannot raise the ceiling. The mechanics are the same at every level. Choose the budget type, which determines the metered product being measured. Choose the scope, which determines whose usage counts against it. Enter a monthly amount. Then, if the option appears, enable Stop usage when budget limit is reached and switch on threshold alerts at 75, 90 and 100 percent. That single checkbox is the whole exercise. Skip it and you have built a notification. What a GitHub Copilot spending limit actually caps GitHub splits its products into license-based and metered. For license-based products, which include Copilot seats, setting a budget does not prevent usage above the amount. It only alerts. For metered products, which include Copilot AI credits, a budget can prevent usage once the threshold is reached. The consequence is worth st

2026-08-29 原文 →
AI 资讯

'Enshittified at a Surprising Clip': A Week of Hacker News on AI Coding Tools

Some fortnights the complaints about AI come from people who barely use it. This one they came from the people who use it most. Scroll Hacker News over the past week — the forum where developers argue about their tools in unusual detail — and the grievances about AI coding assistants weren’t existential. Nobody was worried about the robots waking up. They were worried about their bill, their UI, and the effort of reading what the model just wrote. Quotes sourced from: Hacker News. Every quote below was located at its comment permalink and reproduced verbatim; each is listed with its username, the platform, and the date in the Sources section. As always, we quote experiences, not verdicts — a forum comment is one practitioner’s account, often mid-argument, and we’ve framed them as exactly that. What makes this batch worth reading isn’t volume; it’s specificity. These are checkable complaints. “Enshittified at a surprising clip”: the dark-pattern gripe The sharpest thread of the fortnight was about Cursor, the AI code editor, and it wasn’t about the quality of its completions. It was about the way the product behaves around you. A user posting as jmuguy , on 20 August, laid out a bill of particulars that will sound familiar to anyone who’s watched a beloved tool curdle: “Cursor isn’t covering itself in glory regardless. The flagship app is getting enshittified at a surprising clip. It constantly pops up and interrupts your work pushing new features, changes your model to whatever the latest Grok is without prompting, has this mystery meat UI that is constantly changing, pushes cloud agents in ways that are definitely designed to trick you. We’re actively looking at alternatives, I wouldn’t touch anything this company produces from here on out.” Set aside the verdict at the end — that’s one person’s conclusion, not ours — and look at the specifics, because they’re the kind you can check: interruptions pushing new features, a model silently swapped to Grok, a UI that ke

2026-08-29 原文 →
开源项目

🔥 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 →
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