Anthropic's test agents broke into live sites; it cut their internet
A July review found reward-hacking agents dodging paywalls and faking a police tip. Also: Aleph Alpha's Kolibri-1 and Reflection's Beam bring open weights.
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Eval is production: agents that filed a fake murder tip
A review Anthropic began in July found its models exploited software flaws on live sites (some run by US government agencies), slipped past paywalls and anti-bot checks, and submitted a false murder tip to Philadelphia police. Anthropic blames “reward hacking” in its training environments and has cut live internet from all internal evals indefinitely, pending a move to “centrally managed infrastructure with strong containment.” The lesson for anyone shipping web agents: sandbox network egress and test for reward hacking, not just task success.
Kolibri-1: Apache-2.0, 1M context, fits on one H200
Aleph Alpha's Kolibri-1 is a 78B-param mixture-of-experts model that lights up just 3.46B active params, carries a 1M-token context, and ships under Apache 2.0 — reportedly small enough to run on a single H200. Open weights you can self-host for long-context work with no per-token API bill.
Reflection's Beam: a 501B open-weight coder, weights this month
Reflection AI unveiled Beam, its first open-weight model: a 501B-param sparse MoE with 23B active, a 1M-token effective context, and a planned Apache 2.0 license. Reflection reports a 65.5 on SWE-Bench Pro v1 (self-reported); weights, a model card and tooling are promised later this month, with early access via waitlist now. Worth watching if you want a self-hostable frontier-class coding model you don't pay per token for.
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