Microdose training for position players by position
Premium position players get microdosed training with three to five exercises; infielders get more change-of-direction work, outfielders more high-speed running based on distance covered.
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Premium position players get microdosed training with three to five exercises; infielders get more change-of-direction work, outfielders more high-speed running based on distance covered.
Grubbs advises parents to have their kids play as many sports as possible for as long as they can, producing a holistic, well-developed athlete.
Eugene Cheah advises that once you can measure, you should right-size which teams and workloads deserve the tokens-per-second speed you are paying for, noting that background agents running at midnight probably don't deserve premium speed and that paying for ultra-fast service on a background agent is 'just dumb.' He recommends using measured usage data to advise decisions about dedicated capacity you may purchase, whether for open or closed models, and checking which team spends the most tokens and whether they are productive. Knowing your team workload allows these capacity and model-switching decisions, and knowing your process workload allows splitting things off, such as scheduling background agents to run through the night while the team is asleep so hardware is utilized respectively
You also have to measure outcomes, not usage. A simple rule of thumb formula is tokens times price divided by outcomes, where outcomes are something someone actually uses. You need to be aware that outcomes are hard to measure, respectively, but it's something that you can start measuring and observing. Most importantly, back to the first step, measure. You know the token span per department, per team, and you can start seeing which team is performing better and you can start seeing how much tokens they use. You can start seeing what works.
Process-track teams start early with evals and benchmarks, then find the right-sized model that fits. Asking 'how do I know the smaller model is good enough' is the wrong question. Set up your test cases, evals, and examples, then do the search process from there. You can just download 10 models from Hugging Face and try them rather than needing a provider. Do the search, find the cheapest model that passes most of your evals, and iterate from there.
Nacho Martinez recommends building custom harnesses for specific problems rather than expecting everyone to build a sophisticated general-purpose harness: simplify the problem first, then build a harness that attaches to it, starting from scratch to strip 90% of what you don't need; the smaller everything is in a harness, the better.
Nacho Martinez advises focusing on the data layer of the agent stack because that is the part you can manipulate and work around.
Nacho Martinez advises focusing less on frontier models and more on the harness, which is the interesting part that developers can actually customize.
Nacho Martinez recommends using context engineering to automatically summarize, compact, and shrink the working context window of a model.
Nacho Martinez recommends using tools and skills intelligently to keep the context window limited, noting this is one of the main ideas of the workshop.
A flow generates explainer videos: a research phase scouts and fact-checks details, builds a design brief handed to production that makes and animates individual scenes, and additional phases add sound effects, voiceovers, and scripting. It can be kicked off in Claude Code with the Claude API skill from a single prompt like 'explain how dynamic workflows works'.
When running an outage investigation, prompt the agent to research root cause across services but explicitly instruct it not to touch or change anything in production.
Use the Claude Console to manage API resources; find Managed Agents resources under the Managed Agents dropdown in the left sidebar; in Quick Start, access one-click workflows and scroll to the bottom for the dynamic workflow tag; run the stock watchlist scanner template; create an agent config with Claude Opus 5.5, the multi-agent 2026-10-01 type, and a persona system prompt; give the agent an environment (a computer) via environment configuration, which makes Claude far more capable; then click 'start a session' to kick off the orchestration.
Prompt each agent to focus only on a specific subset of directory paths, since there is no permissioning layer for directory paths; use tool permissions to lock down file system access by inspecting the read request, checking which agent is making it, and denying it if that agent shouldn't have access.
Do not throw your API key into a markdown file that you just give to Claude — that is definitely bad; even as a hidden file in a secret directory, plain-text credentials on disk can leak, and Claude takes on the persona of whatever credentials you give it, so exposed credentials define what your agent can do.
discouraged
Depending on the starting point: prompt into the layer to start from scratch, upload a folder for code on your computer, or clone from repositories with a connected GitHub account for a hosted repo.
Duplicate a code layer inside Figma by option-dragging (alt-drag on PC) to create an independent copy; duplicating creates a new branch that can be worked on independently to try out different ideas, rather than starting over or risking the original state.
Review multiple generated exploration layers by selecting them and hitting return, so all variations can be tried out at once and compared side by side (e.g., OmniSearch field, map-scroll address selection, presets with current location).
When you sell one thing, outside noise will tell you to make a different color, a different shape, a t-shirt, or sweatpants — and giving in complicates your life; Dad Gang was laser focused on getting good at selling that one hat instead, honing in on the why behind Dad Gang rather than using color flips and design flips to push more sales.
The speaker recommends a self-reflection exercise: ask whether the quality of your team has improved since you started; if your team quality has improved and your business has grown, ask how high your bar will be in five years; ask whether your five-years-from-now self would look at your current team and say they aren't up to snuff. Then pull the standards you will have in five years into the present as quickly as possible to create the reality of winning aligned with that standard of excellence.
Choose sources → to get a library of your own and a daily brief.