Presented at AI Engineer. Finance used to be bottlenecked on producing content. It is now bottlenecked on verifying it. An analyst who once spent the day building a model now spends it checking whether a machine’s output can be defended to a client, a committee, or a regulator. ...
Most people now use ChatGPT, Claude, or Gemini every week without a clear mental model of what’s happening underneath. That’s fine right up until the model is confidently wrong and you have no way to tell. AI Model Basics is a new 20-minute LinkedIn Learning course aimed at exactly that gap. What the Course Covers How large language models actually work — enough mechanism to reason about them, without the math What prompting does and why it changes the output as much as it does Why models produce wrong answers, and what kinds of wrong to expect Personalization — what these tools can and can’t retain about you How to evaluate and improve what you get back instead of accepting the first response Who This Is For Anyone using AI tools at work who wants to understand them rather than just operate them. It’s beginner-level and assumes no technical background — no coding, no prior AI experience. ...
Originally published on Anthropic’s blog Inside a platform that indexes 26M+ SEC filings, earnings call transcripts, IR presentations, consensus estimates, and private data across 14,000+ companies and 27 global markets, and how the team behind it built AI that validates every number to the exact filing, page, and line item. The quick pitch NameKepler Founded2025 FoundersVinoo Ganesh (CEO) and John McRaven (CTO) StackAWS, Rust, Python, containers for orchestration GrowthIndexed 26M+ SEC filings, 50M+ public documents, 1M+ private documents, and 14,000+ companies across 27 global markets in less than three months. Financial firms operate in a heavily regulated environment where reporting has to be auditable and accountable. Every figure in a regulatory filing, deal pitch, or research report needs to be verifiable against source documents. ...
Originally published on Next Play If you work in tech, you should learn about Forward Deployed Engineering (FDE). It’s quickly become one of the most popular roles that nearly all fast-growing AI companies are looking for…at the same time, it’s the type of role very few people and companies seem to actually understand. I designed Project Frontline at Palantir, a program that sent over 250 engineers into live customer deployments. Those engineers are now at OpenAI, xAI, Anduril, and dozens of leading companies. ...
Originally published on Kepler Everyone’s talking about context engineering right now, but most of the conversation is focused on the wrong thing. Read the blog posts, the guides, the thought leadership. They’re all asking the same questions: What should I include in the context window? How do I manage tokens efficiently? How do I curate what the model sees? These are valid questions. They’re also the easy part. The hard part isn’t deciding what context to include. It’s building systems that deliver that context reliably, with provenance, at scale, every single time. That’s not a context problem. That’s an engineering problem. And engineering means something specific. ...