🚀 Introducing AI agents that make “self-driving” machine learning usable by anyone — no PhD required. A lifelong dream for me, launched in months… Demo below! Forecasting, key driver analysis, predictions, tipping-point analysis, and anomaly detection now take minutes instead of weeks. 🤖 ✨ To my knowledge, we’re the first team in the world to ship autonomous machine learning (AutoML) guided by an agent — or “agentic AutoML” — usable by anyone. Genuinely in awe of the creativity, ingenuity, and grit every @SpindleAI teammate poured into making that possible. 🙏 With transparent, personalized explanations for “how”, “why,” and “what next,” the new agent also delivers actionable recommendations for setting & hitting business targets 🎯 based on patterns in your data. From churn prediction & customer behavior forecasting, to pricing simulations & cross-sell targeting, beta customers are already sharing compelling use cases with us… But most of all, I’m excited to see all the creative use cases YOU come up with: spindle.ai/agents/autonomous…

Thrilled to have played a very small role in this very big accomplishment. 🚀 Congrats Sam, the Open AI team, and so many in the open-weight LLMs community for making this watershed release a reality. The new OSS models are extremely competitive w/ closed frontier models on multiple benchmarks. AI engineering changes forever today:

Context is king for AI Agents. It’s clear that the more relevant context the agent has to complete the task at hand, the more differentiated and successful the agent will be. Thus, when building AI Agents, you need to work backwards from that ideal context, and figure out what kind of product it would take to deliver it. This is a core part of the moat you’re building in AI. Here are a few of the major categories starting to emerge: * Corporate knowledge: Agents are going to require access to a wide degree of corporate data to make decisions properly, in which case the AI systems with the best access to this data will often win. This can either be because they’re directly have the data in their system already, have a “right” to index or get embeddings from another system (especially for code bases and other knowledge), or have nailed integrations across platforms via MCP, A2A, or otherwise. * User memory: As we’re seeing in the earliest stages of memory inside of products like ChatGPT, having a complete history of what that user has done with the AI can be incredibly powerful. The more the AI Agent can build on from past interactions, and understand all of the nuances about a user, the better results it can deliver. We can also imagine new sources of context beyond chat history, like what is that user’s role in the company, who do they tend to work with, what type of data do they tend to work on, and more. * Domain expertise: Ultimately, AI Agents are going to have the most impact when they’re not just generalists, but when they augment work in particular lines of business or verticals (like financial advisors in banking or researchers in life sciences). This means that the more the agent understands all of the specifics about that industry’s workflows or proprietary data at a granular level will be critical. One can imagine a future where 10,000s or 100,000s of tokens are used up in the context window just explaining the job function and specific instructions for that agent. * Tool use: The best AI agents are going to know their way around a variety of external systems to be able to get work done successfully. Knowing when to search the web vs. answer questions from internal knowledge vs. talking to a specific software platform will make all the difference in successful AI Agents. This problem is *not* easy as we can already see in consumer AI apps. * User experience: While this is hard to sustain as a differentiator, nailing the right Agentic UX is critical for adoption, and successfully completing workflows. Knowing if the agent should be interacting with a user via chat, or in-line with an existing UX, or needs a new interface altogether can make major differences in the customer success and adoption. There are going to be multiple ways to build moats when building AI Agents, but context is going to be king. Some of mix of the above -and probably plenty more- will be necessary to win.

#HIRING another AI Engineer (Principal/Lead IC) for Agents, Agentic Systems, and Applied LLMs! 🤖 Shape the future of Strategic Finance by accelerating multi-agent analytical reasoning, tooling, & fault recovery alongside some of the best builders around: spindle.notion.site/AI-Engin…

#HIRING another AI Engineer (Principal/Lead IC) for Agents, Agentic Systems, and Applied LLMs! 🤖 Shape the future of Strategic Finance by accelerating multi-agent analytical reasoning, tooling, & fault recovery alongside some of the best builders around: spindle.notion.site/AI-Engin…

#HIRING another AI Engineer (Principal/Lead IC) for Agents, Agentic Systems, and Applied LLMs! 🤖 Shape the future of Strategic Finance by accelerating multi-agent analytical reasoning, tooling, & fault recovery alongside some of the best builders around: spindle.notion.site/AI-Engin…

📣 Hiring a Lead/Principal #FrontEnd Engineer (Interactive Data Applications) @SpindleAI! 🚀 @ryanatallah & I invite you to join our world-class team and market-leading FP&A customers in shipping software that augments human cognition: spindleai.com/lead-frontend-…

Delightful contest of #Catan at the @Spindle_app quarterly offsite in summery Gainesville, FL… A game where constrained optimization bleeds over from work into play! ♟️

i call this the Dynamic Interface raw notes:

SO fortunate to work with this amazing product team 🤩 Almost as excited for 2023 as for the many new faces that will join this photo in the next few months