How to use GPT-5.6 Sol in Codex CLI: full setup guide
Install Codex from scratch, sign in, launch GPT-5.6 Sol with the exact terminal command, verify it, and fix common access errors.
Read guide →Practical AI guides
Guides
Practical, source-grounded guides on choosing, evaluating, and running AI tools, models, and research — written to be genuinely useful, not to chase keywords. Each one draws on the same evidence-first approach RepoRadar uses to score its catalog.
How to use these guides
RepoRadar tracks thousands of AI tools, repos, models, and papers, and the hard part is rarely finding something new — it's deciding what deserves your time. These guides are the reasoning behind the radar: how to separate a useful release from a flashy demo, how to weigh popularity against fitness, when to run a model yourself, and how to trust an autonomous tool with real access. They're deliberately tool-agnostic, so they stay useful as the catalog turns over week to week.
How to evaluate an AI tool before you adopt it
A practical 8-point checklist for separating a useful release from a flashy demo, the same way RepoRadar scores the catalog.
Read guide →Local AI vs. hosted APIs: how to choose
When running a model on your own hardware beats a hosted API, weighed across cost, privacy, latency, and maintenance.
Read guide →How to vet an AI agent or MCP server before you wire it in
The permissions, failure modes, and trust checks to run before you give an autonomous tool access to your data.
Read guide →How to read AI benchmarks without getting fooled
Contamination, axis tricks, missing conditions, and hidden variance — how to tell a real result from a marketing chart.
Read guide →A pre-flight checklist before you ship an AI feature
Failure costs, human-in-the-loop, token budgets, hostile input, observability, and planning for the model to change under you.
Read guide →How to reduce AI hallucinations and verify what AI tells you
Why models make things up, how to ground them in real sources, and how to catch the fabrications that slip through.
Read guide →Prompt engineering basics: how to write prompts that work
Task, context, format, role, examples, and iteration — the fundamentals that reliably improve AI output, minus the tricks.
Read guide →RAG vs. fine-tuning vs. long context: how to give an AI your data
The three ways to make AI use your own data, what each is actually good at, and a simple path to picking the right one.
Read guide →Does AI train on your data? Privacy and data controls explained
What really happens to what you paste, how training-use differs by plan, and the three policy questions that actually matter.
Read guide →How to choose the right LLM for your task
Match the model to the job across capability, cost, speed, and privacy — and benchmark finalists on your own tasks, not leaderboards.
Read guide →What is an AI agent — and when you actually need one
What makes something an agent, when the autonomy is worth it, when a simpler tool wins, and the reliability tax of agent loops.
Read guide →How to use AI coding assistants without shipping bugs
Treat generated code as a draft, give it real context, gate it with tests, and watch for security and licensing landmines.
Read guide →How to use AI for research without getting burned
Use AI to orient not conclude, ground it in real sources, and verify every citation before it lands in your work.
Read guide →Do AI detectors work? How to think about AI-generated content
Why AI detectors are unreliable in both directions, why watermarking won't save them, and what to do instead.
Read guide →Understanding AI costs: tokens, context windows, and pricing
What a token is, why long prompts cost, where bills blow up, and how to estimate AI spend before you commit.
Read guide →More guides are added over time. Have a topic you want covered? Tell us, or browse the full radar to put a guide into practice.