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.
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.
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.
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.
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.
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.
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.
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.
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.