Advanced techniques — agents, automation, local AI, and workflow orchestration.
42 results
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66 minutesSee the full beginner workflow for building a small Cursor project, including setup, prompting, debugging, basic Git and the limits of AI-assisted coding.
5 minutesYou can name the three guardrails to set before letting AI write code for you, and know why each one prevents a specific failure.
31 minutesYou can assemble an n8n support workflow that answers tickets with RAG-backed replies and feeds solved tickets back into the knowledge base.
231 minutesSee how a support agent combines retrieval, escalation and workflow tools before production hardening.
24 minutesDesign a marketing workflow where AI drafts and routes work, but humans keep control over strategy, brand voice and publishing.
30 minutesMap the current marketing-tool landscape before deciding which workflows deserve automation.
26 minutesEvaluate where voice agents might fit sales workflows and where disclosure, consent and escalation become blockers.
30 minutesUnderstand the data-enrichment and personalization pipeline behind AI-assisted outbound without confusing automation with quality.
23 minutesStudy a multi-agent newsletter pipeline and identify where sources, approval and failure handling belong.
24 minutesRecognize the browser-agent action loop, where it helps, and where human confirmation is still required.
2 minutesCompare desktop computer-use behavior with browser-only agents.
11 minutesSee the basic NotebookLM source-grounded workflow before building a personal document assistant.
26 minutesUnderstand the personal source-grounded workflow: collect documents, ask bounded questions, and verify citations before trusting the answer.
29 minutesUnderstand generate-score-prune reasoning patterns and when they are too expensive for real work.
25 minutesYou can layer role, structured sections and explicit thinking steps into a prompt without turning chain-of-thought into a ritual.
69 minutesBuild intuition for chunking choices before tuning a real retrieval system.
24 minutesSee how query rewriting, hybrid retrieval, reranking and corrective loops fit into one RAG pipeline.
25 minutesUse the OWASP LLM risk categories to review tool access, output handling and sensitive-data exposure.
11 minutesDistinguish direct and indirect prompt injection and why filtering alone is not enough.
107 minutesLearn the product-builder eval loop: inspect traces, label failures, define criteria, test changes and compare against human judgment.
3 minutesSee the smallest no-code version of a repeatable prompt eval.
26 minutesBuild a first n8n AI-agent workflow while recognizing where tool access, memory and guardrails belong.
92 minutesExtend a simple agent into multi-step workflows with memory, error handling and realistic business integrations.
17 minutesLearn how to design AI workflows that can pause, resume, ask for human judgment and keep business state separate from model guesses.
18 minutesSee how approval gates can be implemented as explicit workflow checkpoints rather than informal manual review after something goes wrong.
14 minutesInstall a local model runner, pull a small model and understand the privacy/performance tradeoff before using it for real work.
6 minutesTry local AI through a GUI and compare small-model behavior with hosted frontier models.
20 minutesUnderstand the protocol roles: host, client, server, tools, resources, prompts and transports.
16 minutesExplain what MCP changes in plain language and decide whether a tool connection should use MCP or a simpler integration.
19 minutesCompare flagship, lite, mid-tier and specialized models so routing decisions are based on task fit, cost and latency instead of brand preference.
9 minutesEvaluate model-routing tradeoffs between quality, cost and reliability before adding orchestration complexity.
30 minutesMap which AI tool belongs in each workflow step instead of forcing every task into one chatbot.
14 minutesYou leave with a stock of concrete use-case ideas mapped to the tool each fits best, instead of defaulting everything to one chatbot.
32 minutesLearn how to introduce AI into localization without removing human ownership of meaning, tone, terminology and final approval.
4 minutesUnderstand why multilingual embeddings matter for private internal search and where local retrieval can reduce data-exposure risk.
10 minutesChoose an automation platform based on hosting, AI-agent fit, integrations and maintenance tradeoffs instead of product hype.
3 minutesSee the original product/research framing that made reasoning models different from ordinary chat models.
28 minutesUnderstand why reasoning-model prompts should specify the problem, constraints and success criteria instead of asking for visible chain-of-thought.
10 minutesUse the C.R.A.F.T. structure as a reusable meta-prompt for generating more consistent prompts across a team library.
20 minutesTurn one-off prompts into reusable patterns with evaluation notes and iteration rules.
8 minutesUse multiple AI future scenarios to plan adoption without betting the company on one forecast.
60 minutesFrame AI adoption around workflow change, capability building and realistic organizational risk.
