QM gives each employee and channel an isolated AI agent workspace with its own memory, files, permissions, and cron jobs while supporting shared scopes for collaboration in Slack or a web app. It’s model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code—and offers admin controls for security postures, org-wide configs, and custom plugins. Deploy with the qm CLI by layering your org’s config and skills over the headless core, all under an MIT license.

• Every user and channel gets an isolated sandbox (memory, files, permissions, cron jobs) while still allowing shared skills across scopes, so teammates don't step on each other
• It's model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code without changing the deployment
• Deployment cleanly separates org-specific config/skills from the upstream core, with helper skills (update-qm, upstream-pr) to sync a private fork in both directions
• Admins get org-wide controls over allowed harnesses/models, security postures (Strict, Auto, Dangerous), and skill/app access

This guide walks you through every step of creating an AI agent from scratch. It highlights tools and techniques that can shrink your build time from two weeks to a single day.

• A working AI agent can be built in ~50 lines of Python: LLM wrapper + registered tool functions + an agent loop.
• Modular tools (discrete functions with clear inputs/outputs, like send_email()) beat hard-coding capabilities into prompts, and make testing easier.
• Adding a FIFO list or vector DB memory buffer (~12 lines) gives the agent multi-turn context without re-prompting.
• Docker plus a GitHub Actions CI/CD pipeline lets you go from prototype to deployed agent in a day, versus two weeks previously.

OpenAI’s data productivity team built Kepler, an AI agent that understands their data platform context and writes iterative SQL to answer complex data questions. It handles table discovery, query refinement, follow-up prompts and even visualizations, cutting analysis time and reducing errors.

• OpenAI built Kepler, an internal AI agent that writes iterative SQL to answer data questions across 70,000 datasets and 600 petabytes daily, deployed via Slack, IDEs, or web UI
• It doesn't just query once—it discovers tables, refines CTEs, verifies numbers against dashboards/Notion, and proposes hypotheses (e.g., duplicate logging) for anomalies like a WAU spike
• Memory for self-learning plus rigorous evals let it improve over time without regressing
• Rolled out to 80% of the company, turning multi-engineer, multi-hour investigations into minutes

Anthropic has turned Claude into a persistent Slack bot that watches channels, answers questions, suggests tasks and can trigger actions without prompting. It acts like an “agentic” AI coworker, raising questions about privacy, control and user consent in workplace chats.

• Claude now runs persistently in Slack, monitoring channels and proactively surfacing suggestions, summaries, and drafts without being explicitly summoned.
• Admins retain control via channel toggles, sensitivity settings, guardrails on sensitive terms, and mandatory review before any suggestion posts.
• Data stays within the Slack workspace unless a user explicitly sends it to Anthropic's servers for fine-tuning.
• It's currently an enterprise preview, positioned to compete with Microsoft Teams Copilot and Google Duet AI, with wider rollout planned later in the year.

GitHub built Qubot, an internal Copilot-powered analytics agent that lets employees query their data warehouse in plain English via Slack, VS Code, or the CLI. It pulls federated context from raw, conformed, and curated data layers, then runs queries on Kusto or Trino to deliver SQL-backed insights within seconds. A continuous evaluation framework and community contributions to the context layer keep responses accurate and fast.

• Qubot lets any employee query GitHub's data warehouse in plain English via Slack, VS Code, or CLI, cutting analytics team question load and freeing analysts for harder work
• It uses a three-tier federated context layer (bronze/silver/gold) maintained collaboratively by product, analytics, and business teams, turning scattered knowledge into a single source of truth
• Investing in well-structured context both improved accuracy and cut answer time by two-thirds
• A continuous offline evaluation framework tests every context or agent update against known SQL answers to catch regressions before release

Ponytail is an always-on ruleset and plugin for AI coding agents (Claude, Codex, Gemini, Copilot, etc.) that enforces a step-by-step “ladder” to include only necessary code. Benchmarks show 80–94% less code, 3–6× faster responses, and 42–75% lower cost by preferring built-ins and one-liner solutions before adding dependencies.

• Ponytail cuts AI-generated code by 80–94% while running 3–6× faster and costing 42–75% less, per Claude API benchmarks across Haiku, Sonnet, and Opus.
• It enforces a strict ladder—skip via YAGNI, use stdlib/native features, add dependencies only if essential, then compress to one-liners—without trimming validation, error handling, security, or accessibility.
• It installs as a plugin (e.g. via Claude Code or Codex commands) and works across Copilot CLI, OpenCode, Gemini/Antigravity, Cursor, Windsurf, Cline, and other agent hosts by copying matching rule files into each tool's config.
• Modes range from lite to full to ultra, adjustable via slash commands or an environment variable, giving control over how aggressively code gets minimized.

SkillSpector scans AI agent skills for vulnerabilities, malicious patterns, and CVEs before installation. It combines fast static checks with optional LLM analysis, covers 64 patterns across 16 categories, and outputs risk scores and detailed reports.

• SkillSpector scans AI agent skills before installation, checking 64 vulnerability patterns across 16 categories (prompt injection, data exfiltration, privilege escalation, etc.) plus real-time CVE lookups via OSV.dev.
• It runs a fast static scan by default, with an optional deeper LLM-based semantic check using providers like OpenAI, Anthropic, or NVIDIA's build.nvidia.com.
• Scans output a 0-100 risk score with severity labels and suggested fixes, in formats including JSON, Markdown, or SARIF for CI/CD pipelines.
• Even without LLM analysis (--no-llm flag), it still catches known code patterns and CVEs.

Ponytail is a plugin and ruleset for AI coding agents that enforces a six-step minimal-code ladder—skip unnecessary code, prefer stdlib or native features, then one-liners—to produce only what each task needs. Benchmarks on Claude models show 80–94% less code, 3–6× faster runs, and 42–75% lower cost. Installation covers Claude Code, Codex, OpenCode, Gemini/Antigravity CLI, Copilot, ClawHub, and more.

• Ponytail enforces a six-step "minimal code" decision ladder before an AI agent writes anything, starting with "does this need to exist" and falling back to stdlib/native features before custom code.
• Benchmarks on five tasks (run 30x for cost, 10x for code/latency) show 80–94% less code, 3–6x faster responses, and 42–75% lower cost versus a vanilla agent.
• It tags every shortcut with a "ponytail" comment for traceability, and preserves validation, error handling, security, and accessibility rather than golfing code.
• Installation is two small Node.js hooks across Claude Code, Codex, Copilot, Gemini/Antigravity, and others, adding slash commands (/ponytail lite|full|ultra|off, /ponytail-review, /ponytail-audit, /ponytail-debt) to control and audit the shortcuts.

Sakana AI released Sakana Marlin, an autonomous research assistant that takes a topic and runs up to eight hours to produce summary slides and a detailed strategy report. It uses long-horizon reasoning and multi-model control to form hypotheses, gather data, and verify findings without human input. After a closed beta with about 300 professionals, it’s now available via pay-per-use and tiered subscription plans.

• Sakana AI launched Marlin, an autonomous research assistant that runs unattended up to 8 hours to produce a ~100-page strategy report plus summary slides.
• It's built on Sakana's prior research (AI Scientist, AB-MCTS, ALE-Agent) to autonomously form hypotheses, gather data, and verify findings without human input.
• About 300 professionals from banks, consulting firms, and think tanks tested it in closed beta for market research, risk assessment, and competitive analysis.
• Now publicly available via free pay-per-use plus Pro/Team/Enterprise subscription tiers.

Two workshops demonstrate hands-on uses of Anthropic’s Claude model. In one, Andrej Karpathy breaks down his 14-minute tutorial on mastering Claude, claiming it takes about 10,000 hours to learn. In the other, Amazon quants show how they built a full AI agent in 25 minutes with just 44 lines of code, now open-sourced.

• This is just a promotional X post, not an actual article with verifiable substance—likely hype/clickbait framing around Karpathy and Claude.
• The "10,000 hours to master Claude" claim contradicts the premise of a 14-minute tutorial teaching it.
• Amazon quants reportedly rebuilt a 3-year framework in 3 days using just 44 lines of Claude-generated code, then open-sourced it.
• The open-sourced package includes pre-trained models, helper tools, and editable prompts, enabling a self-contained AI agent with no external libraries or proprietary backends.