标签 · Productivity — Glean

Recent picks

11picks · chronological

09-03

Management Is the AI Superpower: Know What to Ask For

In an experimental UPenn EMBA class, students who had never coded were asked to build a startup from scratch in four days using Claude Code, Google Antigravity, ChatGPT, Claude, and Gemini. The author estimates their output was roughly an order of magnitude further along than a full pre-AI semester used to produce. The lesson: once AI can execute fast and cheaply, the scarce skill is delegation — specifying what 'done' means and judging whether the result is good. He proposes a trade-off among human baseline time, the AI's probability of success, and AI process time (prompting, waiting, checking). Applying OpenAI's GDPval numbers, experts took seven hours on tasks where GPT-5.2 now ties or beats them in 72% of evaluations; with an hour of review, expected savings are about three hours per task, with failed attempts costing more. He also notes that PRDs, five-paragraph orders, and deliverable specs work naturally as agent instructions. Useful for team leads and domain experts adopting agents.

www.oneusefulthing.org · 13 min · Agents · AI · Management

08-25

AI Amplifies Work, Not Reduces It: 2026 Workplace Report

This in-depth read of ActivTrak's 2026 State of the Workplace report uses over 443 million hours of behavioral data from thousands of organizations to challenge the popular 'AI reduces workload' narrative. AI adoption has hit 80%, companies deploy 7 tools on average, yet daily focused time dropped from 14:23 to 13:07 and focus efficiency hit a three-year low of 60%. The report frames AI as an amplifier, not a replacer: email time rose 104%, instant messaging 145%, and collaboration 34%. Only 3% of employees reach the 7-10% AI usage 'productivity sweet spot.' Meanwhile, the dominant employee risk has shifted from burnout to disengagement, costing mid-cap firms $228-355M annually. For managers assessing AI ROI, AI product leads, and engineers interested in organizational effectiveness.

mp.weixin.qq.com · 9 min · AI · AI Engineering · AI Industry

07-21

12 Claude Shortcuts That Slashed My Workflow in Half. Here's the Full List.

The author tracked daily operations in Claude and Claude Code for a week, finding that about 51 minutes per day were wasted on clicking, navigating, waiting, and resending. By testing every shortcut, 12 were selected (6 browser + 6 terminal) with quantified time and token savings. After adoption, daily ops dropped to 22 minutes, saving 29 minutes and ~35% tokens. Essential reading for heavy Claude users.

x.com · 11 min · Ai Tooling · Claude Code · LLM

07-18

Unibase Memory: Share Context Across ChatGPT, Claude, and Gemini

When using multiple AI tools, context is lost between sessions, wasting hours re-explaining. Unibase Memory is a Chrome extension that captures, organizes, and injects memory across ChatGPT, Claude, and Gemini, enabling shared context. The post covers a 5-step setup from installation to advanced workflows (research-to-draft, persistent brand voice, cross-tool building), with local encryption and optional decentralized sync. For engineers and creators juggling multiple AI models, it offers a practical solution to AI memory fragmentation.

x.com · 12 min · AI Engineering · Ai Tooling · Context Engineering

07-15

How to Create Loops with Claude

This article advocates shifting from writing single prompts to designing loops—automated systems that keep AI agents working without human intervention. It breaks down a loop into six components: automation triggers, git worktrees for parallel isolation, skills (procedure manuals), connectors, sub-agents, and persistent memory files (e.g., STATE.md). The evaluator-optimizer pattern is highlighted: one agent generates, another verifies against objective gates like test suites or type checkers. Stop conditions must be checkable by external signals, not the agent's own claim. An autonomy ladder (suggest, draft, apply low-risk, full auto) helps gradually earn trust. The article also warns about token costs and the need for command allowlists in unattended loops.

x.com · 10 min · Agent Architecture · AI Engineering · Claude Code

07-09

The /teach Skill

This article introduces the /teach skill, an AI agent skill designed for long-term, cumulative learning. Unlike one-off Q&A, /teach turns a directory into a persistent teaching workspace. It grounds lessons in vetted, high-trust resources (documentation, books) with citations, rather than relying on the model's parametric knowledge. It uses ADR-style learning records to track progress and dynamically adjusts lesson difficulty based on the zone of proximal development. The article details the workspace structure (lessons, reference, learning-records) and teaching philosophy: prioritizing storage strength over fluency illusion, and using desirable difficulty, retrieval practice, and spaced repetition for long-term retention. Ideal for engineers who want to learn a language, framework, or theory as a project over multiple sessions.

www.aihero.dev · 3 min · Ai Tooling · Developer Tools · Education

07-07

A Field Guide to Fable: Finding Your Unknowns

The author shares hands-on experience with Claude Fable for agentic coding, emphasizing that the prompt (map) never fully matches the codebase (territory). He categorizes unknowns into four types (known knowns, known unknowns, unknown knowns, unknown unknowns) and provides practical techniques to systematically discover them: blindspot passes, brainstorming & prototypes, interviews, references, implementation plans, implementation notes, pitches, and quizzes. Ends with a real example of editing the Fable launch video. Suitable for engineers using AI-assisted coding.

x.com · 13 min · Agent Engineering · Agents · AI Engineering

07-07

Human-in-the-Loop Workflow Design: From Approval Fatigue to Planned Review

Based on an analysis of 400,000 Claude Code sessions, this article reveals that 93% of permission prompts are approved, leading to 'consent fatigue' where humans are nominally in the loop but functionally tuned out. The author proposes restructuring the workflow into three layers: input (precise task description, constraints, examples), steering (plan-level review instead of per-action approval), and output review (defining quality criteria and self-assessing). A single evaluation checkpoint improved generation quality by 8–10% in controlled tests. The article provides actionable steps to move from per-action approval to strategic intervention, targeting AI engineers and agent developers.

x.com · 11 min · Agent Architecture · AI Engineering · Claude Code

06-27

Agentic Code Review

When coding agents produce thousands of lines of often solid code in minutes, the engineering bottleneck shifts from writing to trusting, making review the most leveraged skill in software. Multi-source 2026 data (Faros AI, CodeRabbit, GitClear, GitHub) shows: AI users generate ~4x raw output but only ~12% more delivered value; code churn up 861%, defect rate from 9% to 54%, review duration up 441.5%, and zero-review merges up 31.3%. The article argues the fix is not to stop using AI but to tier review effort by blast radius: light for solo no-user projects, heavy for large enterprises. Specific advice: triage PRs upfront, require evidence before review, watch test rewrites, run two differently-structured AI reviewers in parallel, and upgrade humans from line-level review to spot-checking and auditing. The durable skill is understanding a system well enough to stand behind it.

addyosmani.com · 29 min · Agent Engineering · AI Engineering · Code Review

06-27

The Orchestration Tax: When Agents Parallelize but Your Attention Doesn't

Starting agents is cheap, but closing the loop (review, merge, judgment) must route through a single serial processor: you. The author frames human attention as the GIL for AI agents — you can spawn 20 agents, but your throughput to main is bounded by your serial review rate. Amdahl's Law applies: the non-parallelizable judgment fraction caps any speedup. Strategies include: scaling agent count to review rate, not UI; sorting tasks into async-able vs. judgment-heavy; batching reviews; making agents prove correctness with tests/screenshots. The insight: busy ≠ productive. Unpaid orchestration tax accumulates both technical debt and cognitive debt, silently eroding your mental model of the system.

addyosmani.com · 9 min · Agent Architecture · Agent Engineering · Cognitive Load

06-22

How to fix your entire life in 1 day

The author argues that most New Year's resolutions fail because people try to change actions without changing their underlying identity. Drawing on Adlerian psychology, Loevinger's stages of ego development, and cybernetics, he presents a one-day protocol: morning psychological excavation (define anti-vision and vision), daytime interrupt-based prompts to break autopilot, and evening synthesis into three-lens goals (year, month, day). The core thesis is that all behavior is goal-oriented at an unconscious level, and real change requires replacing the lens through which you perceive reality. For readers interested in self-development, behavior design, and goal-setting.

x.com · 31 min · Behavior Change · Goal Setting · Identity Change