Stop Reading the Code, Thariq's Field Guide to Fable & Sholto's Classifier Pile-On

The "Stop Reading the Code" Fight

Theo spent the night of the 4th deliberately starting a fire: "How much better do the models have to get before you'll stop reading the code?" (370K views, 653 replies), followed by "At this point I'm genuinely convinced most of you would have kept reading the assembly code after C got popular" and the confession that "I'll be honest, I barely even read the code back when I wrote it by hand..."

The replies are the best part:

Theo says a video is coming on the "you should still read your code" debate that will "piss both sides off" — a direct sequel to Geoffrey Litt's "understanding is the new bottleneck" thread from AIE that dominated Thursday's discourse.

Field Guide to Fable & the Orchestration Meta

Three days into the Fable re-release, the "how to actually drive this thing" genre produced its biggest hits yet.

Thariq's Field Guide. Anthropic's Thariq (Claude Code team) published A Field Guide to Fable: Finding Your Unknowns (1.2M views, ~5K likes) — an X article arguing "the map is not the territory": Fable is the first model where the quality of your output is bottlenecked by your own unknowns, so the job is discovering what you don't know before prompting. Techniques include having Fable quiz you before merging and using HTML artifacts to surface unknowns (examples thread). He credits Geoffrey Litt's AIE discussion for the quiz idea — this is the practitioner's companion to Litt's "understanding is the new bottleneck" thesis. Notable pushback in the replies: multiple people asking Anthropic to publish these on an agent-accessible site instead of X articles, since you can't feed an X article to Claude.

Simon's "use your judgement" tip. Simon Willison shared the most interesting Fable tip he's heard (227K views): tell it "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent" — and let Fable be the router. More notes on his blog. The replies became a mini-survey of delegation patterns:

40K LOC on a $20 plan. LLMJunky posted the most concrete orchestration receipt of the week: Fable proposed ten improvements to his StarSwap app, he picked seven, had Fable write independent plans, delegate each to GPT-5.5 High via Codex Exec in its own worktree, review/fix the results, and merge — 40,000 LOC written using 96% of a single 5-hour session on the Claude Pro $20 plan. Best reply, on the "working out of the box" claim: "The metric I'd watch the next morning: how many files you can still explain... Parallel worktrees are magic until merge debt shows up." LLMJunky: "whats regression? /s"

Theo's effort-level guide. Theo published a one-image guide to maximizing Fable usage whose effort-tier taxonomy tops out at "mental illness" (xhigh/max users happily self-identified in the replies). Consensus in the thread: medium/high is the sweet spot, xhigh+ is for benchmarks, and the pros pair Fable-as-planner with Codex as the workhorse — "Plan and review with Fable, give it tools to drive Codex. 1% used per hour." One cautionary tale: "fable setting off 19 fables in a swarm ate a full 5 hr max 5x limit in under 10 minutes."

Matt Pocock's taxonomy crisis. Related: Matt Pocock asked how anyone categorizes models now — "I used to put models in the bucket of Opus-like, Sonnet-like, or Haiku-like. But now we have Fable. Now Sonnet 5 behaves like Opus." Best answers: "Fable is Opus-like, Opus is Sonnet-like, Sonnet is Sonnet-like", and the pragmatic "I treat different effort levels as different models". Matt's own follow-up: effort levels make everything harder, and on models vs harness he calls it 50/50.

Sholto's Feedback Thread & the Classifier Pile-On

Anthropic's Sholto Douglas asked for feedback post-Fable-jump: "what axes do you feel like we've made great progress on, and what do we need to improve on?" The thread turned into a remarkably consistent bug report, and the dominant axis wasn't capability — it was the safety classifiers:

Buried in the replies, the best data point of the day — Shrivu Shankar's controlled /goal+ultracode experiment, one hard task (replicate an image as a 3D scene) across three stacks:

Stack Time Cost Result
Codex, GPT-5.5 xhigh 19m 31s $11.09 OK-ish
Claude Code, Opus 4.8 ultracode 1h 48m $62.64 broken
Claude Code, Fable 5 ultracode 4h 23m $268.18 "not bad, but still far from the goal"

His conclusion: Fable is still a step function above the other two, but it's "very eager to fan-out to more fables and $$$" — which reinforces the value argument for Codex and explains why the orchestration meta above exists at all.

Honorable mention: Danielle Fong is building "DJ Claude" — Opus 4.8 fast-mode execution plus Fable with variable thinking, in tmux, with the faders on her DJ deck controlling the thinking level. "Perfect kind of 4th of July weekend project."

Harness Corner: Phantom Tool Params & Skill Evals

Armin's phantom parameters. Armin Ronacher has a genuine mystery on his hands: a handful of pi users report Anthropic models injecting extra tool parameters into edit calls, and he can't reproduce it — except that a user-supplied session, when continued, does show it. Replies point at context-prefix effects in resumed sessions; it's an open issue if you have ideas. This follows his AIE-prompted post on pi's intentionally-strict edit tool and the related observation that "Fable really likes its comments. Damn."

Skill evals are unbelievably hard. Matt Pocock, replying to the Effect team: "Evals on skills are hard" is the understatement of the year. His escalation ladder: "Evals on a classifier is trivial. Evals on a coding agent is extremely hard. Evals on a harness-agnostic skill is unbelievably hard" — and it's a data problem, not a token problem. David Cramer pushed back (no harder than any high-value eval). Two useful concretes from the thread: David K's baseline strategy ("does the skill produce better results vs no skill at all, per rubric?") and Will Hampson actually benchmarking the 57K-star ponytail plugin on DeepSWE: double the baseline's solved problems, 26% fewer LOC, 12% fewer tokens, 10% cheaper, 7% faster.

Agents Get Their Own Computers

Peter Steinberger: "Give your agent its own computer to REALLY end to end test stuff" (136K views) — a macOS VM via crabbox.sh (which has a Parallels backend), with the killer feature in all caps: "IT WILL CLICK ON ALL THE ANNOYING MACOS ALERTS FOR YOU." For mobile, he says Codex + computer use "works amazing." The replies are full of people converging on the same pattern — VMs as ephemeral, security-isolated E2E environments, and one user's "minions": one pulls CI, the rest test it with computer-use end-to-end.

In the same spirit, LLMJunky moved his Codex Plugin Marketplace off Vercel onto his Hetzner box by just asking Codex: "Migration, DNS, auto-deploy, SSL, traefik, etc. Codex deftly handled it all via SSH and Chrome in 12 minutes with minimal guidance."

The Creative Writing Gap

Lee Robinson — now doing ML at Cursor — asked the weekend's best non-coding question: Are current LLMs incompatible with great creative writing? Even council-of-models grading ten drafts yields "lowest common denominator slop," because coding has verifiable rewards and writing doesn't. His darker follow-up: "You can't help but wonder if we already have the best writer of all time sitting in the weights, but it just gets fried with RL and inference optimizations."

The standout reply came from Karina Nguyen (ex-OpenAI/Anthropic): non-fiction is fixable — the gap comes from conflicting RL objectives, since chat-pleasant traits fight the precision/density/restraint good technical writing needs (they fixed this for a GPT-4o version). True creative writing is a different problem entirely: coherent worlds and emotional arcs over hundreds of pages, with no training data capturing the process ("hard to simulate Pixar's braintrust process for AI") and no lab investing because it's unmeasurable and low-value economically.

Jerry Liu offered the practical 70% solution: a writing-style skill hill-climbed against your real writing samples — which fixes style but not "the inherent ability to articulate clear, differentiated insights, which is probably an inherent posttraining problem."

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