今日已更新 213 条资讯 | 累计 20683 条内容
关于我们

标签:#X

找到 694 篇相关文章

AI 资讯

Reviving Mandi Central: From 41 Pending Issues to a Live Business Operations Platform

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built and revived Mandi Central , a complete business operations and billing system for mandi-style trading businesses. Live Project: https://mandicentral.in Mandi Central helps manage daily mandi operations like purchases, farmer purchase entries, sales entries, payments, bank and cash ledgers, accounting heads, reports, invoices, outstanding balances, and financial statements from one structured platform. This project was not started as a simple demo. It was a real business-focused software project, but it had many unfinished parts, broken flows, missing reports, pending PDF generation, incomplete mobile API planning, and several UI and accounting issues. For the GitHub Finish-Up-A-Thon Challenge, I brought it back, fixed the pending issues, completed the core business flow, and made the project live on mandicentral.in . Demo Live Website: https://mandicentral.in GitHub Repository: https://github.com/hiijoshi/bill Screenshots and demo cover the completed modules: Business Operations Hub Sales List Bank reconciliation Cash ledger Bank ledger Sales entry Purchase entry Farmer purchase entry Bulk PDF download Accounting reports Party ledger redirection Outstanding reports Mobile API-ready backend The Comeback Story Mandi Central started with a strong idea: create one platform where mandi businesses can manage their complete daily workflow. But before this challenge, the project was stuck with many incomplete and pending items. There were more than 40 pending issues across PDF generation, sales invoices, bank ledger, cash ledger, accounting reports, outstanding reports, purchase calculations, sales calculations, mobile APIs, AWS deployment, domain setup, and UI fixes. Some of the major problems before completion were: PDF generation was not working properly. Bulk sales bill download was missing. Mobile application APIs were pending. Farmer Purchase Entry API was pending. Purchase Entry API w

2026-05-29 原文 →
AI 资讯

Stop Upgrading the Model. Start Engineering the Harness.

When a team hits a ceiling with their coding agent, the first instinct is to reach for a better model. The reasoning feels obvious: the model is the part that produces the code, the code is the part that is wrong, therefore a smarter model will produce more correct code. Wait for the next release. Switch providers. Bump the tier. This is sometimes right. It is much more often wrong, and the cost of being wrong about it is that you spend months waiting for a model upgrade to solve a problem the model was never the cause of. The harness is the cause of the problem more often than the model. Most teams discover this only after they have exhausted the model-upgrade reflex and finally turn to look at everything else. What a model upgrade actually buys you Newer, stronger models do tangibly improve some things. They handle longer contexts more reliably. They make fewer simple reasoning errors on complex tasks. They follow nuanced instructions more closely. On a fixed prompt, with a fixed task, a better model produces a better answer. What model upgrades do not change: The fact that the agent has no idea your team prefers functional components over class components, because the convention is not in any file the agent reads. The fact that your tests do not actually fail when the code is wrong, so the agent can ship broken code that passes CI. The fact that your codebase has three different ways of handling errors and the agent picks one at random on each PR. The fact that the rule the senior engineer keeps repeating in reviews is not encoded anywhere the next session can see. None of these are fixed by a smarter model. They are fixed by a better harness. A smarter model loaded into the same broken harness will produce slightly more sophisticated versions of the same problems. The diagnostic The diagnostic is one question: when the agent fails, does it fail because it lacked information, or because it lacked capability? A capability failure looks like: the task required reas

2026-05-29 原文 →
AI 资讯

Feedback Latency Is the Agent's IQ

The same agent, same prompts, did markedly different work on two codebases I work in. One has a test suite that runs in eight seconds. The other takes twelve minutes. The eight-second project gets a careful, iterative collaborator. The twelve-minute project gets a confident guesser. I noticed it first as a vibe. The agent in the slow codebase would write five files at once, then announce the task complete without having run anything end to end. The agent in the fast codebase would write one function, run the tests, react to the failure, fix it, run them again. Same model. Same configuration. The only difference was how expensive it was to learn whether the previous step was right. That is the whole post in one sentence. An agent's effective intelligence is bounded by how fast it can verify its hypotheses. Cut the verification cost and you raise the agent's apparent IQ. Raise it and you lower the agent's apparent IQ. The model in the middle is unchanged. Why this binds harder for agents than for humans A human engineer can hold a hypothesis in their head. "I think this works. I will check it later." The cost of holding the hypothesis is roughly free; the human has institutional memory, intuition, a sense of what the code does that does not require running the code to confirm. They can defer verification without losing fidelity. An agent cannot. It has no intuition about your codebase. The only ground truth it has access to is what the tests say, what the type checker says, what the build says. When those signals are cheap, the agent uses them constantly. When they are expensive, the agent stops using them and starts speculating. Speculation by an agent looks plausible. It produces code that compiles, follows the patterns it has seen in your repository, names things sensibly. The problem is that plausible is not the same as correct. The agent that speculates is shipping a guess; the agent that iterates is shipping a tested answer. From the diff alone, they can be hard

2026-05-29 原文 →
AI 资讯

Meet phpvm: The PHP Version Manager for Linux (v2.5.1 Released)

Every Linux PHP developer knows the dance. You need to switch from PHP 8.1 to 8.3. You run your sudo commands, update your global symlinks, and then realize your local development server in the other window just crashed because it was running on the old version. Why should managing PHP versions be a system-wide struggle? The Solution: Per-Shell Version Isolation phpvm brings the seamless developer experience of tools like pyenv , rbenv , or nvm to the PHP ecosystem on Linux. Instead of changing /usr/bin/php globally, it uses a lightweight shim directory prepended to your PATH . When you call php , the shim inspects your environment variables and forwards the execution to the correct binary. It supports three layers of resolution, falling back gracefully: Shell pin : Pinned manually via phpvm shell <version> Project default : Resolved from .php-version or composer.json requirements when you cd into a directory Global default : The system fallback managed by update-alternatives Effortless Provisioning No need to look up repository installation guides. The built-in installer automatically detects your distribution (Ubuntu or Debian) and configures the appropriate upstream repositories (Ond?ej Sur�'s PPA or deb.sury.org ) to fetch the exact CLI and FPM packages you need. Polish in v2.5.1 Our latest release focuses on making the environment rock-solid: Tray App Auto-Start : Spawns the GTK desktop tray app immediately after installation by resolving the graphical session environment from active processes. PATH Priority : Actively prevents IDEs, login shells, or snap profiles from overriding the shim's position in PATH . Clean Cleanup : Ensures all background processes are terminated during uninstallation. Getting Started You can install or upgrade using the interactive script: curl -fsSL https://raw.githubusercontent.com/rijverse/phpvm/main/install.sh | sudo bash If you are already running v2.5.0, simply run: phpvm --self-update Check out the project website, or find the

2026-05-29 原文 →
AI 资讯

Building a Japanese-First Read-Later PWA: From Pocket Shutdown to Launch

When Mozilla shut down Pocket in July 2025, I lost my favorite tool. Worse, none of the English alternatives (Instapaper, Readwise, Matter, Raindrop) had Japanese UI, and their article extraction was mediocre on Japanese pages. So I built one. It's called Readbox — Japanese-first, English-too, read-later as a PWA. Here's what I learned shipping it. The stack Next.js 15 App Router + TypeScript strict (no any ) Supabase (Postgres + Auth + RLS) Stripe (JPY + USD prices, locale-routed) Tailwind CSS Service Worker for PWA install + offline read Three things that bit me 1. Article extraction on Vercel serverless First attempt: Mozilla Readability + jsdom. Doesn't bundle on Vercel because of ESM compatibility issues and the 50MB serverless function size limit. I tried 6 approaches — Webpack externals, dynamic imports, edge runtime — none worked cleanly. Ended up using Jina Reader , which returns clean Markdown/HTML from any URL. Trade-off: third-party dependency, rate limits at scale. But it works today, and it's free. 2. Storing article body on-device I didn't want to host millions of articles' worth of HTML on Supabase (cost + privacy). Solution: extracted HTML lives in the browser's IndexedDB only (via Dexie); only metadata (URL, title, tags, read status) syncs to the server. Trade-off: cross-device sync of body content doesn't work seamlessly. Acceptable for a "read it later" workflow where you usually read on the device you saved on. 3. i18n routing — the silent sitemap killer For Japanese + English from one codebase: app/[locale]/ segment with /en prefix for English (default Japanese has no prefix, to preserve old URLs). Middleware detects cookie / Accept-Language and redirects accordingly. The gotcha (cost me a launch-day hour): middleware matcher excludes _next , api , image extensions — but if you forget .xml/.txt/.webmanifest , sitemap.xml and robots.txt get rewritten to /ja/sitemap.xml (which doesn't exist as a route → 404). Fix: export const config = { matcher

2026-05-28 原文 →
AI 资讯

Cloudflare Adds Support for Claude Managed Agents

Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio

2026-05-28 原文 →