The Register
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2 months ago
Microsoft reversed a VS Code change that automatically added "Co-authored-by: Copilot" attribution to git commits, after users complained the notice appeared even when Copilot was disabled or unused. The problematic change was introduced in VS Code 1.110 in early March, and a fix scheduled for version 1.119 will make the attribution trailer opt-in rather than default. The reversal reflects broader tension over how AI code contributions should be documented, with implications for copyright protection, liability, and developer workflows.
OpenAI Blog
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2 months ago
OpenAI and PwC are partnering to help enterprises deploy AI agents that automate financial workflows and strengthen controls within finance departments. The collaboration focuses on modernizing how chief financial officers use AI for tasks including forecasting and workflow automation. The partnership aims to change how large organizations structure and operate their finance functions through AI-assisted processes.
Interconnects
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2 months ago
Chinese labs have used API jailbreaking to extract model outputs for training, prompting U.S. policymakers to target the technique of "distillation," which is a standard industry practice for creating smaller models from larger ones. Congress has moved bills through committee and executive orders are pushing for action, with potential regulatory consequences including restrictions on open-weight models trained on Chinese systems. Overly broad regulation risks permanently damaging the Western academic and open-source AI ecosystem while doing little to stop determined competitors, since the core problem is API abuse rather than distillation itself.
Amazon Science
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2 months ago
Amazon has built a comprehensive responsible AI pipeline across four stages of model development—pretraining, post-training, evaluation, and third-party monitoring—incorporating over 70 internal and external RAI tools and funding or publishing more than 500 research papers. The company teaches RAI principles during pretraining using curated datasets, optimizes policy adherence through reinforcement learning from human feedback, creates model-breaking datasets for evaluation, and partners with third-party experts to assess frontier risks like CBRN and cyberattack research. Amazon's approach aims to anticipate risks, teach models to handle ambiguity, and build systems that adapt to regulatory and social changes across different geographies and applications.
The Register
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2 months ago
● 3 sources
AI agents performing inference workloads generate unpredictable, concurrent data access patterns that overwhelm cloud storage systems designed for human-speed applications, requiring architectures that decouple performance from capacity. AWS EBS volumes experience burst credit exhaustion and latency spikes from 1 millisecond to 50+ milliseconds when AI inference traffic overwhelms the storage layer, as demonstrated by a fintech e-commerce platform whose AI shopping assistant caused a system-wide outage within 15 minutes of launch. Organizations must architect for extreme tail latency performance (p99/p999 under mixed load) and adopt software-defined storage solutions that can deliver consistent sub-millisecond latency even during concurrent OLTP and inference workloads, rather than attempting to scale through read replicas or additional IOPS provisioning.
Import AI
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2 months ago
Researchers and AI developers report that AI systems are demonstrating capabilities to automate components of AI research and development, with progress measurable across multiple benchmarks from code generation to scientific paper reproduction. AI systems have improved from solving 2% of real-world software engineering problems in late 2023 to 93.9% by 2026, while their ability to work independently on complex tasks has extended from 30 seconds in 2022 to 12 hours by 2026. If these trends continue, frontier AI labs may begin delegating larger portions of AI R&D work to autonomous systems, potentially accelerating the cycle of AI model development itself.
The Register
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2 months ago
TeamViewer launched TeamViewer ONE, a platform using agentic AI to shift IT operations from reactive incident response to proactive autonomous remediation across distributed endpoints. According to TeamViewer's survey of 4,200 respondents, digital friction costs organizations an average of 1.3 workdays per month per employee, with 42 percent reporting revenue impact. The platform enables IT teams to detect and resolve issues before users experience disruption, reducing ticket volumes and freeing support staff to focus on strategic work rather than firefighting.
Last Week in AI
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2 months ago
A podcast episode covering major AI developments in late April 2026 discussed OpenAI's GPT-5.5 release with improved coding capabilities, xAI's Grok Voice Think Fast 1.0 achieving 67.3% on voice benchmarks, and DeepSeek's open-sourced V4 model featuring 1M-token context windows. Key business moves included Google planning a $40 billion investment in Anthropic with 5GW compute commitment, Meta's AWS Graviton chip deal, and China blocking Meta's Manus acquisition. Research presented covered AI safety concerns including model sabotage of safety research, document corruption from LLM delegation, and neural network disruption via bit-flip attacks.
Together AI
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2 months ago
● 3 sources
Together AI describes how inference costs dominate production AI systems at 80-90% of total lifetime cost, requiring optimization across latency, throughput, concurrency, and scheduling dimensions. The company has developed a full-stack approach including techniques like adaptive speculative decoding (Aurora delivering up to 1.25x speedup), custom hardware optimization on NVIDIA Blackwell systems, and intelligent batching that ship research to production within weeks. Better inference efficiency directly improves margins for AI-native companies by enabling more requests per GPU-hour and expanding economically viable use cases.
OpenAI Blog
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2 months ago
OpenAI rebuilt its WebRTC stack to handle real-time voice conversations with artificial intelligence at global scale. The system prioritizes low latency and seamless turn-taking, enabling responsive back-and-forth dialogue without noticeable delays. This infrastructure allows OpenAI to deploy voice AI features to users worldwide while maintaining the conversational quality needed for natural interaction.
