713 summarised stories in Tools & Coding, each linking back to the original source. Browse all topics →
OpenAI Blog 1 month ago ● 2 sources
Nextdoor engineers use OpenAI's Codex to debug difficult-to-reproduce issues and develop code across multiple platforms. The company deployed Codex with GPT-5.5 to help engineers spend less time on repetitive coding tasks. Engineers can now focus more on product strategy and outcomes rather than implementation details.
Hugging Face Blog 1 month ago
A coding agent built a 3D gallery of Paris monuments by chaining two Hugging Face Spaces—one for image generation and one for 3D reconstruction—without manual intervention. Hugging Face Spaces now expose agents.md files that provide exact calling instructions, allowing agents to integrate them with zero custom code, and the resulting pipeline was replicated for Egypt and Japan with single-sentence prompts each. This demonstrates how multimedia software can be assembled from documented, callable building blocks rather than built from scratch, reducing integration from a major barrier to a marginal task.
Hugging Face Blog 1 month ago
Trackio migrated its GitHub Actions CI pipeline to run on Hugging Face Jobs, a serverless infrastructure service that supports arbitrary hardware including GPUs. The migration reduced CPU job runtime from 1m40s to 1m10s (30% faster) and enabled GPU tests to complete in 45 seconds on a t4-small machine at minimal cost. The setup requires duplicating a dispatcher Space, creating a GitHub App, and changing the `runs-on` label in workflows from `ubuntu-latest` to labels like `hf-jobs-cpu-upgrade` or `hf-jobs-t4-small`.
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24 July 2026
The guardrails meant to protect AI models from malicious hackers are now frustrating the security researchers trying to stop them. OpenAI, Anthropic, and other leading labs have built walls around their systems—requiring offensive security teams to apply for special access programs like Anthropic's Cyber Verification Program or OpenAI's Trusted Access for Cyber—to use models for legitimate vulnerability hunting. The problem: approval processes move slowly and inconsistently, forcing some researchers to abandon these systems entirely and pivot to unrestricted open-source alternatives, including Chinese models sitting outside U.S. oversight. It's a classic security paradox: the restrictions are well-intentioned, designed to prevent bad actors from weaponizing frontier AI for cyberattacks. But by making it harder for the good guys to do their jobs, companies risk pushing critical vulnerability research away from domestic systems and toward less transparent platforms. The tension exposes a real gap in how AI safety policies are designed—they're built for binary scenarios (allow or deny) when the reality of cybersecurity demands a more nuanced ecosystem where credentialed researchers can move quickly.
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