# messages_out

id: msg-1783561434928-yk1w6r
in_reply_to: 1783561419.485319:ag-1779666729747-9n7kga
timestamp: 2026-07-09 01:43:54
kind: chat
platform_id: slack:C0BD03A2QS3
channel_type: slack
thread_id: slack:C0BD03A2QS3:1783561419.485319
content:
  {"text":"Looks like the link or content didn't come through, James — I just see the message text. Can you share the URL or paste what you want me to ingest?"}


id: msg-1783561550969-6h3q0j
in_reply_to: 1783561487.839679:ag-1779666729747-9n7kga
timestamp: 2026-07-09 01:45:50
kind: chat
platform_id: slack:C0BD03A2QS3
channel_type: slack
thread_id: slack:C0BD03A2QS3:1783561419.485319
content:
  {"text":"**effortmining** — verdict: it's real, and the claim is honest. Worth trying.\n\n**What it does:** Claude Code subagents inherit your session's reasoning effort level — so a subagent that's just grepping a file thinks as hard as one debugging a crash. effortmining classifies each subtask and routes it to the cheapest effort tier that's proven sufficient. The mechanism is clever: Claude Code has no per-spawn `effort` API, so they ship 5 agent files (miner-low.md through miner-max.md) that are byte-identical except one `effort:` frontmatter line. A hook injects the dispatch policy at session start.\n\n**The numbers:** ~450 benchmark runs, pre-registered, published failures included. 64.7% fewer output tokens vs effort inheritance, equal pass rate. `max` effort never beat `xhigh` on a single task — it just costs 7x more.\n\n**Honest caveats (their words):**\n- n=3 per cell → confidence labeled *low*, wide intervals\n- Only calibrated on one model (`claude-opus-4-8`)\n- Low effort **fabricates** in cross-document synthesis (invented a ticket ID that wasn't there) — that's why their R-research class routes hard instances to `xhigh`\n\n**Should you use it?** Yes, probably. Install is one line, ambient mode is default, your explicit effort requests always override, and it's fail-open. The savings are most meaningful in sessions that spawn a lot of subagents doing routine work (extraction, reformatting, running tests). For your workflows — qip-qien's agent pipeline, TradingBot25 strategy loops, nanoclaw subagents — there's real potential.\n\nThe one thing to watch: re-calibrate if you're using it with a different model than opus-4-8. The `python3 bench/effort.py calibrate` refit command is built in and deterministic.\n\n**Not ingesting to wiki yet** — queuing it for low-cost batch ingest. Let me know if you want a wiki page or want to try the install now."}


id: msg-1783562288556-8bmfeq
in_reply_to: 1783562226.754229:ag-1779666729747-9n7kga
timestamp: 2026-07-09 01:58:08
kind: chat
platform_id: slack:C0BD03A2QS3
channel_type: slack
thread_id: slack:C0BD03A2QS3:1783561419.485319
content:
  {"text":"Two parts to clarify:\n\n**Ambient logging = passive, zero cost.** The PostToolUse hook just appends a line to a local dispatch log as you work — no extra calls, no token burn.\n\n**Calibration = active burn.** `effort.py calibrate` reads from that dispatch log *and/or* runs the task matrix (run → grade → analyze). To fit a new model it needs actual benchmark runs: ~6 classes × 5 tiers × 3 reps = 90 tasks minimum (pilot scale). That's real tokens. The good news: it's resumable, so you can spread it across sessions.\n\n**The practical question for your setup:**\n\nThe shipped table is calibrated for `claude-opus-4-8`. For Sonnet, the floor where quality holds might be different — Sonnet at `low` effort could be more fragile than Opus at `low` effort, or it might be fine. If you run on the shipped table without recalibrating, you'd be taking that on faith.\n\nMy honest take: the savings are probably still real on Sonnet even with the Opus-fitted table (Sonnet is capable enough that mechanical tasks at `low` likely still pass), but recalibrating properly would take maybe $5–15 of token spend to do once and get real numbers for your model. Whether that's worth it depends on how many subagent-heavy sessions you run.\n\n**Fable** — not sure which model that is. If it's outside Claude Code's model picker, effortmining wouldn't apply there at all since it hooks into Claude Code's agent dispatch specifically.\n\n**Codex ($20 plan)** — completely separate, effortmining is Claude Code-only.\n\nQueued the repo for wiki ingest. Not much to capture there beyond what I already summarized unless you want a standing reference page."}


id: msg-1783562768971-nb0tap
in_reply_to: 1783562707.158689:ag-1779666729747-9n7kga
timestamp: 2026-07-09 02:06:08
kind: chat
platform_id: slack:C0BD03A2QS3
channel_type: slack
thread_id: slack:C0BD03A2QS3:1783561419.485319
content:
  {"text":"Short answer: it would be a near-complete rewrite, not a port. Three things tie this firmly to Claude Code:\n\n**1. The hook system is Claude Code-only**\nThe whole mechanism runs on Claude Code's `SessionStart` and `PostToolUse` hooks (defined in `hooks/hooks.json` with `CLAUDE_PLUGIN_ROOT` env vars). Codex CLI has no equivalent plugin hook surface — there's nothing to intercept at session start or after a tool call.\n\n**2. The `effort:` frontmatter is Claude Code-only**\nEach miner agent is just an `.md` file with one line difference in the YAML frontmatter (`effort: low` through `effort: max`). Claude Code reads that and pins the subagent's reasoning effort. Codex doesn't have agent definition files or a frontmatter system like this.\n\n**3. The benchmark harness calls `claude` as a subprocess**\n`effort.py` shells out to `claude` to run tasks at each tier. That'd need to be rewritten to call `codex` and handle a different CLI interface.\n\n**What would actually transfer:** the concept — classify the subtask, dispatch to the cheapest tier that passes. OpenAI o-series models do have a `reasoning_effort` param (low/medium/high — 3 tiers vs Claude's 5). But you'd need to find Codex CLI's equivalent extension point, if one exists at all.\n\n**Practical take for you:** your $20/mo Codex plan is probably low-volume enough that the savings wouldn't move the needle much there. The bigger leverage is on the Claude Code side where your heavier multi-agent sessions live (nanoclaw, TradingBot25 loops, etc.). I'd apply effortmining there and not try to cross the gap to Codex — the cost of the port likely exceeds the savings."}


id: msg-1783564153220-uygv2u
in_reply_to: 1783564079.869679:ag-1779666729747-9n7kga
timestamp: 2026-07-09 02:29:13
kind: chat
platform_id: slack:C0BD03A2QS3
channel_type: slack
thread_id: slack:C0BD03A2QS3:1783564079.869679
content:
  {"text":"Yes, it's legitimately as useful as it sounds — this is the real deal.\n\n**Last30Days** is a Claude Code skill that runs parallel searches across Reddit, HN, YouTube, X, TikTok, Polymarket, arXiv, and more, then scores results by real engagement (upvotes, likes, prediction market odds) rather than editorial ranking. 51K stars since January — it trended #1 on GitHub. The core insight: no single AI has native access to all these walled gardens, but you can bridge them with your own keys/sessions.\n\n**Zero-setup sources (works immediately after install):** Reddit, HN, Polymarket, GitHub, arXiv, Techmeme, Digg\n\n**Bring-your-own-auth:** X, YouTube, TikTok, Instagram — 30-second setup wizard\n\nInstall on Claude Code is one line:\n```\n/plugin marketplace add mvanhorn/last30days-skill\n```\n\n**Directly relevant for you:**\n\n- **Your $20 Codex plan** — v3.11 makes this a first-class Codex plugin, not a port. Same engine, renderer-aware citations. `npx skills add mvanhorn/last30days-skill -g`\n- **Congressional trading monitor** — Polymarket integration is a natural signal. Prediction market odds on political outcomes could complement the congressional disclosure data you're already watching.\n- **AI news monitoring** — this overlaps with (and is richer than) your current YouTube + claude.com/blog scheduled tasks. Reddit/HN signal on Anthropic moves faster than blog posts.\n\n**Queuing for wiki ingest** — I'll add a proper tool page overnight.\n\n*NanoClaw aside:* I could use `/last30days` directly in my own research turns — it'd make my ingestion and research tasks considerably sharper without you needing to set up separate monitoring tasks. Worth wiring in as a skill on my end if you want."}


