OpenAI dropped o1-preview and o1-mini in mid-September. Two months in, the hype has settled into something more useful: a clearer picture of where these “reasoning models” actually fit in a practitioner’s toolkit — and where they don’t.

I’ve been building LLM-backed features inside a regulated financial institution for the better part of this year. That means every model choice runs through a mental checklist: latency budgets, cost per thousand calls, data residency, audit trails, and whether the thing hallucinates less than the last one. o1 is the first model that made me update that checklist in a while.

Three weeks ago OpenAI shipped Structured Outputs on the API. Not another model name on a slide — a guarantee that the response will match a JSON Schema you supplied.

If you have been building tool loops or extraction pipelines this year, you already know the pain: the model returns almost the right shape, your parser throws, you retry, and somewhere a retry storm becomes the product. Valid JSON was never the hard part. The hard part was your shape.

OpenAI shipped GPT-4o in mid-May. Anthropic took tool use generally available across the Claude 3 family at the end of May. Every internal slide deck I have seen since then has the word agent on it twice.

Most of those decks are still describing a chatbot with extra steps. The useful shape is simpler and less glamorous: a model that emits a structured tool call, your code that runs it, a result that goes back into the conversation, and a hard stop when the loop has gone on long enough.

In the rapidly evolving world of technology, few innovations have been as transformative as the internet and generative AI. From the early days of the web, when accessing information meant waiting for static pages to load, to the present era of conversational AI, the journey has been marked by groundbreaking developments.

The advent of web applications ushered in a new era of how we interact with software and services. Rather than installing programs locally on individual machines, web apps enabled accessing applications through a web browser over the internet. This fundamental shift democratized access to software and catalyzed new ecosystems of apps delivered as services.

As software developers, we are driven by a passion for creating innovative and impactful solutions. We dedicate ourselves to mastering new technologies, frameworks and methodologies, pouring over lines of code, testing and iterating until we are satisfied with the result. However, despite our best efforts, many of us struggle with a nagging sense of self-doubt. We feel like we are just pretending to be experts, that we are just winging it and that someone will eventually discover our lack of expertise.

Impostor syndrome, also known as impostor phenomenon, is a psychological phenomenon where individuals doubt their abilities and feel like they are just pretending to be competent. Despite their achievements, they feel like they are just lucky or that someone will eventually discover their lack of expertise. This feeling of inadequacy can be paralyzing, causing individuals to doubt their skills, fear failure and even avoid taking risks.

Claude 3 shipped in early March with a 200K context window and a loud story about stuffing whole codebases and policy binders into one prompt. Two weeks ago Anthropic published research on many-shot jailbreaking — a long-context attack that gets more effective the more fake dialogue you pack in front of the real question.

If you are wiring LLMs into internal tools in a bank-shaped environment right now, that combination should change how you design the app, not just how you word the system prompt.