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I deployed a Slack bot app built on the Claude Agent SDK to Railway, and immediately hit a string of landmines around the SDK itself. Every one of them was the "the logs don't tell you what's wrong" kind, and the second one in particular ate a lot of my time. Since other people are likely to get stuck in the same spots, I'm writing it down. This is aimed at junior-to-mid-level devs using @anthropic-ai/claude-agent-sdk ( query() ) in Node.js. TL;DR Gotcha 1 : In a root container, bypassPermissions isn't allowed, and the child process dies with code 1 . Worse, stderr is swallowed, so you can't see why. Gotcha 2 : stdio MCP servers don't wait for connection by default, so on turn 1 the tool list is empty — and the model "acts out" tool calls and fabricates the results. Gotcha 3 : haiku shows up in your API logs, but that's not the model degrading — it's by design. It's used for internal chores. Gotcha 1: bypassPermissions doesn't work in a root container What happened Code that ran fine locally started dying with code 1 the moment I deployed it to Railway — the agent did nothing and just exited. The entire error message was essentially this: Error: Claude Code process exited with code 1 That tells you nothing. The only stack trace was from my app; what the child process (the claude binary) actually said before dying was a complete black box. The cause query() spawns a claude binary internally. That binary refuses --dangerously-skip-permissions (which the SDK calls permissionMode: "bypassPermissions" ) when running as root or under sudo . It's a safety measure — skipping all permission checks as root is far too dangerous. Railway, like many container environments, runs as root by default, so if you've set bypassPermissions you will always hit this. You can't catch it locally if you're running as a normal user. Why there are no logs This is the nasty part. Unless you pass an options.stderr callback, the SDK discards the child process's stderr with "ignore" . In other wor

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Lara get your gun(s).

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A free model that runs 4x faster on your own GPU — and two more shifts for builders Three things landed for builders at once: a free open model that generates text far faster, a more autonomous Codex, and Anthropic owning up to a model that was quietly holding back. Two of them you can act on right now. Here's the 2-minute video version if you want the quick pass first: 1. Google shipped DiffusionGemma — a free open model that runs 4x faster Google released DiffusionGemma , an open-weights model that uses text diffusion instead of standard autoregressive decoding. Instead of generating one token at a time, it generates whole blocks in parallel. It writes blocks of 256 tokens at once , for up to 4x faster generation on a dedicated GPU. It hits 700+ tokens per second on a single RTX 5090 , and fits in 18GB of VRAM quantized — inside consumer GPU limits. It's a 26B Mixture-of-Experts (only 3.8B parameters active), ships under Apache 2.0 , and runs natively in vLLM . The tradeoff Google states openly: output quality is lower than standard Gemma 4, so it's a speed play, not a quality play. Why it matters: this is a fast, free, local draft model you can run on your own hardware. Use it for low-latency drafts and agent loops, then route the hard calls to a stronger model. No inference bill for the cheap 80%. 2. OpenAI gave Codex web search and autonomous goals OpenAI shipped a major Codex update that pushes it further toward an autonomous agent. Code mode can now call web search directly , even from nested JavaScript tool calls — so it can look up current API docs mid-implementation. Goal mode is generally available across the Codex app, the IDE extension, and the CLI. Appshots (macOS) attach an app window to a Codex thread with a hotkey, and MCP tool schemas now preserve oneOf / allOf for richer connectors. Why it matters: Codex can research and chase a goal on its own across every surface. Still — hand it a clear, scoped goal in a branch. Full hand-offs go sideways witho