Akamai announced a $11.6 billion multi-year agreement with Anthropic, with potential expansion to $20 billion and a warrant for up to 5% of Akamai's stock.
Basis reports a 20% improvement in internal evaluation scores alongside the speed gains.
Team Bots are available in public beta on Teams and Enterprise plans, allowing users to share Grok Bots with context, tools, and memory across their organization.
The model supports Zero Data Retention and integrates with the AI SDK, OpenAI-compatible APIs, and Anthropic Messages API.
Researchers from Apple and Georgia Tech introduce LIPPAX to mitigate client drift and improve convergence rates in federated variational inequalities.
Gemma 4 offers five model sizes under Apache 2.0 license, enabling startups to reduce latency and infrastructure costs compared to relying solely on frontier APIs.
UK tech union UTAW released a report based on worker testimonies stating that AI adoption does not reduce workload but instead increases review burdens and stress.
Self-organizing agent teams achieved 66.7% average accuracy across five benchmarks, surpassing the 59.0% achieved by a perfect router selecting the best individual member.
A firsthand account of spending a month refining an AI harness to solve the problem of repetitive initialization in every AI session.
Imp brings DSPy's declarative, self-improving language-model programming paradigm to Elixir, leveraging OTP for reliable, concurrent agent execution.
Cloudflare reports that automated traffic surpassed human traffic in May 2026, six months ahead of previous forecasts.
New research shows that while explicit hidden prompts are ineffective and sanctionable, subtle preference framing significantly alters AI legal analysis outcomes.
The new model uses Glow-TTS architecture and targets edge devices and CPU inference.
The model uses a VeriLoop-Governed Recurrence (VGR) rule that prevents committing state changes that violate any protected obligation, even if total error counts decrease.
Anthropic's Claude Science platform used the Fable 5.1 model to independently calculate the 9-loop scattering amplitude for N=4 super Yang-Mills, a task previously considered impossible by direct computation.
An analysis of OpenAI's research data estimates current AI productivity gains at 2-3% per capability point, significantly below the 15-19% threshold required for a self-sustaining feedback loop.
Benchmarking across 29 datasets shows GLM-5.3-Flash achieves statistical parity with the specialized Jev model for intent classification without fine-tuning, though at higher latency and cost.
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