标签 · Open Source — Glean

Recent picks

16picks · chronological

07-17

Kimi K3: Open 2.8T Frontier Model for Long-Horizon Coding and Knowledge Work

Moonshot AI releases Kimi K3, a 2.8T-parameter open model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), activating 16 out of 896 experts with a reported 2.5× scaling efficiency improvement over K2. It supports native vision and a 1M-token context window. While generally trailing top proprietary models like Claude Fable 5 and GPT 5.6 Sol, K3 achieves competitive scores on coding, knowledge work, and reasoning benchmarks. The post details case studies: GPU kernel optimization, a from-scratch Triton-like compiler (MiniTriton), 3D open-world game development, autonomous chip design (48-hour run), and rapid scientific research reproduction. K3 is available now via Kimi.com, Kimi Work, Kimi Code, and API; full weights open-sourced by July 27, 2026. Recommended for AI engineers, agent developers, and researchers needing long-horizon agentic capabilities.

www.kimi.com · 19 min · Agent Engineering · Coding · Kimi K3

07-13

Lightweight terminal-based coding agent with local execution and cloud integration

Codex CLI is a lightweight coding agent from OpenAI that runs locally in your terminal, powered by your ChatGPT subscription or API key. Unlike IDE plugins or desktop apps, it offers a pure CLI experience tailored for terminal-centric developers. It supports macOS, Linux, and Windows and can be installed via shell script, npm, or Homebrew. Built with Rust and Bazel, it emphasizes performance and portability. Open-sourced under Apache-2.0, it's ideal for developers exploring command-line AI coding assistants.

github.com · 5 min · AI Engineering · CLI · Coding Agent

07-12

Anthropic's Goodwill Drain: Lock-in, Price Hikes, and an Engineer's Reckoning

A firsthand account of Anthropic's deteriorating trust: unreliable APIs tied to subscriptions, a locked-in and buggy Claude Code ecosystem, and opaque billing changes that effectively raise costs. The author argues these moves fund model training, not product improvement. Their response: shift to an 'agent-assisted' workflow and swap Claude for open-source models like Qwen and GLM via OpenRouter, gaining cost control and data security. For engineers feeling trapped in a single AI platform.

raheeljunaid.com · 11 min · Agent Engineering · Anthropic · Claude Code

07-03

Local-first, agentic design workspace with 22 CLI agents and 150+ brand systems

Open Design is a local-first, open-source alternative to Claude Design. It is agent-native, meaning it doesn't ship its own agent but works with 22 coding-agent CLIs (Claude Code, Codex, Cursor, Copilot, etc.) already on your PATH. Using MCP, the agents read DESIGN.md brand systems, skills, and plugins to generate prototypes, live dashboards, decks, images, videos, and HyperFrames. Exports to HTML, PDF, PPTX, MP4. Supports BYOK for any OpenAI-compatible endpoint. Ships 100+ skills, 150+ brand-grade design systems, and 261 plugins. Ideal for engineers and designers who want brand fidelity and local control.

github.com · 35 min · Agent Engineering · Design Tools · Developer Tools

07-01

What I Learned About Agent Skills from Building Popular Ones

The author, having built several popular Skills (PPT, social media cards, logo generator, AI desk card), argues that Agents amplify rather than erase capability gaps. A Skill is defined as a reusable capability unit that bundles expert experience, workflows, taste, and tool calls. Core insights: Skill design is externalizing human taste as constraints (e.g., no pure white/black, text must not cover faces); architecture should be 'short center, thick radius' with SKILL.md holding only high-signal flow; quality must be maintained like code, with gotchas from real failures being the most valuable; the ecosystem should present each Skill as a feature page, not a repository list; distribution relies on GitHub for cross-platform reach and content platforms for community building, creating a flywheel of articles, products, and use cases. A full lifecycle from real need to feedback iteration is proposed. The article is aimed at AI Agent developers, product managers, and content creators, offering concrete cases and actionable design principles.

x.com · 13 min · Agent Architecture · Agent Skill Repository · AI Engineering

07-01

Micro-Agent: Beat Frontier Models with Collaboration inside Model API

The vLLM Semantic Router proposes a different take: a router is not just a request dispatcher but an amplifier of model capability. The core idea is to encapsulate multi-model collaboration inside a single model API call. The user sees one model endpoint (vllm-sr/auto), but behind it the router can automatically select a collaboration pattern — from cost-aware escalation (Confidence), parallel aggregation (Ratings), repeated mixture-of-model reasoning (ReMoM), disagreement-as-signal (Fusion), to budgeted micro-agent workflows (Workflows). These patterns are controlled, configurable, observable runtimes, not application glue. Benchmarks on LiveCodeBench, GPQA-Diamond, and Humanity's Last Exam show the closed-model collaboration scheme (VSR Closed) achieving 92.6%, 96.0%, and 50.0% respectively, matching or beating single frontier models like Fugu Ultra and GPT-5.5. This article is valuable because it sinks multi-model collaboration from the product or application layer down to the serving infrastructure layer, while preserving a single model identity. For engineers building inference routing, multi-model strategies, or cost optimization.

vllm.ai · 14 min · AI Engineering · Cost Optimization · LLM

06-22

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

A step-by-step guide to building a persistent 'second brain' using Claude and Obsidian, based on Andrej Karpathy's LLM Wiki pattern. Obsidian stores all notes as local plain text files, while Claude (via MCP protocol) reads, organizes, and links the entire vault. Key steps: install Claude Desktop (paid plan), install Obsidian with Local REST API plugin, connect via MCP, create a CLAUDE.md profile via interview, structure projects with Inputs/Process/Outputs/Feedback folders, build reusable skills, wire in live data (calendar, email), and set up autopilot scheduling. The author stresses ownership (plain text, vendor-independent) and security ('keys, not prompts'). Aimed at engineers and knowledge workers tired of context loss.

x.com · 11 min · Ai-Memory · Claude Code · Context Engineering

06-22

GLM-5.2: Built for Long-Horizon Tasks

Zhipu AI introduces GLM-5.2, a flagship model for long-horizon tasks with a solid 1M-token context and an MIT license. Architecture innovations include IndexShare, which reuses the sparse attention indexer across four layers to cut per-token FLOPs by 2.9× at 1M context, and an improved MTP layer that raises speculative decoding acceptance length by 20% through IndexShare, KV sharing, rejection sampling, and end-to-end TV loss. Agentic RL post-training is backed by the slime framework, and an anti-hack module detects and blocks reward-hacking behaviors like fetching evaluation files or curl-downloading answers. GLM-5.2 ranks as the top open-source model on long-horizon benchmarks such as FrontierSWE (only 1% behind Opus 4.8) and Terminal-Bench 2.1 (81.0), making it relevant for engineers building coding agents and long-context inference systems.

z.ai · 21 min · Agent Architecture · AI · AI Engineering

06-21

Ponytail: Lazy Senior Dev Inside Your AI Agent, Cuts Code Bloat by ~54%

Ponytail is a rule plugin for 14+ AI coding agents (Claude Code, Codex, Copilot CLI, etc.) that injects a lazy-senior-dev mindset. Before generating code, it forces the agent to climb a ladder: does this need to exist? Can the standard library or native platform feature do it? Can it be one line? Only then writes the minimum viable solution. Benchmarked on real Claude Code sessions editing a real FastAPI + React repository across 12 feature tickets, it cuts lines of code by 54% (mean), tokens by 22%, cost by 20%, and time by 27% while keeping 100% safety on validation, error handling, security, and accessibility. Ideal for developers tired of AI bloat and over-engineering.

github.com · 12 min · Agents · AI Engineering · Code Generation

06-20

Skillify: turn every agent failure into a permanent structural fix

Garry Tan presents 'Skillify': a methodology that turns every AI agent failure into a permanent structural fix instead of relying on prompt tweaks or apologies. Using two real failures—an agent bypassing a local script for calendar search and doing mental timezone math—he walks through a 10-step verification checklist: SKILL.md contract, deterministic script, unit tests, integration tests, LLM evals, resolver trigger, resolver eval, reachability audit, smoke test, and brain filing rules. This workflow is built into GBrain, an open-source knowledge engine that ensures agent judgment improves permanently and verifiably. Targeted at developers frustrated by recurring agent mistakes.

x.com · 22 min · Agent Architecture · Agents · Ai-Memory

06-19

Imagine Naked People Were Stupider. Naked Models Are.

YC partner Garry Tan responds to Kyle Kingsbury's anti-AI essay, arguing that Kingsbury's tests of naked models are like testing an engine on a bench and concluding cars are unsafe. The article details the 'thin harness, fat skills' architecture: skill files (reusable Markdown procedures) constrain model input, resolvers (routing tables) dispatch tasks, deterministic code handles precision operations, and testing covers the full pipeline. Using Kingsbury's own bathroom rendering and stock data hallucination examples, Tan shows how architecture can turn unreliable models into reliable systems, and shares a personal resolver that reduced file misfilings from 10/13 to zero. The automotive metaphor concludes: seatbelts, traffic lights, and crumple zones made cars safe, not skepticism of engines. Targeted at engineers building or evaluating AI systems.

x.com · 18 min · Agent Architecture · Agents · Code

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

06-15

Decomposing the agent harness into swappable workers: the iii engine architecture

Mike Piccolo argues that monolithic agent frameworks force a tradeoff by bundling the loop, tools, memory, and orchestration into one block, which long-running teams inevitably rewrite. He walks through the iii engine's production worker stack, where all thirteen harness responsibilities—credential resolution, policy checks, turn FSM, session persistence, budget tracking, etc.—are decomposed into 11 independently replaceable workers. Each worker connects to the engine via WebSocket and registers functions and triggers using a single primitive (iii.trigger()), making the harness a composable set of installable workers. The post provides a step-by-step trace of a turn through provisioning, streaming, policy-gated tool dispatch, and reactive approval wake-ups, alongside concrete examples of swapping the model catalog, adding a provider, or integrating a Slack approval surface. The core bet: an agent harness should be a slider of composable workers rather than a framework you fork. This is for backend engineers building or scaling custom agent infrastructure who are hitting the composability limits of existing frameworks.

x.com · 20 min · Agent Architecture · AI Engineering · Observability

06-14

Hermes Agent: A Self-Improving, Multi-Platform AI Agent Runtime

Hermes Agent is a self-improving AI agent framework with a closed learning loop. It creates skills from experience, manages persistent memory across sessions, and operates over Telegram, Discord, Slack, and CLI via a single gateway. Any LLM backend can be used without code changes, and it runs on a $5 VPS or serverless infrastructure with near-zero idle cost. Built‑in cron scheduling, subagent delegation, and batch trajectory generation make it suitable for engineers and researchers who need an autonomous agent that evolves with use.

github.com · 11 min · Agent-Memory · Agents · CLI

06-10

AI Agent Skill: Cross-Platform Social Search and 30-Day Synthesis

/last30days is an AI agent skill that aggregates the latest content from Reddit, X, YouTube, TikTok, Hacker News, and more into a 30-day briefing. It uses entity pre-research to identify key people, communities, and topics, then searches in parallel and scores by real engagement (upvotes, likes, money) rather than SEO. An AI synthesizes a cited, in-depth summary. Open-source (MIT), it supports Claude Code and 50+ agent frameworks. Ideal for engineers, PMs, and researchers needing a quick, grounded update before meetings or decisions.

github.com · 27 min · AI Agents · Open Source · Social Media

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