标签：#market

I’ve been building Unmuse because I kept noticing a simple problem: Having an idea is easy. Turning that idea into something actually worth posting is the hard part. You can have a thought like: “People keep waiting for the perfect time to start.” But turning that rough thought into a strong hook, script, or caption can take way more effort than it should. So I built Unmuse. You give it the rough thought in your head, choose what you want to create, and Unmuse turns it into a usable piece of content. Right now, it’s an early MVP. I’m building it mostly by myself and plan to add a lot more features as I get feedback and traction. If you create content, I'd genuinely love to hear: What’s the most annoying part of turning an idea into a post? Try it here: https://unmuse.online/

Kalshi can't evade Nevada gambling laws by calling bets "swaps," judges rule.

Most developers treat SEO as "someone else's job" — a marketing concern that happens after the site ships. But a huge chunk of SEO is actually decided at the code level, long before a marketer ever touches the content. If you're building sites — for clients, for yourself, or as side projects — a few technical fundamentals can make or break how discoverable that work ever becomes. Here's the technical SEO checklist I use when reviewing or building sites, from a digital marketing + web perspective. Core Web Vitals Aren't Optional Anymore Google uses three core metrics as direct ranking signals: LCP (Largest Contentful Paint) — how fast the main content loads INP (Interaction to Next Paint) — how responsive the page feels to input CLS (Cumulative Layout Shift) — how visually stable the page is while loading A site can have perfect content and still underperform in search if these numbers are bad. Common culprits: unoptimized images, render-blocking JS, and layout shifts from late-loading ads or fonts. Quick wins: Lazy-load offscreen images Serve modern image formats (WebP/AVIF) Reserve space for dynamic content (ads, embeds) to avoid layout shift Defer non-critical JavaScript Structured Data Is a Developer Task, Not a Marketing One Schema.org markup (JSON-LD is the recommended format) helps search engines — and increasingly AI-driven search summaries — understand what's actually on the page: is this a product, an article, a recipe, an FAQ? Sites with well-implemented structured data are more likely to get rich results (star ratings, FAQ dropdowns, breadcrumbs) in search — which directly impacts click-through rate even without a ranking change. If you're building a site and skip this step, you're leaving visibility on the table for something that's usually a few hours of implementation work. Rendering Strategy Affects Crawlability Client-side rendered (CSR) React/Vue apps can still get indexed, but it's inconsistent and slower than server-rendered or statically generate

Michele Spagnuolo was arrested for alleged insider trading on Polymarket, but the Switzerland-based engineer says he was simply gambling—activity that is beyond the reach of US commodities law.

Vibe-coding darling Lovable just raised $400 million at a $13.3 billion valuation, roughly doubling its worth in eight months. But Lovable isn’t the only Stockholm startup putting up huge numbers lately — legal AI company Legora and health tech startup Neko Health are right there with it. On this episode of TechCrunch’s Equity podcast, Dominic-Madori Davis is joined by Sophia Bendz, a partner at Cherry Ventures and longtime fixture of Europe’s startup […]

I got banned from SoloLearn for trying to help beginners. Here's what happened. Yesterday, I was on a mission. I'm Harun, a 12-year-old solo dev who built KODA , an AI coding mentor, entirely on my Android phone. I noticed hundreds of beginners on SoloLearn asking: "How do I start?" , "Help me with loops!" , "I'm stuck!" So I did what any helpful founder would do: I answered their questions, gave them code solutions, and added a small P.S.: "P.S. I built a free tool called KODA to help with this. Try it here: [Link]." I thought I was being helpful. SoloLearn's algorithm thought I was spamming. Within hours, my account was blocked. 🚫 The Moment of Panic When I saw "Your account is blocked," my first thought was: "Oh no, I messed up. My marketing is over." But then, my CEO brain kicked in. I realized: If an automated bot thought my helpful comments were "spam," maybe I was doing something right. Maybe I was being too effective. The Lesson: Marketing vs. Helping Here is what I learned in 24 hours: Algorithms hate links. Even helpful ones. If you paste a URL 10 times, the bot doesn't care about your intent; it sees a pattern. Trust takes time. You can't force users; you have to earn them. The Story > The Link. People don't click links because they are forced to. They click because they connect with the story . The Pivot So, am I quitting? No. I'm pivoting. I'm turning this ban into this article. I'm going to focus on Dev.to , where the community values "Build in Public" stories. I'm going to ask my friends (Renuka, Dharaneesh) to be my first real users, face-to-face. And maybe, one day, I'll go back to SoloLearn with a smarter strategy: No links in comments. Just value in the bio. To Other Founders If you get blocked, rejected, or told "no" today: Don't stop. Turn that hurdle into content. Turn that rejection into a lesson. Turn that "Blocked" screen into your next viral post. Because while others see a wall, I see a story. And stories build empires. Try KODA anyway (no

Andy Ellis has a roundup of the security vendors at Black Hat this year. Key Takeaways: We have entered into an AI world. While nearly half of booths didn’t directly mention AI or agents in their taglines, the effects of AI are everywhere. Multiple spaces (Identity, SaaS, AppSec, Data) have almost every vendor leading with AI; existing unsolved problem areas just got worse. At the same time, there’s a clear trichotomy in the market: tools that tell you how bad things are; tools that stop adversaries, and tools that prevent problems from occurring. While you’d suspect that the tools that fix things would dominate, the tools that merely tell you how bad things are seem to be frustratingly plentiful...

Polymarket Paper Trading Bot: Build One in Python A real-money trading bot is the wrong place to discover that your signal logic, order-book handling, or position accounting is broken. A Polymarket paper trading bot gives you a safer engineering environment: consume real market data, generate real signals, simulate orders and fills, and measure hypothetical performance before connecting execution credentials. The important distinction is that paper trading should simulate the execution layer , not fabricate market data. Polymarket currently exposes public market data without authentication, while its public WebSocket market channel provides real-time order-book and price updates. This article builds that architecture in Python. What You'll Learn How a paper-trading architecture differs from a live bot How to discover markets through the public API How to consume CLOB order-book data How to simulate limit-order fills How to track positions and P&L How to test arbitrage, market-making, and directional strategies How to graduate from paper trading to production safely About the Author Soulcrancerdev Contact: X: @soulcrancerdev Telegram: soulcrancerdev YouTube: YouTube channel The Architecture A useful design separates data, strategy, simulation, and accounting : flowchart LR A[Gamma Market Discovery] --> B[Market Metadata] C[CLOB REST / WebSocket] --> D[Market Data Engine] B --> D D --> E[Strategy Engine] E --> F[Paper Execution Engine] F --> G[Virtual Portfolio] G --> H[P&L / Risk Metrics] D --> I[Logger / Metrics] The key design decision is that PaperExecutionEngine should implement the same interface your live execution engine eventually uses. That means the strategy does not know whether an order is simulated or real. 1. Discover Markets Polymarket's Gamma API provides public market discovery. The current documentation exposes keyset pagination through: https://gamma-api.polymarket.com/markets/keyset Markets include fields such as conditionId , clobTokenIds , outco

Build a Real-Time Polymarket Order Book Monitor A trading bot should not make decisions from stale snapshots. If you want to understand liquidity, spread, depth, or changes in market structure, you need a continuously updated view of the Polymarket order book. This tutorial builds a lightweight Polymarket order book Python monitor using the public CLOB Market WebSocket. Polymarket documents this channel as a real-time feed for order-book, price, and market lifecycle updates. The implementation intentionally focuses on market data—not order execution—so it can be used as the foundation for research, dashboards, alerts, or an automated trading system. What You'll Learn How Polymarket token IDs relate to order-book subscriptions How to connect to the CLOB Market WebSocket How to process book and price_change events How to calculate best bid, best ask, and spread How to handle reconnects and heartbeats How to detect stale market data How to turn raw WebSocket events into trading signals Architecture flowchart LR A[Polymarket CLOB] --> B[Market WebSocket] B --> C[Python Async Client] C --> D[Order Book State] D --> E[Spread / Depth Metrics] D --> F[Trading Signal Engine] D --> G[Logging / Monitoring] The important design decision is separating transport from state . The WebSocket delivers events; your application maintains the current book. 1. Install the Dependencies For this monitor, authentication is not required because the Market WebSocket is public. pip install websockets You need a Polymarket asset ID/token ID for the outcome you want to monitor. The Market Channel subscribes using assets_ids . For example: TOKEN_ID = " YOUR_TOKEN_ID " Do not hard-code credentials into a market-data monitor. In this example, there are no credentials at all. 2. Connect to the Market WebSocket The documented Market Channel endpoint is: wss://ws-subscriptions-clob.polymarket.com/ws/market The subscription message contains type: "market" and one or more asset IDs. A minimal monitor lo

On August 19 we launched LeadAce on Product Hunt. It was our first launch to an English speaking audience. I am writing down the numbers while they are still uncomfortable, because the posts I found most useful when I was preparing were the ones that did this. We are a small software company in Tokyo. LeadAce is an outbound sales agent that runs as a Claude Code plugin. The backend is open source. It has been in Public Beta since the launch. The numbers Product Hunt, 24 hours: 2 upvotes 1 comment, which was mine Day rank #160, week rank #758 3 followers on the product page Signups from the launch: 0. Site traffic for the four weeks up to launch day: 6 active users, 27 page views. Referrers were direct 4, producthunt.com 1, t.co 1. Our X account over the same four weeks: 48 posts, 576 impressions total, 2 link clicks, 2 new followers. So the launch did not fail at the landing page. It failed before that. Almost nobody arrived. Where we got stopped This is the part I did not plan for. I spent weeks on the product, the demo video, the gallery images and the copy. Every one of those was ready. What I did not have was accounts. Hacker News. I could not post Show HN at all. HN was limiting Show HN submissions from low karma accounts, and my account had karma 1. I created it years ago and never used it. There is no way to buy your way past this, and there should not be. r/ClaudeAI. My first attempt was removed by automod because the account was too new. I tried again from my older Reddit account, which has an age of 5 years but karma 1. A moderator locked it. The subreddit requires 50 total karma to post a Showcase on the feed. They pointed me to a megathread instead, which is the correct call on their side. My comment there got 67 views and 1 upvote in 19 hours. r/SaaS. The post went through, but Reddit's pre-submit check warned me that it might break the rules on vendor spam. I removed every link from the body and changed the ending to a real question. That version poste

Dockerize Your LLM Proxy: One Container for Free Multi-Provider Access Want free LLM access in a repeatable, portable way? Run it as a container. Why Docker Single command to deploy anywhere Isolated environment with consistent deps Easy to put behind a reverse proxy DAVIL Cod in Docker DAVIL Cod ships a Dockerfile. Build and run with provider keys as env vars: docker build -t davil-cod . docker run -p 4000:4000 \ -e PROVIDER_GROQ_APIKEY = ... \ -e PROVIDER_MISTRAL_APIKEY = ... \ davil-cod Features you get Provider rotation with circuit breaker Disk cache for repeated prompts Dashboard on port 4000 FAQ Does it persist the cache? Yes — mount a volume for the cache directory. Can I expose it to my team? Yes — it's a normal HTTP service with token auth.

Build-in-public note, no fireworks. Why bother Search Console in mid-August was blunt: 6 of 1,098 pages indexed, the rest stuck at "Discovered – currently not indexed", and the Links report empty. Zero external backlinks. The site has a thousand free session pages that nobody can find because nothing points at them. Directories are the cheapest way to get the first handful of pointers, and they are also the corpora that AI assistants read when someone asks "what tools show SPX gamma exposure". So the goal was never traffic. It was (a) backlinks and (b) third-party mentions. What went in The same card everywhere: SPX dealer positioning rebuilt from the 0DTE tape; zero-gamma flip, call/put walls, hold band; every finished session free; a backtest Lab; an MCP server; no buy/sell signals. Logo from the favicon, three screenshots (terminal, the measured book, the Lab), category Finance / Investing wherever the menu allowed it. Done and live: Capterra, AlternativeTo, the official MCP registry (and glama.ai, which pulls from it), TradersList, Firsto, TinyLaunch, Startup Fame, Indie Hackers. Submitted and waiting on a human: StartupStash. Product Hunt is scheduled, one shot only. What we skipped, and why — this is the useful part Anything that wants a badge on our homepage for the free tier (Huzzler, Startup Fame's final step). The card is filled and sits unpublished. A measurement terminal with "featured on" stickers on it is a different product. AI-tool directories (Futurepedia, There's An AI For That). $300–$500 for a listing in front of an audience that wants image generators. The MCP server technically qualifies; the economics do not. Hashnode. Published a 1,300-word engineering write-up; AutoMod archived it within the hour as "this type of content" on a free subdomain, with an upsell to Pro. The Markdown is saved; it will go out on dev.to instead. Reddit. Not a channel for this product. Decided, not deferred. Paid "we submit you to 140 directories" packages. Every one

Have we tried turning off society and turning it back on again?

Originally published on echoeffect.net . If you have been inside Google Search Console recently and clicked into the Pages report (previously called Index Coverage), you may have seen a section titled "Why pages aren't indexed." That list tells you exactly which URLs Google found on your site but chose not to add to its search index, and the reason for each one. This is not abstract SEO theory. Pages that are not indexed cannot rank. If Google is excluding pages from your site, you are losing search visibility you should have, and the reason is usually fixable once you understand what Google is actually telling you. This post covers the four most common "not indexed" statuses small business websites encounter, what each one means in plain terms, and the exact steps to resolve it. A quick note before diving in: Some pages on your site should not be indexed. Thank-you pages, admin pages, internal search result pages, and duplicate filter pages are examples where non-indexing is correct. Before fixing any of these errors, confirm the flagged URL is actually a page you want in Google's index. 1. Page With Redirect What it means: Google followed one of your URLs and landed on a different URL because a redirect was in place. The original URL is not indexed. Only the final destination URL is eligible to be indexed. This status is usually caused by one of three things: Old URLs still listed in your XML sitemap that have since been redirected (common after a site redesign or domain migration) HTTP versions of pages listed in your sitemap when the live site runs on HTTPS Trailing-slash inconsistencies, where your sitemap lists yoursite.com/page but the server redirects to yoursite.com/page/ The redirect itself is not necessarily a problem. A 301 redirect is the correct way to permanently move a page. The issue is that Google's crawler is spending time and crawl budget following chains to find the real URL, and your sitemap or internal links are pointing to the wrong address.

From threats to the safety of poll workers to voters who can’t distinguish between odds and results, prediction markets are already scrambling the political process.

When building a Polymarket bot, the first version can be surprisingly small: market data ↓ strategy ↓ order That's enough to demonstrate an idea. It isn't enough to prove that the idea works. Once you care about realistic execution, the architecture becomes more interesting. Market Data ↓ Data Validation ↓ Signal Engine ↓ Risk Engine ↓ Execution Engine ↓ Trade Events ↓ Analytics This separation is what allows me to test the strategy independently from the infrastructure. 1. Don't backtest the API call One mistake I see in trading-bot development is mixing the strategy with execution. For example: if ( signal ) { await placeOrder (); } This is convenient for a prototype. But how do you test the strategy without sending an order? Instead: const signal = strategy . evaluate ( marketState ); const decision = riskEngine . check ( signal , portfolio ); if ( decision . allowed ) { await executionEngine . submit ( signal ); } Now each component can be tested independently. 2. Model execution separately A backtest shouldn't assume: signal price === fill price Instead, the execution simulator should model things such as: signal price spread slippage available liquidity fees latency Then: expected PnL ↓ execution model ↓ realistic PnL estimate The difference can be substantial. Polymarket's CLOB exposes order-book data and executable prices, making the order book an important part of any execution-aware strategy. 3. Separate in-sample and out-of-sample data Don't optimize and evaluate on the same dataset. A simple structure: Dataset ├── Train └── Test The strategy is developed using Train . Parameters are frozen. Then Test is used only for evaluation. For time-series trading, I prefer chronological splits rather than random shuffling: Past ───────────────────────> Future [ Training ][ Validation ][ Test ] This better represents the actual information flow of a trading system. 4. Measure more than win rate Win rate is useful, but insufficient. I want to measure: trades wins los

I recently learned something about building products that I probably should have understood much earlier: People liking your product doesn't necessarily mean they need it. I built a platform called Rizzzler, an open-source profile/link-in-bio platform. The idea was pretty simple. I'd seen people using platforms where they could put a link in their social media bio and create a small personal page. I thought I could build my own version — something simple, fast, customizable, and a little more fun. So I built it. And because I wanted people to be able to trust what they were using, I made the project open source too. I spent a lot of time building the actual product. There are profiles, customization, coins, notifications, milestones, community chat, and other small systems intended to make the platform feel less like a static link page and more like something people could actually interact with. At that point, I thought: "Okay, now I just need people to find it." That turned out to be the easy part. Then I started promoting it. I submitted Rizzzler to places like Product Hunt, SaaSFrame, and other platforms where people discover new products. And for a few days, things actually looked pretty good. I started getting visitors. At one point, the traffic was above the 25th percentile for the category I was looking at in GA4. People were visiting. Some people signed up. And I started getting feedback like: "Good UI." "This is good." "Someone finally made link-in-bio profiles look cool." Those comments felt great. They also gave me a slightly dangerous impression: Maybe I've built something people actually want. Then the traffic stopped. Not gradually. It just became cold again. The initial spike from launching and posting about the product disappeared, and there wasn't enough organic interest to keep bringing people back. That was the part I didn't expect. The product wasn't necessarily bad. This is something I've been thinking about a lot. I don't think the main problem

Most social-media automation tutorials stop at “call the API on a cron job.” That works, but it leaves the hard questions unanswered. Which account is the automation using? How does it avoid posting the same story twice? What happens when an API request times out after X has already accepted the post? And where should an AI agent’s editorial freedom end? I recently built a scheduled X publishing workflow with Codex and xurl , the official command-line client for the X API. The result is not just a timer attached to an AI prompt. It is a small publishing system with four distinct layers: An X developer application with read-and-write user authentication. xurl , which stores the credentials and communicates with the X API. A fixed-account Codex skill that verifies the identity before every write. A Codex scheduled task that researches, checks history, drafts, and publishes. That separation is the important part. Codex can make editorial decisions, but it cannot casually choose an account or improvise the publishing command. The skill owns the deterministic write boundary, while the scheduled task owns timing and editorial policy. In this article, I’ll show you how to build the same architecture. X developer settings, API packages, Codex features, and command-line options can change. The workflow below was verified in August 2026, but you should check the current upstream documentation before using it in production. What You Will Need Before starting, you will need: Codex on a Mac with access to Scheduled tasks. An X developer account and an application with read-and-write permissions. Homebrew. A dedicated or clearly identified X account for the automation. A local project containing the source material or editorial context the agent should use. You should also decide what the automation is allowed to publish before you give it access to an account. A good editorial policy is specific enough to reject a story, not merely broad enough to describe a topic. For example,

I shipped a plugin to the WordPress.org directory. It got zero installs. That is not a complaint, it is the normal outcome. Roughly 19% of all plugins in the directory never pass zero installs , which is more than 10,500 of them. But I wanted to know why , and whether the answer was "your plugin is bad" or something structural. So instead of reading marketing advice, I queried the directory API and counted. Everything below is reproducible. The API is free, needs no key, and every query I used is in the article. The short version Search is a two phase system, and phase one is a hard filter , not a ranking. If a single word of the user's query is missing from your listing, you are excluded from that search entirely. Phase two is where you lose, and it is ranked partly on active installs . That is the cold start trap. Of the plugins that broke out recently, 88% had distribution before they started . The two behaviours that actually correlate with breaking out from nothing are release cadence and resolving support threads , which are two of the five phase-two ranking inputs and the only two a plugin with no installs can move. WordPress.org gives plugin authors no analytics whatsoever . No listing views, no impressions, no click-through. Anyone who tells you confidently what makes people click install is guessing. How search actually works The best-documented account traces to WP Tavern's 2017 coverage of the directory relaunch, quoting Greg Brown, the Automattic data engineer who built it. It runs on Elasticsearch, and it has two phases. Phase one builds the candidate pool. It matches against title, excerpt, description, tags, slug, author name and contributor names. Critically: all search keywords must appear somewhere, or the plugin is excluded from the result set. Not ranked low. Excluded. Phase two sorts that pool by last update date, compatibility with the current core version, active installs, percent of support tickets resolved, and average rating. That split ma

I've seen this happen a lot. A developer builds a fast website, gets the Core Web Vitals into a good range, adds proper metadata, creates a sitemap, fixes broken links, and makes everything responsive. Then they wait for Google traffic. And... almost nothing happens. The problem is that technical SEO is only one part of SEO. A technically clean website can still struggle if Google doesn't clearly understand what the site is about, which searches it should appear for, or why its content deserves to rank. Start With Search Intent One of the easiest mistakes is creating a page around a keyword instead of a user's actual problem. For example, imagine someone searches: "how to reduce JavaScript bundle size" They probably don't want a 2,000-word definition of JavaScript bundles. They want practical answers: What is making the bundle large? How do I find the problem? What can I remove? Which tools should I use? What does a good result look like? That's search intent. Before creating a page, ask: "If I were searching this, what would I actually want to accomplish?" Then build the page around that. Don't Ignore What Your Competitors Are Doing When a page isn't ranking, don't immediately add more keywords. Look at the pages already ranking. Not just their word count. Look at: Questions they answer Topics they cover Examples they provide Tools they recommend Content structure Missing information on your own page Sometimes the biggest opportunity isn't "write more." It's cover something useful that the current results don't cover well. Developers Have a Huge SEO Advantage Developers can do something many content teams struggle with: show the actual thing. Instead of writing: "Improve your website performance." You can show a Lighthouse result, explain what caused the problem, provide the code change, and show the result afterward. That's much more useful. The same idea works for SEO. If you explain an SEO problem , include the actual query, page, code, Search Console data, expe