dk189

It's named throughout our main website, the RL is on Kimi K2.6, benchmarks are vs K2.6: https://cosine.sh/blog/introducing-lumen-outpost. The ArgusRed page is a week old so it's not on there yet, but nothing's hidden. And K2.6 only needs attribution above a certain scale, the threshold Cursor hit and we haven't.

On Shannon airgapped in your VPC, if it works for you, you might not need us. A normal model will refuse or hedge on offensive tasks, we post-trained ours to just run the authorised stuff. For this one narrow job, a specialist that'll actually attack beats a generalist that won't.

Agreed, and that's basically our premise. If a 5 person team can post-train an open model to do this, so can the people you don't want doing it, model-level refusals on open weights are a speed bump. Which is the argument for defenders having it too, not against.

Fair, should've been precise. What's free today is the scan: read-only. The Bank of Anthos integer overflow is a scan finding, clone it and you'll get the same. The active mode that actually sends the exploit and shows the response is gated for now, that's the part that's really 'pen test'. Juice Shop's a fair target for showing it, will try to get this done and post an update.

Did you follow the link? There is a brew install binary you can install and test. It's live.

The tool is live, you can test it.

The RL is easy to describe, hard to do. The nice thing about pen testing is the reward isn't a vibe like training for code quality, the exploit either lands or it doesn't. The day to day is not glamorous at all, mostly fighting for stable gpu access, watching a cluster sit half-idle with nodes you somehow can't book.

You need both, scanning for your own code, pen testing to actually prove vulnerabilities, otherwise it can be very noisy and one of the things that most tools currently suffer from is they give you too many false positives. For the moment. The pen testing we gated it for now until we resolve the debate of safety.

I think the policy universally makes sense, who would want to give a tool like this to bad actors? But it does leave a big section of the market underserved. Particularly when Mythos was made accessible to very large orgs and then Fable was pulled on export grounds.

Large Language Models like ChatGPT have showcased exceptional capabilities in natural language conversations, leading to a revolution in customer service automation. Unlike rigid, complex traditional NLP chatbots, a simple wrapper application around the ChatGPT API can significantly outperform them. Consequently, the market has now flooded with ‘Custom ChatGPTs’ solutions offered by a wide range of solutions, from solopreneurs to large, experienced companies in conversational AI.

At Algomo, we believe that the potential extends even further. We see language models as the foundational 'intelligence' driving autonomous AI agents in customer service.

Autonomous AI agents?

Standard conversational language models work by taking text input and producing text output. You ask a question, and the language model responds with ‘static’ information from a knowledge base. You can think of them as a ‘conversational search engine.’

LLM-based agents, however, are a step ahead. They not only take text as input but can also interact with 'modules' such as memory, call other specialized LLMs, utilize functions (tools & APIs), and even integrate human judgement. Essentially, they can strategize and execute solutions to any presented problem. Application in Customer Service

Autonomous AI agents can literally redefine the interaction landscape between businesses and their customers. For instance, an agent can integrate into a company's CRM system, using its memory module to recall past interactions and personalize responses. It can employ specialized LLMs to handle technical queries or access APIs for real-time order tracking, or product information, ensuring customers receive accurate and timely information. Even more impressively, these agents can autonomously escalate complex issues or ask feedback from human agents, combining AI efficiency with human empathy.

What is the difference against competitors eg Intercom Fin?