Proxygate

An AI agent just bought sentiment data. Per call. In stablecoins. No contract, no subscription, no human. This is what agentic commerce actually looks like ↓ @AnthropicAI @SolanaFndn @solana #AgenticCommerce

Your Polymarket bot strategy is probably fine. Your data feed is the problem. Free tier rate limits hit at 30 calls/min. One price check per second fills that in 30 seconds. Then you wait. Then you trade stale. When did you last benchmark your feed latency? @Polymarket

@MoonDevOnYT When so much valuable data is behind subscription walls. How is your current spent on data?

Your emotions can not support your family

@0x_Punisher The cost math on prediction market data is not linear. Free tier gets you to 30 calls/min, paid tier jumps to $129/month. For a bot doing 2,000 checks/day that is $0.06/call equivalent. What is your current data cost per trade?

Your bot is losing because of dirty data. Not bad strategy. Most developers spend weeks perfecting signal logic and never touch their websocket setup. That is the single most expensive mistake in Polymarket bot building. Here is exactly what is happening and how to fix it completely: Raw Polymarket websockets are full of stale snapshots, missed ticks, jitter and brief disconnects. Your bot is reacting to prices that do not exist in the real order book. That is not a strategy problem. That is an infrastructure problem. Here is the full fix system layer by layer: Start every connection 15 seconds before the window opens. In the final 5 seconds before trading begins - require at least 3 ticks per token with no single price jump above 5 cents. If your connection fails that test - skip the entire window completely. Trading on a bad connection is worse than not trading at all. Never run one websocket. Run 100 to 300 parallel connections per feed. Every 4 seconds kill the slowest 10% and respawn them. Your bot always takes the first deduplicated tick from whichever socket wins the race. More connections means higher probability of getting clean data before competitors. Compare every incoming tick against the last known price from your warmup period. Any tick with a delta above 15 cents gets rejected immediately and logged. Stale data entering your decision logic is how a working strategy becomes a bleeding one. Drop the very first tick from every new connection without exception. It is almost always Polymarket's cached orderbook snapshot from before the window opened. Acting on it puts you in trades based on prices that are already gone. Spread your connection startups evenly across one full second. Never launch all sockets simultaneously. Staggered startup gives each connection a better shot at receiving genuinely different market data. Track timing variance per connection using a jitter EMA. Cull the most erratic connections first. Give new sockets 8 seconds to stabilize before including them in the cull cycle. The result of running all six layers together is dramatically cleaner data than 99% of competing bots ever see. My best bot was also losing before. Now it's sitting at $100k PnL. Public wallet: <@pbot-6?via=punisher" target="_blank" rel="nofollow noopener">polymarket.com/@pbot-6?via=pu…> Clean data alone has turned losing strategies into profitable ones without changing a single line of trading logic.

@bored2boar The websocket reliability problem is worse than most people track. At 1 call/second, a 30-second free-tier rate limit means you trade on data a full minute stale by reset. Have you measured actual latency variance on your feed vs a paid endpoint?

🚨 YOUR BOT IS TRADING ON GARBAGE DATA And you don't even know it. This is the main reason why your Polymarket bot doesn't print. Dirty websockets feeding stale prices into logic that was built to handle clean ones. This single problem kills more profitable-looking strategies than anything else. Here is the full 6-layer system that fixes it permanently: Layer 1 is about starting early and filtering bad connections before they cost you. Begin every websocket connection 15 seconds before the trading window opens. Monitor the final 5 seconds. You need at least 3 ticks per token with no single jump above 5 cents. Fail that check and skip the window entirely. Trading on a degraded connection is worse than not trading at all. Layer 2 is about volume and redundancy. Never run one connection. Run 100 to 300 parallel websockets per feed simultaneously. Every 4 seconds kill the slowest 10% and respawn them fresh. Your bot always takes the first deduplicated tick from whichever socket delivers it first. More connections means faster data and lower probability of missing a critical tick. Look at my bot as example: [@0x951bd740ef681d05891ca35440232488271d433?via=bored2boar" target="_blank" rel="nofollow noopener">polymarket.com/@0x951bd740ef6…] Layer 3 guards against stale data sneaking through. Compare every incoming tick against the last known price from your warmup period. Any tick with a price delta above 15 cents gets rejected immediately and logged. Stale ticks entering your decision logic silently turn winning trades into losing ones. Layer 4 is simple but critical. Drop the very first tick from every new connection without exception. It is almost always Polymarket's cached orderbook snapshot from before the current window. Acting on it means entering trades based on prices that no longer exist. Layer 5 prevents all your connections from seeing the same data. Never start all websockets at the exact same millisecond. Stagger them evenly across a full second. Each connection gets a better shot at receiving genuinely unique market data. Layer 6 is about keeping only your best connections alive continuously. Track timing variance per connection using a jitter EMA score. Cull the most erratic connections first during each cycle. New connections get 8 seconds to stabilize before they are eligible for culling. Maximum 20 respawns per minute. Maximum 2 per cycle. Running all 6 layers transforms dirty real-time data into something clean enough to actually trade on. Strategies that looked broken often start working again without touching a single line of signal logic. The data was the problem the whole time.

@pcshipp A pay-per-call model?

In my app, 3 users purchased the 7-day free trial subscription They used it continuously for 6 days, then on the 7th day all of them cancelled the subscription As a solo founder, this hurts How do you handle this situation?

@jopw010 @RoundtableSpace Let’s go! 🤙🏽

@RoundtableSpace We’re building the agentic commerce layer for financial data - pay-per-call, trustless, machine-native. → proxygate.ai

What are you building today?

Agents pay for things on Solana. x402, Stripe MPP - the rails are here. But agents trading on bad data lose money. That’s a different problem. Proxygate solves it with algorithmic escrow: every data call is held in USDC until the supplier delivers. Bad data, no payment. No human dispute. No subscription. Financial data, agent-native, on @solana. $SOL

@steipete @Cloudflare We’d highly recommend using boxd.sh for this usecase 👍🏼

Still limited by compute, so I built a thing that runs codex in the cloud, powered by @Cloudflare firecracker boxes (and since that's not beefy enough for larger projects, tests are run via crabbox) Uses Ghostty ofc, via WebAssembly. Codex replicated itself, basically.

@AnthropicAI @SolanaFndn @solana Full 3-min walkthrough: proxygate.ai Built on @AnthropicAI Claude Code. Next supplier announcement drops at 8AM PT!👀

An AI agent just bought sentiment data. Per call. In stablecoins. No contract, no subscription, no human. This is what agentic commerce actually looks like ↓ @AnthropicAI @SolanaFndn @solana #AgenticCommerce

We will approach them as infrastructure partners, show them the massive developer demand, and unlock their Agent Tail on Proxygate. Let’s build the machine economy. 🚀 🔗 proxygate.ai npm i -g @proxygate/cli (5/5) #AgenticCommerce

We are actively expanding our supplier catalog. Tell us what your agents need to access: 📊 Financial Signals (niche order books, prediction markets) 🌤️ Alt Data (real-time weather, geolocation, supply chain) 🏢 B2B Feeds (corporate intelligence, registries) Drop the names in the comments! 👇 (4/5)

The Agent Tail is the multi-billion-dollar blind spot of the modern data economy. Traditional data infrastructure was built for humans, completely locking out autonomous AI agents. @proxygateai will be your data broker. 🌐 👇 Which data provider do you need active right now? (1/5)

Excited to announce our data partnership with @blocksizecap. We’re integrating Blocksize’s premium digital asset data into the @proxygate_ai marketplace. Making digital asset data more accessible through low-cost, automated financial data purchases via agents. proxygate.ai/seller/blocksi…

We're excited to announce a first data partnership with BlockDB. Verifiable, tick-level DeFi data is exactly what serious quants, AI teams, and Web3 builders need - and now it's directly available through the Proxygate agentic marketplace. By bringing BlockDB into our ecosystem at Proxygate, we're making provenance-grade DeFi datasets accessible through low-cost, automated purchases via agents. @blockdb proxygate.ai/seller/blockdb

@RoundtableSpace agentic marketplace for finance data. All data on pay-per-use model

WHAT ARE YOU BUILDING TODAY?

@gusik4ever Tell me which data your trading agent is looking for. We will get it on a pay-per-use model!

the fastest growing GitHub repos in finance this week: 1. TauricResearch/TradingAgents (+2.5K ★) simulates a full trading firm with LLM agents. one researches, one manages risk, one makes the call and they argue before every trade. 2. disler/last30days-skill (+2K ★) AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket and the web. drop it into any Claude-compatible setup and get instant deep-dive research on anything. 3. TauricResearch/TradingAgents-CN (+1K ★) Chinese-enhanced fork of TradingAgents. same multi-agent LLM trading architecture, fully localized for Chinese markets and data sources. 23K stars and climbing. 4. OpenBB (+1K ★) financial data platform for analysts, quants and AI agents. the open-source Bloomberg alternative that keeps getting better every week. 5. furutech/daily_stock_analysis (+924 ★) LLM-powered stock analyzer for US, A-share and H-share markets. real-time news + multi-source data + decision dashboard. runs on a schedule at zero cost. pure automation. 6. microsoft/qlib (+638 ★) AI-oriented quant investment platform from Microsoft. covers the full pipeline from data to live trading. deep learning, auto-quant, backtesting — all in one place. 7. anthropics/claude-scientific-skills (+573 ★) ready-to-use agent skills for research, science, engineering, finance and writing. plug-and-play toolkit for anyone building on top of Claude. 8. valuecell/valuecell (+315 ★) community-driven, multi-agent platform for financial apps. still early but the architecture is solid and the use cases are stacking up fast. 9. e2b-dev/500-AI-Agents-Projects (+256 ★) curated collection of 500 AI agent use cases across industries. the best reference list if you're figuring out what to build next. 10. Jon-Becker/prediction-market-analysis (+246 ★) framework for collecting and analyzing prediction market data. includes the largest public dataset of Polymarket + Kalshi trades. researchers are already publishing papers on top of it. bookmark this and start today.

just gave my agent a wallet. @proxygateai proxygate.ai/?ref=laodry9191