Alpesh Kumar, Author at Alpesh Kumar » Page 8 of 17

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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Build an AI Agent Evaluation Flywheel That Improves Prompts — a text-free circular technical schematic shows recorded agent traces being separately evaluated for outcomes and trajectories, classified into recurring failure types, turned into prompt candidates, and checked against development, hold-out, and regression cases before feeding evidence back into the next run. Visual direction: idea: a trace-aware evaluation loop turns recurring agent failures into verified prompt changes; subject: AI agent evaluation flywheel; mechanism: captured tool traces flow through separate outcome and trajectory evaluation, failure taxonomy, prompt variants, and hold-out/regression verification with a safety gate; composition: a single clockwise five-stage circular schematic with evidence records at the top and a verification boundary closing the loop; palette: warm off-white, graphite, deep forest green, muted rust, clay orange, restrained golden yellow; exclusions: dark navy, neon-blue glow, floating UI cards, network nodes, generic AI circuitry, people, robots, logos, readable artwork text.

AI Agents, Artificial Intelligence

Build an AI Agent Evaluation Flywheel That Improves Prompts

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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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Vector Quantisation: How Code Books Compress Similar Data — an illustrated flow of many similar input vectors being matched to a small numbered code book, then represented by one compact index to show lossy shared-dictionary compression.

Machine Learning

Vector Quantisation: How Code Books Compress Similar Data

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