Building AI So Everyone Can Flourish
The future of AI requires mechanism design: shaping rules and affordances to build an ecosystem, not just a product. Human context and protocols are key to guiding LLMs.
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System Architecture
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"My name is... my name is...": A Linguistic Map for Voice Agents — Midam Kim, ServiceNow Voice AI failures are not random bugs, but structural issues. Kim introduces a linguistic framework mapping communication to two channels (listening/speaking) and four interdependent levels: sounds, words, interaction, and mental model.Build context
If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread Designing AI agents for knowledge work requires mimicking human orchestration. Agents must break down complex, ambiguous problems using specialized sub-agents and multiple search primitives to overcome performance ceilings. Where RL Will Take Search — Maximilian-David Rumpf, SID.ai RL is revolutionizing search by creating specialized sub-agents. These agents can adaptively search, leading to results that are 20 times faster and 100 times cheaper than using frontier models. Vertical Mobility: Inference from MVP to Trillion-Parameter Workloads — Sitanshu Gupta, CoreWeave The inference platform supports two models: serverless (pay-per-token) and dedicated (billed per GPU hour). Optimization relies heavily on KV cache awareness and techniques like 4-bit quantization.Recent signals
Building AI So Everyone Can Flourish The future of AI requires mechanism design: shaping rules and affordances to build an ecosystem, not just a product. Human context and protocols are key to guiding LLMs. Workshop: Training Agents at the frontier of AI Industry The future of agentic AI requires moving beyond model weights to optimizing the entire system. Speakers discuss advanced RL techniques, modular architecture, and the critical role of vetted environments in achieving reliable, scalable outcomes. 1,368 AI Failures and What They Teach About Evals — Vishal Srivastava & Tanmay Sah, New York Life AI failures require a holistic, risk-based evaluation framework. Assess systems across Technical Strength, Deployment Controls, and Business Context using a 'build-break-fix' loop to ensure safety.
Workshop: Training Agents at the frontier of AI Industry
The future of agentic AI requires moving beyond model weights to optimizing the entire system. Speakers discuss advanced RL techniques, modular architecture, and the critical role of vetted environments in achieving reliable, scalable outcomes.
Read article
1,368 AI Failures and What They Teach About Evals — Vishal Srivastava & Tanmay Sah, New York Life
AI failures require a holistic, risk-based evaluation framework. Assess systems across Technical Strength, Deployment Controls, and Business Context using a 'build-break-fix' loop to ensure safety.
Read article
My Jev Pi Durable Agent CURATES News: HYPE, FACT, or SIGNAL
The video details building a Durable News Agent to filter AI noise. By moving from 'in the loop' to 'out the loop' using proactive agents, engineers can build autonomous systems that gather, filter, and curate high-signal information without constant supervision.
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AI News & Strategy Daily | Nate B Jones
What Really Happens When AI Agents Meet in the Wild?
Self-replicating AI agent swarms are here. The internet is changing, requiring builders to adopt 'agent-aware' security and build robust control systems to manage autonomous compute.
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Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
Anthropic introduces Flows in Claude Managed Agents, a multi-agent orchestration engine that allows a single goal to be executed by up to 1,000 collaborating agents. This solves the scaling problem of single agents and enables complex, verifiable AI workflows.
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Bridging the Gap Between AI Builders and Domain Experts
The future of AI agents lies in specialization. We must move from 'god models' to thousands of hyper-scoped, domain-specific agents, requiring standardized runtimes and a microservices mindset.
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How MasterClass Builds Multi-Agent AI That Learners Can Trust
MasterClass details its multi-agent AI system, NCX, and the advanced observability stack built on CoreWeave. Learn how tracing, MCP servers, and an AgentOps loop enable self-improvement in complex AI applications.
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AI Tokenomics Explained
The economics of AI inference are dominated by agentic workflows, which use significantly more tokens than basic chat. Scaling requires co-design across model, system, and software layers to minimize cost per token.
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Why 99% Accurate Browser Agents Still Fail — Derek Meegan, Browserbase
A browser agent that's 99% accurate per step succeeds only about 36% of the time over 100 steps. Here's how to beat that math.
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Build Your Own Open-Source Dots with Pi and Telegram
Learn to build open-source, always-on AI agents using Pi and pi-gateway. This guide covers agent workspaces, Google Workspace skills, and setting up a Telegram message gateway for persistent, multi-platform AI assistants.
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AI News & Strategy Daily | Nate B Jones
The AI Bottleneck: Why Your Team Isn't Shipping. Here's the Fix.
Scaling AI productivity requires fixing the system, not the engineer. Learn six principles—from making agents 'multiplayer' to removing obsolete processes—to build a 'faster factory' for AI-assisted development.
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AI in Healthcare Series: Have We Already Bent the Healthcare Cost Curve?
AI is poised to fundamentally reshape healthcare, moving the industry from a scarcity-based model of expertise to one of abundance. This shift demands that institutions re-architect their systems to leverage agentic intelligence and avoid legacy constraints.
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How AI Agents, LLMs & APIs Use Real-Time Data at the US Open
Learn how AI Agents combine specialized APIs and LLMs to analyze complex, real-time data, using the US Open tennis serve as a case study. This pattern divides labor between computation and reasoning.
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Meta's VR Codebase Nobody Wanted to Touch — Until This
Learn how to modernize legacy codebases by treating them like functioning cities, not 'balls of mud.' Discover how to decouple AI workflows from single providers using specialized servers and parallel agent processing.
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When Should AI Systems Use Super Agents?
Super agents offer centralized intelligence but carry risks of privilege abuse and expanded attack surfaces. Secure design requires agent swarms, least agency, isolation of tools, and observability.
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Vertical Mobility: Inference from MVP to Trillion-Parameter Workloads — Sitanshu Gupta, CoreWeave
The inference platform supports two models: serverless (pay-per-token) and dedicated (billed per GPU hour). Optimization relies heavily on KV cache awareness and techniques like 4-bit quantization.
Read article
Where RL Will Take Search — Maximilian-David Rumpf, SID.ai
RL is revolutionizing search by creating specialized sub-agents. These agents can adaptively search, leading to results that are 20 times faster and 100 times cheaper than using frontier models.
Read article
If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread
Designing AI agents for knowledge work requires mimicking human orchestration. Agents must break down complex, ambiguous problems using specialized sub-agents and multiple search primitives to overcome performance ceilings.
Read article
"My name is... my name is...": A Linguistic Map for Voice Agents — Midam Kim, ServiceNow
Voice AI failures are not random bugs, but structural issues. Kim introduces a linguistic framework mapping communication to two channels (listening/speaking) and four interdependent levels: sounds, words, interaction, and mental model.
Read article