Arun C Murthy

Most AI products give you a chatbot. You ask, it answers. But that doesn’t work for serious analytical work. Your team doesn't watch every step. They start an analysis, go to meetings, come back the next day. Colleagues need to understand what happened. Sometimes you need to rewind and try a different path. A chatbot can't do any of that. So we threw out the chatbot model and rebuilt around one idea: the event stream. Every action aidnn takes is emitted as a structured, ordered event. That single decision unlocks everything: → Teammates jump in, ask questions anchored to specific moments, and keep going → Close your laptop, come back tomorrow — zero lost progress → Branch from any checkpoint and compare results → See assumptions and reasoning before execution, not after The result feels less like querying a black box and more like working with a teammate who documents their work and can be reviewed for methods, not just results. Full breakdown on the @isotopes_ai blog: blog.isotopes.ai/building-a-col…

We recently talked about the "Team of Rivals" architecture which we bring to bear @isotopes_ai to scale sophisticated AI Agents, here is our paper on @arxiv: arxiv.org/abs/2601.14351 Discuss: news.ycombinator.com/item?id=468023…

We just published our latest whitepaper "The Orchestration Imperative for AI Agents" and this one is especially interesting for leaders who want to learn about the next evolution of AI. Here's the core insight: Production-grade AI Agents aren't a chatbot problem. They are an orchestration problem. Most multi-agent AI systems fail for predictable reasons—coordination overhead scales nonlinearly, context gets contaminated and rots progressively, and debugging becomes impossible. We've watched teams spend months building "agentic" solutions only to hit these walls. This whitepaper distills the principles @isotopes_ai uses to make sophisticated AI agents work in production: structured workflows, plans as contracts, hierarchy that tames complexity, and humans owning the "what" while the system handles the "how", and more. If you're evaluating any agentic AI for your business - or building one - this is definitely worth a read! Get the whitepaper to learn more → isotopes.ai/whitepapers/or…

Orchestrating a Team of Rivals We @isotopes_ai just published our in-depth technical paper on how aidnn achieves production-grade reliability! Model scaling is slowing down. But AI impact doesn't have to plateau. The path forward isn't faster clock-speeds. It's multi-core. Instead of waiting for a single smarter model, aidnn orchestrates multiple LLMs with opposing incentives. No business relies on one employee to handle critical operations. We shouldn't architect AI systems around single-agent execution either. aidnn deploys 50+ specialized agents organized as specialized teams: * Planners create execution strategies * Executors perform the work * Critics validate outputs with veto authority * Remote code execution keeps data isolated from reasoning models Each of these agents are coding agents resulting in a highly repeatable & reliable agentic system. We call it "orchestrating a team of rivals." Multiple agents with different failure modes catch what individual models miss. Hierarchical veto authority prevents errors from propagating. The result: 90%+ error prevention through layered validation. And your data stays secure. This is the architecture behind financial close automation at Invisible Technologies and others — turning weeks of manual work into hours of validated analysis. Read the full technical paper here: isotopes.ai/technology

Orchestrating a Team of Rivals We @isotopes_ai just published our in-depth technical paper on how aidnn achieves production-grade reliability! Model scaling is slowing down. But AI impact doesn't have to plateau. The path forward isn't faster clock-speeds. It's multi-core. Instead of waiting for a single smarter model, aidnn orchestrates multiple LLMs with opposing incentives. No business relies on one employee to handle critical operations. We shouldn't architect AI systems around single-agent execution either. aidnn deploys 50+ specialized agents organized as specialized teams: * Planners create execution strategies * Executors perform the work * Critics validate outputs with veto authority * Remote code execution keeps data isolated from reasoning models Each of these agents are coding agents resulting in a highly repeatable & reliable agentic system. We call it "orchestrating a team of rivals." Multiple agents with different failure modes catch what individual models miss. Hierarchical veto authority prevents errors from propagating. The result: 90%+ error prevention through layered validation. And your data stays secure. This is the architecture behind financial close automation at Invisible Technologies and others — turning weeks of manual work into hours of validated analysis. Read the full technical paper here: isotopes.ai/technology

Recently, @far33d from @a16z nailed it: AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. With aidnn from @isotopes_ai you can live in the future. We built aidnn to be multiplayer with shared Organizational Memory from day one. Everyone sees the same context, numbers, assumptions, and process - updated in real time. While others are trying to make single-player AI tools work for individuals, we're already delivering multiplayer intelligence with collaboration, shared history, and organizational memory built in - today.

Recently, @far33d from @a16z nailed it: AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. With aidnn from @isotopes_ai you can live in the future. We built aidnn to be multiplayer with shared Organizational Memory from day one. Everyone sees the same context, numbers, assumptions, and process - updated in real time. While others are trying to make single-player AI tools work for individuals, we're already delivering multiplayer intelligence with collaboration, shared history, and organizational memory built in - today.