Stack
Which services to use and what they cost: model access, rented GPUs, coding agents and the pipeline around them.
Agent-first coding: use the frontier tools and pay for them
Claude Code or OpenAI Codex, desktop app or command line. Generic and budget setups hide what the new tools can do.
What you may do with an AI agent on licensed data
Most data licenses forbid putting the content into AI systems. Here is what that covers, what it leaves open, and how to keep working without risking your campus's access.
Negotiating an AI contract for beginners: retention, zero data retention, training and models
Four clauses decide whether an AI service is safe for research data. What each one means, what a good answer looks like and what to get in writing.
API, MCP, CLI or computer use: how to connect an AI agent to your research tools
Use the lowest-level, most structured interface the job allows. What that means for data pulls, vendor platforms and software with no API.
Fully automated research pipelines: what exists and how to point one at finance
Machine learning, mathematics, biomedicine and finance each have a system that runs most of the research loop. Two of them you can install today.
Give your agent a memory: the instruction file and your first skill
Two plain text files turn an agent from a stranger into a research assistant who knows the project. How to write both, for Claude Code and OpenAI Codex.
OpenRouter or AWS Bedrock: where a researcher should buy model access
OpenRouter is the quick way to try many models with one key. Bedrock is the one to use when the data is licensed or the university already buys from AWS.
Automate SEC filings research with a coding agent, from EDGAR to a finished panel
The SEC's data service needs no key. Here are the endpoints, the instruction file, a skill and a scheduled run, plus linking to CRSP and Compustat through the wrds library.
Token management for researchers, and why to buy usage before you hire help
What a token costs, the three settings that cut the bill, and the case for spending on usage ahead of spending on support staff.
Vast.ai or RunPod: renting a GPU for research without overpaying or leaking data
Vast.ai is a marketplace with the lowest floor prices and the most variance. RunPod has fixed prices and a data-center tier. Which one depends on what is on the disk.
Why data vendors are scared, and how to deal with them as a researcher
Their interface is worth less every month and they cannot see what your agent does. That gives you more room at renewal than you have had in twenty years.
If the platform forbids agents, build the dataset yourself from the source
Filings, financial statements and earnings calls were public before any vendor packaged them. An agent can go back to the source, and the tools to do it are free.
Should you still pay for call transcripts when an agent can do the parsing?
An agent now does the cleaning, splitting and scoring that used to justify a data budget. What it cannot produce is history, identifiers and the right to use the text.
The one public review of WRDS's Ask AI chatbot finds a lookup tool with a six-question limit
Librarians in Singapore tested the beta and found it useful for locating variables, unreliable on first try and capped at six questions a session.