Hihi, I'm Alex, co-founder and CEO. Subimage maps infrastructure the way an attacker does so security teams at scale-ups and enterprises can find and fix problems. We're built on Cartography (https://cartography.dev), the open source infra mapping tool we helped create at Lyft and donated to the CNCF. It runs at over 70 companies, including 7 in the Fortune 100.
We're a tiny seed-stage team and our customers are companies your parents have heard of. We have more demand than we can build for, which is why this post exists. This is a chance to get in on the ground floor of something big (or to find out quickly that it won't work; just being upfront about it).
Some of the problems you'd be working on:
- Vulnerability triage with real context: is the asset internet-facing, through which services, is the vulnerable function actually reachable in code, are there compensating controls, and what sensitive data does it reach once exploited, across how many hops.
- Proving that the security fixes our agent proposes won't break anything.
- A near-real-time, self-updating map of an environment so you can see an attack as it happens, plus "time travel" in the graph so you can replay it afterward.
- Teaching an agent to answer questions about a large graph quickly, with only the context it needs and without making mistakes.
- Determining who owns a given resource from messy signals like tags, actions, logs, and files.
Stack: Cartography for ingestion, Neo4j for the graph, Python/FastAPI backend, Svelte frontend with a homegrown WebGL graph renderer, an MCP server that exposes all of it to agents, Terraform for IaC.
Who we're looking for: 3+ years at high-growth companies, strong distributed systems and cloud experience, a real sense of ownership, first-principles thinking, and a willingness to implement gnarly business logic (elegantly, ideally) if it makes a jaded security engineer's life easier. Kubernetes and security knowledge are nice to have, not required. You're in the Bay Area and want to be in the office five days a week.
Process: technical screen, call with the founders, a paid day working with the team, then offer. Health/vision/dental, 401k match, gym, team lunches, unlimited PTO.
I like the concept of deontological ethics where instead of just plain good and bad, we consider what is one’s duty. People make decisions based on who they are responsible for.
My M4 Pro 48GB gets about 13tok/s, in both 3.6 and 3.8 27b Qwens. Qwen A3B and Gemma get closer to 100tok/s from memory but the results are pretty poor for coding tasks.
Edited to add: for agentic workflow I’m running omlx which tells me it has about a 90% cache hit rate (tradeoff is some disk and mem space) - that noticeably changes the felt speed.
I feel like it's 50/50 between people doing that, and people that have spent a lot of time tuning a system they are pointing at focused and well specified problems.
Seems threads about local LLMs on Apple hardware feature comments listing M3/4/5 at 48GB 64GB and not 128GB.
That is, users with M-series hardware that have less-than-max RAM share results whereas users with max RAM do not.
Speculating (not extrapolating), maybe users with machine that have max RAM are less interested in running local LLMs and are less averse to paying services for compute?
Personally, I’d love to see what output max RAM M-series Apple hardware in these threads.
Qwen3.8-27B runs at 59.5 tok/s on my M4 Max, 40-core GPU, 128 GB
I use it occasionally for classification and other tasks but I wouldn't trust those smaller models with the real work and for larger data processing it's too slow, e.g. a dataset I wanted to classify would've taken 56 days on my laptop vs just paying the cheap Luna prices to openai and getting it done in a few hours.
I've been using Qwen3.6-37B-A3B on an M1 Max w/ llama.cpp and for my practical uses I prefer it to qwen3.8. When 3.8 does answer its slower and, qualitatively, marginally better than qwen3.6, but 3.8 often ends up in unresolved thought loops and runs slower. The Moe 3.6 on my setup is much faster, 500t/s peaks, 30t/s typical, vs 3.8 150 peak, 4-9 t/s typical.
While I've spend a little time tuning, I'm assuming there will be deeper tuning for 3.8 that might close the gap.
Yeah, that's my experience. It's a big "wow" factor to get a non-trivial LLM running on my Mac, but it's actually not that useful. Like trying to use Photoshop at 8 FPS.
It’s not because you didn’t find use cases for local LLMs that there are none.
I use local LLMs on my Mac Mini M4 Pro with 48G to review text messages tone, act as a text correction tool, act as a code review tool, to do code agent work, generate code snippets, etc
Regarding this analogy, fps don't matter as much for Photoshop, since it's not an immediate mode GUI. 8 fps would be quite ok for comfortably getting feedback on live image filters and such.
It basically ended up with me learning that what is mostly pursued is the sharing part of piracy, and not doing that.
I got in a bit of trouble by my parents at the time and probably just stopped pirating stuff for a bit. No more issues from the ISP as we complied with the C&D and I stopped seeding stuff or uploading to shared FTPs (I can't really recall which was the issue at the time)
Hihi, I'm Alex, co-founder and CEO. Subimage maps infrastructure the way an attacker does so security teams at scale-ups and enterprises can find and fix problems. We're built on Cartography (https://cartography.dev), the open source infra mapping tool we helped create at Lyft and donated to the CNCF. It runs at over 70 companies, including 7 in the Fortune 100.
We're a tiny seed-stage team and our customers are companies your parents have heard of. We have more demand than we can build for, which is why this post exists. This is a chance to get in on the ground floor of something big (or to find out quickly that it won't work; just being upfront about it).
Some of the problems you'd be working on:
- Vulnerability triage with real context: is the asset internet-facing, through which services, is the vulnerable function actually reachable in code, are there compensating controls, and what sensitive data does it reach once exploited, across how many hops.
- Proving that the security fixes our agent proposes won't break anything.
- A near-real-time, self-updating map of an environment so you can see an attack as it happens, plus "time travel" in the graph so you can replay it afterward.
- Teaching an agent to answer questions about a large graph quickly, with only the context it needs and without making mistakes.
- Determining who owns a given resource from messy signals like tags, actions, logs, and files.
Stack: Cartography for ingestion, Neo4j for the graph, Python/FastAPI backend, Svelte frontend with a homegrown WebGL graph renderer, an MCP server that exposes all of it to agents, Terraform for IaC.
Who we're looking for: 3+ years at high-growth companies, strong distributed systems and cloud experience, a real sense of ownership, first-principles thinking, and a willingness to implement gnarly business logic (elegantly, ideally) if it makes a jaded security engineer's life easier. Kubernetes and security knowledge are nice to have, not required. You're in the Bay Area and want to be in the office five days a week.
Process: technical screen, call with the founders, a paid day working with the team, then offer. Health/vision/dental, 401k match, gym, team lunches, unlimited PTO.
Apply here and mention HN: https://www.ycombinator.com/companies/subimage/jobs/NCTFgKK-...
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