Alpesh Kumar
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AI automation

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How Jev Works: The Parallel Decision Model That Skips Token-by-Token JSON
Artificial Intelligence, Software Architecture

How Jev Works: The Parallel Decision Model That Skips Token-by-Token JSON

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AI Agent Evaluation in Production: Trace the Path, Verify the Outcome
AI Agents, Software Architecture

AI Agent Evaluation in Production: Trace the Path, Verify the Outcome

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Your First Personal AI Agent: A Focused Weekend Build — a warm cream technical schematic shows current-week meeting notes constrained by an agent contract, optionally checked against one read-only source, transformed into a structured priorities draft, and stopped at a human approval boundary before any external action; visual direction: idea: a first personal AI agent produces a reviewable weekly-priorities draft from tightly bounded meeting notes, with no autonomous external action; subject: focused personal AI-agent weekend build; mechanism: current-week notes pass through a clearly labelled contract boundary and one limited read-only lookup into a structured priorities draft, then stop at an explicit human approval gate before any external-action area; composition: landscape editorial explainer card with the exact title large across the upper third and a lower left-to-right paper-like workflow schematic, ending at a prominent approval stamp boundary and a muted crossed-out external-action zone; palette: warm cream background, charcoal typography, oxblood red boundary accents, muted olive green for approved draft, dusty apricot for source notes, slate gray connectors; exclusions: dark navy, neon-blue glow, floating UI cards, network nodes, generic AI circuitry, people, robots, hands, dashboards, physical-machine metaphors, decorative charts, logos, secondary readable text.
AI Agents, Artificial Intelligence

Your First Personal AI Agent: A Focused Weekend Build

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Why Final-Answer Evals Leave AI Agent Failures Invisible — a warm editorial execution-trace schematic contrasts a reassuring green final-response indicator with the visible failed path beneath it: wrong tool selection, a quoted-number parameter mismatch, and a missing update state, all examined by a trajectory evaluator. Visual direction: idea: an apparently successful answer hides a failed execution path; subject: AI-agent trajectory evaluation; mechanism: evaluator compares final claim with recorded tool selection, typed arguments, missing action, and state transitions; composition: left-to-right audit strip beneath a detached green outcome capsule with an inspection bracket; palette: warm ivory, charcoal, muted sage, terracotta, ochre; exclusions: dark navy, neon-blue glow, floating UI cards, network nodes, generic AI circuitry, people, robots, logos, readable artwork text.
AI Agents, Artificial Intelligence, Software Architecture

Why Final-Answer Evals Leave AI Agent Failures Invisible

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