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Nothing can escape Agent Shankar’s vision. Be careful with the package Agents. Happy Unboxing! #666OperationDreamTheatre Teaser 2 Days To Go | 1 PM DrShivaRajkumar Dhananjaya Priyanka Mohan Aditi Balan Advaitha Gurumurthy 𝐓𝐡𝐞 𝐁𝐢𝐠 𝐋𝐢𝐭𝐭𝐥𝐞 #666ODTteaser

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Dhananjaya

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13,266 Aufrufe • vor 1 Monat •via X (Twitter)

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666 AGENTS ASSEMBLE Agent Prakash, has a secret hamper for you just before the teaser on July 3rd. Happy Unboxing. #666OperationDreamTheatre #666ODTteaser Dhananjaya Hemanth M Rao Priyanka Mohan Aditi Balan Advaitha Gurumurthy 𝐓𝐡𝐞 𝐁𝐢𝐠 𝐋𝐢𝐭𝐭𝐥𝐞

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Hemanth M Rao

117,086 Aufrufe • vor 1 Monat

You can now create a REAL-TIME chatbot with any voice and customize its personality. Steps: 1. Go to ElevenLabs 2. Click on voices 3. you can type in a name for the voice 4. then you can drag in an audio file to use for your voice 5. then go to the agents tab in side panel 6. and create a new agent 7. the voice 8. Create system instructions 9. Optional (Upload knowledge docs) 10. Test This can be easily implemented into your applications. I'll make a tutorial on that soon.
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You can now create a REAL-TIME chatbot with any voice and customize its personality. Steps: 1. Go to ElevenLabs 2. Click on voices 3. you can type in a name for the voice 4. then you can drag in an audio file to use for your voice 5. then go to the agents tab in side panel 6. and create a new agent 7. the voice 8. Create system instructions 9. Optional (Upload knowledge docs) 10. Test This can be easily implemented into your applications. I'll make a tutorial on that soon.

Riley Brown

40,108 Aufrufe • vor 1 Jahr

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!
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We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 Aufrufe • vor 1 Jahr

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
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Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Guohao Li 🐫

223,252 Aufrufe • vor 1 Jahr

I know a real estate agent that got 2 million views on Instagram posting AI-generated property videos. $0 spent on videographers. $0 spent on editors. Here are 4 AI video formats agents are using to go viral on social media: 1. Room renovation animations (before-to-after transformations that build themselves) 2. Listing photo walkthroughs — cinematic videos with music, voiceover, and captions 3. Day-to-night property transitions 4. Home construction timelapses (new builds assembling from the ground up) Most agents are still posting the same 10 listing photos. You need to build video hooks that actually stop the scroll of your buyers/sellers. I put together a free PDF with the exact prompts for all 4 along with full tutorial videos. Comment "CALICO" and I'll send you everything (must be following so I can dm you!).
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I know a real estate agent that got 2 million views on Instagram posting AI-generated property videos. $0 spent on videographers. $0 spent on editors. Here are 4 AI video formats agents are using to go viral on social media: 1. Room renovation animations (before-to-after transformations that build themselves) 2. Listing photo walkthroughs — cinematic videos with music, voiceover, and captions 3. Day-to-night property transitions 4. Home construction timelapses (new builds assembling from the ground up) Most agents are still posting the same 10 listing photos. You need to build video hooks that actually stop the scroll of your buyers/sellers. I put together a free PDF with the exact prompts for all 4 along with full tutorial videos. Comment "CALICO" and I'll send you everything (must be following so I can dm you!).

David Roberts

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🚨BREAKING (Minneapolis): ICE/Border Patrol agents just took a toddler. Around 1:00 PM today, agents smashed a car window, in what appears to be a residential driveway, and detained a father and his young child, on Chicago & 35th St. The child is believed to be 2-year-old Chloe Renata. As of now, it is said the mother has no information on where they were taken and is actively searching for answers. And this isn’t random. Over the past few days in Minneapolis, federal operations have been targeting families, children, and areas around schools… directly contradicting DHS claims that they’re only going after the “worst of the worst.” Please share this, and related posts, so we can get answers before DHS tries to control the narrative with lies.
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🚨BREAKING (Minneapolis): ICE/Border Patrol agents just took a toddler. Around 1:00 PM today, agents smashed a car window, in what appears to be a residential driveway, and detained a father and his young child, on Chicago & 35th St. The child is believed to be 2-year-old Chloe Renata. As of now, it is said the mother has no information on where they were taken and is actively searching for answers. And this isn’t random. Over the past few days in Minneapolis, federal operations have been targeting families, children, and areas around schools… directly contradicting DHS claims that they’re only going after the “worst of the worst.” Please share this, and related posts, so we can get answers before DHS tries to control the narrative with lies.

Jesus Freakin Congress

214,814 Aufrufe • vor 6 Monaten

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Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.

Santiago

73,062 Aufrufe • vor 9 Monaten

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Today I'm excited to share Sigilum! This is Payman's solution for Auditable Identity for AI Agents. (think One Password-ish but for AI Agents) I recorded a quick walkthrough showing how it all works (video below). This answers three pains we've seen within Financial Services (Banking) AI Agents we've built and OpenClaw🦞 AI Agents we deploy. Security, Auditability, and Control. 1. Security Making sure keys are secure and not just freely given to an AI Agent is a big deal. When working with money, you can't just expose these or skip putting controls in place. Sigilum provides a local gateway that prevents access to keys by the AI Agent without explicit authorization from a person. We provide namespaces through the service so you always know who authorized what key, for what service, to which agent. 2. Auditability If I could hit on the importance of this 100 times I would. It comes up in every financial services conversation. Sigilum provides you with the answer to "Who authorized this AI Agent to act on my behalf?" Audit logs trace back to the person, the service, and the AI Agent. With more audit logs being built through our managed service, this will be the key source for determining how an AI Agent is behaving on your behalf. This is needed for agents from OpenClaw, and especially for banking/money movement. 3. Control Revoke keys, limit access, grant authorization. All seemingly simple things, but complex to implement and make elegant. These controls dictate what the AI Agent can or cannot do. Sigilum allows you to do all of this through the managed Dashboard. We've made Sigilum open source and encourage others to contribute and keep building on the gateway. It's been a source of a lot of visibility and productization of AI Agents for us. We'll keep contributing and adding to it. Link in comments. If you want to try it out, we do have a managed service that makes it easy to spin up. Go to to sign up. Note: even though we've been pushing 100+ commits a day to get this out to folks, there are still some noticeable areas for improvement we're working on, which should get resolved soon (by us or you!): - Deeper audit trails - More providers (currently supports all OpenClaw providers) - Deeper scanning of existing keys your agent is hiding from you (we'll find them) - OpenClaw gateway persistence - Auto-purging keys - And more... If you want to contribute or have feedback, please DM or go to the GH. Happy building!

tyllen

18,497 Aufrufe • vor 5 Monaten

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)
42:49

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

31,931 Aufrufe • vor 22 Tagen

This is how you can start automating your entire life: (No need to install or configure anything. You don't have to mess with OpenClaw.) 1. Go to SureThing 2. Connect a few apps (1000+ available) 3. Start building automations by just asking I recorded a few examples in the video. Surething is an autonomous agent with a chat interface. There's absolutely nothing to configure. It literally takes 5 seconds to be up and running. You start connecting with different apps. For example, your email, Slack, your calendar, Discord, Zoom, whatever. It has memory. It updates it as you interact with it and as it consumes more information about you. You can also build and schedule automations by just asking. No code necessary. The system does it all automatically. This is pretty incredible! Thanks to the team for partnering with me on this post.
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This is how you can start automating your entire life: (No need to install or configure anything. You don't have to mess with OpenClaw.) 1. Go to SureThing 2. Connect a few apps (1000+ available) 3. Start building automations by just asking I recorded a few examples in the video. Surething is an autonomous agent with a chat interface. There's absolutely nothing to configure. It literally takes 5 seconds to be up and running. You start connecting with different apps. For example, your email, Slack, your calendar, Discord, Zoom, whatever. It has memory. It updates it as you interact with it and as it consumes more information about you. You can also build and schedule automations by just asking. No code necessary. The system does it all automatically. This is pretty incredible! Thanks to the team for partnering with me on this post.

Santiago

107,878 Aufrufe • vor 5 Monaten

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.
0:40

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.

INFINIT

176,699 Aufrufe • vor 9 Monaten

Loved this 22-minute talk on continual learning for AI agents. Must watch for anyone looking to get agents performant and into production. Credit: Soheil Feizi at AI Engineer • Agent learning can happen at three layers: the model (weights), the harness (prompts, tools, skills, code, workflows), and memory (session or persistent). • Two fundamental challenges: (1) getting feedback, meaning how do we know if the agent did well and what it should have done instead, and (2) acting on that feedback, meaning deciding which layer or component to change and how. • Feedback sources differ by stage: In development you have benchmarks with evaluators that score pass/fail. In production you only have logs, which can be judged either automatically (LLMs or code analyzing the log, which is scalable) or by human experts (low volume but critical domain knowledge). • Logs plus feedback aren't enough because they're not testable: A single log with feedback is one observation of what happened. You need to lift it into a replayable learning environment, a simulation with tools, users, and defined evaluators, so candidate fixes can be run, verified, and compared. • Three ways to optimize the agent, with tradeoffs: Model-layer updates (SFT, RL post-training like DPO/GRPO, LoRA) are expensive and need benchmarks and evaluators. Harness updates (trace-to-harness coding agents, prompt search like GEPA) are flexible but either untestable and "vibe-based" or benchmark-dependent. Memory updates (fact storage like Letta/Mem0, skill distillation) are cheapest and fastest but usually unverified. • A good learning engine makes "the smallest durable change at the right layer" of the agent. • Verifiable continual learning (VCL): Improve an agent from its own experience where every fix is proven to help and proven to break nothing that already worked. It requires an executable test (replayable failure), a measured delta (score before and after), and regression tests (prior tests still pass). • Four principles of practical VCL: Replayability (turn one-off failures into rerunnable tests), holisticness (one failure can have causes in memory, prompts, tools, workflow, or model, so route the fix to the right layer), lifelongness (fix new failures subject to no regression on past environments, with regression handled inside the optimization loop rather than post-hoc), and efficiency (the loop must run frequently and cheaply, without scaling linearly as past environments accumulate). • Three takeaways: (1) Agent continual learning isn't necessarily fine-tuning; many useful updates live in the harness and memory layers. (2) Production logs are not learning environments and must be transformed into replayable ones. (3) The frontier is regression-aware improvement: fixing new failures while verifying you don't break old ones.
22:34

Loved this 22-minute talk on continual learning for AI agents. Must watch for anyone looking to get agents performant and into production. Credit: Soheil Feizi at AI Engineer • Agent learning can happen at three layers: the model (weights), the harness (prompts, tools, skills, code, workflows), and memory (session or persistent). • Two fundamental challenges: (1) getting feedback, meaning how do we know if the agent did well and what it should have done instead, and (2) acting on that feedback, meaning deciding which layer or component to change and how. • Feedback sources differ by stage: In development you have benchmarks with evaluators that score pass/fail. In production you only have logs, which can be judged either automatically (LLMs or code analyzing the log, which is scalable) or by human experts (low volume but critical domain knowledge). • Logs plus feedback aren't enough because they're not testable: A single log with feedback is one observation of what happened. You need to lift it into a replayable learning environment, a simulation with tools, users, and defined evaluators, so candidate fixes can be run, verified, and compared. • Three ways to optimize the agent, with tradeoffs: Model-layer updates (SFT, RL post-training like DPO/GRPO, LoRA) are expensive and need benchmarks and evaluators. Harness updates (trace-to-harness coding agents, prompt search like GEPA) are flexible but either untestable and "vibe-based" or benchmark-dependent. Memory updates (fact storage like Letta/Mem0, skill distillation) are cheapest and fastest but usually unverified. • A good learning engine makes "the smallest durable change at the right layer" of the agent. • Verifiable continual learning (VCL): Improve an agent from its own experience where every fix is proven to help and proven to break nothing that already worked. It requires an executable test (replayable failure), a measured delta (score before and after), and regression tests (prior tests still pass). • Four principles of practical VCL: Replayability (turn one-off failures into rerunnable tests), holisticness (one failure can have causes in memory, prompts, tools, workflow, or model, so route the fix to the right layer), lifelongness (fix new failures subject to no regression on past environments, with regression handled inside the optimization loop rather than post-hoc), and efficiency (the loop must run frequently and cheaply, without scaling linearly as past environments accumulate). • Three takeaways: (1) Agent continual learning isn't necessarily fine-tuning; many useful updates live in the harness and memory layers. (2) Production logs are not learning environments and must be transformed into replayable ones. (3) The frontier is regression-aware improvement: fixing new failures while verifying you don't break old ones.

Alex Lieberman

20,085 Aufrufe • vor 1 Monat

OpenClaw - the agentic software spreading like wildfire - was built on top of Pi, a minimalist, self-modifying agent. I sat down with Pi's creator, Mario Zechner and longtime Pi user (+ the creator of Flask) Armin Ronacher ⇌ to talk Pi, and their (very grounded!) takes on building with AI. Timestamps: 00:00 Intro 07:30 How Mario, Armin, and Peter Steinberger met 15:15 How 30 dev teams use AI agents: learnings 21:50 The importance of judgment 24:26 Challenges when non-engineers write code 28:30 Downsides of over-automation 32:18 Pi 48:09 OpenClaw + Pi 50:54 “Clankers” 57:32 Open source and AI 1:00:22 Complexity as the enemy 1:02:50 Building an AI-native startup 1:11:52 “Slow the F down” 1:16:40 MCPs vs. CLI 1:25:03 Predictions and staying up to date • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar — The makers of SonarQube, the industry standard for code verification and automated code review. Try it out for yourself. • WorkOS – WorkOS gives you APIs to ship enterprise features – SSO, directory sync, RBAC, audit logs – in days, not months. Visit learn more. --- Three parts I found especially interesting in this discussion: 1. New trend: AI makes it harder for senior engineers to reject pointless complexity. Historically, senior engineers kept software complexity at bay simply by saying “no” a lot. But Armin observes that these days, more junior engineers and product managers deploy agent-scripted counterarguments when a senior colleague kicks an idea to the curb. This makes decision-making exhausting, and more bad ideas make it into production as a result. 2. It should be MUCH easier to build specialized tools for specific tasks. Different projects need different harness types because, as Mario points out, the same hammer is not ideal for every single construction job. As such, Pi is built with the goal of allowing the creation of specialized harnesses. It can modify itself so that a user can create the bespoke harness needed for any task. Mario believes it’s a preview of how self-modifiable software might look in the future. 3. Automation bias is one of the biggest risks of working with AI agents. Once devs confirm that an AI agent can produce acceptable code, they start to review its output less often, even though agents can – and do! – produce slop. Mario advises being far more sceptical with agents, and cautions that the quality of their output isn’t guaranteed, however well they performed previously.
1:33:55

OpenClaw - the agentic software spreading like wildfire - was built on top of Pi, a minimalist, self-modifying agent. I sat down with Pi's creator, Mario Zechner and longtime Pi user (+ the creator of Flask) Armin Ronacher ⇌ to talk Pi, and their (very grounded!) takes on building with AI. Timestamps: 00:00 Intro 07:30 How Mario, Armin, and Peter Steinberger met 15:15 How 30 dev teams use AI agents: learnings 21:50 The importance of judgment 24:26 Challenges when non-engineers write code 28:30 Downsides of over-automation 32:18 Pi 48:09 OpenClaw + Pi 50:54 “Clankers” 57:32 Open source and AI 1:00:22 Complexity as the enemy 1:02:50 Building an AI-native startup 1:11:52 “Slow the F down” 1:16:40 MCPs vs. CLI 1:25:03 Predictions and staying up to date • YouTube: • Spotify: • Apple: Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar — The makers of SonarQube, the industry standard for code verification and automated code review. Try it out for yourself. • WorkOS – WorkOS gives you APIs to ship enterprise features – SSO, directory sync, RBAC, audit logs – in days, not months. Visit learn more. --- Three parts I found especially interesting in this discussion: 1. New trend: AI makes it harder for senior engineers to reject pointless complexity. Historically, senior engineers kept software complexity at bay simply by saying “no” a lot. But Armin observes that these days, more junior engineers and product managers deploy agent-scripted counterarguments when a senior colleague kicks an idea to the curb. This makes decision-making exhausting, and more bad ideas make it into production as a result. 2. It should be MUCH easier to build specialized tools for specific tasks. Different projects need different harness types because, as Mario points out, the same hammer is not ideal for every single construction job. As such, Pi is built with the goal of allowing the creation of specialized harnesses. It can modify itself so that a user can create the bespoke harness needed for any task. Mario believes it’s a preview of how self-modifiable software might look in the future. 3. Automation bias is one of the biggest risks of working with AI agents. Once devs confirm that an AI agent can produce acceptable code, they start to review its output less often, even though agents can – and do! – produce slop. Mario advises being far more sceptical with agents, and cautions that the quality of their output isn’t guaranteed, however well they performed previously.

Gergely Orosz

172,822 Aufrufe • vor 3 Monaten

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.
0:38

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.

Santiago

34,977 Aufrufe • vor 1 Jahr

The BEST prompt for AI agents I've heard over the last 12 months is ONLY 3 words, and it comes from someone who managed multi-billion dollar P&Ls in AI, led a 100 person org at AWS, and now runs a brilliant AI workforce of 34 agents: 1. Her best prompt is just "do smart things" She gives the agents all her context first, her calendar, email, Stripe, goals, and transcripts, then lets them decide what's worth doing. She realized every task still started as a thought in her own head, which meant the company could only ever be as good as what she remembered to ask for. 2. Her human team talks to her agents in Slack. She has a channel where a teammate can ask "did that financial services client reply to Ali's email," and the agents answer directly, so people stop waiting hours for her to get back to them. 3. Since an AI agent costs almost nothing, she hired the person she'd never put on payroll. One does nothing but ask how to make everything ten times better. Another just watches the other agents work and flags where they get stuck. 4. She avoids old job titles, because the second you call one your CMO, you've rebuilt a 2015 company with robots. Most of her sub-agents run on cheaper, smaller models and do the job fine. 5. Run an AI watchdog instead of a dashboard. Point one at your Slack to catch two people doing the same work, at your calendar to flag conflicts, at your analytics to tell you what to post tomorrow. Almost nobody does this yet. 6. She keeps a daily "brain dump" that feeds the agents. At the end of each day she dictates what's in her head that isn't written down anywhere, the stuff that only lived in a meeting or a Slack thread, and it goes into a wiki the agents read. That's how they get the context that email and calendar miss. 7. She uses a "last 30 days" research skill to spin up on anything fast. Before running a workshop for 200 execs in an industry she doesn't know, she fans out agents to scan and synthesize the last month of news, then walks in sounding like she's followed it for years. That's my convo with Allie K. Miller on The Startup Ideas Podcast (SIP) 🧃 . Pretty much her biggest tips for using AI agents. Full episode below. Happy building, I'm rooting for you.
48:27

The BEST prompt for AI agents I've heard over the last 12 months is ONLY 3 words, and it comes from someone who managed multi-billion dollar P&Ls in AI, led a 100 person org at AWS, and now runs a brilliant AI workforce of 34 agents: 1. Her best prompt is just "do smart things" She gives the agents all her context first, her calendar, email, Stripe, goals, and transcripts, then lets them decide what's worth doing. She realized every task still started as a thought in her own head, which meant the company could only ever be as good as what she remembered to ask for. 2. Her human team talks to her agents in Slack. She has a channel where a teammate can ask "did that financial services client reply to Ali's email," and the agents answer directly, so people stop waiting hours for her to get back to them. 3. Since an AI agent costs almost nothing, she hired the person she'd never put on payroll. One does nothing but ask how to make everything ten times better. Another just watches the other agents work and flags where they get stuck. 4. She avoids old job titles, because the second you call one your CMO, you've rebuilt a 2015 company with robots. Most of her sub-agents run on cheaper, smaller models and do the job fine. 5. Run an AI watchdog instead of a dashboard. Point one at your Slack to catch two people doing the same work, at your calendar to flag conflicts, at your analytics to tell you what to post tomorrow. Almost nobody does this yet. 6. She keeps a daily "brain dump" that feeds the agents. At the end of each day she dictates what's in her head that isn't written down anywhere, the stuff that only lived in a meeting or a Slack thread, and it goes into a wiki the agents read. That's how they get the context that email and calendar miss. 7. She uses a "last 30 days" research skill to spin up on anything fast. Before running a workshop for 200 execs in an industry she doesn't know, she fans out agents to scan and synthesize the last month of news, then walks in sounding like she's followed it for years. That's my convo with Allie K. Miller on The Startup Ideas Podcast (SIP) 🧃 . Pretty much her biggest tips for using AI agents. Full episode below. Happy building, I'm rooting for you.

GREG ISENBERG

169,811 Aufrufe • vor 6 Tagen

HOW TO MAKE $50K/MONTH SELLING MANAGED AI AGENTS (FULL COURSE) The model: sell managed AI agents to businesses for $5K/month each. You handle the infrastructure, they get an employee that never sleeps. 10 clients puts you at $50K MRR with 85%+ margins, run entirely by you and a fleet of agents. Nick Vasilescu is doing exactly this, and he came on the pod to walk through the whole playbook. Here's what I learned: 1. The arbitrage is that nobody knows this is possible. 99% of business owners are still asking ChatGPT what the weather is. One working agent hooks them on the spot. 2. Sell abundance. Unlimited agents, unlimited infrastructure. They don't care what an MCP is, they care that their problem is gone. 3. Don't niche too early. Say yes to everyone and let the market pull you. You find the niche by doing reps, not guessing. 4. Paid audit into managed service. Charge $1K to map every automation opportunity, then credit it toward month one. Qualifies the lead, makes the upsell a no-brainer. 5. First call, don't sell. Record it, map the workflow tip to tail, find the automation with the most value and least effort. Start there. 6. The stack is Hermes + Composio + Orgo. Composio connects all their apps in one click. Orgo spins up a working Hermes agent in 26 seconds. 7. Productize with a golden snapshot. Build one perfect agent, clone it, and every copy comes over one for one with auth intact. 8. Turn client call transcripts into skills in 10 minutes. Feed the recording to Claude Code, write the skill, port it to the client's agent via Orgo MCP. 9. Watchdogs make you look elite. Get alerted before the client notices anything broke. "Already fixed it" is why they keep paying you. 10. You become their guy. You drive more outcomes than their own employees, they credit every win to you, and churn drops to almost nothing. His 2 key takeaways: 1. Bet on cost going to zero. They launched unlimited tokens when it was barely profitable because they knew they'd capture the spread. Build for where the puck is going. 2. One client every six weeks gets you to $600K a year. The model isn't hard, it's just unevenly executed. That's the entire opportunity. Nick is crushing this model and we had a blast diving deep on how you can do the same. Go follow Nick Vasilescu Full video below. (Also available on the Build With AI podcast wherever you get your pods)
47:49

HOW TO MAKE $50K/MONTH SELLING MANAGED AI AGENTS (FULL COURSE) The model: sell managed AI agents to businesses for $5K/month each. You handle the infrastructure, they get an employee that never sleeps. 10 clients puts you at $50K MRR with 85%+ margins, run entirely by you and a fleet of agents. Nick Vasilescu is doing exactly this, and he came on the pod to walk through the whole playbook. Here's what I learned: 1. The arbitrage is that nobody knows this is possible. 99% of business owners are still asking ChatGPT what the weather is. One working agent hooks them on the spot. 2. Sell abundance. Unlimited agents, unlimited infrastructure. They don't care what an MCP is, they care that their problem is gone. 3. Don't niche too early. Say yes to everyone and let the market pull you. You find the niche by doing reps, not guessing. 4. Paid audit into managed service. Charge $1K to map every automation opportunity, then credit it toward month one. Qualifies the lead, makes the upsell a no-brainer. 5. First call, don't sell. Record it, map the workflow tip to tail, find the automation with the most value and least effort. Start there. 6. The stack is Hermes + Composio + Orgo. Composio connects all their apps in one click. Orgo spins up a working Hermes agent in 26 seconds. 7. Productize with a golden snapshot. Build one perfect agent, clone it, and every copy comes over one for one with auth intact. 8. Turn client call transcripts into skills in 10 minutes. Feed the recording to Claude Code, write the skill, port it to the client's agent via Orgo MCP. 9. Watchdogs make you look elite. Get alerted before the client notices anything broke. "Already fixed it" is why they keep paying you. 10. You become their guy. You drive more outcomes than their own employees, they credit every win to you, and churn drops to almost nothing. His 2 key takeaways: 1. Bet on cost going to zero. They launched unlimited tokens when it was barely profitable because they knew they'd capture the spread. Build for where the puck is going. 2. One client every six weeks gets you to $600K a year. The model isn't hard, it's just unevenly executed. That's the entire opportunity. Nick is crushing this model and we had a blast diving deep on how you can do the same. Go follow Nick Vasilescu Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

85,412 Aufrufe • vor 1 Monat

🏆 STEPN GO Alpha Draw 🏆 Ready to win your first #STEPNGO Sneakers and join the Alpha Testing? The Alpha Draw is your gateway to early access in STEPN GO 🚀 Over 30 days, lock your pGMT on Polygon and participate in daily raffles to win STEPN GO Sneakers! 👟 Please note that for now, the Alpha Draw is available for Android devices only. How it works: 📆 Start: June 19, 1 AM UTC ⌛️ Duration: 30 days 🥊 Daily Raffles at 1 AM, 9 AM, 5 PM UTC ⏱️ 12 Rounds per Raffle 🎉 Extra Raffles at 2:05 AM, 10:05 AM, 6:05 PM UTC 🎫 Lock 5 pGMT to get 1 ticket + 1 Extra Round Ticket For each ticket used, you will receive a card. Scratch it, and you may win a set of 3 STEPN GO Sneakers! 🏆 Prizes also include three different collections of stickers. Gather the entire collection, and who knows, you may win a secret prize later 👀 How to participate? 1️⃣ Download STEPN GO on the Play Store on June 18, 10 AM UTC 2️⃣ Log in with your FSL ID 3️⃣ Enter the daily raffles by locking your pGMT 4️⃣ After each raffle, scratch your card and reveal your prize! And there’s more… we’ll have some exclusive perks for our STEPN users who participate in the Alpha Draw 🎉 Notes: 🔹 Your GMT will be locked until April 19, 2028. 🔹 After the locking period, you can withdraw all your GMT, whether you won a prize or not. 🔹 Soul-bound Sneakers and Shoe boxes cannot be traded. For more details and tutorial guides, visit our website: Ready? STEPN… GO!
0:51

🏆 STEPN GO Alpha Draw 🏆 Ready to win your first #STEPNGO Sneakers and join the Alpha Testing? The Alpha Draw is your gateway to early access in STEPN GO 🚀 Over 30 days, lock your pGMT on Polygon and participate in daily raffles to win STEPN GO Sneakers! 👟 Please note that for now, the Alpha Draw is available for Android devices only. How it works: 📆 Start: June 19, 1 AM UTC ⌛️ Duration: 30 days 🥊 Daily Raffles at 1 AM, 9 AM, 5 PM UTC ⏱️ 12 Rounds per Raffle 🎉 Extra Raffles at 2:05 AM, 10:05 AM, 6:05 PM UTC 🎫 Lock 5 pGMT to get 1 ticket + 1 Extra Round Ticket For each ticket used, you will receive a card. Scratch it, and you may win a set of 3 STEPN GO Sneakers! 🏆 Prizes also include three different collections of stickers. Gather the entire collection, and who knows, you may win a secret prize later 👀 How to participate? 1️⃣ Download STEPN GO on the Play Store on June 18, 10 AM UTC 2️⃣ Log in with your FSL ID 3️⃣ Enter the daily raffles by locking your pGMT 4️⃣ After each raffle, scratch your card and reveal your prize! And there’s more… we’ll have some exclusive perks for our STEPN users who participate in the Alpha Draw 🎉 Notes: 🔹 Your GMT will be locked until April 19, 2028. 🔹 After the locking period, you can withdraw all your GMT, whether you won a prize or not. 🔹 Soul-bound Sneakers and Shoe boxes cannot be traded. For more details and tutorial guides, visit our website: Ready? STEPN… GO!

STEPN GO

283,449 Aufrufe • vor 2 Jahren

‼️🚨🔵PRESSER 🔵🚨‼️ 🎙 Enzo Maresca’s Full Pre-Cardiff City Press Conference – Part 1/2 🔹 Enzo Maresca on Saturday’s comments: “I already spoke about that and I don’t have nothing to add. It’s Cardiff tomorrow, please. I already spoke about that and I think I was quite clear. No more than that. I respect your opinion and people’s opinion. I have nothing to add. My focus is on tomorrow’s game 🎯 and that we can achieve the third semi-final in my time at the club. We are in an era where everyone can say what they think. I respect people’s opinions, your opinion, but I have nothing to add. My focus is on tomorrow. I already answered the question and my focus is on tomorrow. I don’t have nothing to add.” ⚽⏭️ 🔹 Enzo Maresca on if he is happy with people saying he has a bad relationship with owners/sporting directors: “I said after the game that I love Chelsea supporters ❤️ — they deserve the best. Again, I don’t have nothing to add.” 🔵 🔹 Enzo Maresca on if he is committed to the Chelsea job: “Absolutely, yes.” ✅💙 🔹 Enzo Maresca on Cardiff clash: “He is doing a great job 👏🏽. They are top of the league, playing nice football and winning games. We need to pay attention and be careful ⚠️. We can achieve the third semi-final in 18 months.” 🏆📈 🔹 Enzo Maresca on if Palmer will be rested: “I already said many times, Cole is one of the players that deserves to be protected 🛡️. He is not available to play two games in three days.” ⏳⚠️ 🔹 Enzo Maresca on why he said what he did: “I can speak Italian 🇮🇹, my language, Spanish 🇪🇸 very well, French 🇫🇷, English 🇬🇧 — I think I was clear with what I said. It’s done, it’s finished. It was after the game, I said what I said, and it’s done. It’s finished.” ✔️ 🔹 Enzo Maresca on if he is enjoying the season: “Absolutely, yes 😄. It’s fantastic. No matter if we win or lose, it is a fantastic opportunity because you can always learn 📚 and I am very happy with that.” 💙 #CFC | #Chelsea | #CarabaoCup | #Interviews 📲 CFC_ChelseaFC via Telegram 📹MightyBluesNews via YouTube
9:47

‼️🚨🔵PRESSER 🔵🚨‼️ 🎙 Enzo Maresca’s Full Pre-Cardiff City Press Conference – Part 1/2 🔹 Enzo Maresca on Saturday’s comments: “I already spoke about that and I don’t have nothing to add. It’s Cardiff tomorrow, please. I already spoke about that and I think I was quite clear. No more than that. I respect your opinion and people’s opinion. I have nothing to add. My focus is on tomorrow’s game 🎯 and that we can achieve the third semi-final in my time at the club. We are in an era where everyone can say what they think. I respect people’s opinions, your opinion, but I have nothing to add. My focus is on tomorrow. I already answered the question and my focus is on tomorrow. I don’t have nothing to add.” ⚽⏭️ 🔹 Enzo Maresca on if he is happy with people saying he has a bad relationship with owners/sporting directors: “I said after the game that I love Chelsea supporters ❤️ — they deserve the best. Again, I don’t have nothing to add.” 🔵 🔹 Enzo Maresca on if he is committed to the Chelsea job: “Absolutely, yes.” ✅💙 🔹 Enzo Maresca on Cardiff clash: “He is doing a great job 👏🏽. They are top of the league, playing nice football and winning games. We need to pay attention and be careful ⚠️. We can achieve the third semi-final in 18 months.” 🏆📈 🔹 Enzo Maresca on if Palmer will be rested: “I already said many times, Cole is one of the players that deserves to be protected 🛡️. He is not available to play two games in three days.” ⏳⚠️ 🔹 Enzo Maresca on why he said what he did: “I can speak Italian 🇮🇹, my language, Spanish 🇪🇸 very well, French 🇫🇷, English 🇬🇧 — I think I was clear with what I said. It’s done, it’s finished. It was after the game, I said what I said, and it’s done. It’s finished.” ✔️ 🔹 Enzo Maresca on if he is enjoying the season: “Absolutely, yes 😄. It’s fantastic. No matter if we win or lose, it is a fantastic opportunity because you can always learn 📚 and I am very happy with that.” 💙 #CFC | #Chelsea | #CarabaoCup | #Interviews 📲 CFC_ChelseaFC via Telegram 📹MightyBluesNews via YouTube

Miki Djan

23,609 Aufrufe • vor 8 Monaten

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.
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RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 Aufrufe • vor 3 Monaten