FOSSA head of Applied AI after acquisition of EdgeBit in July 2025.
FOSSA (acquired EdgeBit) - Head of Applied AI
EdgeBit (YC W23) - CEO - https://edgebit.io
Red Hat (acquired CoreOS) - Director of Product
CoreOS (YC S13) - early employee
Really cool. It would be interesting to see a demo of an app that is clearly more bespoke, like your Tesla account, online banking, movie theatre ticketing, etc.
I commented elsewhere but our team built a custom static analysis engine for JS/TS specifically for the dep update use-case. It was hard, had to do synthetic execution, understands all the crazy remapping and reexporting you can do, etc. Even then it’s hard to penetrate a complex Express app due to how the tree is built up.
We’ve built a modern dependabot (or works with it) agent: fossabot analyzes your app code to know how you use your dependencies then delivers a custom safe/needs review verdict per upgrade or packages groups of safe upgrades together to make more strategic jumps. We can also fix breaking changes because the agents context is so complete.
We have some of the best JS/TS analysis out there based on a custom static analysis engine designed for this use-case. You get free credits each month and we’d love feedback on which ecosystems are next…Java, Python?
Totally agree with the author that static analysis like govulncheck is the secret weapon to success with this problem! Dynamic languages are just much harder.
We have a really cool eval framework as well that we’ve blogged about.
Thanks for sharing, really helpful to see your thinking. I haven't fully embraced FaaS myself but never regretted it either.
Curious to hear more about Renovate vs Dependabot. Is it complicated to debug _why_ it's making a choice to upgrade from A to B? Working on a tool to do app-specific breaking change analysis so winning trust and being transparent about what is happening is top of mind.
When were you using quay.io? In the pre-CoreOS years, CoreOS years (2014-2018), or the Red Hat years?
no, but still being super impressive. CEO of a company rebuilding a CAD rendering engine because they put an LLM on top of it. So you describe the mechanical specs of the part you want and it models it. Takes all the tedium out of modeling stuff. Super cool and many applications.
Same author talked about adversarial license plates that trick these cameras with a sequence of black blocks, discussed here in original form [1]. He is interested in breaking both the plate detection (ideal) and character recognition (good). The examples are pretty cool looking.
Totally agree that AI is great for this, it will work harder and go deeper and never gets tired of reading code or release notes or migration guides. What you want instead of summaries is to find the breaking changes, figure out if they matter, then comment on _that_.
If a giant chunk of the constellation can act as a truly huge antenna, what can you get from that? Super high resolution? Seek/dwell time on a target that is effectively infinite?
Curious if auto-merge philosophy changes between libraries and applications. The library definitely has a larger user base to break and a wider matrix of use-cases. IMO, auto-merge is more palatable for an application – do you agree? Especially when you're under SOC2/FedRAMP/etc.
How do you scale this besides keeping the dep list short? Are you reading every item or just scanning for words like "deprecated" or "breaking change"?
Fair criticism. I was going for the colloquial usage of "you get one shot" but yeah I did read that Google paper the other day referring to these as zero-shot.
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