ollama VS OpenAI-DotNet

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.

This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b. Same tools, same workflows, no data leaving your network.

Related: 35B MoE on 2× 1080 Ti · Ollama

The sample is a tiny console app running entirely against a local Ollama model — no cloud keys, and every HTTP call is traced so I can see exactly what goes over the wire (complete sample code). There's a single skill on disk:

irm https://ollama.com/install.ps1 | iex

Provider-side metadata. Both major providers expose per-user tagging. OpenAI accepts a user parameter on the Chat Completions and Responses APIs, and the OpenAI Usage API (launched December 2024) supports group_by=user_id for programmatic per-user cost breakdown. The Costs endpoint requires an admin key. Anthropic accepts metadata.user_id on every API request, capped at 256 characters and explicitly not for PII. The trade-off: provider metadata is the lowest friction, but it is provider-tied and does not show real-time per-customer running totals inside the application. (Side note: the request-to-Usage-API binding for OpenAI's user parameter should be re-verified against the live API reference before treating it as definitive.)

For solo founders who don't run their own gateway: use Claude direct for highest quality, OpenAI for proven reliability, or wire up multi-provider routing via something like Prism (or build your own — see Prism's architecture once it's published).

Composer 2 originally gained attention because Cursor delivered strong coding performance at dramatically lower token costs than frontier proprietary models. Cursor positioned it as a cheaper alternative to systems from Anthropic and OpenAI. (Cursor)

The most extreme case arrived in April 2026. In 2024, OpenAI CEO Sam Altman predicted that a one-person billion-dollar company "would have been unimaginable without A.I., and now it will happen." He maintained a betting pool with fellow tech CEOs over when it would arrive. In April 2026, he emailed the New York Times claiming he won the bet and that he "would like to meet the guy."

Everyone sees what they want in their benchmarks. OpenAI sees reasoning capability. Anthropic sees safety and helpfulness. Google sees multimodal understanding. The academic community sees generalization. Same numbers, completely different conclusions.

OpenAI provides access to code generation through ChatGPT and the API. The free tier of ChatGPT allows code generation conversations with GPT-4o, which handles code tasks competently across most standard languages and frameworks.

1. Get the Codex app onto your machine. You go to http://openai.com, find Codex up in the menu, hit the install button and grab the build for Mac or Windows I guess. Whole thing is about a minute, zero setup decisions along the way. A tip from me, even if you have been poking around Codex in the browser, get the Desktop version running from day one, that is where the real usage happens later and you do not want to redo the setup then.

To understand why this certification matters, it helps to look at how we got here. About three years ago, when ChatGPT/OpenAI took the world by storm with the GenAI and LLM revolution, we saw AWS flagbearer GenAI service Amazon Bedrock being used primarily for setting up chatbots, statbots, and AI assistants with Retrieval Augmented Generation (RAG) enabled and basic agentic setups. Those were small-scale and mostly proof-of-concept (POC)-grade solutions. Before Agentic AI became mainstream, the focus was narrow—build a chatbot, add some retrieval capabilities, and call it done.

Multi-provider failover (Azure OpenAI -> OpenAI -> Anthropic -> Gemini)

The timing was strategic. OpenAI had announced ad integration for ChatGPT's free tier, creating immediate anxiety among users who'd grown comfortable with an ad-free experience. Anthropic's ads essentially said, "If you don't like where ChatGPT is heading, we're the alternative."