We're seed-staged, 3 people, building an AI for cybersecurity, looking for a founding AI engineer who wants to learn/apply SOTA techniques for AI. Ideal background is experience building production agentic AI systems (by this I mean something like Simon's definition: https://simonwillison.net/2025/Sep/18/agents/ definition) who also likes to think about WHAT to build and not just how.

We are a Golang/Python shop (although I'm not sure that matters so much any more).

Email jobs@polarsky.ai with subject HN.

In general, treating LLM outputs (no matter where) as untrusted, and ensuring classic cybersecurity guardrails (sandboxing, data permissioning, logging) is the current SOTA on mitigation. It'll be interesting to see how approaches evolve as we figure out more.

(To clarify, I meant that some engineers mostly use CC while others mostly use Codex, as opposed to engineers using both at the same time.)

It’s also interesting to see how instead of a plan mode like CC, Codex is implementing planning as a skill.

Comments URL: https://news.ycombinator.com/item?id=46005687

Points: 3

# Comments: 0

Generative AI is rewriting how organizations use data, and breaking traditional security models in the process. We’re a team of cybersecurity, AI, and systems experts building the foundation for secure, trustworthy AI in the enterprise.

We're looking for a Founding AI Engineer who loves building with AI -- crafting context pipelines, integrating and evaluating LLMs into production systems, and delivering AI-native product experiences. You'll work on all parts of Polar Sky, from the data and eval systems to the reasoning, retrieval, and orchestration systems.

Apply online here: https://ats.rippling.com/polar-sky/jobs/a04ed5b7-6202-45e6-b....

We're a well-funded, pre-seed cybersecurity startup focused on data security. I'm looking for a founding AI lead with experience in fine-tuning LLMs (expertise around RL + reasoning models a big plus). This person would own the full AI stack from data to training to eval to test-time compute.

Who's a good fit:

* If you've always thought about starting a company, but for whatever reason (funding, life, idea), this is a great opportunity to be part of the founding team. We're 2 people right now.

* You enjoy understanding customer problems and their use cases, and then figuring out the best solution (sometimes technical, sometimes not) to their problems.

* You want to help figure out what a company looks like in this AI era.

* You enjoy teaching and sharing knowledge.

Questions, interest, just email jobs@polarsky.ai.

Comments URL: https://news.ycombinator.com/item?id=45354995

Points: 2

# Comments: 4

We're a well-funded, pre-seed cybersecurity startup focused on data security. I'm looking for a founding AI lead with experience in fine-tuning LLMs (expertise around RL + reasoning models a big plus). This person would own the full AI stack from data to training to eval to test-time compute.

Who's a good fit:

* If you've always thought about starting a company, but for whatever reason (funding, life, idea), this is a great opportunity to be part of the founding team. We're 2 people right now.

* You enjoy understanding customer problems and their use cases, and then figuring out the best solution (sometimes technical, sometimes not) to their problems.

* You want to help figure out what a company looks like in this AI era.

* You enjoy teaching and sharing knowledge.

Questions, interest, just email jobs@polarsky.ai.

* The "stack-centric" approach such as vLLM production stack, AIBrix, etc. These set up an entire inference stack for you including KV cache, routing, etc.

* The "pipeline-centric" approach such as NVidia Dynamo, Ray, BentoML. These give you more of an SDK so you can define inference pipelines that you can then deploy on your specific hardware.

It seems like LLM-d is the former. Is that right? What prompted you to go down that direction, instead of the direction of Dynamo?

Comments URL: https://news.ycombinator.com/item?id=43797033

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=43573785

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=43332234

Points: 6

# Comments: 0

- They used QwQ to generate training data (with some cleanup using GPT-4o-mini)

- The training data was then used to FT Qwen2.5-32B-Instruct (non-reasoning model)

- Result was that Sky-T1 performs slightly worse than QwQ but much better than Qwen2.5 on reasoning tasks

There are a few dismissive comments here but I actually think this is pretty interesting as it shows how you can FT a foundation model to do better at reasoning.

Comments URL: https://news.ycombinator.com/item?id=42961877

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42870111

Points: 1

# Comments: 0