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Perform the hinge exercise: take a wide stance, sweep hands along the floor, reach through, come up and reach overhead, squeeze glutes at the top. Do 10 repetitions. Use a wide stance to force the hinge to occur at the hips, not the waist. Maintain a natural curvature in the spine at the bottom; do not let the back cave in.
Perform the relay exercise: stand with feet close together, weight on one side, one hand near cheek, other hand behind, then lunge the opposite leg back while driving the arm up, reaching out to challenge core stability. Do 5 reps on each side. Maintain a stable knee position over the toe; do not let the knee cave inward.
Perform the sweeping lunge: get on the floor on one toe (or flat foot if possible), other leg straight out front, hands on ground, then slide the front foot down, let hands follow, and sweep to the side. Having hands in contact with the ground provides stability and safety, allowing beginners to work on the movement.
An agent architecture should include an agent runtime, a memory and knowledge layer, a memory manager sitting on top of a data platform, and a continual learning loop that feeds model updates back into the agent runtime.
GEPA's Pareto-based candidate selection maintains a much more balanced search process, eventually converging to a much higher score than an LLM-in-a-loop approach, with almost twice the performance gains and more than half of GEPA's total gains attributable to this strategy.
GEPA keeps a Pareto pool where it keeps every single candidate that wins on even one training example, not just the top scorer.
OptimizeAnything is a universal API for optimizing any text parameter. To use it, provide the set of problems you care to be solved along with an evaluator (fitness) function that returns a score plus any available domain-specific side information. The evaluator's side-information return value is an open-ended dictionary that can contain literally anything. Call OptimizeAnything with a fitness function and a set of problems, and it returns an optimized solution.
Roman Ugarte says when the model gets smart enough to do something natively, delete the scaffolding built around it; adding features instead of deleting old scaffolding bloats the product, and hacky pro tips and crazy knobs make it confusing.
A task is essentially a unit test for your model, described as YAML files. In ARIA's tasks, an LLM judge determines what correctness means in the context of the question, a second LLM judge determines if the insights are actually interesting, and a rule-based judge checks if the agent was able to generate a result within a set number of tool calls, e.g., six, for expediency. About 200 tasks are clustered together into an eval suite that runs nightly, tracked in Weave with a listing of every night's eval.
The team's evaluation results are emitted to Weave, where they have a common dashboard for making go, no-go decisions on prompt or architectural changes.
The team's offline research loop involves forming hypotheses, implementing candidate agents, and analyzing evals, with data fed from Weave.
The team runs a live production loop where they trace production logs, review them as humans, use LLMs to complement that analysis, and transform findings into tasks.
Jemima Abu recommends telling your agent to default to the best way of writing things rather than the most popular way, and including this instruction in your skill — for example, custom JavaScript accordions are the popular approach but not the best approach.
Jemima Abu discourages throwing an accessibility overlay on a website and acting like that's the fix-all; she says she hates the stick-person overlay widgets in the corner of websites and calls them terrible. Shoving a bunch of ARIA labels into a completed product and calling it an accessible website also does not work.
Several anti-patterns emerged: trusting a good-looking AI forecast result (like 4% error) without expert review because errors can cancel out; relying on AI-extracted numbers from filings without verifying them against disclosed figures; applying a growth rate to the wrong population base (US-Canada increase applied to the whole international membership); and accepting AI-invented assumption ranges (5–30%) that miss the real disclosed figure (45%).
EvoUndo captures a pre-state witness before applying a mutation, which provides the information to restore the original state and later verify that recovery actually worked.
Once the model has read the pointers, it structurally cannot generate numbers — unless it dereferences a pointer, that number or fact cannot appear in Kepler's output. If a model cannot de-resolve a pointer, it cannot print that number out. If you grab something out of a hash map that doesn't exist, you don't get anything back.
The source argues that reaching customers who aren't searching requires a different medium: short-form video (TikTok, Reels, Shorts) serving as a discovery channel at the top of the funnel where strangers find you before searching your name.
If you only do OCR on a figure and want to know a value like Intel's revenue in Q2 2025, you lose all information and will never really be able to match the page. Instead, create an embedding on the visual representation — let the model look at the page screenshot and create the embedding based on that — to get rid of the lossy OCR-based pipeline.
Requests per second (demand) is the bottleneck for GPU latency, and time-to-first-token is what to optimize for. Throughput matters less than time-to-first-token: 200 tokens per second is enough for interactivity. As soon as time-to-first-token exceeds five seconds, user complaints blow up — that's the empirical limit. The aim is sub-three-second time-to-first-token.
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