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startuphub.ai/startups/anthropicLive

Anthropic

Claimed·aDR 90·eDR 82·Agent 57·Speed 61

Anthropic is an AI safety and research public benefit corporation building reliable, interpretable, and steerable AI systems, best known for the Claude series of advanced AI models.

Last updated Nov 6, 2026

2021Active19San Francisco, United States

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OverviewProducts155

About

Anthropic is an American artificial intelligence public benefit corporation dedicated to AI research and products that prioritize safety, aiming to secure the benefits of AI while mitigating its risks. Its flagship product is Claude, a series of proprietary large language models (LLMs) including Opus, Sonnet, and Haiku, designed for various applications from summarization to code assistance and enterprise use.

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Every Correction You Give Your Agent Is Data - Brian Douglas, Paper Compute

Your agent scolding is training data

What happens after you correct your agent? Brian Douglas told AI Engineer to save the correction and label it.

Douglas works at Paper Compute. He said he spent six to eight months capturing agent sessions with tapes.dev. Tapes.dev is open source session capture that writes prompts, tool calls and diffs to a searchable store. He runs agents in a sandbox runtime his team calls stereos. Traces flow to a central console where teammates tag pushback, apologies and regressions.

That gap drives his demo, Douglas said. Senior engineers push back when his agent duplicates services or writes Rust that compiles but breaks Paper API conventions. He collects each pushback from an engineer named Matt. He post-trained a small reviewer that comments on pull request 381 as Matt would. The pattern needs no GitHub scrape, Douglas said, because most review now happens inside the harness and never reaches

His advice is to start with skills, which are reusable checks built from labeled traces, before paying for post-training. Specialized fine-tuning adds small adapters on top of a base model, Douglas said. It worked for him on a 4B Qwen model quantized on a single 5090. Direct preference optimization needed 70B-plus scale he could only reach on rented Brev GPUs. Do not confuse the tooling with Tape to Tape, a roguelite

Team leads who let agents write in languages juniors do not know will have to budget for trace storage and labeling, or keep paying seniors to catch the same errors in review. Douglas kept his reviewer private because it holds sensitive data, and Paper console remains waitlisted.

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