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Describes an automated pull-request workflow: when a PR is created, a review runs; critical issues block the PR; the system then iterates to make everything green, fixing CI failures and code review issues. Once green PRs are ready for approval, the next step is to unmerge and fix any merge conflicts.
Joe Liemandt advises against moving students to the next level at 80-85% accuracy, because the remaining gaps compound; students should stay in the mastery loop until they have mastered material before advancing.
In the Alpha School example, a kindergartner climbs a wall with a guide coaching from the bottom, the kid self-talks 'I can do it' and finishes the climb alone, ringing the bell; parents are told to just watch, and Alpha runs these life-skill tests about 10 weeks into the school year.
Ben recommends designing systems so the data plane — the databases holding critical user data — is isolated from other components like control planes, observability pipelines, analytics frameworks, apps, and MCPs, so that a bad deploy to the control plane cannot take down access to the database.
Design AI to operate with a least-trust policy so users can interrogate and highlight outputs to dig deeper. Because pushing an AI hard enough makes it agree with you, design the system to resist user pressure and base its answers only on the causal chain of evidence it created.
Treat models as general-purpose reasoners and harness them into a specific domain with six pillars. Build an agent architecture that answers the same questions across teams by structuring it around these six major parts: model orchestration, context engineering, causal reasoning, governed actions, a learning system, and evals.
When facing a task you don't want to do — like a large 3-month project with no clear starting point — break it into smaller subtasks so the activation energy required for each piece doesn't seem formidable. Start by researching one area to get a handle on it, use an if-then structure to sequence pieces, and initiate just the first small step when effort feels overwhelming.
endorsed
Dr. Husain advises thinking twice before taking a stimulant like Adderall or Ritalin to get a better grade, because the evidence suggests you might get worse. If you're already a high performer, stimulants risk degrading performance — the inverse U-shaped curve. If you start from a low baseline of performance, you might get a small positive cognitive boost, but it's quite small.
Hormozi advises speeding up cycles as the fourth step of the algorithm, asking 'how do we do it faster?' once the process is simplified.
Hormozi advises bringing up automation last — only after the process has been maximized for effectiveness — as the final step of the optimization sequence.
Hormozi advises simplifying — which he equates with optimizing — to get the most output for the least input, as the third step of the algorithm.
When auditing AI/tool spend, start with questioning the requirements, delete all stupid requirements, simplify/optimize to get the most for the least, speed up cycles, and only then bring in automation. A litmus test for AI usage: though I'm using AI, are we getting more views? Though I'm using AI, am I making more money? AI companies bill you whether what you built was useful or not — Claude/Anthropic, OpenAI, Gemini, whoever is doing the billing. If your token costs are going up month over month but your money is not, you're probably blowing money on shit that doesn't matter.
Hormozi advises starting any optimization process by questioning the requirements: asking why things must happen in a given order and why each requirement exists at all.
Hormozi advises deleting all stupid requirements as part of the optimization process, following the questioning of requirements.
With data slice technology, there are workflows where you can put it on a set schedule so it's auto-refreshed. Put your data slice on a set schedule so your chatbot never gets stale. When new files are added to or deleted from SharePoint, the system automatically picks them up and adds them to the data slice on a set schedule. The SDK picks up refreshed data so the chatbot's vector database always has the freshest metadata.
Commercial context: The creator presents this as their own offering and directs viewers to get it.
After ingesting files, users can manually create a user-defined tag, leverage reusable tags from a tag library, or leverage AI to auto-suggest tags. To create a tag, type the tag name (e.g., data document categorization), give it a prompt, and this prompt is sent to the LLM to determine how the file should be tagged. If you know what you're looking for, you can optionally put in classification values. If you have your taxonomy and valid values already, you can upload them via CSV. You can also add conditions on the tagging tree — for only legal and contract files, use sub-tagging to pull out more data around contract type (supplier contract, NDA, materials and time contract) — plus unconditional tags and any number of different tags.
To generate metadata, choose what files you want, choose what tags you want, and say generate metadata, then see the results. You can see the document category (e.g., engineering documents, contracts and legal with 68 different files) and clicking any of those automatically filters. You can see the evidence for why AI tagged something — where in the document it found evidence that it's a legal contract. You can also do thumbs up and thumbs down, which is few-shot learning, so you can give it an example if you want to.
For freshness, you can say I only want files within X number of days, or within a specific date range. For data quality, specify the pieces of metadata that you absolutely have to have, and then it will figure out of the 339 files, maybe you really need 200 files. From there, you can create a data slice. You can also give it context as well. A data slice can be filtered based on specific criteria — in this case, 14 files that are only relevant for legal.
When AutoClusters detects a critical hardware error such as an XID79 error where a GPU is completely unusable, the user is notified with no action required; the Slurm operator sets the node down and cancels running jobs, the process receives a SIGTERM with up to two minutes to save checkpoints or flush logs, the canceled job is automatically re-queued, AutoClusters verifies no pod label disables automatic replacement, the Kubernetes node is cordoned and drained, the unhealthy node is removed from the node pool and replaced with a healthy node from spare capacity, a record of the alert and remediation is logged, and once the healthy node comes online the Slurm job restarts, loads the model and checkpoint, and training resumes.
Conor Hale advises that when a node is being replaced, you have up to two minutes to handle the SIGTERM signal to either save your checkpoints or flush your logs before the node goes away.
deterministic
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