Claude + Cowork merge 🛠️, ChatGPT sponsored agents 💰, harness tax 🤖
Agents are smarter when they can search the web. But scraping Google gets expensive when you're burning extra tokens to parse messy SERPs into readable text.
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Agents are smarter when they can search the web. But scraping Google gets expensive when you're burning extra tokens to parse messy SERPs into readable text.
Current solutions are complex, but they can't see API calls outside of proxy, subprocesses, or config rewrites made by agents. That's unsustainable in the agentic era.
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Most large-scale AI training runs operate at 35–45% Model FLOPS Utilization (MFU). You're paying for twice the compute you're using.
The most ambitious companies are training specialized models to outperform the frontier on their domain.
Bad tool calls, malformed output, and token spend you cannot account for. Agent tracing shows all of it. Serge from Sentry instruments three agents live: a chatbot in an ecommerce store, a custom Slack agent, and a GitHub Action that reviews PRs.
Many organizations have AI policies, but far fewer have a practical way to decide whether an agent is ready for production, who signs off, and how that decision gets documented.
The Wispr Flow Notetaker is here - and it's got all the context with none of the bots (Sponsor)
Ramp's latest research suggests something entirely different is happening (something almost nobody predicted).
TLDR is hiring its first PM to help build the agent-first operating layer used across the company. We're looking for a builder who has shipped real products / systems with LLMs. Click here to learn more.
If you ship software, you already know: agents are builders. Is your API ready for them?
Atlas is a world generation model pretrained from scratch to natively operate on text, images, video, and 3D. It combines all inputs into a shared spatial context and uses that context to generate what comes next.
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