Category: AI
You are here if AI showed up in your job and the names on the screen do not match the buttons. This category is practical. It is not a model-release calendar, and it is not one lesson pasted under a new product name. The series split the pile so a Claude post and a ChatGPT post can teach different work.
Everyday paths are for a person with no account yet, or a person whose chat forgot a file: setup, prompting, memory, agents, safety, and which product fits a job. Product paths stay on one tool: Claude, ChatGPT, Gemini, or Grok, from a first login to the surface you should not open. Open-weight paths are Llama, DeepSeek, Qwen, Kimi, and GLM: what the name refers to, and when a normal paid chat is the easier tool. Engineering paths are for running a model, retrieving from your own files, and fine-tuning. Several of those hubs are still waiting on a first live part, and the hub page says so.
Where to start: no account yet, use AI setup from zero. You already pay for one product, open that product’s series and use the first part. You want the idea before the brand, use Practical AI. You want every series in order, use Learn and the AI tracks.
How to use DeepSeek for coding without trusting untested changes
Use DeepSeek for coding drafts, then run the tenant-scoped pytest that would catch a missing filter. Paste a function, not the secrets file.
DeepSeek thinking mode: why its step-by-step reasoning is not proof
Treat DeepSeek reasoning_content as a scratch pad. Instant vs Expert vs thinking toggles change style, not audit rights. Verify margins in the sheet before Slack.
What you get with DeepSeek’s free chat app and its paid plans
Use official chat.deepseek.com (Instant and Expert) before you buy a random Plus extension. Chat can be free; the API is the metered wallet. Confirm the publisher on mobile…
When Claude or ChatGPT is easier than running a Llama model yourself
Choose Llama when local or open weights earn the ops cost. Choose a closed $20 seat when the job is writing and the GPU project has no owner.…
Community versions of Llama: how to pick a fine-tuned model you can trust
Avoid the Llama variant zoo. Keep one default Instruct stack, match the model card to the file, and remember license and Acceptable Use still follow the weights.
