Anthropic's classifiers reduced jailbreak success from 95% to 4%, and then their latest two-stage system brought it down to under 0.5%.
Anthropic's classifiers reduced jailbreak success from 95% to 4%, and then their latest two-stage system brought it down to under 0.5%.
Where this was said
At 1:06:57 · chapter starts 1:02:50
The FUD around Fable being 'nerfed' after its return gets a systematic takedown. Ben notes that public benchmarks comparing Fable before and after the export ban are mostly hitting prompts that now trigger rerouting to Opus — the model itself hasn't changed. Theo then digs into the actual architecture behind rerouting, drawing on an Anthropic paper he'd reviewed that day. [1] — Theo "Anthropic's rerouting system has two stages: a cheap first-stage classifier (~0.05% compute) that watches which expert pathways activate in…" 1:02:50 The system is two-stage: a first-stage classifier monitors which expert pathways activate during inference (essentially watching the direction of the weights) and costs roughly 0.05% of full inference compute. When it flags a request, a far more expensive secondary classifier fires — one that adds 27–80% to the compute cost per request. By building the cheap first-stage gate, Anthropic can catch the vast majority of concerning requests without paying the full secondary cost on every token. The combined system has driven jailbreak pass rates from 95% down to under 0.5%, though Theo notes that since all of this runs on every token of output, there's an interesting lower bound related to how many tokens it takes to encode any given piece of dangerous information. Both hosts agree rerouting is annoying but rare in practical coding use — and recommend just sending a normal follow-up message to exit Opus and return to Fable.
Anthropic's rerouting system has two stages: a cheap first-stage classifier (~0.05% compute) that watches which expert pathways activate in the model, and an expensive second-stage classifier (27–80% extra compute) that only fires when the first flags a request. The result: jailbreak pass rates dropped from 95% to under 0.5%.
Anthropic's secondary safety classifier increases compute per request by 27% to 80%, prompting them to build a cheaper first-stage check costing only ~0.05% of compute.
GLM-5.2 is fast, cheap, and produces clean code easily controlled by a smarter orchestrator. Sonnet 5 edges it out only in one scenario: very long-running jobs requiring recursive self-calling and review loops. Outside that, GLM-5.2 wins on price-performance — and even Theo would pick it over Sonnet if he still had Fable.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
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A hard paywall is a screen that blocks all app features unless the user pays or starts a free trial — it cannot be dismissed.
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Vasco stated that the majority of his app's user base came directly from his YouTube channel.
SEO Bot features a 'Boost My Domain Rating' button that routes users directly to Listing Bot, an example of in-product cross-selling.
The founder's entire product portfolio is AI-related, making it easier to package products attractively for directories.
The founder attached their SaaS demo to the trending debate about whether AI coding is actually good enough to build a full SaaS product.
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