Lilith · selected stories
What is actually happening in AI. Selected stories, context and opinion without the promotional noise.
Atom feed ↗Sea Limited is deploying Codex across engineering, with OpenAI citing 87% weekly active users. The interview with Shopee's David Chen is not just about faster coding. It frames agents as a layer over complex codebases, CI/CD, tests, and system design.
Latent Space uses the pullback of part of OpenAI's fine-tuning API as a useful reality check: for most AI products today the first step is not tuning weights but better evaluation, context, retrieval, tool use and workflow. Fine-tuning remains a strong tool, just not a universal fix for a poorly designed system.
OpenAI details how Codex runs in isolated environments: per-repo sandboxes, network restrictions, approval gates, and agent-native telemetry for safe enterprise adoption.
OpenAI outlines its layered approach to ChatGPT community safety: model safeguards, abuse detection, policy enforcement, and collaboration with external safety experts.
OpenAI launches a bio bug bounty targeting universal jailbreaks in GPT-5.5, with rewards up to $25,000 for critical biological safety findings.
ChatGPT Images 2.0 brings improved image generation focused on text accuracy in graphics, multilingual support, and advanced visual reasoning for production workflows.
Cisco and OpenAI describe deploying Codex as an agent in enterprise engineering workflows: build automation, defect fixes, and a shift toward agent-native development.
GPT-5.2-Codex targets long-horizon coding tasks across large context: large-scale code transformations, security fixes, and multi-file consistency.
OpenAI introduces FrontierScience: a benchmark for scientific reasoning tasks in physics, chemistry, and biology, focused on reasoning processes rather than factual recall.
The GPT-5.1-Codex-Max system card describes two safety layers: model-level safety training and prompt injection protection, and product-level sandboxing with configurable network access.
OpenAI released gpt-oss-safeguard-120b and 20b: open-weight reasoning models where content classification policy is not baked into the weights but supplied at runtime. Organizations bring their own rules; the model reasons over them.
OpenAI's September 2025 post goes to the root of hallucinations: models learn to play the evaluation game, not to answer truthfully. If evals penalise admitted uncertainty more harshly than confident errors, models calibrate toward persuasiveness.
OpenAI and Anthropic published results of a joint safety evaluation: they tested each other's models for misalignment, instruction following, hallucinations, and jailbreaking. For the first time, two leading labs show where outside eyes find their blind spots.
