US labor productivity kept rising in Q1, but more slowly. Output per hour for nonfarm workers increased at a 0.8% annual rate, down from 1.6% in Q4. Compared w/a year earlier, however, productivity was up 2.9%; the biggest gain since 2024. Productivity has been on a strong run since 2023. JPMorgan cautions against attributing too much of this strength to AI, noting it’s still early. Still, ongoing business investment in AI could help sustain the trend. Faster productivity growth may also be helping to contain unit labor costs, which rose just 1.2% YoY; suggesting limited wage-driven inflation pressure.

Sharing a Claude Code productivity tool I recently built 👇 🔔 cc-remote-approval — so Claude Code's long-running tasks don't stall waiting for your approval. 【😩 The pain】 You kick off a long Claude Code task, step away to do something else, come back — and the agent is frozen on "Approve this Bash?" / "Pick an option" / "Fill in this MCP form." Those twenty minutes? Nothing moved. Every approval point in Claude Code is a wall: - Bash / Edit / Write permissions - AskUserQuestion options - MCP Elicitation forms - The idle wait at the end of every turn The moment you're away from the screen, the agent stops working. 【✨ The fix】 cc-remote-approval is a Claude Code plugin. When Claude needs approval or asks a question: 1. The native local dialog still shows (no replacement, no hijacking) 2. A background hook starts a timer — 20s by default 3. No local response → the request is forwarded to Telegram, with full context and buttons 4. On your phone, tap ✅ Allow / ❌ Deny / ⭐ Always, or type an option 5. Whichever side responds first wins; the other side auto-syncs 📱 On the subway, between meetings, scrolling before bed — you're away from the screen, the agent keeps moving. 【🛠 Features】 - Pure Python stdlib, zero third-party dependencies - Local-first: no external calls other than the Telegram API - Concurrency-safe across sessions: flock + pending queue — multiple agents can request approvals in parallel without crossing wires - Hook-level integration — works across CLI, desktop, and plugins - 📖 Full context button: expand the last N turns of the conversation in one tap when the truncated preview isn't enough - SessionStart hint injection: steers Claude toward the AskUserQuestion tool so choices render as buttons on your phone, instead of making you type a number 【🔒 Security】 - 100% local: data flows directly between your machine ↔ Telegram — no self-hosted server, no cloud relay, zero analytics or telemetry - Pure Python stdlib, zero third-party deps → minimal supply-chain surface - Bot token stays in process memory: never appears in ps, never written to logs. Sensitive content (API keys, passwords, tokens, etc.) is auto-masked before anything leaves your machine 【🤔 Anthropic already has options — why build another?】 Anthropic ships /remote-control and an official telegram plugin, but in practice both have constraints: - /remote-control is web-based: you have to keep a browser tab open and actively watch it; no push-notification model; doesn't support the Claude Code desktop app. - Official telegram plugin: concurrent sessions steal each other's messages; the setup/startup flow is a bit clunky; also no desktop support. 【💡 Recommended config: flip Stop hook on for full-workflow coverage】 Set stop_hook_enabled: true: - Before the agent idles each turn, Telegram gets a message with Continue / Dismiss buttons - Tap Continue, reply with your next instruction, and the agent picks it up - The whole loop — approvals, questions, forms, "what's next?" — can now happen from your phone - Side effect: each turn blocks for up to stop_wait_seconds (default 180s); if you're at the screen and don't want to wait, just press ESC in Claude Code to skip GitHub: Manta-Network/cc-remote-approval Install: /plugin marketplace add Manta-Network/cc-remote-approval /plugin install cc-remote-approval@manta Or just paste the GitHub link into Claude Code and ask it to install.

A life devoid of productivity and genuine meaning ought to be empty and insignificant for the modern individual. : The author asserts that for the modern individual, a life lacking both productivity and genuine meaning should feel profoundly empty and insignificant. This is not a moral judgment but a statement of psychological and existential reality. In an era where self-realization and contribution are central to personal identity, mere survival or passive consumption no longer suffices. Productivity here means the active creation of value, whether through work, relationships, art, or service. Genuine meaning arises when that productivity is aligned with deeper purpose. When both are absent, life loses its weight and luster. The individual senses a void that no external distraction can fill. This serves as a quiet challenge. It reminds us that modern freedom and opportunity carry a corresponding responsibility: to live deliberately, to create, and to invest our time in what truly matters. A life without productivity and meaning is not merely unfortunate; it is, in the author’s view, incompatible with the dignity and aspirations of the contemporary human being.

2025 marked an inflection point in AI productivity, as AI agents evolved from something you could chat with to something that can perform work on your behalf. Read @JozefARK's Substack "2025: The Year Chatbots Became Agents" for a look into Big Ideas. 🔗

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation @thdxr (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • @WorkOS – everything you need to make your app enterprise ready • @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

A reminder that if unions aren’t raising productivity, they’re unable to significantly boost wages without resorting to tactics that shield their members from competition (from, for instance, automation) but impose costs on everyone else.