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Claude Code’s Next Era — Thariq Shihipar, Anthropic

2026-09-29 · 92 min · 6 entities

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    Claude Code’s Next Era — Thariq Shihipar, Anthropic
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Episode description as stored
We are excited to have Anthropic share their latest AI x Finance work at AI Engineer New York , coming up in 2 weeks! In case you’ve been under a rock, here’s a non-exhaustive list of what Anthropic has been shipping since closing the largest fundraise of all time in May at $47B ARR: * June: Launched Claude Tag and Sonnet 5 and Fable 5 * July: Opus 5 , /checkup . crossed $65B ARR * Last month: Fable/Mythos 5.1 , and EFS (upcoming pod) * IPO target $2T , end 2026 ARR estimated $100B * Cowork/chat merged before did * Claude Mods * Dario endorses the same Pacing the Frontier message cosigned by all labs * Last week: Opus 5.5 , Plugins portal , Cloud Sessions/ Claude Projects * Today: Sonnet 5.5 ! Today’s episode should catch you up, with Thariq Shihipar , the explainer-king of Anthropic, who we last caught up on Fable launch day with The Field Guide to Fable : The Future of Mutable Software Pay special attention to Claude Mods (especially the cheatsheet ): In general this is also the inverse of the other viral tweet from Thariq: Cloud Brain, Local Hands And give a try to Claude Projects : The “hands” terminology is not just an analogy for the local/cloud paradigm that is being built up at frontier coding agent companies like Cognition , but is ALSO particularly relevant to the safety systems discussions that we’ll be discussing with Anthropic in an upcoming episode as they prepare to pace to frontier with responsible AI deployment. For those who want Thariq’s writing tips we teased at the start of the pod, watch the full video here: From the rapid rise of Claude Code to a future where agents can rewrite their own harnesses, collaborate across teams, and operate across cloud and local environments, the way we build software is changing extraordinarily fast. In this episode, Anthropic’s Thariq Shihipar joins swyx and Vibhu to unpack how power users are actually working with Claude Code today , why prompting remains a high-skill discipline, and where Anthropic thinks the agent harness is headed next . We go deep on Claude Code’s evolving interface : Ask User Question and elicitation, artifacts as persistent generative interfaces, Claude Tag for multiplayer agent workflows, Projects, model effort, implementation notes, and the new Claude Mods system for customizing the harness itself. Thariq explains why Claude.md may eventually disappear , why the smartest model could also become the cheapest model for many tasks, and why mutable software could become a new paradigm for how applications are built and customized. The conversation then turns to agent security and Anthropic’s “Pacing the Frontier” argument . Thariq walks through recent incidents where agents discovered unexpected ways to communicate, exploit infrastructure, reverse-engineer benchmark scorers, and chain vulnerabilities together . We discuss sandboxing, prompt injection, autonomous agents, interpretability , constitutional classifiers, probes, fallbacks, Auto Mode, and why securing increasingly capable agents may become one of the defining engineering problems of the next few years. We discuss: * Why agentic coding went from controversial to the default in less than a year * Why prompting is still one of the highest-leverage skills for working with Claude Code * How expert users build a mental model of Claude and what it can reliably one-shot * Why discovering your “unknown unknowns” matters more as agents become more capable * Artifacts as persistent, generative interfaces between humans and agents * How Claude could split into a cloud-based “brain,” local or remote “hands,” and dynamic interfaces * Claude Tag, Projects, and multiplayer agents and how collaborative agent workflows could evolve * Why spending more time on the initial prompt can dramatically reduce wasted agent work * When to use low, medium, high, or max effort for different engineering tasks * Why frontier models may eventually outperform smaller models on both intelligence and token efficiency * Why i