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Wake Arena: multi-agent AI audit with graph-driven reasoning and LLM-tailored static analysis. 43/94 high-severity vulnerabilities found in historical audit competitions. 26 findings, including 5 criticals in 4 production audits by Akcee in Nov 2025. Full benchmarking report: Built by senior auditors securing Lido, Aave, Axelar, and Safe. 50%+ true...

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Ackee Blockchain Security

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1,997,525 次观看 • 7 个月前 •via X (Twitter)

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EVMbench for Solana: Trident Arena, a multi-agent AI security scanning solution. 21 out of 30 critical/high-severity vulnerabilities found across 6 open source Solana code bases in benchmarks. 70%+ true positive rate. See it in action: During a manual audit, TridentArena helped us find a critical-severity issue (later fixed), making MetaDAO even more secure. Built by the School of Solana senior auditors, securing leading protocols. Time to IBRL of Solana security audits! Launch blog post: Request access → Follow Trident for product updates and insights.
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EVMbench for Solana: Trident Arena, a multi-agent AI security scanning solution. 21 out of 30 critical/high-severity vulnerabilities found across 6 open source Solana code bases in benchmarks. 70%+ true positive rate. See it in action: During a manual audit, TridentArena helped us find a critical-severity issue (later fixed), making MetaDAO even more secure. Built by the School of Solana senior auditors, securing leading protocols. Time to IBRL of Solana security audits! Launch blog post: Request access → Follow Trident for product updates and insights.

Ackee Blockchain Security

1,838,331 次观看 • 4 个月前

The accounting services industry has historically been 1) underserved by technology and 2) constrained by capacity, not demand. Founders Arush Jain, Pranav Pillai, and Vinay Kasat saw this as an opportunity. They brought together their backgrounds in private equity M&A, forward-deployed software engineering, and growth operations. In doing so, they created Modus to partner with auditors ready for the next generation of firms that have the right to grow. Through co-creating and tightly iterating on powerful, scalable workflows with frontline auditors, Modus freed up tens of thousands of hours in their first platform partner that they have already sold into. The co-founders joined Justin M. Overdorff and Amish Desai this week on The Investment Memo, where they go into more detail about our partnership, their vision for innovating in the industry, and how their past career experiences prepared them for this journey. 0:00 Welcome, Arush Jain, Vinay Kasat, & Pranav Pillai! 2:40 Why Audit Is a Massive Opportunity 3:17 The Core Problem Auditors Face Today 4:10 The “Audit in 2 Weeks” Vision 5:04 Proving the Model Works Without an Audit Background 5:53 Why Audit Software Has Failed Before 6:25 Reinventing Audits from the Ground Up 7:25 Inside 7 AI Workflows & 40% Time Savings 9:03 Palantir’s Influence on Their Strategy 10:00 Embedding Engineers Inside Audit Firms 11:12 Turning Time Savings into Growth 12:25 Improving Audit Quality with AI 14:01 Where Audit Errors Actually Happen 16:24 The First Acquisition Journey 17:50 Early Results: Growth & Efficiency Gains 19:05 The “This Works” Moment 20:18 From Friends to Co-Founders 22:34 Hiring in the Age of AI 24:08 Advisors Behind Modus 25:41 Choosing the Right Investors 26:55 The Future of AI in Audit 28:03 What Excites the Founders Most 28:48 Advice for Founders
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The accounting services industry has historically been 1) underserved by technology and 2) constrained by capacity, not demand. Founders Arush Jain, Pranav Pillai, and Vinay Kasat saw this as an opportunity. They brought together their backgrounds in private equity M&A, forward-deployed software engineering, and growth operations. In doing so, they created Modus to partner with auditors ready for the next generation of firms that have the right to grow. Through co-creating and tightly iterating on powerful, scalable workflows with frontline auditors, Modus freed up tens of thousands of hours in their first platform partner that they have already sold into. The co-founders joined Justin M. Overdorff and Amish Desai this week on The Investment Memo, where they go into more detail about our partnership, their vision for innovating in the industry, and how their past career experiences prepared them for this journey. 0:00 Welcome, Arush Jain, Vinay Kasat, & Pranav Pillai! 2:40 Why Audit Is a Massive Opportunity 3:17 The Core Problem Auditors Face Today 4:10 The “Audit in 2 Weeks” Vision 5:04 Proving the Model Works Without an Audit Background 5:53 Why Audit Software Has Failed Before 6:25 Reinventing Audits from the Ground Up 7:25 Inside 7 AI Workflows & 40% Time Savings 9:03 Palantir’s Influence on Their Strategy 10:00 Embedding Engineers Inside Audit Firms 11:12 Turning Time Savings into Growth 12:25 Improving Audit Quality with AI 14:01 Where Audit Errors Actually Happen 16:24 The First Acquisition Journey 17:50 Early Results: Growth & Efficiency Gains 19:05 The “This Works” Moment 20:18 From Friends to Co-Founders 22:34 Hiring in the Age of AI 24:08 Advisors Behind Modus 25:41 Choosing the Right Investors 26:55 The Future of AI in Audit 28:03 What Excites the Founders Most 28:48 Advice for Founders

Lightspeed

12,553 次观看 • 3 个月前

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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Himanshu Kumar

228,185 次观看 • 3 个月前

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:
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Andrew Ng

126,450 次观看 • 1 年前

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc
4:32

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc

RIDWAN

12,958 次观看 • 3 个月前

NEAR AI Ecosystem – “What Did You Ship This Week?” Ep. 7 A big week across the NEAR AI ecosystem with updates on personalized data agents, TypeScript tooling, crowdsourced AI data, and non-custodial flows for token coordination + much more. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:00 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI explains how their platform lets users earn stablecoins and points by uploading and verifying native language audio clips. Data builders can now claim earnings directly to a NEAR Protocol wallet. Public beta is live. 09:35 – Matt Hamilton from ASIMOV Protocol demos their CLI tools for uploading structured datasets to NEAR, plus new knowledge graph visualizations and early experiments transcribing voice into semantic graphs. 15:55 – @tahpot_au from Verida 💜 shows how developers can access private user data for AI agents using the Verida Vault. Users retain full control over their data, with API keys gated by permission. 25:25 – Zahidul Islam from Jutsu | Work Smarter with AI Agents drops updates on the NEAR AI TypeScript SDK. One-command agent deployment, fully local dev support, and matching features with the Python SDK. Also demoed new AI-generated video tooling with Postt. 34:20 – Matt Lockyer from Proximity Labs shares the launch of their Base naming agent Basednames. Users post on X to mint names, pay with Bankr, and finalize with verified chain signatures powered by NEAR. All non-custodial. All live on Base. 50:35 – Charles previews Divvy Agent. Users can set portfolio growth and swap thresholds with natural language. Latest update adds persistent memory, improved LLM matching, and MPC integration for safe, automated execution. 59:36 – Cameron.near🇺🇸 closes out with updates from the NEAR AI core team. Hiring in progress, governance agents coming soon, and progress on privacy tooling and agent infra powering the NEAR AI Hub. The future is NEAR - the blockchain for AI. Catch the full replay:
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NEAR AI Ecosystem – “What Did You Ship This Week?” Ep. 7 A big week across the NEAR AI ecosystem with updates on personalized data agents, TypeScript tooling, crowdsourced AI data, and non-custodial flows for token coordination + much more. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:00 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI explains how their platform lets users earn stablecoins and points by uploading and verifying native language audio clips. Data builders can now claim earnings directly to a NEAR Protocol wallet. Public beta is live. 09:35 – Matt Hamilton from ASIMOV Protocol demos their CLI tools for uploading structured datasets to NEAR, plus new knowledge graph visualizations and early experiments transcribing voice into semantic graphs. 15:55 – @tahpot_au from Verida 💜 shows how developers can access private user data for AI agents using the Verida Vault. Users retain full control over their data, with API keys gated by permission. 25:25 – Zahidul Islam from Jutsu | Work Smarter with AI Agents drops updates on the NEAR AI TypeScript SDK. One-command agent deployment, fully local dev support, and matching features with the Python SDK. Also demoed new AI-generated video tooling with Postt. 34:20 – Matt Lockyer from Proximity Labs shares the launch of their Base naming agent Basednames. Users post on X to mint names, pay with Bankr, and finalize with verified chain signatures powered by NEAR. All non-custodial. All live on Base. 50:35 – Charles previews Divvy Agent. Users can set portfolio growth and swap thresholds with natural language. Latest update adds persistent memory, improved LLM matching, and MPC integration for safe, automated execution. 59:36 – Cameron.near🇺🇸 closes out with updates from the NEAR AI core team. Hiring in progress, governance agents coming soon, and progress on privacy tooling and agent infra powering the NEAR AI Hub. The future is NEAR - the blockchain for AI. Catch the full replay:

NEARWEEK

35,143 次观看 • 1 年前

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)
0:36

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)

Suryansh Tiwari

141,998 次观看 • 2 个月前

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf
1:08

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

Seppmos

25,942 次观看 • 4 个月前

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇
10:37

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇

YanXbt

95,587 次观看 • 1 个月前

Today marks 8 years of QuillAudits. Most Web3 security firms didn't exist 8 years ago. Most won't exist 8 years from now. We've built through 3 bear markets, 2 exploit waves, and the full evolution of smart contract attacks from simple reentrancy to cross-protocol economic exploits. 1,500+ protocols. $3B+ protected. The biggest lesson from 8 years and 1,500+ engagements : One team, one method, one pass doesn't cut it when you're protecting hundreds of millions in user funds. So we rebuilt the model. Multi-Layer Audit → four independent security layers, delivered in the same timeline as a traditional audit: > Senior auditors who've collectively reviewed 1,500+ protocols > AI security agents trained on 5,000+ real exploits since 2017 > Independent bug bounty through curated security researchers > Continuous monitoring, because threats don't stop at deployment 4 layers. Each one catches what the others miss. Web3 has a $100T addressable market if institutions show up. They won't show up until security is embedded in every layer, every transaction, every deployment, the way HTTPS is embedded in the internet. That's the problem worth solving for the next 8 years. QuillAudits built the foundation, QuillShield is the next chapter — an AI security agent that brings what we learned from 1,500+ manual audits into every developer's workflow, before code ever hits mainnet. 8 years in. Still early.
1:07

Today marks 8 years of QuillAudits. Most Web3 security firms didn't exist 8 years ago. Most won't exist 8 years from now. We've built through 3 bear markets, 2 exploit waves, and the full evolution of smart contract attacks from simple reentrancy to cross-protocol economic exploits. 1,500+ protocols. $3B+ protected. The biggest lesson from 8 years and 1,500+ engagements : One team, one method, one pass doesn't cut it when you're protecting hundreds of millions in user funds. So we rebuilt the model. Multi-Layer Audit → four independent security layers, delivered in the same timeline as a traditional audit: > Senior auditors who've collectively reviewed 1,500+ protocols > AI security agents trained on 5,000+ real exploits since 2017 > Independent bug bounty through curated security researchers > Continuous monitoring, because threats don't stop at deployment 4 layers. Each one catches what the others miss. Web3 has a $100T addressable market if institutions show up. They won't show up until security is embedded in every layer, every transaction, every deployment, the way HTTPS is embedded in the internet. That's the problem worth solving for the next 8 years. QuillAudits built the foundation, QuillShield is the next chapter — an AI security agent that brings what we learned from 1,500+ manual audits into every developer's workflow, before code ever hits mainnet. 8 years in. Still early.

QuillAudits 🥷

11,599 次观看 • 4 个月前

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉
0:34

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉

Kredo AI

16,988 次观看 • 1 年前

2026 Fulton County Election Fraud Report finds THOUSANDS of FALSE BALLOTS added to the post-election hand count/audit, which lowered Biden's margin of victory and vote total by about 50,000 to 60,000 votes, indicating that Trump did indeed win Georgia in the 2020 election. This isn't even counting all of the other fraud found GA. As a result of the 36 errors, 6,691 fictitious ballots that do not exist were added to the "Total Ballots Cast" column. After removing the false ballots from the total, because they do not exist, Fulton County's corrected Total Ballots Cast for the hand count/audit is 521,341, or 7,436 ballots less than the certified Nov. 3rd total of 528,777. Of these, candidate Trump received 1,025 false votes that do not exist, while candidate Biden received 5,618 false votes that do not exist. Correcting for the errors from only the absentee ballots of one county, the hand count/audit results yield a margin of victory that is 4,593 votes less than the November 3rd results. 11,779 was the total margin of victory for the entire state. A review of only 3 percent of the ballots yielded errors that falsely inflated the margin of victory for the hand count/audit by one-third. Based on these errors alone, the margin of victory drops to 7,185. The 36 errors mysteriously added a sufficient number of ballots and votes to substantiate the November 3rd results, albeit falsely. Just as the hand count/audit was used as a metric to corroborate the November 3rd results, the errors call those results in to question. First, there was no investigation beyond that which was carried out by Mr. Rossi and the Governor's office as the Secretary of State's investigator did not perform an investigation. Second, the claim that the error were unintentional is refuted by the fact that 35 of the 36 inconsistencies benefited one candidate. Next, the failures were not the product of data entry errors. Lastly, the conclusion and excuse that the errors did not affect the outcome of the presidential contest is irrelevant and not responsive to the allegation, as the race for president extended past the Fulton County line, and so did the "errors" that were found. It gets even better... A total of eight 8 false batch entries are included in the results in which candidate Trump erroneously receives ZERO votes, and almost all are supported with a batch tally sheet. Errors with a batch tally sheet that don’t match the corresponding ballots are not the product of mistake or unintentional error. It is indisputable that the batch tally sheets identified by Mr. Rossi and Governor Kemp were intentionally fabricated to falsely pad the hand-count/audit results in line with the November 3rd results. Georgia law explicitly states that any superintendent or employee who intentionally destroys or alters tally papers, or permits them to be destroyed or altered, shall be guilty of a felony. Fulton County's chaotic, unaccountable curation and processing of cast ballots, cast BMD printout, and electronic records make a true risk-limiting audit impossible. It is unreasonable for voters to trust that their votes were counted at all, much less counted correctly. Voters have good reason to believe that some votes counted more than others, some votes were included twice or 3x in the totals. There is no way to know how many votes were omitted from the tabulation, absent access to the physical ballots and BMD printout and evidence that the chain of custody is intact. From the records produced so far, it Is impossible to determine whether malware, bugs, misconfiguration, or malfeasance disenfranchised voters or altered the election results. The fact that thousands of false ballots and votes were unveiled should have triggered a real investigation, not only of Fulton County's November 3rd election results, but those of the entire state. We can use the same mathematical basis as the Risk Limiting Audit by using Fulton County’s rate of error- and extrapolate. Approximately 148,000 absentee ballots were cast in Fulton County, and out of those, 6,691, 4.52%, were found to be false, or in error. A total of 1,311,061 absentee ballots were cast in the state of Georgia for the 2020 General Election. Using the same percentage, 4.52%, of false ballots/votes as that confirmed in Fulton County: 1,311,061 x .0452 = 59,259 false ballots/votes Using the same ratio of distribution of false ballots/votes as that confirmed in Fulton County: Candidate Biden: 83.9% of 59,259 = 49,718 false votes Candidate Trump: 15.3% of 59,259 = 9,066 false votes Therefore, using the Fulton County hand count/audit error rate, as established by the Governor's report on just the absentee ballots cast, the error rate is determinative, and it is possible that the wrong candidate did take office. Ms. McGowan's assertion that the hand count/audit errors did not affect the outcome of the race, is not supported by fact. Given the egregious manipulation in Fulton County, failing and/or refusing to check the hand count/audit results of the other 158 counties constitutes gross negligence, if not willful misconduct and fraud. On top of these findings, Fulton County election officials knew the hand-count/audit results were falsely inflated. There are a confirmed 6,691 fictitious votes that were added to the hand-count/audit results, but were NEVER corrected. An email found corresponds, which is located in this report, establishes the fact that Fulton County election officials knew of the errors at the time, even the same day that the Secretary of State released the results. (PAGE 171 in the video report above contains the email to view.) For some five 5+ years, the hand-count/audit results, known by Fulton County to be materially defective, have been used to falsely substantiate the official results. The same fraudulent results have also been used against those who rightfully questioned Georgia's election results. Lastly, for just this report and presentation, the hand-count tally sheets DO NOT MATCH results from advance voting polling locations. Poll tapes were compared for all tabulators at each polling location to the corresponding batch tally sheets as produced during the hand-count/audit. The fact that such differences between the hand counted audited ballot tallies and the official machine count tallies differs by this much signals that tabulation and auditing processes are flawed and strongly argue for intense objective expert examination and considerable mitigation efforts. It should be the case that such counts are consistent and exact. The fact that such audit discrepancies at a precinct level did not cause precertification investigations of the count variances is unacceptable, it essentially defeats the purpose of an audit if significant discrepancies are ignored and chalked up to human error. As they seem to have been at least in the case of the Fulton County audit. It is irrefutable that the hand count/audit results were indeed the result of intentional human acts, aka, FRAUD. Read full report for all details and visual evidence above in video report. The 2020 Election was stolen.
5:18

2026 Fulton County Election Fraud Report finds THOUSANDS of FALSE BALLOTS added to the post-election hand count/audit, which lowered Biden's margin of victory and vote total by about 50,000 to 60,000 votes, indicating that Trump did indeed win Georgia in the 2020 election. This isn't even counting all of the other fraud found GA. As a result of the 36 errors, 6,691 fictitious ballots that do not exist were added to the "Total Ballots Cast" column. After removing the false ballots from the total, because they do not exist, Fulton County's corrected Total Ballots Cast for the hand count/audit is 521,341, or 7,436 ballots less than the certified Nov. 3rd total of 528,777. Of these, candidate Trump received 1,025 false votes that do not exist, while candidate Biden received 5,618 false votes that do not exist. Correcting for the errors from only the absentee ballots of one county, the hand count/audit results yield a margin of victory that is 4,593 votes less than the November 3rd results. 11,779 was the total margin of victory for the entire state. A review of only 3 percent of the ballots yielded errors that falsely inflated the margin of victory for the hand count/audit by one-third. Based on these errors alone, the margin of victory drops to 7,185. The 36 errors mysteriously added a sufficient number of ballots and votes to substantiate the November 3rd results, albeit falsely. Just as the hand count/audit was used as a metric to corroborate the November 3rd results, the errors call those results in to question. First, there was no investigation beyond that which was carried out by Mr. Rossi and the Governor's office as the Secretary of State's investigator did not perform an investigation. Second, the claim that the error were unintentional is refuted by the fact that 35 of the 36 inconsistencies benefited one candidate. Next, the failures were not the product of data entry errors. Lastly, the conclusion and excuse that the errors did not affect the outcome of the presidential contest is irrelevant and not responsive to the allegation, as the race for president extended past the Fulton County line, and so did the "errors" that were found. It gets even better... A total of eight 8 false batch entries are included in the results in which candidate Trump erroneously receives ZERO votes, and almost all are supported with a batch tally sheet. Errors with a batch tally sheet that don’t match the corresponding ballots are not the product of mistake or unintentional error. It is indisputable that the batch tally sheets identified by Mr. Rossi and Governor Kemp were intentionally fabricated to falsely pad the hand-count/audit results in line with the November 3rd results. Georgia law explicitly states that any superintendent or employee who intentionally destroys or alters tally papers, or permits them to be destroyed or altered, shall be guilty of a felony. Fulton County's chaotic, unaccountable curation and processing of cast ballots, cast BMD printout, and electronic records make a true risk-limiting audit impossible. It is unreasonable for voters to trust that their votes were counted at all, much less counted correctly. Voters have good reason to believe that some votes counted more than others, some votes were included twice or 3x in the totals. There is no way to know how many votes were omitted from the tabulation, absent access to the physical ballots and BMD printout and evidence that the chain of custody is intact. From the records produced so far, it Is impossible to determine whether malware, bugs, misconfiguration, or malfeasance disenfranchised voters or altered the election results. The fact that thousands of false ballots and votes were unveiled should have triggered a real investigation, not only of Fulton County's November 3rd election results, but those of the entire state. We can use the same mathematical basis as the Risk Limiting Audit by using Fulton County’s rate of error- and extrapolate. Approximately 148,000 absentee ballots were cast in Fulton County, and out of those, 6,691, 4.52%, were found to be false, or in error. A total of 1,311,061 absentee ballots were cast in the state of Georgia for the 2020 General Election. Using the same percentage, 4.52%, of false ballots/votes as that confirmed in Fulton County: 1,311,061 x .0452 = 59,259 false ballots/votes Using the same ratio of distribution of false ballots/votes as that confirmed in Fulton County: Candidate Biden: 83.9% of 59,259 = 49,718 false votes Candidate Trump: 15.3% of 59,259 = 9,066 false votes Therefore, using the Fulton County hand count/audit error rate, as established by the Governor's report on just the absentee ballots cast, the error rate is determinative, and it is possible that the wrong candidate did take office. Ms. McGowan's assertion that the hand count/audit errors did not affect the outcome of the race, is not supported by fact. Given the egregious manipulation in Fulton County, failing and/or refusing to check the hand count/audit results of the other 158 counties constitutes gross negligence, if not willful misconduct and fraud. On top of these findings, Fulton County election officials knew the hand-count/audit results were falsely inflated. There are a confirmed 6,691 fictitious votes that were added to the hand-count/audit results, but were NEVER corrected. An email found corresponds, which is located in this report, establishes the fact that Fulton County election officials knew of the errors at the time, even the same day that the Secretary of State released the results. (PAGE 171 in the video report above contains the email to view.) For some five 5+ years, the hand-count/audit results, known by Fulton County to be materially defective, have been used to falsely substantiate the official results. The same fraudulent results have also been used against those who rightfully questioned Georgia's election results. Lastly, for just this report and presentation, the hand-count tally sheets DO NOT MATCH results from advance voting polling locations. Poll tapes were compared for all tabulators at each polling location to the corresponding batch tally sheets as produced during the hand-count/audit. The fact that such differences between the hand counted audited ballot tallies and the official machine count tallies differs by this much signals that tabulation and auditing processes are flawed and strongly argue for intense objective expert examination and considerable mitigation efforts. It should be the case that such counts are consistent and exact. The fact that such audit discrepancies at a precinct level did not cause precertification investigations of the count variances is unacceptable, it essentially defeats the purpose of an audit if significant discrepancies are ignored and chalked up to human error. As they seem to have been at least in the case of the Fulton County audit. It is irrefutable that the hand count/audit results were indeed the result of intentional human acts, aka, FRAUD. Read full report for all details and visual evidence above in video report. The 2020 Election was stolen.

The SCIF

45,719 次观看 • 10 天前

This Claude MCP AI Agent replaces your $200K+ Operations Teams. I probably shouldn't be sharing the exact system for free... while I was trying to catch Pikachu at 3am on Pokemon Go, it audited my entire business, found 12 bottlenecks, and built me 5 production-ready n8n agents the efficiency gain is absolutely INSANE most founders burn months hiring ops consultants who charge $500/hour just to tell you what's broken this agentic system does their entire job in minutes here's what happens when you deploy it: → runs complete business intelligence audit in 3 minutes (what takes consultants weeks) → identifies 12+ workflow bottlenecks killing your efficiency → architects custom agentic systems tailored to your business → builds 5+ autonomous AI agents with advanced error handling → creates intelligent orchestration layer syncing everything together → delivers complete operational transformation in under 10 minutes the entire audit that consultants charge $50K for now happens in 10 minutes ZERO technical knowledge needed ZERO expensive consultants required ZERO months of back-and-forth just describe your current setup and watch it build your agentic empire the math is stupid simple: $200K ops team salary vs one-time MCP deployment that's $16,600 saved monthly the system includes: - intelligent business stack analyzer - bottleneck detection AI - custom agentic architect - autonomous agent builder - complete deployment documentation this is the exact system building 7-figure operational infrastructure and you're getting it for free Follow + RT + comment "MCP" & I'll send you the FULL setup guide tonight don't sleep on this every week you wait is 30+ hours of manual work you'll never get back
0:24

This Claude MCP AI Agent replaces your $200K+ Operations Teams. I probably shouldn't be sharing the exact system for free... while I was trying to catch Pikachu at 3am on Pokemon Go, it audited my entire business, found 12 bottlenecks, and built me 5 production-ready n8n agents the efficiency gain is absolutely INSANE most founders burn months hiring ops consultants who charge $500/hour just to tell you what's broken this agentic system does their entire job in minutes here's what happens when you deploy it: → runs complete business intelligence audit in 3 minutes (what takes consultants weeks) → identifies 12+ workflow bottlenecks killing your efficiency → architects custom agentic systems tailored to your business → builds 5+ autonomous AI agents with advanced error handling → creates intelligent orchestration layer syncing everything together → delivers complete operational transformation in under 10 minutes the entire audit that consultants charge $50K for now happens in 10 minutes ZERO technical knowledge needed ZERO expensive consultants required ZERO months of back-and-forth just describe your current setup and watch it build your agentic empire the math is stupid simple: $200K ops team salary vs one-time MCP deployment that's $16,600 saved monthly the system includes: - intelligent business stack analyzer - bottleneck detection AI - custom agentic architect - autonomous agent builder - complete deployment documentation this is the exact system building 7-figure operational infrastructure and you're getting it for free Follow + RT + comment "MCP" & I'll send you the FULL setup guide tonight don't sleep on this every week you wait is 30+ hours of manual work you'll never get back

Aryan Mahajan

146,304 次观看 • 1 年前

What does skydiving have to do with AI sales? 🪂 We didn't want to build yet another AI agent focused on flooding inboxes with spam, so we built Ora. 🔮 Introducing Lavender Ora: our agent for thoughtful, effective selling. Ora is built for sellers who choose value over volume, modeling the best practices of top performers—researching, personalizing, and timing impactful and optimized emails. All built on Lavender's ACI - Augmented Communication Intelligence - and powered by our insights from analyzing nearly 2B sales emails. 💜 Saves 4+ hours/day by handling busywork. ✈️ Smart Sequences precisely time follow ups based on real-world news and market events. ✅ Protects deliverability—no spam, just trust. 50 well-researched, personalized, and optimized emails every day - just like the best human sellers. Whether you're team manual or autonomous, Ora adapts to your workflow with: 🧐 OCD Mode: Manually review, refine, and approve 50 emails in 5 min. 🚀 LFG Mode: Ora goes fully autonomous, building passive pipeline and freeing up more of your time. Time saved at work = more time to do fun stuff. Like flying. Join the waitlist:
0:52

What does skydiving have to do with AI sales? 🪂 We didn't want to build yet another AI agent focused on flooding inboxes with spam, so we built Ora. 🔮 Introducing Lavender Ora: our agent for thoughtful, effective selling. Ora is built for sellers who choose value over volume, modeling the best practices of top performers—researching, personalizing, and timing impactful and optimized emails. All built on Lavender's ACI - Augmented Communication Intelligence - and powered by our insights from analyzing nearly 2B sales emails. 💜 Saves 4+ hours/day by handling busywork. ✈️ Smart Sequences precisely time follow ups based on real-world news and market events. ✅ Protects deliverability—no spam, just trust. 50 well-researched, personalized, and optimized emails every day - just like the best human sellers. Whether you're team manual or autonomous, Ora adapts to your workflow with: 🧐 OCD Mode: Manually review, refine, and approve 50 emails in 5 min. 🚀 LFG Mode: Ora goes fully autonomous, building passive pipeline and freeing up more of your time. Time saved at work = more time to do fun stuff. Like flying. Join the waitlist:

Lavender 💜

14,397 次观看 • 1 年前

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI
51:54

Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience leading pre-training efforts for Google’s PaLM and early Gemini models to argue that pre-training itself must be rethought to move beyond static benchmarks. We explore the limitations of next-token prediction for multi-step workflows and examine how attention mechanisms, loss objectives, and training data must evolve to support long-form reasoning and planning. Aakanksha shares insights on the difference between context retrieval and actual reasoning, the importance of "trajectory" training data, and why scaling remains essential for discovering emergent agentic capabilities like error recovery and dynamic tool learning. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 02:26 - Reflection 04:54 - Limitations of post-training for building agents 07:31 - Rethinking pre-training in agents 10:51 - Scaling 11:27 - Evolving attention mechanisms for agentic capabilities 12:39 - Memory as a tool 14:13 - Loss objectives and training data 15:50 - Fine-tuning loss in agent performance 19:37 - Training data 21:29 - Augmenting dominant training data source 24:11 - Overcoming challenges in training on synthetic data 25:47 - Benchmarks 30:44 - Scaling laws in large models versus small models 33:20 - Long-form versus short-form reasoning 37:57 - Agent’s ability to recover from failure 40:15 - Hallucinations and failure recovery 43:53 - Tool use in agents 46:38 - Coding agents 48:37 - How researchers can contribute to agentic AI

The TWIML AI Podcast

44,888 次观看 • 7 个月前

🎉 Watch our Founder Justin Banon - @BosonProtocol reveal Boson’s most ambitious roadmap to date in Boson Masterplan Part V, including plans for: ● Immediate adoption by partnering with Web3's first movers in luxury and beyond. ● Near-term adoption by extending Boson to all physical assets with Fermion Protocol (built on Boson). ● Crypto x AI - developing Boson as a foundational platform for Decentralized Agentic Commerce. ● Driving civilization-scale adoption of Boson and unlocking a Computable Economy. Timestamps: 00:00 - Intro 00:19 - Boson’s mission 02:08 - Review of Boson 2024 Roadmap 04:45 - Boson Roadmap 2025 Introduction 05:08 - Q1 Initiatives Immediate Adoption 06:50 - Luxury Initiatives 07:43 - Major luxury partnership (Project Alpha) 08:48 - Multichain deployment 09:20 - Q2 Boson dApp + widgets 2.0 09:47 - Boson Digital Product Passports 10:22 - Crypto Gaming Integrations 10:43 - Web3 Fashion campaigns 11:04 - Q3 and Q4 overview 12:20 - Extending Boson to all physical assets with Fermion Protocol 16:30 - Introducing offchain web 17:20 - High level Fermion Roadmap 18:50 - Crypto x AI “Decentralized Agentic Commerce” on Boson 22:25 - Long-term vision of Boson
25:25

🎉 Watch our Founder Justin Banon - @BosonProtocol reveal Boson’s most ambitious roadmap to date in Boson Masterplan Part V, including plans for: ● Immediate adoption by partnering with Web3's first movers in luxury and beyond. ● Near-term adoption by extending Boson to all physical assets with Fermion Protocol (built on Boson). ● Crypto x AI - developing Boson as a foundational platform for Decentralized Agentic Commerce. ● Driving civilization-scale adoption of Boson and unlocking a Computable Economy. Timestamps: 00:00 - Intro 00:19 - Boson’s mission 02:08 - Review of Boson 2024 Roadmap 04:45 - Boson Roadmap 2025 Introduction 05:08 - Q1 Initiatives Immediate Adoption 06:50 - Luxury Initiatives 07:43 - Major luxury partnership (Project Alpha) 08:48 - Multichain deployment 09:20 - Q2 Boson dApp + widgets 2.0 09:47 - Boson Digital Product Passports 10:22 - Crypto Gaming Integrations 10:43 - Web3 Fashion campaigns 11:04 - Q3 and Q4 overview 12:20 - Extending Boson to all physical assets with Fermion Protocol 16:30 - Introducing offchain web 17:20 - High level Fermion Roadmap 18:50 - Crypto x AI “Decentralized Agentic Commerce” on Boson 22:25 - Long-term vision of Boson

Boson

138,130 次观看 • 1 年前

I built an AI system that automates product video creation for entire e-commerce catalogs. (Saves $30K per collection shoot, increases conversion rates by 40% on sites) Here's how the automation works: → Firecrawl scrapes product photos from any e-commerce product page → Nano Banana create 4 images showcasing each angle of the product → Google's Veo 3.1 animates a 360° motion video using the generated nano banana images → Each animation starts and ends with the original photo for seamless looping → Videos are automatically organized and stored in Google Drive → Everything processes in batches while you handle other business priorities This system can transform how DTC companies showcase products and consistently drives higher engagement from potential customers. Static product photos just don't cut it anymore. Customers want to see what a product looks and feels like before they buy. Want the complete blueprint? Here are the steps: 1. Like & RT this post ✅ 2. Follow me for more e-commerce automation insights 3. Comment "MOTION" I'll send you the entire n8n workflow, all the prompts, and a full setup video for free. No more expensive video shoots or wondering if your product photos are converting.
4:27

I built an AI system that automates product video creation for entire e-commerce catalogs. (Saves $30K per collection shoot, increases conversion rates by 40% on sites) Here's how the automation works: → Firecrawl scrapes product photos from any e-commerce product page → Nano Banana create 4 images showcasing each angle of the product → Google's Veo 3.1 animates a 360° motion video using the generated nano banana images → Each animation starts and ends with the original photo for seamless looping → Videos are automatically organized and stored in Google Drive → Everything processes in batches while you handle other business priorities This system can transform how DTC companies showcase products and consistently drives higher engagement from potential customers. Static product photos just don't cut it anymore. Customers want to see what a product looks and feels like before they buy. Want the complete blueprint? Here are the steps: 1. Like & RT this post ✅ 2. Follow me for more e-commerce automation insights 3. Comment "MOTION" I'll send you the entire n8n workflow, all the prompts, and a full setup video for free. No more expensive video shoots or wondering if your product photos are converting.

David Roberts

170,098 次观看 • 7 个月前

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.
3:40

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

Mike Fishbein

10,137 次观看 • 1 个月前

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.
0:22

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.

ChainOpera AI

17,042 次观看 • 7 个月前

Open-sourcing the first unified LLM routing library 🔥 Meet LLMRouter 16+ routers in ONE framework. Stop reimplementing routing papers from scratch, simply run pip install llmrouter-lib And instantly deploy SOTA LLM routing tailored to your exact needs! 🔗 Get Started with LLMRouter 🚀 Code: 🌐 Project Page: 💸 Why LLMRouter? Why pay GPT-5 prices for "What's the weather?" Smart routing = Significant Savings Simple query → Cheap model Complex query → Powerful model Save 30–50% on inference costs without sacrificing quality 🧭 The 16+ Router Arsenal Switch between state-of-the-art methods with a single flag change. We cover every paradigm: ✅ Single-Round: From classic ML (KNN, SVM) to neural approaches (Graph, Contrastive). ✅ Multi-Round: RL-powered reasoning that "thinks" before routing. ✅ Agentic: Breaks down complex tasks and routes step-by-step. ✅ Personalized: Learns from user history to fit unique needs. 🛠️ Complete "Out-of-the-Box" Toolchain We didn't just ship the code; we shipped the pipeline. ✅ Unified CLI: Train, infer, and chat with any router. ✅ Interactive UI: Full Gradio interface included. ✅ Benchmarks: 11 datasets ready to use + full data generation pipeline. From LLM routing research to production in minutes. 🚀
1:08

Open-sourcing the first unified LLM routing library 🔥 Meet LLMRouter 16+ routers in ONE framework. Stop reimplementing routing papers from scratch, simply run pip install llmrouter-lib And instantly deploy SOTA LLM routing tailored to your exact needs! 🔗 Get Started with LLMRouter 🚀 Code: 🌐 Project Page: 💸 Why LLMRouter? Why pay GPT-5 prices for "What's the weather?" Smart routing = Significant Savings Simple query → Cheap model Complex query → Powerful model Save 30–50% on inference costs without sacrificing quality 🧭 The 16+ Router Arsenal Switch between state-of-the-art methods with a single flag change. We cover every paradigm: ✅ Single-Round: From classic ML (KNN, SVM) to neural approaches (Graph, Contrastive). ✅ Multi-Round: RL-powered reasoning that "thinks" before routing. ✅ Agentic: Breaks down complex tasks and routes step-by-step. ✅ Personalized: Learns from user history to fit unique needs. 🛠️ Complete "Out-of-the-Box" Toolchain We didn't just ship the code; we shipped the pipeline. ✅ Unified CLI: Train, infer, and chat with any router. ✅ Interactive UI: Full Gradio interface included. ✅ Benchmarks: 11 datasets ready to use + full data generation pipeline. From LLM routing research to production in minutes. 🚀

Jiaxuan You

75,244 次观看 • 7 个月前