标签：#Python

GitHub热门项目 | Agentic RL Training at Scale | Stars: 1,656 | 13 stars today | 语言: Python

GitHub热门项目 | 闲鱼自动回复管理系统是一个基于 Python + FastAPI 开发的自动化客服系统，专为闲鱼平台设计。系统通过 WebSocket 连接闲鱼服务器，实时接收和处理消息，提供智能化的自动回复服务。同时集成闲鱼自动发货，自动评价，自动擦亮等功能，实现闲鱼虚拟商品自动化流程。 | Stars: 5,759 | 42 stars today | 语言: Python

GitHub热门项目 | Biomni: a general-purpose biomedical AI agent | Stars: 3,461 | 32 stars today | 语言: Python

GitHub热门项目 | Lightweight, open-source AI agent for your tools, chats, and workflows. | Stars: 45,639 | 120 stars today | 语言: Python

GitHub热门项目 | DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/. | Stars: 26,041 | 128 stars today | 语言: Python

I built a small MCP server a while back — developer-presence , seven tools wrapping the GitHub REST API and the DEV.to API so an agent can check my repo stats, list my articles, or draft a new post without me leaving the chat. It's mine, I wrote every line, there's no third-party package doing anything sketchy under the hood. By the usual "vet your MCP servers before installing them" checklist, it passes clean. I've written that checklist article before. What I hadn't thought carefully about until recently is that vetting the server doesn't vet the data. Two of its tools go straight to the point: @mcp.tool () def get_repo_stats ( repo : str ) -> dict : """ Get stars, forks, watchers, open issues for enjoykumawat/<repo>. """ r = _gh ( f " /repos/ { GITHUB_USERNAME } / { repo } " ) return { " name " : r [ " name " ], " stars " : r [ " stargazers_count " ], " forks " : r [ " forks_count " ], " watchers " : r [ " watchers_count " ], " open_issues " : r [ " open_issues_count " ], " language " : r . get ( " language " ), " description " : r . get ( " description " ), } description is free text. Any repo owner can put anything in it. If I ever point this tool at a repo I don't control — someone else's fork, a dependency, anything — that field lands in my agent's context exactly the same way a trusted instruction would: as text in a tool result, with no marker distinguishing "this came from GitHub's database, unfiltered" from "this is something I told the agent to do." The server is safe. The channel is safe. The payload was never vetted at all, because there was nothing to vet — it's just whatever a stranger typed into a form. I only really felt this because of a task I run on a schedule: check dev.to for trending posts in a few tags, score them, and use the highest scorers as source material for what to write about next. Step one of that job is a loop over tag pages: for tag in [ " ai " , " llm " , " mcp " , " claudecode " , " agents " , " productivity " ]: url = f " http

DeepSeek vs Qwen vs Kimi vs GLM: Which One Wins My Freelance Budget? Last Tuesday I spent two hours building a client dashboard that needed AI-powered text summarization. The client is a small e-commerce shop, they get maybe 500 product descriptions a week that need condensing into bullet points. Sounds simple, right? Except when I ran the numbers on my usual OpenAI setup, the bill was going to eat into my margin harder than I'd like. That's when I went down the rabbit hole of Chinese AI models. DeepSeek, Qwen, Kimi, GLM — I've been hearing about these for months from other devs in Discord, but I never actually committed to testing them because, honestly, who has the time? Well, apparently I do, because that Tuesday I decided to run all four head-to-head against my actual workload. Here's what happened. Why I Even Bothered (The Real Math) Before we get into the benchmarks and pricing tables, let me put this in perspective. My hourly rate as a freelance dev sits at $85. Every hour I spend wrestling with a subpar API that hallucinates or charges too much is an hour I'm not billing a client. The "free" model is never free — either it costs me time or it costs me money, and usually both. I was paying roughly $0.60 per 1M output tokens on GPT-4o for the summarization work. For 500 product descriptions, each averaging maybe 150 tokens output, that's about $0.045 per batch. Sounds tiny, right? But multiply that across multiple clients, and suddenly I'm watching $40-60 a month vanish into API costs that I can't really pass along without awkward pricing conversations. So I started shopping. And what I found genuinely surprised me. The Contenders at a Glance All four model families run through Global API's unified endpoint, which means I didn't have to maintain four different SDKs, four different auth setups, four different billing dashboards. Just swap the model name in the request and ship. For a one-person operation, that's huge. Here's the landscape I was working with: Di

i've been the incident commander who forgot to write down the first 20 minutes of the timeline because i was too busy reading logs. more than once. the war room is chaos — five engineers pasting logs, someone asking if the deploy from 30 minutes ago is related, nobody documenting anything. you start logging events in a doc while reading error logs while drafting a stakeholder update while deciding whether to rollback. you're the bottleneck. not because you're bad at your job — because you're doing four jobs at once. i got tired of watching smart people spend their incident energy on documentation instead of decisions. so i built ai-incident-commander — a CLI tool that handles the mechanical parts. timeline, updates, remediation research, postmortem draft. you make the calls. it does the paperwork. runs on your laptop with a local LLM. no API keys, no cloud, no docker. github.com/deghosal-2026/ai-incident-commander — MIT licensed. what it does one command: pip install git+https://github.com/deghosal-2026/ai-incident-commander.git incident-commander simulate --scenario db-connection-pool --auto-approve 8 pre-built scenarios ship with it. database connection pool, bad deploy, memory leak, cert expiry — the usual suspects. no real data needed to try it. for actual incidents, you point it at a directory with your alert, logs, messages, and github PRs. it outputs 10 markdown files: timeline, stakeholder updates, comms blocks you can paste straight into slack, remediation suggestions, a blameless postmortem, and a cost report. the safety part was the real engineering. three points in the pipeline where the graph pauses and waits for you to say yes — stakeholder update, remediation, postmortem. the AI never ships anything without approval. every remediation comes with a citation. suggestions below 0.7 confidence get suppressed. the postmortem prompt enforces blameless language. all AI content gets labeled [AI-GENERATED — review carefully] . and it never executes anything. i

Somebody on Reddit posted about 997s that just say AK5*R*5 — one or more segments in error — no AK3 , no AK4 . Preach. That's the problem this free doohickey* is for: rejectdecoder.com *If you'd prefer a "gizmo", I can make that happen. What it does Paste the rejection (997, 999, 824, TA1) plus the original bounced document. It parses both locally in your browser and cross-audits them: control number agreement segment counts envelope consistency code validity required segments It then quotes the exact segment byte-for-byte and ranks the likely causes for anything it finds. If it finds nothing, it says the answer isn't in the docs and tells you to escalate to your partner with your control numbers — which beats pulling a diagnosis out of my... AIs. Where the AI does (and doesn't) fit I know how and appreciate WHY "AI-powered EDI" is sneered at. So the audits here are deterministic parser code, not a model. The AI only writes the plain-English narration of facts the parser already verified, every card says so, and if the narration fails you still get the full audit results. No hallucinations or guesswork. Privacy Parsing runs entirely in-browser (the real Python parser, compiled to WebAssembly via Pyodide) and even works with the WiFi off. If you use narration, only a masked summary you preview first ever leaves the page. Don't take my word for it — check your network tab. Free. No signup for the examples or the deterministic audits; narration is a handful of decodes a month with just an email. Built it solo from an in-house tool of mine, so it's young AND kinda old. Please tell me where it's wrong. Walmart's rejection quirks are encoded so far. Whose partner nonsense should be next...? -jjg

GitHub热门项目 | 👩🏿‍💻👨🏾‍💻👩🏼‍💻👨🏽‍💻👩🏻‍💻中国独立开发者项目列表 -- 分享大家都在做什么 | Stars: 52,940 | 1,194 stars today | 语言: Python

I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take I'll be honest — I went into this expecting a clear winner. I came out with a scatter plot, three regressions, and a deeper appreciation for why "best" is the most dangerous word in machine learning. Over the past three weeks I've been grinding through prompts with ten different LLMs, all routed through the same endpoint, scoring every output on a 1–10 rubric that I tried very hard not to bias. The pricing data is pulled directly from the provider pages. The scores are mine. If you disagree with a score, you're probably right — n=1 per task per model is a laughably small sample size, and I say that as someone who publishes papers with bigger samples. But trends still emerged. Let me walk you through what I found. The Lineup Before I touch a single benchmark, here's the cast. I've grouped them by family so you can see the obvious concentration in the open-source Chinese ecosystem, which personally I find fascinating — three of the top five are DeepSeek or Qwen variants. # Model Provider Output $/M Category 1 DeepSeek V4 Flash DeepSeek $0.25 General (strong code) 2 DeepSeek Coder DeepSeek $0.25 Code-specialized 3 Qwen3-Coder-30B Qwen $0.35 Code-specialized 4 DeepSeek V4 Pro DeepSeek $0.78 Premium general 5 DeepSeek-R1 DeepSeek $2.50 Reasoning (code thinking) 6 Kimi K2.5 Moonshot $3.00 Premium general 7 GLM-5 Zhipu $1.92 Premium general 8 Qwen3-32B Qwen $0.28 General purpose 9 Hunyuan-Turbo Tencent $0.57 General purpose 10 Ga-Standard GA Routing $0.20 Smart routing One quick note on Ga-Standard — it's a routing layer that picks a backend model per request. So the score fluctuates. I averaged across runs. How I Tested Five prompts. Each one designed to probe a different cognitive layer: Function implementation — flatten a nested list recursively in Python Bug fix — chase down an async/await race condition in JavaScript Algorithm — Dijkstra's shortest path in TypeScript with proper types Code review — sec

Build a Local LLM Chatbot with Ollama and Python Build a Local LLM Chatbot with Ollama and Python Imagine typing a question into your chatbot and getting a response in milliseconds, completely offline, with zero data leaving your machine. No API keys, no monthly subscription fees, and no privacy concerns about your data being sent to a cloud server. This isn’t a futuristic dream—it’s the reality of running a Local Large Language Model (LLM) on your own computer. With the rise of tools like Ollama , building a private AI chatbot in Python has become as simple as installing a few packages and writing a short script. Let’s dive in and build one together. Why Go Local? Before we write any code, it’s worth understanding why running an LLM locally is a game-changer. Cloud-based AI services like OpenAI or Anthropic are powerful, but they come with trade-offs: you pay per token, your data is processed on their servers, and you’re dependent on their uptime. A local LLM flips this model. You download the model once, run it on your hardware, and you have full control. Ollama is the engine that makes this accessible. It’s a lightweight, open-source tool that simplifies running LLMs like Llama 3, Phi 3, or Mistral on macOS, Linux, and Windows. It handles model downloads, memory management, and inference, exposing a simple API that Python can easily interact with [1][2]. Step 1: Install Ollama and Pull a Model The first step is getting Ollama on your machine. Visit ollama.com , click Download , and install the version for your operating system [2]. Once installed, verify it’s working by opening your terminal or Command Prompt and running: ollama --version If you see a version number, you’re ready to go. Next, you need a model. Ollama supports dozens of open-source models, but for a beginner-friendly chatbot, Llama 3.2 is a great choice. It’s small, fast, and surprisingly capable. To download it, run: ollama pull llama3.2 This command fetches the model and stores it locally. Depen

GitHub热门项目 | The Python programming language | Stars: 73,794 | 438 stars this week | 语言: Python

GitHub热门项目 | Open-source meeting transcription API for Google Meet, Microsoft Teams & Zoom. Auto-join bots, real-time WebSocket transcripts, MCP server for AI agents. Self-host or use hosted SaaS. | Stars: 2,520 | 74 stars today | 语言: Python

GitHub热门项目 | Animation engine for explanatory math videos | Stars: 88,514 | 133 stars today | 语言: Python

GitHub热门项目 | A collection of projects showcasing RAG, agents, workflows, and other AI use cases | Stars: 13,132 | 18 stars today | 语言: Python

GitHub热门项目 | Our library for RL environments + evals | Stars: 4,344 | 15 stars today | 语言: Python

GitHub热门项目 | 26m function call model that runs on incredibly small devices | Stars: 3,071 | 113 stars today | 语言: Python

If you have written more than a couple of scrapers, you already know the pattern. The first few hundred requests fly through. Then responses slow down, you start seeing 429 Too Many Requests , a captcha wall appears, and finally the target just returns empty pages or a hard 403 . Your code did not change. Your IP did. Scraping at any real volume is less about parsing HTML and more about managing where your requests come from. This post is a practical walk-through of how proxies fit into a scraping pipeline: why a single IP fails, what proxy types actually matter, how rotation works, and how to wire it all up in Python with requests , aiohttp , and Scrapy. There is code you can copy, plus the mistakes that cost me the most time. Why one IP is never enough Every site you scrape sees the same thing: a stream of requests from one address, arriving faster and more regularly than a human ever would. Anti-bot systems are built to spot exactly that. The signals they use are boring but effective: Request rate per IP. Too many hits in a short window trips a rate limiter. Volume over time. Even a slow scraper eventually stands out if every request comes from the same address for hours. Behavioral fingerprint. No mouse, no scroll, identical headers, requests in perfect intervals. Reputation. Datacenter ranges that have been abused before are pre-flagged. You can soften some of these with headers, delays, and a real browser, but there is a ceiling. Once a single IP has made enough requests, it gets throttled or blocked regardless of how polite you are. The only way past that ceiling is to spread requests across many addresses, so no single one crosses the threshold. That is the entire job of a proxy pool. The proxy landscape, minus the marketing Providers love to complicate this. For scraping, the distinctions that actually change your results are these: Shared vs private. Shared proxies are handed to many customers at once. You inherit everyone else's behavior, so an address ca

Here's the thing: speed Test: I Found AI APIs 99% Cheaper Than Premium I have a confession: I've been overpaying for AI APIs for years. Like, embarrassingly overpaying. When I finally sat down and actually benchmarked 15 different models on speed and cost, I couldn't believe what I found. Some of the fastest models out there cost literal pennies per million tokens. Here's the thing — if you're still defaulting to whatever the big labs are pushing, you're leaving serious money on the table. So I spent a week running tests through Global API's infrastructure, hitting endpoints from multiple regions, and crunching numbers until my eyes hurt. What I discovered genuinely surprised me. Check this out: there's a model that pushes 80 tokens per second and costs $0.15 per million output tokens. Compare that to premium options charging $3.00/M and you'll understand why I had to write this down. Let me walk you through everything I found. Why I Even Started This Whole Thing My monthly AI bill got out of control. I'm running a few production apps that do text generation, summarization, and chat, and my December bill made me physically flinch. I knew there had to be faster, cheaper models hiding in the ecosystem — I just hadn't taken the time to actually measure them properly. That's the whole reason I ran these benchmarks. Not for clout, not for content marketing. Pure self-interest. I wanted to know where the actual sweet spots are. Where you get the best speed-per-dollar ratio. Where you can save 70%, 80%, even 99% without tanking your user experience. What I found was honestly kind of shocking. My Testing Setup (For the Nerds) I kept the methodology tight and consistent. Here's exactly how I ran everything: Date: May 20, 2026 Test regions: US East (Ohio) and Asia (Singapore) Prompt: "Explain recursion in 200 words" Output target: ~150 tokens per run Iterations: 10 runs each, averaged the results Streaming: Enabled via SSE Base URL: https://global-apis.com/v1 I measured two k