AI for Business

Propeller Consulting. Discusses governance, operating discipline, workforce adoption and ROI measurement as connected parts of scaling AI beyond experiments. That fits the article's maturity model because adoption is treated as changed work with owners and metrics, not as tool usage or workshop attendance.

World Wide Technology. Connects build-or-buy choices to business outcomes, workload placement, cloud economics, data sovereignty, security, infrastructure readiness and hybrid operating models. That makes it a useful strategic companion for deciding when to buy a tool, extend a platform, build a thin custom layer or own more of the deployment stack.

IBM Technology. Discusses the uneven "barbell" shape of AI coding performance, architecture ownership, agent orchestration, context limits, open-source versus proprietary tooling and why models can solve hard tasks while still failing ordinary engineering details. That supports the article's rule that tests and human review remain the shipping gate.

AI adoption should not be measured by how many people tried ChatGPT. A practical framework for measuring workflow ROI, quality, risk, maturity, and scale-readiness.

Most teams should buy before they build, but not always. A decision framework for AI tooling, workflow automation, RAG, agents, privacy, integration depth, total cost, and strategic differentiation.

A practical workflow model for Estonian companies working across Estonian, English, Russian, Finnish, and other customer languages without losing tone, terminology, privacy, or accountability.

Cursor, Copilot, Claude Code, and repository-aware agents change software work only when teams add boundaries. A practical workflow for codebase context, planning, tests, review, secrets, and production safety.

LLM-powered products face economics that are harder than traditional SaaS. Variable costs that scale with usage, margins squeezed by inference, commoditization risk, and competitors with the same foundation models. How to build a product that's actually defensible — and the patterns that lead to LLM

LLM inference costs are 60-90% reducible with the right techniques. Prompt caching, model routing, output control, batching, and a few less-known patterns. The numbers, the patterns, and the production discipline that distinguishes well-run inference from a runaway bill.

Prompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.

Classic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.

A production RAG pipeline is six stages, each with specific patterns that determine quality. The architecture, the choices at each stage, and the iterative evaluation discipline that distinguishes RAG that works from RAG that disappoints.

Most MCP tools we see are technically correct and practically useless. LLMs ignore them, misuse them, or call them in unhelpful ways. The principles for designing tools LLMs adopt naturally, with examples of common failures and their fixes.

Building a production Model Context Protocol server requires more than wiring up a few tools. The patterns for schema design, auth, error handling, streaming, observability, and the production realities that make MCP servers useful at scale.

LLM applications fail in unique ways that traditional observability misses. The patterns for tracing multi-step flows, tracking costs that vary 100x per call, monitoring quality drift, and debugging hallucinations at production scale.

Most eval suites look impressive but miss real regressions. Building evals that catch what matters requires careful dataset construction, sensitive metrics, judge calibration, and a culture of trust. The patterns from teams that get this right.

Structured outputs and function calling are the bridge from 'LLM that generates text' to 'system that does work'. In production, the patterns that matter are about schemas, error handling, idempotency, and graceful degradation — not just JSON mode.

Evals — systematic measurement of AI output quality — are usually treated as an engineering concern. But every team running AI workflows needs them, and the basics are accessible without code. The how-to.

A practical AI sales stack that handles research, personalization, sequencing, and follow-up — without becoming the spam everyone deletes. The architecture, the tools, the prompts, and the guardrails that separate effective from annoying.

A practical, end-to-end AI marketing stack for content, SEO, and social — the tools, the workflows, the prompts, and the discipline that separates real automation from spam. Built for teams of one to small teams, not enterprise.

Most teams fail at AI adoption not because the technology doesn't work, but because the rollout doesn't. A practical playbook: how to pick use cases, train people, set policy, measure impact, and avoid the common failures.

Y Combinator. The Lightcone hosts work through why vertical AI agents — not horizontal wrappers — are the defensible shape for application-layer companies, with concrete examples and a clear-eyed take on which categories the model providers will eat. That is the anti-moat trap the article warns about, expressed as a positive playbook.

Sequoia Capital. Bret Taylor walks through the shift from per-seat SaaS to outcomes-based pricing — what to anchor on (resolution, CSAT, NPS), why incumbents struggle to follow, and how vertical specialisation creates pricing power. It directly mirrors the article's pricing and margin sections.

OpenAI. OpenAI's own Build Hour on prompt caching — the 1024-token threshold, the prefix-stability requirement, audio caching at 99% discount for realtime, time-to-first-token impacts at long inputs. Useful when you are sizing the engineering effort to actually hit the cache reliably on your production prompts.

Prompt Engineering. Walks through Anthropic's prompt caching against Gemini's context caching with concrete latency-and-cost reductions per use case (long-document chat, few-shot, multi-turn). The breakdown of cache-write surcharge vs. cache-read discount is exactly what the article assumes when it talks about when caching pays off.

IBM Technology. Tighter focus on the two techniques teams most often confuse. Goes deeper on data freshness, source attribution, and the inference-time speed argument for fine-tuning. Worth watching if you are specifically trying to argue against an unnecessary fine-tune project.

IBM Technology. A clear whiteboard pass through all three techniques with their respective costs — retrieval latency, training compute and catastrophic forgetting, the limits of prompt-only solutions — and the combinations that actually make sense in production. The closing example of a legal AI system using all three is almost exactly the article's "when to combine" argument.

Prompt Engineering. A focused walkthrough of Microsoft's GraphRAG — entity extraction, community summaries, query-focused summarization — set up on a local machine with cost notes. Watch it for the graph-RAG section of the article specifically; the cost discussion is the part most write-ups skip.

Cole Medin. A working agentic-RAG-plus-knowledge-graph build, with the agent deciding when to do vector search, when to hit Neo4j, and when to do both. It's the cleanest demonstration on YouTube of the "agent as the retrieval planner" pattern the article describes, in code you can actually pull down and run.

AI Engineer. Douwe Kiela led the original RAG paper at FAIR and now ships RAG into regulated enterprises. The talk is mostly about what stops working at scale — chunking strategies that don't survive 100k documents, "accuracy is table stakes, inaccuracy is the real problem," and why attribution and observability matter more than the embedding model. Good calibration before re-reading the article's eval and monitoring sections.

AI Engineer. LlamaIndex's CEO walking the gap between "naive RAG demo" and a real pipeline — small-to-big retrieval, sub-question routing, hybrid search, evaluation. The shape of his slides maps almost directly onto the article's pipeline sections; watch first, then re-read the article with his diagrams in your head.

Anthropic. Hannah Moran and Jeremy Hadfield from Applied AI walking through how to phrase tool calls and agent prompts on a real Pokemon-playing agent — when to push behavior into the system prompt versus the tool description, what the model needs to know about each tool's preconditions. Useful immediately after you write your first MCP server and find Claude calling it in unexpected ways.

Anthropic. Anthropic engineers walking through what they actually changed when their multi-agent systems were misusing tools — collapsing endpoints, returning names instead of IDs, leaning on MCPs and Agent Skills instead of stuffing more tools into the system prompt. Maps point-for-point onto the article's checklist for tool descriptions and return-shape design.

AI Engineer. Anthropic's Mahesh Murag walking through MCP's design — why tools, resources, and prompts are separated, how clients negotiate capabilities, what production hosts actually do with the protocol. Watch it after the build to understand the parts of MCP the SDK quietly hides and to calibrate the article's "production-ready" checklist against the spec authors' intent.

Web Dev Simplified. A full, code-along build of both an MCP server and a client in TypeScript — tool definitions, schemas, prompts and resources, stdio transport, inspector debugging. It's the closest video on YouTube to actually doing what the article asks you to do, at a pace where you can pause and follow along in your own editor.

Hamel Husain. Hamel Husain, Eugene Yan, Brian Bischof, Harrison Chase, and Shreya Shankar working through tracing, log analysis, LLM-as-judge, and the workflow around looking at real production data. Sit with it the same way you would a long podcast — it is the single best deep treatment of the article's "look at your traces" thesis on YouTube.

LangChain. A guided tour of an LLM trace, project, and dataset by LangChain's co-founder — token cost, latency, error rate, feedback aggregation, drilling into a single retrieval-step span. It's the closest visual analogue to what the article describes when it talks about "every call is a span" and why structured traces beat print logging.

Stanford Online. Methodical pass through rule-based metrics, LLM-as-judge biases, factuality and agent evaluation, and the failure modes of static benchmarks. Use it as the theory companion to the article's section on choosing what to measure and why most off-the-shelf metrics under-predict real regressions.

Dave Ebbelaar. A working AI engineer walking through his actual eval ladder — assert-style unit tests, reference-free metrics, LLM-as-judge alignment with humans, and the analyze/measure/improve loop. The structure is the closest match on video to the article's argument that evals are a regression-catching system, not a leaderboard.

OpenAI. Walks through `strict: true`, the difference from old JSON mode, refusal handling, and how function calling and response-format schemas compose. Useful precisely because it describes the contract the API gives you, which is what the article's production patterns are built on top of.

AI Engineer. The talk that crystallised the modern "define a Pydantic model, hand it to the LLM, let validation do the rest" pattern, with concrete examples of nested objects, validators that catch hallucinated URLs, and Chain-of-Thought as a typed field. Watch it before re-reading the article's section on validators and you will recognise where its retry and refusal rules come from.

Anthropic. A three-minute Anthropic walkthrough of running a real eval inside the Workbench — auto-generating realistic test cases, grading outputs, tweaking the prompt, and re-running the same suite side-by-side. The view count sits below the usual bar, but for "how do I actually do this without writing code" this is the cleanest official demo and slots neatly under the more strategic Husain/Shankar conversation.

Lenny's Podcast. Hamel Husain and Shreya Shankar walk through the entire eval workflow on a real property-management AI assistant — looking at traces, open and axial coding of errors, deciding when to stop, building an LLM-as-judge, and validating it against human judgment. This is the rare long-form conversation that is genuinely aimed at PMs and team leads rather than ML engineers, and it covers the same "30 minutes a week after setup" rhythm the article recommends.

Liam Ottley. A live build of an AI voice agent that calls inbound leads, qualifies them, and tries to book a discovery call — Make.com plus a voice provider, with the qualification script and handoff logic shown. Good complement to the email side: same enrichment-then-personalization pattern, different channel, different failure modes.

Nick Saraev. Saraev builds the exact pipeline the article describes — Apollo for leads, Apify for scraping, n8n to enrich and run a multi-line icebreaker generator off each lead's site, then Instantly for sending — and is candid about per-lead costs and reply rates. It's the cleanest demonstration of "real personalization at scale," not just "mail merge with a first name."

Greg Isenberg. A wider tour of the current AI marketing stack — workflow automation, model routing, AI video and voice tools, ad creation from competitor analysis. Good way to see which tools are doing what across the category before you decide where to put the first three Zaps or n8n flows for your own team.

Greg Isenberg. Isenberg builds a real content pipeline in n8n with The Boring Marketer — scraping top-performing posts on YouTube and X, drafting new pieces with Claude, researching with Perplexity, generating images, and publishing to LinkedIn with a human-approval step. It is exactly the "agent in the middle, tools on either side" shape the article describes, and the human-review stage is shown rather than just mentioned.

Big Think. A tight 8-minute version of Mollick's "four scenarios" model — static, linear, exponential, AGI — and why teams should plan against scenario two or three rather than betting everything on either extreme. Useful when you're trying to get a leadership team to agree on what they're actually preparing for before you write the playbook.

Sana. An hour with Mollick on what AI inside organizations actually looks like — why "cut costs" is the wrong framing, why traditional org charts are bending, and what "AI-native" teams do differently. Sits below the usual 100k bar but it is the cleanest practitioner-level conversation about adoption strategy from the researcher most consistently cited on this topic, and the playbook concerns in the article map almost 1:1 onto his framing.