We made an anime about SBOMs (with a lot of help from @claudeai). Then shipped v0.3.0 🎉 CBOMs now get checked against CNSA 2.0 + post-quantum rules, and if sbom-tools can't tell what an algorithm is. github.com/sbom-tool/sbom-to…

Anthropic's Frontier Red Team believes small AI models may now be months away from being capable of self replicating without access to a datacenter. Quoting from the blog: 'The capabilities that make AI models generally powerful coding agents also generalize to cyber tasks, including autonomous replication. Like coding agents in 2024, we’re seeing signs of life for AI worms. We don’t yet have a full rogue replicator, but agents are becoming increasingly capable of completing the individual steps in the replication chain (and passing on their adaptations to future copies of themselves). At the same time, increasing amounts of inference compute are now available both inside and outside of datacenters (see Appendix D). It’s uncertain exactly what form autonomous AI replication will take. It could be a slow, distributed “digital slime mold,” living on relatively insecure compute. It could also be a powerful cyber model that breaches datacenters at a high pace, or a model that scams people for compute credits. As a prediction that goes beyond our experiments, we expect a future awash with agents running on all types of hardware, including some that aren’t under human control. While it’s true that IoT devices have been vulnerable for a long time, lightweight agents may be much more willing than human attackers to put in the effort to compromise large fractions of the world’s IoT compute. The rise of slow replicators would also be a bellwether for more aggressive, faster ones that can breach more hardened machines or be effective API key thieves. We should aim to harden our systems to make it difficult for such replicators to spread, but also prepare to deal with a world where they are here to stay.'

Agree on verification, but I'd push it further. Memory safety is a symptom, not the cause. We got here because of design decisions and trade-offs we made all along, and you can see it in pretty much every recent Linux kernel disclosure. Throwing more tokens at the attack surface won't save us either. Nothing we have today is ready for AI-driven vuln research at this scale, and finding and fixing bugs with AI doesn't change that. What we actually need is redesign and architecture validation with AI, formal methods where we can, and not accepting attack surface at the design phase in the first place. That's just not how we build software today. Patching known bugs doesn't touch the root cause, and hardware has the same problem.

Security will become a larger and larger function in software companies. Security is verification engineering (eg: “my code is probably memory-safe”), as well as capital allocation (“what surface should I throw most tokens at?”) For startups, it’s a challenge and opportunity. As the world’s paranoia grows given ever-more-sophisticated AI adversaries, how can you trust a 2-person-and-a-dog company? But conversely, given the dire state of global cybersecurity and increasing reliance on centralization, as always, there’re vast areas that small teams can now disrupt that I’m excited to invest in.

CVE-2026-86950: The Great Glyph Grift calif.io/research/the-great-…