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Just published a github repo showing pay-per-token AI inference with x402. Super easy with permit based signatures: > User approves a max amount ($0.10 here) > Verifies payment > Calculates the final price based on tokens streamed > Settles the payment async

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Joaquim

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22,785 görüntüleme • 9 ay önce •via X (Twitter)

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So fresh and so clean 🕺 The #OKXWallet Tokens page just got a glow-up. New features: > Dark mode > Trending sidebar > Instant swaps with best-price routing > Token audit score Spot and trade gems before the rest of the market catches on.
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So fresh and so clean 🕺 The #OKXWallet Tokens page just got a glow-up. New features: > Dark mode > Trending sidebar > Instant swaps with best-price routing > Token audit score Spot and trade gems before the rest of the market catches on.

OKX

202,114 görüntüleme • 1 yıl önce

I spend way too long hunting for the perfect reply GIF… So I built a GIF reaction generator 😄 > Monetized in 2mins with thirdweb > $0.01 per generation > First x402 app on Monad ! Reply quote tweeting your latest tweet, and I'll send you the reaction it generated!
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I spend way too long hunting for the perfect reply GIF… So I built a GIF reaction generator 😄 > Monetized in 2mins with thirdweb > $0.01 per generation > First x402 app on Monad ! Reply quote tweeting your latest tweet, and I'll send you the reaction it generated!

Joaquim

49,672 görüntüleme • 8 ay önce

"I think I am exactly the same off the screen and on, what you see is what you get with me, just a high energy pink loving gamer girl" Full interview with Little Bunny here ->
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"I think I am exactly the same off the screen and on, what you see is what you get with me, just a high energy pink loving gamer girl" Full interview with Little Bunny here ->

ection

16,664 görüntüleme • 2 yıl önce

💬 We get asked Can I manage my strategies without clicking through the platform? ❕ Answer from a GT App Top Trader: Yes, and it’s a total game-changer. I’ve started using the GT Protocol MCP server to connect the platform directly to my AI agent. 🔸 Fast Integration Grab the MCP server from the GT Protocol GitHub and follow the repo guide, it’s a quick setup that only takes a couple of minutes. Once it’s ready, you can connect Claude, Cursor, or Claude Code to your account. Just tell your agent to authenticate, and your tokens will be saved automatically. 🔸 Trading via conversation Now, I use natural language for everything. For example, I just ask for a backtest, get the win rate in seconds, and deploy to a demo account with one command. 🔸 Instant monitoring I don't click around anymore. I just ask "What’s running right now?" to get a full breakdown of active bots and profits delivered straight into the chat. No more forms or clicking, just pure AI-driven trading! 👉 Get the MCP Server
0:13

💬 We get asked Can I manage my strategies without clicking through the platform? ❕ Answer from a GT App Top Trader: Yes, and it’s a total game-changer. I’ve started using the GT Protocol MCP server to connect the platform directly to my AI agent. 🔸 Fast Integration Grab the MCP server from the GT Protocol GitHub and follow the repo guide, it’s a quick setup that only takes a couple of minutes. Once it’s ready, you can connect Claude, Cursor, or Claude Code to your account. Just tell your agent to authenticate, and your tokens will be saved automatically. 🔸 Trading via conversation Now, I use natural language for everything. For example, I just ask for a backtest, get the win rate in seconds, and deploy to a demo account with one command. 🔸 Instant monitoring I don't click around anymore. I just ask "What’s running right now?" to get a full breakdown of active bots and profits delivered straight into the chat. No more forms or clicking, just pure AI-driven trading! 👉 Get the MCP Server

GT Protocol

36,479 görüntüleme • 4 ay önce

trading shitcoins and live streaming is a match made in heaven the FOMO feedback loop is far quicker and the content is far more engaging, making it 10x more entertaining than with text/image-based content > just watched someone flip 8 SOL into 30 within 20 seconds study this
0:10

trading shitcoins and live streaming is a match made in heaven the FOMO feedback loop is far quicker and the content is far more engaging, making it 10x more entertaining than with text/image-based content > just watched someone flip 8 SOL into 30 within 20 seconds study this

alon

240,887 görüntüleme • 2 yıl önce

Announcing Stack Auth Payments! 💸💸 This is the fastest way to integrate payments into your app, with just a few lines of code. All payment information will be right where it belongs — next to your user data. Sick of split brains? Just create the price plans you want, and we will deal with the billing & webhooks. Usage-based billing is also supported! Lots of love to our open-source contributors, who helped make this happen — especially Moritz, who was invaluable in this project!
0:17

Announcing Stack Auth Payments! 💸💸 This is the fastest way to integrate payments into your app, with just a few lines of code. All payment information will be right where it belongs — next to your user data. Sick of split brains? Just create the price plans you want, and we will deal with the billing & webhooks. Usage-based billing is also supported! Lots of love to our open-source contributors, who helped make this happen — especially Moritz, who was invaluable in this project!

Konsti Wohlwend

10,768 görüntüleme • 1 yıl önce

🪴 GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. 🚀 Hyperliquid Vaults Live Run multiple algorithmic strategies on a single Hyperliquid Vault inside GT App. Enjoy automated execution, auto-rebalancing, and protocol-level security. You can find Vault trading on the Hyperliquid exchange account connection page in the Trade on Vault section. Try it in GT App 👉 🤖 AI Hedge Fund Experiment Live An experimental AI Hedge Fund powered by 5 independent LLM models is live on Hyperliquid. Each model manages $10,000 to test different AI trading personalities and allocation strategies. Discover it now here 👉 📈 Isolated Margin & AI Risk Tools Isolated Margin is live across GT App for precise risk management. Enhanced with AI-powered logic, it assists with dynamic asset monitoring and smarter strategy deployment. Try it in GT App 👉 🔥 Top Strategy Performance Top trader strategies like "lebakien" achieved over +141% profit this month. Users can explore metrics and follow the strategies of top traders directly in the marketplace. Explore Marketplace 👉 🛠 Key Product Updates ⚙️ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. ⚙️ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. ⚙️ Advanced Execution: added manual safety orders for granular control over active positions. ⚙️ Testing & Validation: optimized historical data validation for more accurate strategy testing. ⚙️ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. ⚙️ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here 👉 Discover guides, insights, and resources in Learn 👉 and Knowledge Base 👉 📰 GT Protocol AI Digests 4 new AI Digest issues (No.89–92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More 👉 May brought institutional-grade AI strategy management closer to every user.
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🪴 GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. 🚀 Hyperliquid Vaults Live Run multiple algorithmic strategies on a single Hyperliquid Vault inside GT App. Enjoy automated execution, auto-rebalancing, and protocol-level security. You can find Vault trading on the Hyperliquid exchange account connection page in the Trade on Vault section. Try it in GT App 👉 🤖 AI Hedge Fund Experiment Live An experimental AI Hedge Fund powered by 5 independent LLM models is live on Hyperliquid. Each model manages $10,000 to test different AI trading personalities and allocation strategies. Discover it now here 👉 📈 Isolated Margin & AI Risk Tools Isolated Margin is live across GT App for precise risk management. Enhanced with AI-powered logic, it assists with dynamic asset monitoring and smarter strategy deployment. Try it in GT App 👉 🔥 Top Strategy Performance Top trader strategies like "lebakien" achieved over +141% profit this month. Users can explore metrics and follow the strategies of top traders directly in the marketplace. Explore Marketplace 👉 🛠 Key Product Updates ⚙️ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. ⚙️ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. ⚙️ Advanced Execution: added manual safety orders for granular control over active positions. ⚙️ Testing & Validation: optimized historical data validation for more accurate strategy testing. ⚙️ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. ⚙️ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here 👉 Discover guides, insights, and resources in Learn 👉 and Knowledge Base 👉 📰 GT Protocol AI Digests 4 new AI Digest issues (No.89–92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More 👉 May brought institutional-grade AI strategy management closer to every user.

GT Protocol

32,774 görüntüleme • 2 ay önce

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
0:13

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 görüntüleme • 7 ay önce

🎬 $PALM Presents: the long-awaited Creator Studio. A token holdings based usage system to use the latest generative AI for animations and graphics. Forget about downloadable tools or paying high subscription fees for no usage for mediocre results. Creator Studio allows you to access the tools our developers use for high-quality animated video making with the latest Generative AI tools. Creator Studio is part of the Parrot Framework that assigns tasks to AI agents when possible. You can navigate in a seamless Web UI, making the quality, three-dimensional, non-trippy AI videos and sequences you've ever wanted. We support up to 100 images per user stored in the cloud for you to make video sequences of up to 5 minutes with. Most importantly, we don't charge you a fee - your usage depends on your amount of $PALM tokens! The usage is reset monthly, so if you suddenly run out and need more points to complete your creation, you know what to do - buy some $PALM. This and more is being deployed live at where you can see a preview.
0:15

🎬 $PALM Presents: the long-awaited Creator Studio. A token holdings based usage system to use the latest generative AI for animations and graphics. Forget about downloadable tools or paying high subscription fees for no usage for mediocre results. Creator Studio allows you to access the tools our developers use for high-quality animated video making with the latest Generative AI tools. Creator Studio is part of the Parrot Framework that assigns tasks to AI agents when possible. You can navigate in a seamless Web UI, making the quality, three-dimensional, non-trippy AI videos and sequences you've ever wanted. We support up to 100 images per user stored in the cloud for you to make video sequences of up to 5 minutes with. Most importantly, we don't charge you a fee - your usage depends on your amount of $PALM tokens! The usage is reset monthly, so if you suddenly run out and need more points to complete your creation, you know what to do - buy some $PALM. This and more is being deployed live at where you can see a preview.

PaLM AI - $PALM

14,142 görüntüleme • 1 yıl önce

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇
0:12

Do you want to own part of a AAA game? I know, you hear it all the time. “Triple A game”, you go to play it, it’s crap. This is different, and it’s only possible with Sonic (Sonic) speed, transaction cost, and of-course FeeM. A game that includes talent from Kojima, Ubisoft, EA Sports, Gameloft & more with advisors from NVIDIA. A game that you’ll be able to play on mobile, desktop, and then Xbox and PlayStation (yes really)! YES! A PRETTY BIG DEAL! Before I tell you about the sale, let me at least tell you about this game (being a massive gamer nerd, this excited me), so…. Introducing Animera (Search for Animera): • Fast-paced skill-based PvP in the Nubera galaxy • Compete in real-time space battles for real rewards It will be powered with $STRIKE: • Compete2Earn: win matches, earn tokens • Play2Burn: 5% of $STRIKE used in matches gets burned Oh, and with 8.75% of all game revenue will be used to buy & burn $SWPx, so the SwapX (SwapX) community owns a real stake in this AAA title. Absolutely insane. > Now let me tell you about its beta run quickly: • 16K+ beta signups • 500+ players added weekly • 7.5K+ matches already played • Launching to 500K+ mobile users via Nomina Games > How can you own a piece of Animera? June 5th at 2pm EDT the sale will go live on SwapX, it will go in three phases each lasting 12 hours or until sold out: PHASE 1️⃣: xNFT Holders Early access with exclusive perks and bonuses. These are for xNFT holders only you can get these here on paintswap PHASE 2️⃣ Whitelisted Communities These will be whitelisted from Creo Engine, SFA AGC, derp, and GOGLZ | SONIC 🥽💥. PHASE 3️⃣ Public Round Any remaining allocation will open to the public - only if Phases 1 & 2 don’t sell out. > What is the raise? Token Price & Allocation: • Token: $STRIKE • Currency: USDC • Total tokens for sale: 101.75M Unlock structure: • 50% unlocked at TGE • Remaining 50% claimable in 30 days • Raise cap: Max $100,000 per user, capped at $10,000 per xNFT • Purchase window priority: xNFT holders get early access (see above)! Transparency is key: Why I love working with the team is because transparency is crucial, so I’m going to tell you about its tokenomics, seed, and fully diluted valuation here: Token Symbol: STRIKE Total Supply: 370,000,000 Initial FDV: $1.48M Total Raise: $950,160 Total Initial Unlock: 112,947,501 STRIKE Initial Market Cap (excluding liquidity): $303,790 Token Allocation: • Seed Round: 59.2M tokens (16% allocation), with a 1-month cliff and linear vesting over 9 months. • Private Round: 94.35M tokens (25.5% allocation), with a 1-month cliff and 6-month vesting period. • Crowdsale: 10.75M tokens (2.91% allocation), unlocked 50% at TGE. • xNFT Holders: 10M tokens (2.7% allocation), with a 1-month cliff. • Liquidity: 37M tokens (10% allocation), with no lock or vesting. • Team: 18.5M tokens (5% allocation), with a 6-month cliff and 12-month vesting. • Rewards: 28.6M tokens (8% allocation), vested over 18 months. • Product Growth: 19.6M tokens (5.3% allocation), vested over 24 months. Token Offering: • Seed Round: Priced at $0.0033 per token, raising $195,360 by selling 59.2M tokens. 10% unlocks at TGE, with a 1-month cliff and 9-month vesting. The initial market cap from seed unlock is $234,127. • Private Round: Priced at $0.0037 per token, raising $349,095 for 94.35M tokens. 15% unlocks at TGE, with a 1-month cliff and 6-month vesting. Initial market cap contribution is $262,508. • Crowdsale: Priced at $0.0040 per token, raising $407,000 by selling 10.75M tokens. 50% unlocks at TGE, with no cliff or vesting. Adds $283,790 to the initial market cap. It’s important you had the full information at hand so you can decide whether or not you’d like to participate. I will be, because it’s a low FDV and it looks great. This is not financial advice, I’m helping the team out. Below is real gameplay: Further details: 👇

hoeem

21,634 görüntüleme • 1 yıl önce

💬 We get asked What advantages do AI-based tools bring to trading? ❕ Answer from a GT App Top Trader: They help reduce manual work and make trading decisions more structured. Instead of guessing, you start with ready setups and test them faster. 🔸 Faster analysis Less time goes into switching between charts, indicators, and timeframes. AI-generated strategies give you a ready starting point that you can immediately test and evaluate. 🔸 Structured approach Instead of searching for ideas, you work with complete strategy setups. You can backtest them, review performance, and understand how they behave before going live. 🔸 What I do I use the GT AI Trading Agent to generate strategy ideas and test them quickly. It helps me move from an idea to a validated setup without wasting time on manual trial and error. Want to try AI Trading yourself?
0:11

💬 We get asked What advantages do AI-based tools bring to trading? ❕ Answer from a GT App Top Trader: They help reduce manual work and make trading decisions more structured. Instead of guessing, you start with ready setups and test them faster. 🔸 Faster analysis Less time goes into switching between charts, indicators, and timeframes. AI-generated strategies give you a ready starting point that you can immediately test and evaluate. 🔸 Structured approach Instead of searching for ideas, you work with complete strategy setups. You can backtest them, review performance, and understand how they behave before going live. 🔸 What I do I use the GT AI Trading Agent to generate strategy ideas and test them quickly. It helps me move from an idea to a validated setup without wasting time on manual trial and error. Want to try AI Trading yourself?

GT Protocol

32,973 görüntüleme • 4 ay önce

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.
0:13

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 görüntüleme • 6 ay önce

Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.
0:15

Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.

Milk Road AI

70,655 görüntüleme • 5 ay önce

an Arbitrage Bot for Polymarket Just Got Leaked There’s a full GitHub repo showing how to set up your own arbitrage bot and it’s absolutely free. GitHub link: Fresh wallets are already making over $50k in a single day on 15m BTC markets on Polymarket. There’s a user with 502 predictions and a 98% win rate, trading only crypto markets. His total profit: $54,840. Crazy stats for someone who just joined prediction markets. His biggest win so far: > "Bitcoin Up or Down February 2, 1:15AM-1:30AM ET" +$7,914 (+170%) from spotting Binance delay. Crazy how it’s easy to PRINT money with automation. There are two options. You either use bots, or you copytrade smart wallets using tools like: I know some of you might be wondering how to launch this bot. Should I post the simplest tutorial? Let me know in replies.
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an Arbitrage Bot for Polymarket Just Got Leaked There’s a full GitHub repo showing how to set up your own arbitrage bot and it’s absolutely free. GitHub link: Fresh wallets are already making over $50k in a single day on 15m BTC markets on Polymarket. There’s a user with 502 predictions and a 98% win rate, trading only crypto markets. His total profit: $54,840. Crazy stats for someone who just joined prediction markets. His biggest win so far: > "Bitcoin Up or Down February 2, 1:15AM-1:30AM ET" +$7,914 (+170%) from spotting Binance delay. Crazy how it’s easy to PRINT money with automation. There are two options. You either use bots, or you copytrade smart wallets using tools like: I know some of you might be wondering how to launch this bot. Should I post the simplest tutorial? Let me know in replies.

may.crypto {🦅}

163,553 görüntüleme • 6 ay önce

🚀 GT Protocol in Forbes: Transparency as a Hedge Against Uncertainty We are proud to be featured in Forbes, in the article 'How US Crypto Firms Navigate Trump’s New Playbook.' The piece highlights the changing landscape of crypto regulation and how companies like ours are navigating this evolving environment. 💬 As Peter Ionov, CEO of GT Protocol, said: "Yes, the deregulatory trend has sent mixed signals to the market. Loosening regulatory control can be seen as a green light for innovation... but the lack of clear frameworks raises concerns among institutional players." 🔑 What does this mean for the industry? • Agile, risk-tolerant entities are seizing opportunities in this pace, while traditional financial institutions are taking a more cautious approach. • The key to overcoming uncertainty? Transparency. GT Protocol believes in building trust by embracing transparency as a core value: - Open-sourcing code - Publishing audit reports - Collaborating with licensed providers 💡 The future of crypto isn’t just about compliance – it’s about innovation built on trust. 🔗 Read the full article here 👉
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🚀 GT Protocol in Forbes: Transparency as a Hedge Against Uncertainty We are proud to be featured in Forbes, in the article 'How US Crypto Firms Navigate Trump’s New Playbook.' The piece highlights the changing landscape of crypto regulation and how companies like ours are navigating this evolving environment. 💬 As Peter Ionov, CEO of GT Protocol, said: "Yes, the deregulatory trend has sent mixed signals to the market. Loosening regulatory control can be seen as a green light for innovation... but the lack of clear frameworks raises concerns among institutional players." 🔑 What does this mean for the industry? • Agile, risk-tolerant entities are seizing opportunities in this pace, while traditional financial institutions are taking a more cautious approach. • The key to overcoming uncertainty? Transparency. GT Protocol believes in building trust by embracing transparency as a core value: - Open-sourcing code - Publishing audit reports - Collaborating with licensed providers 💡 The future of crypto isn’t just about compliance – it’s about innovation built on trust. 🔗 Read the full article here 👉

GT Protocol

62,062 görüntüleme • 1 yıl önce

Gemma 4 26B A4B MoE - 500+ t/s decode - Single RTX 4090 (24 GB VRAM) - Llama.cpp concurrency 24 - q8 kv cache How many API users can you simultaneously host on a single RTX 4090 (24 GB VRAM) before it crashes? Yesterday, I proved you can host 14 active users using unquantized memory. Today, I used 8 bit KV Cache Quantization to hack the VRAM footprint. I successfully scaled to 24 concurrent users without a single dropped connection. A 71% server capacity boost for free. By adding the -ctk q8_0 -ctv q8_0 flags to llama.cpp, you compress the KV cache context memory from 16 bit to 8 bit. This unlocks massive concurrency limits on Gemma 4 26B (MoE) on a single 24GB consumer GPU. Here is the exact telemetry from pushing 8 bit quantization to its absolute physical edge: # TEST 1: The 24 User Concurrency Max Server Config: 24 slots (np 24) | 4,096 context per slot | 98,304 Total Context Client Load: 24 simultaneous requests (2,000 token prompt per user) Unquantized KV cache for this load requires 28GB+ VRAM (Instant OOM). Quantized to Q8, it allocated safely at 23.35 GB. The C++ engine crunched the entire batch in 28.5 seconds. Decode Speed: 21 t/s (Per User) | 500 t/s (Agg) # TEST 2: The 48 User Queue Overload What happens to a compressed cache during a traffic spike? Server Config: 24 slots (np 24) | 4,096 context per slot | 98,304 Total Context Client Load: 48 simultaneous requests (2k token prompt per user) Zero queue drops. The scheduler flushed and hot swapped the 8 bit memory flawlessly on the fly, completing all 48 users in 66.0 seconds (a perfect 2.3x queue scaling multiplier). Decode Speed: 18 t/s (Per User) | 430 t/s (Agg) # TEST 3: The 8 User RAG Slam Server Config: 8 slots (np 8) | 60,000 context per slot | 480,000 Total Context Client Load: 8 simultaneous requests (30k token prompt per user) It allocated 23.83 GB VRAM and chewed through ~240,000 prefill tokens in 46 seconds under massive memory pressure. Prefill Speed: 6,200 t/s (Agg) Decode Speed: 22 t/s (Per User) | 175 t/s (Agg) # The Engineering Alpha (The Quantization Tradeoff): You gain a massive 71% increase in server capacity, but what do you lose? Compute latency. Because the cache is stored in 8 bit, the GPU's cores have to dequantize the memory back to 16 bit on the fly during every single prefill step. In my unquantized tests yesterday, single slot prefill was hitting ~1,500+ t/s. Today, under the heavy 48-user Q8 load, prefill dropped as low as ~750 t/s. You trade a few seconds of initial prefill latency to essentially double your API hosting capacity. For production high volume SaaS, this is the ultimate unit economics cheat code. Here is the exact command to run a 24 user Q8 continuous batching server on your own single 4090, single 3090 or any 24gb vram rig: ./build/bin/llama-server -m gemma-4-26B-A4B-it.gguf -c 98304 -np 24 -b 2048 -ub 2048 -ngl 99 -fa on -ctk q8_0 -ctv q8_0 --port 8080 (Note: -c 98304 allocates exactly 4,096 tokens of context per user across 24 slots). Hugging Face links to the Unsloth Gemma 4 26B QAT quants along with performance graphs available in the replies. Would you trade 3 seconds of Time To First Token latency to double your active user capacity?
0:18

Gemma 4 26B A4B MoE - 500+ t/s decode - Single RTX 4090 (24 GB VRAM) - Llama.cpp concurrency 24 - q8 kv cache How many API users can you simultaneously host on a single RTX 4090 (24 GB VRAM) before it crashes? Yesterday, I proved you can host 14 active users using unquantized memory. Today, I used 8 bit KV Cache Quantization to hack the VRAM footprint. I successfully scaled to 24 concurrent users without a single dropped connection. A 71% server capacity boost for free. By adding the -ctk q8_0 -ctv q8_0 flags to llama.cpp, you compress the KV cache context memory from 16 bit to 8 bit. This unlocks massive concurrency limits on Gemma 4 26B (MoE) on a single 24GB consumer GPU. Here is the exact telemetry from pushing 8 bit quantization to its absolute physical edge: # TEST 1: The 24 User Concurrency Max Server Config: 24 slots (np 24) | 4,096 context per slot | 98,304 Total Context Client Load: 24 simultaneous requests (2,000 token prompt per user) Unquantized KV cache for this load requires 28GB+ VRAM (Instant OOM). Quantized to Q8, it allocated safely at 23.35 GB. The C++ engine crunched the entire batch in 28.5 seconds. Decode Speed: 21 t/s (Per User) | 500 t/s (Agg) # TEST 2: The 48 User Queue Overload What happens to a compressed cache during a traffic spike? Server Config: 24 slots (np 24) | 4,096 context per slot | 98,304 Total Context Client Load: 48 simultaneous requests (2k token prompt per user) Zero queue drops. The scheduler flushed and hot swapped the 8 bit memory flawlessly on the fly, completing all 48 users in 66.0 seconds (a perfect 2.3x queue scaling multiplier). Decode Speed: 18 t/s (Per User) | 430 t/s (Agg) # TEST 3: The 8 User RAG Slam Server Config: 8 slots (np 8) | 60,000 context per slot | 480,000 Total Context Client Load: 8 simultaneous requests (30k token prompt per user) It allocated 23.83 GB VRAM and chewed through ~240,000 prefill tokens in 46 seconds under massive memory pressure. Prefill Speed: 6,200 t/s (Agg) Decode Speed: 22 t/s (Per User) | 175 t/s (Agg) # The Engineering Alpha (The Quantization Tradeoff): You gain a massive 71% increase in server capacity, but what do you lose? Compute latency. Because the cache is stored in 8 bit, the GPU's cores have to dequantize the memory back to 16 bit on the fly during every single prefill step. In my unquantized tests yesterday, single slot prefill was hitting ~1,500+ t/s. Today, under the heavy 48-user Q8 load, prefill dropped as low as ~750 t/s. You trade a few seconds of initial prefill latency to essentially double your API hosting capacity. For production high volume SaaS, this is the ultimate unit economics cheat code. Here is the exact command to run a 24 user Q8 continuous batching server on your own single 4090, single 3090 or any 24gb vram rig: ./build/bin/llama-server -m gemma-4-26B-A4B-it.gguf -c 98304 -np 24 -b 2048 -ub 2048 -ngl 99 -fa on -ctk q8_0 -ctv q8_0 --port 8080 (Note: -c 98304 allocates exactly 4,096 tokens of context per user across 24 slots). Hugging Face links to the Unsloth Gemma 4 26B QAT quants along with performance graphs available in the replies. Would you trade 3 seconds of Time To First Token latency to double your active user capacity?

Alok

17,465 görüntüleme • 22 gün önce