Revolut replaces Claude and Codex with self-hosted GLM
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Revolut has switched the default coding tool for about 2,000 software engineers from Anthropic's Claude Code and OpenAI's Codex to GLM, the open-weight model from Chinese lab Z.ai, self-hosted by the company on its own infrastructure in the United States rather than using Z.ai's hosted service, cutting its AI coding bill to about one-tenth of the previous amount. The savings cover only model calls, not cloud, staffing, and monitoring costs, and depend on extensive tuning: Revolut executive Abitbol says out-of-the-box inference was "terrible," and the team had to cut time to first token from about 14 seconds to 1.7 seconds on the same batch of GPUs before it was usable. Image tasks still go to Gemini, and Claude Code and Codex are kept for the hardest work. Abitbol says smaller companies lacking Revolut's scale and infrastructure team may not achieve the same results.
AI overview based on source reports · Updated 2 days ago
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- Latent Space[AINews] It's Swapping Season
Cat Wu is leaving Anthropic's Claude Code team to lead Core Products PM at OpenAI, while Sharon Zhou is leaving AMD for Anthropic, alongside other moves including Factory acquiring HumanLayer and Meta acquiring Conductor. The roundup also covers OpenAI's autonomous pipeline that patched 948 Linux kernel privilege-escalation bugs for $1.2M in API cost, open-weight releases including Kimi K3.1 Pro, Ripple-65B and Tencent Hy4, and reports that GPT-6.2 Sol and Astra could ship as soon as Friday.
- Hacker News AIRevolut slashes AI coding costs 10x by cutting out Claude and going self-hosted
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- Hacker News AIFeaturedRevolut swaps Claude for GLM open-weight model, cutting AI coding bill by 90%
Revolut has moved its roughly 2,000 software engineers off Anthropic's Claude Code and OpenAI's Codex as default coding tools onto GLM, an open-weight model from Chinese lab Z.ai that Revolut hosts itself, bringing its bill to about a tenth of what it paid Anthropic and OpenAI. David Abitbol, who runs AI operations and infrastructure at Revolut, said the company serves GLM from about 56 Nvidia B200 GPUs at a blended cost of around $0.30 per million tokens, and reported roughly 60% shorter coding time and 70% shorter lead time in a sample of about 2,000 engineers, though the comparison does not isolate the model change from other causes. Abitbol said the 90% saving excludes cloud, staff and monitoring costs and depended on heavy tuning, with out-of-the-box inference "terrible" until time to first token fell from about 14 seconds to 1.7 seconds on the same GPUs.
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