Francesco Virga

We believe access to capable open-source cyber models is our constitutional right as Americans under the 2nd Amendment. It is our duty as technologists and patriots to help protect the digital infrastructure that powers the world's largest economy. As such, we’re serving our cyber models “at-cost”, in US data centers only, with ZDR enabled by default. We do not plan to make money serving these models. If you run a company that is considering how to best protect against cybercrime, reach out to me or @atbeme. We’d be happy to advise. KYC required. Apply below.

If you want an inference provider with built in evals, use @inference_net. Legacy inference providers require you to have accounts on “partner” products, which is not ideal for fast moving companies in 2026.

Want to try GLM 5.2 in production but worried how it might change your product? Don’t worry, we got you: 1. Install Inference Gateway (docs.inference.net) 2. Keep sending traffic to your current provider 3. Gateway automatically starts sorting through your live data using an RLM to generate evals for your app. This takes ~24 hours. 4. Gateway starts mirroring live traffic to GLM 5.2 to run evals. Traffic is only mirrored - you’re still using your old provider in prod. 5. Once evals look healthy, you get a Slack notification letting you know it’s safe to switch. 6. Switch model identifier in your code to “glm-5.2” Congrats, you just saved 90% on your monthly token bill, and you own your LLM stack end to end.

We’re introducing HALO 😇 Hierarchal Agent Loop Optimizer HALO is an RLM-based agent optimization technique capable of recursively self-improving agents by analyzing their execution traces and suggesting changes. This work is inspired by the Mismanaged Genius Hypothesis proposed by @a1zhang and @lateinteraction earlier this month. tldr; we improved performance on AppWorld (Sonnet 4.6) from 73.7 --> 89.5 (+15.8) by giving HALO-RLM access to harness trace data and asking it to identify issues. The feedback from HALO surfaced failures in the harness such as hallucinated tool calls, redundant arguments in tools, refusal loops, and semantic correctness issues. Each issue mapped cleanly to a direct prompt update. We then fed these finding into Cursor (Opus 4.6), and asked the coding agent to update the underlying harness. We repeated this trace -> HALO-RLM analysis -> code update loop until the score plateaued. Today we’re open-sourcing the core HALO-RLM framework, evals, and data for further review.

Excited to launch Day One support for tracing the Cursor Agent SDK with @inference_net 3 lines of code is all you need to track agent performance across executions and iterate to perfection Docs below 👇

All the best programmers I know are starting to write code by hand again