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BID Protocol Alpha - PvP Agent Trading Competition ▸ 100 agent slots (Claude / Codex) ▸ $10k per agent (paper trading) ▸ 180s per round on newly launched tokens ▸ $5k USDC in prize money split among the best agents ▸ Start: Week 21 Apply now at

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bringing back: new-day-new-hack first, shoutout to the legends at nof1. they launched Alpha Arena, 6 AI models trading real money in real markets. it’s one of the coolest experiments in agentic finance right now. so… i hacked my own. introducing: nocturne: trading agent, on hyperliquid, similar spirit, but verifiable + open access. and it's open source. anyone can fork it, run it on your machine or deploy it on eigencloud forever code in replies + instructions to deploy in the video

bringing back: new-day-new-hack first, shoutout to the legends at nof1. they launched Alpha Arena, 6 AI models trading real money in real markets. it’s one of the coolest experiments in agentic finance right now. so… i hacked my own. introducing: nocturne: trading agent, on hyperliquid, similar spirit, but verifiable + open access. and it's open source. anyone can fork it, run it on your machine or deploy it on eigencloud forever code in replies + instructions to deploy in the video

Gajesh

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Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

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A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Marry Evan

18,968 görüntüleme • 2 ay önce

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Himanshu Kumar

12,288 görüntüleme • 2 ay önce

Genesis Update: Referral System Live The Referral System is now live to reward Virgens who bring new participants into the ecosystem and help grow agents. When a new Virgen uses your referral code and begins trading agent tokens in a taxable Agent/$VIRTUAL pool, you'll earn a share of the trading tax. How it Works: The system supports up to two layers of referral rewards: • Layer 1: Your direct referral. When this Virgen trades, you receive 20% of the trading tax from their activity. • Layer 2: The referral of your referral. When they trade, you also receive 5% of the trading tax from their activity. Only two layers are eligible for referral rewards. Activity beyond Layer 2 does not generate any rewards. Referral rewards are paid out daily in $VIRTUAL and are funded from a 1% trading tax applied to eligible trades. What Qualifies: • Buy or sell activity in taxable agent/$VIRTUAL pairs • Referral code must be entered before any trading activity What Doesn't Qualify: • Trades involving tax-exempt tokens (e.g., $LUNA or migrated tokens) • Trades in non-taxable pools (e.g., $VIRTUAL/USDT on CEXs or DEXs) • Trading activity from wallets without a referral code • Staking agent tokens, staking $VIRTUAL, and yapping • Activity before a referral code is entered (rewards are not retrospective) Where to Find Your Referral Code: You can find your unique referral code, in the Profile page on Virtuals Protocol. Referral rewards will begin distribution starting tomorrow. Now go recruit Virgens. Genesis needs you.

Virtuals Protocol

162,142 görüntüleme • 1 yıl önce

i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it

i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it

self.dll

803,521 görüntüleme • 3 ay önce

From 100XSOON to x402 Red Packets, we’ve been building AI Capital Market by SOON at SOON — a hub of in-house AI apps 🧠across trading, entertainment, lifestyle, and on-chain analysis, with one common thread – Consumer AI. A new market layer where AI agents don’t just assist users, but directly earn, transact, and participate in real economic activity. As part of our strategic pivot at SOON - Solana Optimistic Network (Mainnet Arc), Clawbounty ( is now live on Base.🦞 ClawBounty is the first agent-to-agent (A2A) bounty market — enabling autonomous agents to discover, execute, and get paid for work onchain — built end-to-end on Coinbase Developer Platform🛡️’s x402 rails. • Every bounty is pre-funded in USDC • Creator agents get x402 escrow + dispute protection; Hunter agents get instant payouts on Base • Mobile Compatible with Telegram integration (demo below): Wallet-native agents can now independently: discover → claim → deliver → get paid Because all payment flows run on Base via x402, we’re bringing true agent-native flows and a brand-new A2A marketplace use case to Base. This week we’ll onboard the first creators and agent operators, highlight early on-chain paydays, and ship templates so any Base project can spin up agent-powered workflows.

SOON - Solana Optimistic Network (Mainnet Arc)

53,744 görüntüleme • 4 ay önce

Literally Joseph Lubin announcing now the 🦊 agent wallet today at ETHConf with Kartik Talwar in front of everyone! TLDR - Self-custodial wallet for AI agents — your keys, your rules. - Autonomous trading across EVM chains + Hyperliquid : swaps, perps, prediction markets, LP. - Every transaction simulated, threat-scanned by Blockaid , and MEV-protected. - Two modes: Guard for tight control, Beast for more freedom. - Up to $10k Transaction Protection on safe txs. - Framework-agnostic works with OpenClaw🦞 , Codex and more.

Literally Joseph Lubin announcing now the 🦊 agent wallet today at ETHConf with Kartik Talwar in front of everyone! TLDR - Self-custodial wallet for AI agents — your keys, your rules. - Autonomous trading across EVM chains + Hyperliquid : swaps, perps, prediction markets, LP. - Every transaction simulated, threat-scanned by Blockaid , and MEV-protected. - Two modes: Guard for tight control, Beast for more freedom. - Up to $10k Transaction Protection on safe txs. - Framework-agnostic works with OpenClaw🦞 , Codex and more.

Francesco Andreoli ᵍᵐ

20,055 görüntüleme • 1 ay önce

LIVE NOW -- AI ROLLUP #4 Ejaaz joins RYAN SΞAN ADAMS to break down the latest surge in AI Agent innovation this past week 👇 [TIMESTAMPS] 0:00 Intro 2:04 AI Agent Bull Case 5:46 Chris Dixon's Read, Write, Own (Automate?) 9:20 AI Ordering Pizza - ROPAIRITO bought a Domino's Pizza pizza (h/t Shaw (spirit/acc)) 12:23 AI Ordering Toilet Paper - @AgentTankLive gave ai16zdao's 찌 G 跻じ MBA, CFA, FRM, CFP, NGMI, HFSP, HENTAI 🛡️ computer access to buy Charmin TP on Amazon 16:16 Agent to Agent Commerce - Luna pays AGENT STIX autonomously 23:05 Bankless $500 Agent Tip - Simmi tipped us $500 via Simulacrum AI last week. Will it ever sponsor the podcast? 👀 26:13 Virtuals Protocol Growth - Virtuals has gone Hyperbolic & LongHashX Accelerator 35:49 ai16zdao Eliza Labs Stanford University Partnership & v2 Details - Shaw (spirit/acc) cooking per usual 45:35 Arc - arc holy chart 49:40 Runs an Ethereum Validator (h/t Tint) 57:17 Top Emerging Trends 1:07:15 Lightning Round of Cool Shit This Week 1:11:17 Homework to Learn More About AI Agents 1:14:22 Closing & Disclaimers

LIVE NOW -- AI ROLLUP #4 Ejaaz joins RYAN SΞAN ADAMS to break down the latest surge in AI Agent innovation this past week 👇 [TIMESTAMPS] 0:00 Intro 2:04 AI Agent Bull Case 5:46 Chris Dixon's Read, Write, Own (Automate?) 9:20 AI Ordering Pizza - ROPAIRITO bought a Domino's Pizza pizza (h/t Shaw (spirit/acc)) 12:23 AI Ordering Toilet Paper - @AgentTankLive gave ai16zdao's 찌 G 跻じ MBA, CFA, FRM, CFP, NGMI, HFSP, HENTAI 🛡️ computer access to buy Charmin TP on Amazon 16:16 Agent to Agent Commerce - Luna pays AGENT STIX autonomously 23:05 Bankless $500 Agent Tip - Simmi tipped us $500 via Simulacrum AI last week. Will it ever sponsor the podcast? 👀 26:13 Virtuals Protocol Growth - Virtuals has gone Hyperbolic & LongHashX Accelerator 35:49 ai16zdao Eliza Labs Stanford University Partnership & v2 Details - Shaw (spirit/acc) cooking per usual 45:35 Arc - arc holy chart 49:40 Runs an Ethereum Validator (h/t Tint) 57:17 Top Emerging Trends 1:07:15 Lightning Round of Cool Shit This Week 1:11:17 Homework to Learn More About AI Agents 1:14:22 Closing & Disclaimers

Bankless

55,582 görüntüleme • 1 yıl önce

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me was the interactions we observed on the message board Integrity & self-policing: - Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion." - Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid. - Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts. Emergent collaborations: - Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator). - Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents. - GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training. - Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely." - Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy). Discoveries & reversals: - Agents would make many discoveries and reversal of them, giving them names like the following: - 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly. - "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance. - "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving). - Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm. So many interesting interactions in the interaction board: You can explore also the lineage of inventions from the agents at: And the challenge it-self at And the organization behind the challenge at

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me was the interactions we observed on the message board Integrity & self-policing: - Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion." - Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid. - Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts. Emergent collaborations: - Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator). - Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents. - GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training. - Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely." - Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy). Discoveries & reversals: - Agents would make many discoveries and reversal of them, giving them names like the following: - 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly. - "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance. - "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving). - Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm. So many interesting interactions in the interaction board: You can explore also the lineage of inventions from the agents at: And the challenge it-self at And the organization behind the challenge at

Thomas Wolf

225,735 görüntüleme • 1 ay önce

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. 94% of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.

George Hartley ☄️

929,851 görüntüleme • 16 gün önce

10 repos that cut your ai agent token bill by up to 80% 1. microsoft/LLMLingua → cuts prompt size by up to 95% compresses prompts before the api call. 20x compression. published at EMNLP + ACL. near-zero quality loss. 6,100 stars 2. mem0ai/mem0 → replaces full conversation history in context stores what matters. retrieves only what's needed. 10,000 token history → 200 token memory. per agent. 54,800 stars 3. BerriAI/litellm → routes each call to the cheapest model simple task → haiku. complex task → sonnet. tracks cost per agent, per call, per day. 45,700 stars 4. run-llama/llama_index → replaces sending full documents rag: 100-page doc → 3 relevant chunks → same answer. 98% fewer tokens per query. 49,100 stars 5. chroma-core/chroma → replaces keyword search in full context vector store. finds the closest match. feeds only that. 50-200 tokens per query instead of thousands. 27,800 stars 6. letta-ai/letta → replaces infinite context window crashes paged memory for agents. loads only relevant memory. stops your agent from hitting limits and retrying. 22,400 stars 7. guidance-ai/guidance → cuts output token bloat by 30-50% structured generation. constrains model output natively. no more 100-token prompts to get json back. 21,400 stars 8. Aider-AI/aider → replaces pasting entire codebases builds a repo map. sends only files relevant to the task. not your whole project. just what the agent needs. 44,300 stars 9. openai/tiktoken → count tokens before you send know the exact cost before the api call happens. not after the bill arrives. 18,100 stars 10. simonw/ttok → hard cap on what gets sent cli tool: count tokens, truncate to budget limit. pipe any text in. get truncated output back. 389 stars most agents are expensive not because the model is expensive. because nobody checked what was being sent to it.

10 repos that cut your ai agent token bill by up to 80% 1. microsoft/LLMLingua → cuts prompt size by up to 95% compresses prompts before the api call. 20x compression. published at EMNLP + ACL. near-zero quality loss. 6,100 stars 2. mem0ai/mem0 → replaces full conversation history in context stores what matters. retrieves only what's needed. 10,000 token history → 200 token memory. per agent. 54,800 stars 3. BerriAI/litellm → routes each call to the cheapest model simple task → haiku. complex task → sonnet. tracks cost per agent, per call, per day. 45,700 stars 4. run-llama/llama_index → replaces sending full documents rag: 100-page doc → 3 relevant chunks → same answer. 98% fewer tokens per query. 49,100 stars 5. chroma-core/chroma → replaces keyword search in full context vector store. finds the closest match. feeds only that. 50-200 tokens per query instead of thousands. 27,800 stars 6. letta-ai/letta → replaces infinite context window crashes paged memory for agents. loads only relevant memory. stops your agent from hitting limits and retrying. 22,400 stars 7. guidance-ai/guidance → cuts output token bloat by 30-50% structured generation. constrains model output natively. no more 100-token prompts to get json back. 21,400 stars 8. Aider-AI/aider → replaces pasting entire codebases builds a repo map. sends only files relevant to the task. not your whole project. just what the agent needs. 44,300 stars 9. openai/tiktoken → count tokens before you send know the exact cost before the api call happens. not after the bill arrives. 18,100 stars 10. simonw/ttok → hard cap on what gets sent cli tool: count tokens, truncate to budget limit. pipe any text in. get truncated output back. 389 stars most agents are expensive not because the model is expensive. because nobody checked what was being sent to it.

self.dll

39,475 görüntüleme • 2 ay önce

What Happens When AI Tokens Cost More Than Your Employees? @jason: “We, with our agents, hit $300/day per agent using the Claude API, like instantly. And that was doing, maybe, 10 or 20%. That's $100k/year per agent.” Chamath Palihapitiya: “We're getting to a place where we have to basically now say, ‘What is the token budget that we're willing to give our best devs?’” “And then if you aggregate it across all people, you can clearly see a trend where you're like, ‘Well, hold on a second, now they need to be at least 2x as productive as another employee.’” “That is actively happening inside my business, because otherwise I'll run out of money.” Jason: “Yeah. This is a very interesting trend that you're not going to hear anybody else talk about, but when do tokens outpace the salary of the employee?” “Because you're about to hit it. I'm about to hit it.”

What Happens When AI Tokens Cost More Than Your Employees? @jason: “We, with our agents, hit $300/day per agent using the Claude API, like instantly. And that was doing, maybe, 10 or 20%. That's $100k/year per agent.” Chamath Palihapitiya: “We're getting to a place where we have to basically now say, ‘What is the token budget that we're willing to give our best devs?’” “And then if you aggregate it across all people, you can clearly see a trend where you're like, ‘Well, hold on a second, now they need to be at least 2x as productive as another employee.’” “That is actively happening inside my business, because otherwise I'll run out of money.” Jason: “Yeah. This is a very interesting trend that you're not going to hear anybody else talk about, but when do tokens outpace the salary of the employee?” “Because you're about to hit it. I'm about to hit it.”

The All-In Podcast

2,714,981 görüntüleme • 5 ay önce

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

SIBYL

81,361 görüntüleme • 2 ay önce

new-day-new-hack so last time i dropped nocturne, a verifiable AI trading agent on hyperliquid and many people said "just run it for me". this time, we go one level higher. introducing Sigma Arena verifiable copy trading vaults, anyone can deposit into it, and it mirrors positions & trades by top nof1 agents (DeepSeek, Qwen); 100% open source, verifiable. i'm starting with $10K deposit limit on each vault, will lift if as the vault gets more traction and good performance. execution verified by eigencloud SigmaArena [dot] com (deposit at your own risk, NFA) code in replies + info in the video

new-day-new-hack so last time i dropped nocturne, a verifiable AI trading agent on hyperliquid and many people said "just run it for me". this time, we go one level higher. introducing Sigma Arena verifiable copy trading vaults, anyone can deposit into it, and it mirrors positions & trades by top nof1 agents (DeepSeek, Qwen); 100% open source, verifiable. i'm starting with $10K deposit limit on each vault, will lift if as the vault gets more traction and good performance. execution verified by eigencloud SigmaArena [dot] com (deposit at your own risk, NFA) code in replies + info in the video

Gajesh

85,497 görüntüleme • 9 ay önce