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GitHub热门项目 | ⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary. | Stars: 5,511 | 29 stars today | 语言: Rust
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The “5-minute market” concept is gaining attention because of how fast new prediction rounds appear and how quickly volume builds up. Each cycle is short, which creates both opportunity and risk for anyone trying to analyze or trade it. In this article, I’ll break down how I’ve been approaching this space from a data perspective, how I’m thinking about building an edge, and the tools I’ve been experimenting with. What is the 5-minute market? A 5-minute market is a fast-cycle prediction or trading window where outcomes resolve quickly and new markets appear frequently. Compared to longer timeframes (like 15-minute markets), these shorter cycles: Generate more trading opportunities per hour Require faster data collection and processing Make latency and execution extremely important Increase noise in price action Because of this, traditional slow analysis often doesn’t work well here. Data collection approach My current setup focuses on continuously pulling market data in real time. The idea is simple: Connect to a market data source (I’m using a Gamma API as part of the pipeline) Stream or request live market updates Store order book + price movement data Aggregate it into 5-minute windows for analysis The goal is to build a dataset that can later be used for backtesting and feature extraction. Right now, I’m mainly focusing on a single asset (PPC) to keep things simple while testing the pipeline. Where the potential edge might come from The key question is: can we predict short 5-minute movements better than random chance? Some areas I’m exploring: 1. Order book behavior Tracking: Liquidity changes Bid/ask imbalances Sudden volume spikes 2. Session-based behavior Some traders observe patterns during different market sessions: Asian session behavior London session volatility Overlap periods These may or may not hold in 5-minute markets, but they’re worth testing. 3. Micro momentum patterns Since markets reset frequently, short momentum bursts might matter more than lo
What was released / announced Lore is an open source version control system designed with scalability in mind, allowing developers to efficiently manage large-scale projects. According to the official website, Lore aims to provide a more efficient and scalable alternative to traditional version control systems. This release is particularly exciting for developers and engineers working on complex projects that require robust version control. Why it matters As someone who works with large-scale AI infrastructure and cloud systems, I can attest to the importance of reliable version control. With Lore, developers can expect improved performance and reduced latency when managing massive codebases. This is especially crucial in environments where multiple teams collaborate on the same project, and version control becomes a bottleneck. I believe Lore has the potential to streamline development workflows and enhance overall productivity. How to use it To get started with Lore, you can begin by installing the command-line tool using the following command: pip install lore Once installed, you can initialize a new Lore repository using: lore init Lore also provides a REST API for integrating with other tools and services. For example, you can use the following Python code snippet to interact with the Lore API: import requests response = requests . get ( ' https://your-lore-instance.com/api/repo ' ) print ( response . json ()) You can explore more API endpoints and usage examples in the official Lore documentation. My take As an AI infrastructure engineer and DevOps architect, I'm excited about the potential of Lore to improve our development workflows. In my experience, traditional version control systems often struggle with large-scale projects, leading to performance issues and frustration. Lore's focus on scalability and performance could be a game-changer for teams working on complex AI and machine learning projects. I'm looking forward to exploring Lore further and integr
If you've ever tried to build a trading dashboard, a crypto portfolio tracker, or any financial app, you probably ran into the "charting problem" pretty quickly. The standard industry approach goes something like this: Embed a heavy <iframe> from a 3rd party provider. Realize it doesn't quite match your app's UI/theme. Struggle with limited postMessage APIs to push real-time data. Watch the UI lag when you try to render multiple charts on the same page. I got tired of fighting with iframe embeds and DOM-based SVG charts that couldn't handle thousands of real-time ticks. I needed something native, fast, and entirely under my control. So, I built one. And today, I'm fully open-sourcing the core engine. Meet Exeria Charts Exeria Charts is a source-available, high-performance financial charting library designed for self-hosted web applications. Instead of embedding external widgets, Exeria renders directly inside your application using a highly optimized Canvas architecture. Here’s a quick look at what it can do: https://exeria.dev The Tech Constraints (Why build another charting lib?) Building a financial chart isn't just about drawing boxes and lines. It’s about performance under pressure. When designing the architecture, we had a few strict requirements: Zero iframes: It had to be a native JavaScript/React module that lives in the main DOM tree, styled perfectly to match the host application. High-frequency updates: Crypto and forex markets move fast. The library needed to handle sub-millisecond tick updates without dropping frames or blocking the main UI thread. Unified runtime: I didn't want a separate library for line charts, another for candlesticks, and another for volume histograms. We needed one engine that could switch views instantly. How to use it We designed the API to be as straightforward as possible. Here is what a vanilla JS implementation looks like: import { createChart } from " @efixdata/exeria-chart " ; // 1. Grab your container const container = d
Five months after returning to OpenAI, Barret Zoph - the company's head of enterprise AI sales - has departed, The Verge has learned. Zoph returned to OpenAI in mid-January after a stint as co-founder and CTO of Thinking Machines Lab, the competing AI company founded by former OpenAI CTO Mira Murati. Shortly after Zoph returned […]
You added .env to .gitignore . You felt responsible. But three weeks later it's still in the repo, still pushed to GitHub, still in every clone — because adding a path to .gitignore does nothing to a file git already tracks. That's not a bug. It's documented behavior: .gitignore only stops untracked files from being added. Anything already committed keeps getting tracked, ignore rule or not. So the secrets, build artifacts, and 40 MB log files that were committed before someone wrote the rule just... stay. The fix is one command — git rm --cached — but only once someone notices . And nobody notices, because git status is clean and the file looks ignored. So I built gitslip : a zero-dependency CLI that finds every tracked file your own ignore rules say should be gone, and hands you the exact fix. $ npx gitslip 2 tracked files are ignored by your rules but still committed: config/secrets.env ↳ .gitignore:7 *.env logs/app.log ↳ .gitignore:2 *.log Fix — stop tracking them (keeps your local copy): git rm --cached -- config/secrets.env git rm --cached -- logs/app.log or let gitslip do it: gitslip --apply It tells you which rule caught each file ( .gitignore:7 *.env ), so there's no guessing. And --apply runs the git rm --cached for you — it only un-tracks, it never deletes your working copy. Why not just grep? You can grep your .gitignore patterns against git ls-files . But: A raw grep '\.env' can't tell a still-tracked leftover from a file that's correctly excluded, and it has no idea about !negation rules, build/ directory rules, nested .gitignore files, .git/info/exclude , or your global core.excludesFile . Reimplementing gitignore's matching semantics to get this right is exactly the kind of subtly-wrong code you don't want guarding your secrets. gitslip doesn't reimplement anything. It asks git. How it works (the fun part) Detection is a single git incantation: git ls-files -i -c --exclude-standard -c = tracked (cached), -i = ignored, --exclude-standard = use all the
Building a prototype of an AI agent is fun. Building a production-ready agent is a nightmare. In a perfect world, your agent always gets the perfect context, the API never fails, and the model never gets "lazy." But in the real world, transient errors are a constant, and models love to take shortcuts. If you aren't testing your agent against the messy reality of production, you’re setting yourself up for failure. This is where our Agent Profiler comes in. We’ve designed it to be an "adversity sandbox." It doesn’t just ask your agent a question; it challenges it. We inject transient runtime errors, introduce "lazy-agent traps" that force the model to stay focused, and validate structural AST matches to ensure the agent is actually outputting what it claims to output. It’s an active testing loop designed to stress-test your agent’s self-recovery mechanics. If your agent can’t handle a little chaos in the test suite, it certainly won’t survive your users.
What if you could build a production-ready AI agent workflow in 10 lines of YAML — and have it handle retries, observability, and multi-model routing out of the box? Dify is an open-source LLM app development platform with 145,764 GitHub stars, 22,915 forks, and 460+ contributors. It just shipped v1.14.2 (May 2026) with security hardening, agent groundwork, and workflow reliability improvements. Yet most teams only use it as a no-code chatbot builder — completely missing the infrastructure underneath. In 2026, AI workflows have moved from "prompt and pray" to orchestrated multi-step pipelines with memory, tool calling, and observability. Dify sits at the center of this shift, combining visual workflow design, RAG pipelines, agent capabilities, and LLMOps in a single platform that runs on your own infrastructure. Here are 5 hidden uses of Dify that most teams never discover. Hidden Use #1: Visual Workflow as Code — Export, Version Control, and Replay What most people do: Build workflows in the Dify web UI, click "Run," and hope for the best. When something breaks, they debug by clicking through nodes manually. The hidden trick: Every workflow in Dify can be exported as YAML. You can version-control it in Git, diff changes between deployments, and replay any historical execution step-by-step using the built-in tracing API. # dify-workflow.yaml — a production RAG + agent pipeline app : name : " customer-support-agent" mode : " workflow" version : " 1.14.2" nodes : - id : " start" type : " start" variables : - name : " user_query" type : " string" required : true - id : " retriever" type : " knowledge-retrieval" dataset_ids : [ " faq-dataset-v3" ] top_k : 5 score_threshold : 0.7 depends_on : [ " start" ] - id : " llm-agent" type : " llm" model : " gpt-4o" prompt_template : | Context: {{ retriever.documents }} Question: {{ start.user_query }} Answer concisely using only the context above. depends_on : [ " retriever" ] - id : " output" type : " end" output : " {{ llm-agen