37,617 results · Most relevant
Outside of not training at all, the number one mistake people make in pursuit of fitness goals is failing to progressively overload. Progressively overload your training — increasing sets, reps, and weight over time — to continue causing adaptation and see gains in strength and hypertrophy.
Dr. Andy Galpin advises that if you're trying to grow a specific body part or muscle, it's very important that you're actually seeing progress in that area. Don't worry about what the textbook says an exercise is supposed to target — if you're not in the right position or at the right angle, it may do very little for that muscle. If an exercise isn't targeting the intended muscle, you may need to adjust to an incline bench, decline bench, or pec fly. He describes a coaching scenario where squats are excellent for quad development in general but may not work for a specific individual, in which case a coach may move them to a machine to isolate that muscle group and ensure development.
Steve Jamison recommends calculating the intelligence cost of a PR by combining what you pay your engineers plus what you pay for AI into one cost bucket, and taking the ratio of cost to throughput to get the answer on whether AI spend is worth it.
Attributed to Steve Jamison as his recommendation; not a system guarantee.
Somya Rai's advice: use two separate models rather than one combined model, because physical and online transaction portfolios have different base rates and different signal geometry.
MD Sharique's advice: concatenate three order-invariant statistics (mean, max, standard deviation) rather than just averaging. A single dangerous neighbor among many genuine ones is exactly the pattern fraud rings create, and mean pooling is precisely the operation that would hide it; standard deviation distinguishes a uniform, boring neighborhood from one with a live outlier.
Somya Rai's advice: optimize for precision at a fixed K (the alert slice) rather than area under the curve — a fraud model must move real fraud into the top alert slice at the same latency to be worth anything.
MD Sharique's advice: hold a soft target fraud rate of 0.5% for card-present merchant transactions (about 10× the 0.05% natural rate) and 6% for online transactions (about 10× the 0.6% natural rate). Holding a soft target fraud rate without breaking connectivity was, per Somya Rai, the key to graph sampling.
MD Sharique's advice: make each calendar month its own disjoint graph with no edges crossing the boundary, and fit every scalar and vocabulary on training months only, then freeze them, to prevent distribution leakage. Building graph features leakage-safe is harder than it sounds when edges carry timestamps plus 32 derived fraud-detection features.
For aggregation, don't just average — concatenate three order-invariant statistics: mean, max, and standard deviation, then combine with the node's own vector through learned weights. With four neighbors where three look normal (scores around 0.1) and one is 0.92, averaging gives 0.32 and dilutes the ring-linked neighbor into the crowd, but taking the max lets it come through untouched at 0.92. A single dangerous neighbor among many genuine ones is exactly the pattern fraud rings create, and mean pooling is precisely the operation that would hide it. Standard deviation adds a third view: how much the neighborhood disagrees with itself. Concatenating three statistics costs nothing extra at serving time — no additional learned parameters, and still a fixed-size output no matter how many
The four-neighbor example is hypothetical, as stated by the speaker; some figures carry a possible numeric error flag.
Michele Grant's third artifact is the autonomy scorecard, which the steering committee uses to grant autonomy in four stages. Stage one is shadow mode: analysts build the report as they do today, the agent runs alongside, and the team compares the two. Stage two is the supervised draft: the agent compiles the first draft and the senior analyst reviews every line item. Stage three is exception and sample: the analyst reviews lagged initiatives plus a random sample of claims. Stage four is exceptions only: the analyst reviews only high-risk anomalies, supported by periodic independent sampling.
Michele Grant says that for every agent, leadership has to decide four things: what it is permitted to do, who owns it, what evidence supports it, and where its authority stops. She stresses this is an executive operating mandate, not an engineering task.
Michele Grant says evidence for greater agent autonomy comes from how the agent is built, and she looks for three artifacts: an auditable decision log, a validation document, and an autonomy scorecard.
Michele Grant insists that every single metric requires a pre-deployment baseline. If the IMO cannot state the baseline today, do not deploy — if you can't measure where you started, you can never prove the agent moved the needle.
Michele Grant says the steering committee needs a written value case agreed upon before the agent ever touches production data. The value case should use concrete metrics: cycle time with a hard target (e.g., cutting a five-day report to two days), analyst capacity (e.g., four analysts spending half their week is 80 hours a week the agent gives back), and detection time — how long from the first sign of a slip to the moment the steering committee sees it — which she calls the most important metric.
Michele Grant says the agent needs one employee assigned to oversee it every day — in the example, the senior analyst who reviews the draft before it reaches the committee. That overseer's role requires four specific capabilities: understanding the agent's operational boundaries, interpreting its output with context, having authority to override or reverse it, and having a clear kill switch to shut it down safely the second it behaves unexpectedly.
Michele Grant's first artifact is an auditable decision log: for every work stream the agent evaluates, it records the raw data ingested, the tool calls executed, output generated, and the human sign-off. The log also captures the accountable executive, data lineage, dependencies across source systems, validation results, escalation prompt, and the kill switch runbook, and documents regression triggers — any updates to the prompt, base model, or source API that require full revalidation before running in production again.
Michele Grant closes with a call to action: ask the three questions of every agent you build, fund, or govern — what value does it create, who is on the hook for it, and what evidence gives it permission to run with greater autonomy. Leadership earns agent trust by answering these three questions, and building an agent that works is just the first step; answering them is what earns it the trust to run at scale.
Michele Grant defines accountability as defining where the agent's authority stops, documented in a one-page policy she calls the Human Work Standard. The standard maps, for every fork in the process, the decision, the human role authorized to make it, the required evidence, and the escalation path. In the example, the senior analyst decides whether a variance is an issue, the work stream lead decides how to remediate the shortfall, and the integration lead decides whether to formally flag an initiative as at risk to the steering committee.
Dr. Chris Thompson recommends that everyone should start getting screened for colon cancer at age 45, arguing that starting at 45 is better than starting at 50, 55, or 60. If you have a family member that has had cancer, you want to start at 40, or 10 years younger than when they were diagnosed if they were younger.
Consequential creator advice requires independent verification before Today can promote it.
Dr. Chris Thompson recommends that when choosing a surgeon, patients should check their case volume — how many procedures they do and how many they have done over their career — because volume is important, though it is not the whole story.
Consequential creator advice requires independent verification before Today can promote it.
Choose sources → to get a library of your own and a daily brief.