标签：#ENSO

Modern collaboration needs more than documents and chat messages. Teams need a shared visual space where ideas can be created, organized, and refined together in real time. That's why I built SyncCanvas — an AI-powered collaborative whiteboard that combines real-time multiplayer collaboration, infinite canvas drawing, and AI-assisted brainstorming into one modern workspace. The Problem Most collaboration tools focus on only one aspect of teamwork. Some are great for drawing. Others are excellent for documentation. AI tools often live in separate windows, disconnected from the creative workflow. I wanted a platform where teams could brainstorm visually while AI actively helped generate and organize ideas directly on the canvas. Introducing SyncCanvas SyncCanvas is an infinite multiplayer whiteboard designed for students, developers, teams, and creators. Key features include: Real-time collaboration with live synchronization Infinite canvas with pan and zoom Drawing tools, shapes, text, and sticky notes AI-powered content generation using Gemini Private room sharing Guest mode access PNG export support WiFi Rooms for local collaboration Real-Time Collaboration Collaboration is at the heart of SyncCanvas. Using Yjs and WebSockets, multiple users can work on the same board simultaneously while seeing updates instantly. Users can: View live cursors Track online participants Join private rooms using secure room codes Collaborate anonymously through guest mode This creates a seamless experience similar to working together in the same room. Infinite Canvas Experience The whiteboard is designed to be limitless. Users can: Draw freehand sketches Create rectangles and circles Add text elements Organize ideas with sticky notes Move freely across an infinite workspace Export workspaces as images The canvas is powered by Fabric.js, providing smooth rendering and flexibility for future enhancements. AI-Powered Brainstorming One of the most exciting features is the integration of G

A few months ago I was about to buy a flight. The page showed "Only 2 seats left at this price" in red letters. I hesitated, then refreshed the page out of curiosity. The counter said "Only 2 seats left" again. Same number. It had been resetting on every page load the whole time. That's when I started cataloguing every trick I'd seen on shopping sites and built a Chrome extension that flags them automatically, in real time, on any page. This isn't just my opinion — it's documented research In 2019, Princeton and University of Chicago researchers scraped 11,000 shopping sites and found dark patterns on more than 1,250 of them. The FTC has since fined several companies specifically for fake countdown timers and pre-checked subscription boxes. This isn't a grey area anymore — it's a known, studied, and increasingly regulated practice. The extension targets four categories that account for most of what's out there. The four patterns Fake urgency. Countdown timers that reset, "X people are viewing this" badges that never change, "only N left in stock" that's been static for a week. Trap checkboxes. A checkbox for "Yes, sign me up for the newsletter" that's pre-checked and styled to blend into the page so you don't notice it. Confirmshaming. The decline button reads "No thanks, I don't want to save money" instead of just "No thanks." Psychological pricing. Prices ending in .99 or .95 designed to register as a lower price bracket than they are. Why no AI this time My phishing detector used Claude because language and intent are genuinely ambiguous — you need a model that understands context. Dark patterns are different. They're structural. A countdown timer either resets on reload or it doesn't. A checkbox is either pre-checked or it isn't. That's a DOM query, not a judgment call. So this one runs entirely on regex and DOM inspection. No API calls, no latency, no cost per scan, works offline. Sometimes the boring solution is the correct one. Detecting the urgency pattern T

Hey everyone, I've been deep in prediction market infrastructure for a while and just open-sourced a market maker bot designed for CLOB-based prediction markets like Polymarket. What it does: Quotes both sides of a binary market automatically Adjusts spreads based on order book depth and volatility Manages inventory risk to avoid getting stuck on the wrong side of a resolved market Built on top of Polymarket's CLOB API with Gnosis Safe / EOA wallet support on Polygon The core challenge with prediction markets vs. regular markets: Normal market making is about capturing spread. Prediction markets add a brutal edge case — resolution risk. If you're holding YES at 0.6 and the market resolves NO, you're not just down on the spread, you're down the full position. So the bot has to: Track time-to-resolution and widen spreads as resolution approaches Reduce inventory exposure on markets with high directional momentum Use FAK orders to avoid resting limit orders too long near resolution Stack: Rust Polymarket CLOB API Polygon (USDC settlement) SQLite for order state tracking What's next: Dynamic spread model based on implied volatility Multi-market portfolio rebalancing Better signal integration (news feeds, oracle data) GitHub: https://github.com/HarrierOnChain/Prediction-Markets-Trading-Bot-Toolkits Happy to answer questions on the architecture, risk model, or anything CLOB-related. Always looking for feedback from others building in this space.

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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

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