标签 · Ai Tooling — Glean

Recent picks

28picks · chronological

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

07-17

Graphify: Turn Any Codebase into a Queryable Knowledge Graph for AI Coding Assistants

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, and videos into a knowledge graph for AI coding assistants like Claude Code, Cursor, and Gemini CLI. It uses tree-sitter AST for deterministic, local-only code parsing, and delegates semantic extraction for non-code assets to the assistant's model. The output includes an interactive HTML visualization, a Markdown report, and a reusable graph.json, enabling natural-language queries, path traversal, and concept explanations. Every edge is tagged EXTRACTED or INFERRED, so users always know what was read vs. guessed. Ideal for engineers onboarding large unfamiliar codebases or augmenting long-tail maintenance workflows.

github.com · 47 min · Agent Engineering · Ai Tooling · Code Intelligence

07-17

Fable's judgement

Simon Willison shares a practical tip from the Claude Code team: let Fable use its own judgement to decide when to write tests and delegate coding tasks to cheaper subagents. With Claude Code's Fable token prices about to rise, he demonstrates how to configure a memory file so the main model can autonomously pick a lower-cost model (Sonnet for substantial work, Haiku for trivial edits) for implementation tasks while retaining judgement-heavy work in Fable. Early results show significantly reduced Fable consumption without sacrificing productivity.

simonwillison.net · 2 min · Agent Engineering · Ai Tooling · Claude Code

07-16

Cut Claude Code token costs by rendering system prompts & history as images

pxpipe is a local proxy that intercepts Claude Code API requests, rendering bulky text parts like system prompts, tool docs, and old history into compact PNG images. Since image token pricing depends on pixel dimensions rather than text length, the approach cuts input tokens by ~60%, leading to a 59–70% end-to-end cost reduction. It rewrites requests before they leave the machine, preserving prompt caching. By default it works with Claude Fable 5 and GPT-5.6, with dashboard controls for opt-in models. It includes profitability gates and benchmarks showing near parity in coding tasks, though exact-string recall is lossy. The project is aimed at developers using LLM coding agents who want to slash API costs without sacrificing functionality.

github.com · 12 min · Ai Tooling · Anthropic · CLI

07-14

Deep Comparison of AI Agent Skill Frameworks: Matt Pocock Skills, Superpowers, and Agent Skills

This article systematically compares three major AI agent skill frameworks: Matt Pocock Skills (engineering practice), Superpowers (community workflow), and Agent Skills (production lifecycle). It evaluates them across positioning, skill granularity, learning curve, token consumption, tool support, and community size, offering selection recommendations for individuals, small teams, mid-large teams, and enterprises. Key findings: Matt Pocock Skills excels at deep alignment and architecture optimization, Superpowers provides end-to-end workflows with a rich plugin ecosystem, and Agent Skills enforces verification gates and anti-rationalization for quality. It also suggests combination strategies. Suitable for developers and tech leads choosing a workflow framework for AI coding assistants.

www.besthub.dev · 8 min · Agent Engineering · Ai Tooling · Comparison

07-09

v1.1: /wayfinder, /to-spec, /to-tickets, grilling improvements, and much more

This article covers the v1.1 release of the skills repository, a toolset for AI agents. Key changes: /to-prd renamed to /to-spec to unify the term 'spec'; /to-plan and /to-issues merged into /to-tickets with blocking edges for both local files and real trackers. Grilling skills now prevent multiple simultaneous questions, add a confirmation gate, and avoid self-grilling. A new /wayfinder skill decomposes large tasks into GitHub issues with dependency graphs, accompanied by /research and /prototype skills. Code review skill integrates Fowler's ten code smells (e.g., mysterious name, duplicated code) with just 10 lines of guidance. TDD skill becomes reference-only, moving refactoring to code review. The recommended workflow: Grilling → Spec → Tickets → Implement → Code Review. Suitable for engineers building with AI agents.

www.aihero.dev · 12 min · Agent Engineering · Ai Tooling · Context Engineering

07-09

The /writing-great-skills Skill

This article introduces `/writing-great-skills`, a meta-skill that serves as a reference framework for authoring and editing predictable AI skills. The core idea is the trade-off between **cognitive load** and **context load**: model-invoked skills cost context load but fire automatically, while user-invoked skills cost zero context load but require you to remember their existence. The article provides tools for managing these loads, including leading words (compact anchors for execution), information hierarchy (progressive disclosure), pruning (single source of truth and no-op test), and failure modes (premature completion, duplication, sediment, sprawl). A must-read for system builders writing consistent, maintainable skills for agents.

www.aihero.dev · 3 min · Agent Engineering · Ai Tooling · Context Engineering

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

07-08

Loop Patterns in Claude Code: A Practical Guide

The Claude Code team's official blog post introduces four loop modes: turn-based, goal-based, time-based, and proactive loops. It covers how each is triggered, stopped, and when to use them, along with token management and code quality tips. Practical CLI commands and SKILL.md examples are provided. The article emphasizes starting simple and gradually automating repetitive tasks. Essential reading for engineers using or evaluating Claude Code for autonomous development.

x.com · 9 min · Agents · AI Engineering · Ai Tooling

07-03

Continually Improving Our Agent Harness

Cursor shares how it continuously improves its agent harness, covering context window evolution from static to dynamic fetching, a two-layer evaluation system (offline benchmarks and online A/B tests measuring code keep rate and user satisfaction), tool call error classification and repair pipeline (anomaly detection + automated log analysis with Cloud Agents), per-model customization of tool formats and prompts (e.g., patch vs. string replacement), and mid-chat model switching with specialized instructions. The post concludes with a vision of multi-agent architectures where the harness orchestrates specialized sub-agents.

cursor.com · 13 min · Agent Engineering · Ai Tooling · Context Engineering

07-03

The Claude Opus 4.8 Setup Guide: How to Get Maximum Quality for Minimum Cost (Exact Config Inside)

A hands-on configuration guide published day after Claude Opus 4.8's release. The core value lies not in benchmark improvements (SWE-bench 87.6% → 88.6%) but in three operational features: Effort Control for per-task reasoning depth, Fast Mode at 3x cheaper than before, and Dynamic Workflows supporting up to 1,000 parallel subagents. The author provides a cost-optimization matrix routing tasks to Haiku/Sonnet/Opus at different effort levels, claiming ~50% monthly savings ($400-600 down to ~$205) for heavy users. Includes copy-paste configs for environment variables and settings.json. Practical for Claude Code users focused on cost control, though the savings claims are unverified estimates.

x.com · 9 min · Agents · Ai Tooling · Claude Code

07-02

Steering Claude Code: CLAUDE.md, skills, hooks, rules, subagents and more

This official guide from Claude Code maps out seven mechanisms for injecting instructions: CLAUDE.md, rules (with optional path scoping to save tokens), skills (dynamically loaded on invocation), subagents (fully isolated context, ideal for side tasks), hooks (deterministic triggers with low context cost), output styles (highest instruction weight, but replace defaults), and append-system-prompt (additive but has diminishing returns). It details when each loads, its context cost, and typical use cases. Key advice: use hooks for deterministic behavior over CLAUDE.md, skills for multi-step procedures, path-scoped rules for API-specific constraints, and managed settings for non-overridable guardrails. Aimed at engineers customizing Claude Code for production workflows.

claude.com · 11 min · Agent Architecture · Ai Tooling · Claude Code

06-26

Human in the /loop

The author shares a practical workflow for coding with AI agents: define a verifiable 'definition of done' (model eval score, QA pass, green tests, performance benchmark), wrap it in a loop for the agent to iterate autonomously, and get notified via Slack when a decision is needed or the task completes. Loops run in the cloud, not on the local machine. The author runs 3-5 long loops concurrently plus shorter tasks. For engineers looking to level up from one-shot agent interactions to long-running autonomous optimization tasks.

x.com · 4 min · Agent Engineering · Ai Tooling · Context Engineering

06-26

Loop engineering: the 14-step roadmap from prompter to loop designer

This post from @0xCodez on X provides a comprehensive 14-step roadmap for transitioning from manual prompting to designing autonomous loop systems in AI-assisted coding. Based on Anthropic engineering docs, Addy Osmani's essay, and recent studies, it's structured in three tiers: first, a 4-condition test to decide if a loop is warranted; second, five building blocks (automations, worktrees, skills, connectors via MCP, sub-agents with maker-checker split); third, building the minimal viable loop and avoiding failure modes like the 'Ralph Wiggum loop', comprehension debt, and security tax. The author emphasizes that loops are not universal—they only earn their cost when tasks repeat, verification is automated, the token budget can absorb waste, and the agent has senior engineer tools. Ideal for engineers already using coding agents who want to orchestrate them into batched, automated workflows.

x.com · 23 min · Agents · AI Engineering · Ai Tooling

06-24

Loop Engineering: How One Loop Ships 259 PRs a Month

This article breaks down the engineering of AI-driven development loops, contrasting a single engineer shipping 259 PRs in a month with a runaway loop that burned $47,000. It dissects six essential components—state file, automation/scheduling commands (e.g., /loop, /schedule, /goal), git worktrees, skills, MCP connectors, and sub-agents (writer vs. checker)—with concrete configuration examples for both Claude Code and OpenAI Codex. The piece provides a brake configuration template (max_turns, max_budget_usd, scope, circuit_breaker), describes four failure modes, and offers low-cost starting strategies. Aimed at engineers building or evaluating AI agent workflows.

x.com · 12 min · Agent Engineering · Ai Tooling · Claude Code

06-23

Loop Engineering

Loop Engineering proposes a shift from hand-prompting coding agents to designing autonomous loops: a system with five components (scheduled automations for discovery, worktrees for parallel isolation, skills to codify project context, plugins/connectors via MCP, and verifier sub-agents) that lets agents iterate without manual intervention. The post maps these primitives across Codex and Claude Code, noting that memory persisted outside the conversation (via AGENTS.md or Linear) is the critical sixth piece. The core insight is that loop design is harder than prompt engineering—the engineer's role moves from operator to system architect, while verification burden, comprehension debt, and cognitive surrender remain unresolved challenges that the loop itself cannot eliminate.

addyosmani.com · 14 min · Agent Architecture · Agent-Memory · Ai Tooling

06-22

Vercel's AI Design Spec: A Textbook Example

This article deeply analyzes Vercel's DESIGN.md, showcasing how to write an efficient and executable design specification for AI. It breaks down Vercel's approach across color, spacing, typography, motion, copy, and accessibility, revealing the thinking behind it. The color system uses a 100-1000 scale where each number corresponds to a fixed UI state (default, hover, click), eliminating AI guesswork. Spacing is limited to 9 values based on 4px increments, enforcing rhythmic consistency. Typography adopts role-based thinking (heading/label/copy/button) instead of pixel-based thinking. Motion design advocates 'no animation is often best' and gives precise durations per scenario. This piece is valuable for product managers, front-end engineers, and AI tool developers aiming to improve AI-generated UI consistency or build their own design specs.

x.com · 4 min · Ai Tooling · Design System · Developer Tools

06-21

A local HTML editor built for human-AI collaboration

Lavish-axi is a local CLI tool that opens AI-generated HTML artifacts in a local browser, allowing developers to annotate elements, select text, take screenshots, and send structured feedback directly back to the AI agent. It runs a local server with a browser chrome, supporting live reload, layout auditing (overflow, clipped text, overlapping text), feedback queuing, and long polling. Built as an AXI, it requires no setup beyond `npx` and can be integrated as a skill into agents like Claude Code. It's ideal for engineers who need to iterate on AI-generated visualizations, plans, or UI mockups with precise feedback.

github.com · 18 min · Agents · Ai Tooling · CLI

06-20

Stop building Foxconn factories for your agents

Garry Tan reflects on his experience building a 540,000-line Rails app, using the Foxconn factory as a metaphor for the dominant AI agent development pattern: wrapping hyper-intelligent models in mountains of code, tests, and guardrails. He argues the economics have inverted—model calls are now cheap and the models are smarter, making the old instinct to ration and control them obsolete. The new paradigm is 'just-in-time software' and 'skill packs,' where lean markdown instructions and minimal TypeScript replace bloated engineering frameworks. A concrete example shows a hackathon judge agent built in an afternoon, doing what previously required a full software project. The essay challenges engineers to abandon the 2013 mental model of measuring capability by lines of code and to embrace 'tokenmaxxing' to gain a 2-3 year competitive advantage. It is aimed at engineers who are coding with AI but still trapped by traditional software metrics and mistrustful architectures.

x.com · 14 min · Agents · Ai Tooling · Code

06-20

Thin Harness, Fat Skills

YC partner Garry Tan argues that the bottleneck in AI agents is not model intelligence but context and process management. He introduces five definitions: Skill files (reusable Markdown procedures), a thin harness (a ~200-line loop for running the model and managing context), resolvers (routing tables that load the right context at the right time), the latent-versus-deterministic boundary (judgment vs. repeatable execution), and diarization (distilling structured intelligence from unstructured data). A real-world example from YC Startup School demonstrates how the same skill file, invoked with different parameters, handles breakout grouping, lunch matching, and real-time pairing, and then improves itself by analyzing mediocre feedback. The piece offers concrete design principles for engineers building agent systems that compound improvements over time.

x.com · 12 min · Agent Architecture · Agents · Ai Tooling

06-19

On the LOC controversy: doing the math on a 810x developer output increase

Garry Tan, CEO of Y Combinator, responds to criticism of his claim of shipping 600,000 lines of production code in 60 days. He concedes LOC is a flawed metric but provides a rigorous before-and-after comparison: in 2013, as a part-time coder, he averaged 14 logical lines per day; in 2026, with the same day job, he now averages 11,417. Even after deflation for logical SLOC and an aggressive 2x AI-verbosity factor, the daily rate is 5,708 lines – a 408x increase. Quality data is provided: 2.0% revert rate, 6.3% fix commits, and a test suite that grew from 100 to over 2,000 tests. He details his testing infrastructure (Playwright-based browser CLI, slop-scan), product traction (75k GitHub stars, ~7k WAU), and argues the real shift is the collapse of the “idea to shipping" cycle from weeks to hours. The core argument is that the productivity ground has shifted for all engineers, not just him.

x.com · 12 min · AI · Ai Tooling · Claude Code

06-18

Stop Giving Every Agent Its Own Skull

Pejman argues that we are replicating a core human limitation—knowledge siloed in individual brains—inside agent systems. Using OpenClaw, Codex, and Claude Code, each agent retains isolated context about him and his projects. The critical gap is not in the repo's artifacts but in the session itself: the debates, dead ends, and pruned idea branches that markdown cannot capture. With literal physical separation across machines, this fragmentation intensifies. The missing layer is a shared, user-owned memory substrate that transcends agent boundaries. He highlights GBrain and CASS as early signals tackling parts of this problem. The piece resonates with engineers building or deeply integrating multi-agent workflows.

x.com · 7 min · Agent Architecture · Agents · Ai-Memory

06-17

Kimi Code + K2.7 Code Hands-On: Can It Replace Claude Code?

A hands-on evaluation of Kimi Code paired with the K2.7 Code model as a potential Claude Code replacement. Tests include using video understanding to replicate an ink-wash animation, using the /goal command to autonomously compress a 2.1MB image to below 120KB, and running a suite of web UI, game, and animation programming challenges. Kimi Code is found to be highly compatible with Claude Code's commands and permission system. The /goal command enables fully unattended task execution. The K2.7 model demonstrates stable code generation capability with a claimed 30% average reduction in reasoning token consumption. A unique built-in Datasource plugin allows querying real-time financial data, company records, and academic papers via natural language within the CLI.

mp.weixin.qq.com · 1 min · Agent Architecture · Ai Tooling · Claude Code

06-10

AI Skills Marketplace for Product Managers: 100+ Structured Workflows from Discovery to Growth

pm-skills is an AI skills marketplace for product managers, packing 100+ codified PM skills and 42 chained workflows into 9 installable plugins. It transforms established product methodologies by Teresa Torres, Marty Cagan, and others into structured, step-by-step AI-guided processes, going beyond generic text generation to enforce product-thinking rigor. Covering the full lifecycle from discovery to growth, it works as a Claude Code/Cowork plugin and offers cross-platform skill support. Ideal for PMs and founders who want to embed AI into their decision-making workflow, not just accelerate document output.

github.com · 29 min · Agent Skill Repository · Ai Tooling · Claude Code Marketplace

06-09

AI Agents: What They Are and How to Build a Telegram Bot with Claude Code

This guide clarifies that AI agents are not a category but a spectrum from simple chat to autonomous loops, defined by tools, memory, and a loop. It then provides a no-code, step-by-step tutorial on building a Telegram bot agent with Claude Code, including system prompt templates, systemd deployment, persistent memory, cost tracking, and practical skills. Also addresses the common memory problem and offers concrete fixes. Suitable for engineers who want a practical agent without writing code themselves.

x.com · 17 min · Agents · Ai Tooling · Claude Code

06-04

A harness for every task: dynamic workflows in Claude Code

Anthropic engineer Thariq Shihipar details dynamic workflows in Claude Code, where Claude auto-generates custom JavaScript harnesses to orchestrate multiple subagents. It explains how this overcomes single-context-window failures like agentic laziness, self-preferential bias, and goal drift. Common patterns such as classify-and-act, fan-out-and-synthesize, adversarial verification, and tournament are illustrated with concrete use cases from migrations and deep research to root-cause analysis. The post candidly advises that workflows are token-heavy and not needed for routine coding, offering practical tips on token budgets, saving workflows as skills, and pairing with /goal and /loop.

x.com · 15 min · Agent Architecture · Agents · Ai Tooling

06-03

Meta-Meta-Prompting: The Secret to Making AI Agents Work

Garry Tan, CEO of Y Combinator, presents GBrain, his personal AI agent system built on 100,000 pages of structured knowledge and over 100 modular skills. The core architecture follows a “thin harness, fat skills, fat data” philosophy: a lightweight runtime like OpenClaw routes messages to self-contained skill files, which are themselves created and improved by a meta-skill called Skillify. Tan illustrates the compounding value through the “book-mirror” pipeline, which cross-references a book’s ideas with his actual life events, journal entries, and meeting notes. He details the evolution from an error-prone first version to a reliable workflow using multi-model cross-modal evaluation and deep brain retrieval. Other examples include automated meeting preparation that synthesizes months of accumulated context and entity propagation that updates every related person or company page after a conversation. The article provides a concrete architecture overview, evidence of iterative improvement, and a four-step starting guide for developers building personal compounding AI systems.

x.com · 16 min · Agents · Ai Tooling · Knowledge Graph

06-02

A Multi-Agent IDE to Run Claude Code, Codex, and Others in Parallel Git Worktrees

Orca is a desktop and mobile IDE designed to run multiple AI coding agents—such as Claude Code, Codex, and Grok—concurrently. It leverages Git's worktree mechanism to give each agent an isolated working directory, eliminating the need for stashing or branch juggling. Users can observe and control all agents from a single interface with tabbed panes, built-in diff review, and direct GitHub Issue/PR integration. It's built for developers who rely on CLI-based coding agents and need to handle multiple features or refactors in parallel.

github.com · 9 min · Agents · Ai Tooling · CLI