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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>digihub.li Drop (English)</title><link>https://digihub.li/en/drop</link><atom:link href="https://digihub.li/en/drop/feed.xml" rel="self" type="application/rss+xml"/><description>Drop by digihub.li: short, curated finds on AI, crypto, security and fintech, each with its source and a note on why it matters.</description><language>en</language><lastBuildDate>Thu, 12 Nov 2026 09:00:00 +0100</lastBuildDate><item><title>Vercel Schedules brings scheduling for AI agents</title><link>https://digihub.li/en/drop/2026-11-12-vercel-schedules-brings-scheduling-for-ai-agents</link><guid isPermaLink="true">https://digihub.li/en/drop/2026-11-12-vercel-schedules-brings-scheduling-for-ai-agents</guid><pubDate>Thu, 12 Nov 2026 09:00:00 +0100</pubDate><description>Vercel Schedules is, according to the provider, available as a public beta. Applications and agents can use it to create or change one-off and recurring tasks at runtime, without redeploying for every schedule change. A concrete building block for automation — permissions and the actual execution remain the application&#x27;s job. (Source: Vercel, https://vercel.com/blog/introducing-vercel-schedules)</description><category>AI</category></item><item><title>Cloudflare Zero Wire classifies critical vulnerabilities</title><link>https://digihub.li/en/drop/2026-11-11-cloudflare-zero-wire-classifies-critical-vulnerabilities</link><guid isPermaLink="true">https://digihub.li/en/drop/2026-11-11-cloudflare-zero-wire-classifies-critical-vulnerabilities</guid><pubDate>Wed, 11 Nov 2026 09:00:00 +0100</pubDate><description>Cloudflare is introducing Zero Wire, a public feed on critical vulnerabilities in internet-facing software. The briefings, according to the provider, bundle impact, possible actions and links to technical sources. For SMEs this can be a starting point for checking their own exposure; the feed does not replace such a check or any necessary updates. (Source: Cloudflare, https://blog.cloudflare.com/zero-wire)</description><category>Security</category><category>AI</category></item><item><title>oMLX splits prompt processing across two Macs</title><link>https://digihub.li/en/drop/2026-11-10-omlx-splits-prompt-processing-across-two-macs</link><guid isPermaLink="true">https://digihub.li/en/drop/2026-11-10-omlx-splits-prompt-processing-across-two-macs</guid><pubDate>Tue, 10 Nov 2026 09:00:00 +0100</pubDate><description>With oMLX 0.7.4.dev1, a second Mac can according to the release notes support processing of long prompts while the large model stays on the main device. This is explicitly a development version, not a stable release. The published benchmarks come from the project itself. (Source: jundot, https://github.com/jundot/omlx/releases/tag/v0.7.4.dev1)</description><category>AI</category></item><item><title>DeepSeek locally on the DGX Spark cluster</title><link>https://digihub.li/en/drop/2026-11-09-deepseek-locally-on-the-dgx-spark-cluster</link><guid isPermaLink="true">https://digihub.li/en/drop/2026-11-09-deepseek-locally-on-the-dgx-spark-cluster</guid><pubDate>Mon, 09 Nov 2026 09:00:00 +0100</pubDate><description>The maintainer of spark-ds41f documents in release 1107 how to run DeepSeek on several DGX Sparks. The published performance figures come from his own tests and are not independently confirmed. What is interesting for local AI projects are mainly the described hardware requirements, tests and supported cluster configurations. (Source: christopherowen, https://github.com/christopherowen/spark-ds41f/releases/tag/spark-ds41f-1107)</description><category>AI</category></item><item><title>Revolut moves 2,000 engineers to self-hosted open-source AI</title><link>https://digihub.li/en/drop/2026-11-03-revolut-glm-self-hosted</link><guid isPermaLink="true">https://digihub.li/en/drop/2026-11-03-revolut-glm-self-hosted</guid><pubDate>Tue, 03 Nov 2026 21:15:00 +0100</pubDate><description>According to an X post by @guilleflorvs, around 2,000 Revolut engineers now code with the self-hosted model GLM by default, with Claude Code and Codex kept as the exception. The figures come from a public talk by David Abitbol, who leads AI operations at Revolut: 56 Nvidia B200s, about 30 cents per million tokens and roughly a tenth of the previous Anthropic and OpenAI bill, all self-reported. For SMEs the interesting part is less the model than the question behind it: once tokens are no longer rationed, teams hand more work to agents and need clear guardrails for that. (Source: @guilleflorvs, https://x.com/guilleflorvs/status/2117379073785696395)</description><category>AI</category><category>Fintech</category></item></channel></rss>
