Total Articles: 9 from 4 sources
AWS News
1. Introducing enhanced custom event buses in Amazon EventBridge for enterprise-scale event-driven applications
Published: 2026-09-24 20:58
Summary:
Amazon EventBridge announces an enhanced custom event bus for organizations scaling event-driven applications across teams and accounts Deploy a single event bus shared across all AWS accounts in your organization, with ordering guarantees, a simplified Subscriber resource, and improved economics at scale.
Cloudflare Engineering
1. Agents can now set up your website’s security with Turnstile Spin
URL: https://blog.cloudflare.com/turnstile-spin/
Published: 2026-09-25 13:00
Summary:
Misconfiguring Turnstile by skipping backend validation leaves sites exposed to bots Turnstile Spin fixes incomplete setups by using your preferred AI coding agent to wire up server-side verification.
2. How Cloudflare addressed a cross-tenant data exposure vulnerability in Containers
URL: https://blog.cloudflare.com/containers-cross-tenant-vulnerability/
Published: 2026-09-24 15:00
Summary:
External security researchers at Accomplish identified a vulnerability in Cloudflare Containers that could expose residual disk data from previous workloads We explain how the issue worked, how we investigated it, and the steps we took to remediate it.
3. How we could save petabytes of cache storage with Zstandard and Pingora
URL: https://blog.cloudflare.com/cache-transcoding/
Published: 2026-09-01 12:59
Summary:
Could we get more cache space with the same hardware We prototyped compression inside Cloudflare’s cache to find out.
GitHub Blog
1. When chat is the wrong UI
URL: https://github.blog/ai-and-ml/github-copilot/when-chat-is-the-wrong-ui/
Published: 2026-09-24 20:00
Summary:
What is a developer to do when they need something more tangible than a chat box The post When chat is the wrong UI appeared first on The GitHub Blog.
2. AI-powered fuzzing with the GitHub Security Lab Taskflow Agent
Published: 2026-09-24 18:26
Summary:
In this blog post, I explain how to use the new fuzzing taskflow based on the GitHub Security Lab Taskflow Agent AI framework The post AI-powered fuzzing with the GitHub Security Lab Taskflow Agent appeared first on The GitHub Blog.
Google Developers
1. The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents
Published: 2026-09-25 14:24
Summary:
While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent’s logic broke down To solve this, developers should adopt behavioral evaluations—fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications rather than final string equality By building these inexpensive micro-checks alongside macro benchmarks, engineering teams can confidently iterate on system prompts and upgrade models without the risk of regressions.
2. Autonomous LLM post-training with Tunix on TPUs
URL: https://developers.googleblog.com/autonomous-llm-post-training-with-tunix-on-tpus/
Published: 2026-09-25 14:24
Summary:
The “autofinetune” project introduces an autonomous research loop that fully automates LLM post-training workflows, including Supervised Fine-Tuning (SFT) and Reinforcement Learning via GRPO By defining boundary conditions and evaluation metrics in a single Markdown specification, developers can deploy an AI agent to iteratively edit training scripts, launch experiments, and automatically commit verified hyperparameter optimizations to Git Built on Google’s AI stack—including Tunix, Gemma, and Cloud TPUs—this framework eliminates manual tuning cycles, successfully demonstrating hands-off performance gains in both function calling and math reasoning models.
3. Build zero-trust AI agents that judge intent, not just syntax
URL: https://developers.googleblog.com/build-zero-trust-ai-agents-that-judge-intent-not-just-syntax/
Published: 2026-09-25 14:24
Summary:
This blog post explores how to transition AI agents from static, build-time security controls to dynamic runtime governance using the Gemini Enterprise Agent Platform It highlights three primary managed defenses: Model Armor for screening edge prompts, Semantic Governance Policies for evaluating tool intent against business rules, and Agent Anomaly Detection for catching multi-turn exploits By shifting these capabilities to the platform level, security administrators can dynamically enforce policies and neutralize complex attacks without needing to modify or redeploy the agent’s underlying code.
Generated on 2026-09-25 14:24:25