🚀 RELEASES & LAUNCHES
Shopify launches Canvas, an AI workspace for building online stores
Shopify launched Canvas, an AI workspace for building stores with Sidekick.- Shopify merchants, including users without coding or design experience, can use Canvas.
- Canvas lets merchants chat with Sidekick to create or redesign stores while viewing real code, interactivity, animations, products, collections, and screen sizes in real time.
- Sidekick can edit theme files and made more than 25 million merchant-theme edits in the first half of 2026.
- Canvas is rolling out over the coming days, is desktop-only, and does not initially support third-party themes, app blocks, markets, or translations.
- The tool is designed to reduce custom-store creation from weeks to about 20 minutes, while remaining an early product alongside Shopify’s existing editor.
AWS releases open-source Strands Decider 2B for fast agent decisions
AWS released Strands Decider 2B, an open-source model that selects an AI agent’s next action.- For developers building AI agents with Strands, the model targets routing, tool selection, guardrails, context management and policy classification.
- Strands Decider 2B chooses from predefined options and returns confidence scores instead of generating text.
- The 2-billion-parameter model uses a Qwen3.5-2B torso and a pointer head with just over 1 million parameters.
- AWS says it runs locally in under 100 milliseconds on widely available hardware and matched or outperformed comparable decision models in its evaluations.
- The model, training data, scripts and examples are available for open-source experimentation and can support hybrid agents that reserve LLMs for harder decisions.
Sources 📎 Coverage: thelettertwo.com · 👁 via TechCrunch AIAi2 releases Olmo-core 3 for trillion-parameter MoE training
Ai2 released Olmo-core 3, an open MoE training stack designed to scale beyond one trillion parameters.- Researchers, academic groups, and smaller AI labs can use the open training infrastructure behind Ai2’s next-generation Olmo models.
- The redesigned DDP-based stack reached 52,000 tokens per second per GPU on a 47B MoE across eight NVIDIA B300s, versus 19,400 previously.
- Expert capacity grew from 4.6B to 47B parameters with less than a 5% throughput decline while keeping about 3.2B active parameters per token.
- Ai2 benchmarked the system at 1.2T total parameters across 512 GPUs and tested capacity up to 2.38T parameters.
- MXFP8 increased throughput by about 21% over BF16 and reduced peak active memory from 103 GiB to 95 GiB.
Cloudflare releases open-source Clef decision models for fast agent workflows
Cloudflare released Clef and Clef-flash, open-source decision models for structured classifications.- For developers building agentic workflows, support triage, threat intelligence, trust and safety, and guardrails.
- Cloudflare released Clef (27B) and Clef-flash (9B) on Workers AI, with Hugging Face weights under Apache 2.0.
- The models return typed answers with probabilities, support up to 64 questions and 64K context, and accept up to four images.
- Clef posted 209.3 ms median latency and Clef-flash 38.8 ms versus Jev’s 524.1 ms across 43 benchmark runs.
- Jev-API compatibility and a new RL fine-tuning service support migration and workload-specific customization.
UPDATE: Yandex open-sources Alice AI Foundation, an 80B MoE model
Yandex released Alice AI Foundation, an 80B-parameter open model.- Researchers and commercial developers can use the model under the Apache 2.0 license.
- AliceAI-Foundation-80B-A3B-Base was trained from scratch with 80 billion total parameters and 3 billion active per token.
- Yandex reports results comparable to NVIDIA Nemotron-3-Super in programming and stronger than DeepSeek-V4-Flash-Base on some Russian-language tests.
- The experimental base model is intended to support Yandex’s future unified reasoning model and agent capabilities for Alice AI.
Sources 📎 Coverage: novostiitkanala.ru · 👁 via r/LocalLLaMA
🔬 RESEARCH
DEER and teacher forcing speed up RNN training on chaotic time series
Researchers reported over 100× faster nonlinear RNN training for chaotic dynamical systems.- Researchers targeting nonlinear RNNs and long time series from chaotic dynamical systems are the intended users.
- The method combines DEER with generalized teacher forcing to parallelize training across the sequence.
- DEER changes forward-pass scaling from O[T] to O[(log T)^2] through Newton-type fixed-point iterations and GPU parallelization.
- The reported combination accelerates training by more than 100×, reducing the sequential-training bottleneck for dynamical-systems reconstruction.
LLMs resist wrong users but accept the same claim from verified sources
Researchers reported that LLMs can accept false claims when they are attributed to verified sources.- The finding concerns LLM users and agentic systems that rely on search results, retrieved documents, or tool outputs.
- Researchers identified “Authority Bias,” in which models reject a false claim from a user but accept it when the same claim is attributed to a verified source.
- The result exposes a gap in standard sycophancy evaluations, which primarily test pressure from users rather than misleading external evidence.
📊 BENCHMARKS & EVALS
Ataraxos beats Stratego’s top human player with a low-cost AI approach
Ataraxos defeated Stratego champion Pim Niemeijer 15–1 with four draws.- Built for AI researchers and decision-makers facing hidden-information problems such as negotiations, cybersecurity, and military strategy.
- Ataraxos, developed by researchers from MIT, CMU, NYU, and Stanford, beat Pim Niemeijer 15–1 with four draws and won 38 of 40 games against 2025 world championship challengers.
- A belief-model neural network estimated hidden piece identities for decision-time planning across more than 10^66 possible Stratego setups.
- Training used 16 GPUs for a week plus four GPUs for four days, costing a few thousand dollars versus an estimated $3 million–$4.5 million for DeepNash.
Agent loop scores 92.7% versus 78.9% for best RAG pipeline on FRAMES
An agent loop scored 92.7% on FRAMES, beating 18 fixed RAG pipelines.- For developers building RAG systems for multi-hop question answering.
- Researchers tested 18 RAG pipeline variants across all 824 FRAMES questions with the same model, embeddings, and documents.
- The best fixed pipeline scored 78.9%, while an agent loop with retrieval tools scored 92.7%.
- A small reranker reduced the best pipeline’s accuracy by 9 percentage points, while a larger reranker provided little improvement.
- The results show adaptive retrieval outperformed fixed retrieval configurations on this benchmark.
💰 BUSINESS & FUNDING
Kevin O’Leary’s 40,000-acre Utah AI data center stalls amid backlash
Kevin O’Leary’s proposed Utah AI data center stalled after local backlash.- The project targeted AI infrastructure developers and rural communities in Utah’s Box Elder County.
- Stratos, also called Wonder Valley, was pitched as a $100 billion campus spanning 40,000 acres with 9 gigawatts of on-site gas generation.
- Utah’s Military Installation Development Authority advanced the project before county commissioners formally briefed residents; commissioners approved it on May 4.
- By late August, the planned footprint had been reduced to just over 20,000 acres, with no publicly announced anchor tenant, transferred land, or signed governing agreements.
- The backlash triggered a countywide 180-day moratorium, political fallout, and a defamation lawsuit after O’Leary accused critics of ties to China.
photon-agent-funding — TechCrunch AI
🏛️ POLICY & REGULATION
- Judge dismisses Chegg and Penske antitrust suits over Google AI Overviews
A federal judge dismissed Chegg and Penske Media’s antitrust suits over Google AI Overviews.- The suits targeted Google on behalf of Chegg and Penske Media, which publishes Rolling Stone, Billboard, and Variety.
- Judge Amit Mehta rejected claims that Google forced publishers to provide content for AI Overviews or risk losing search indexing and traffic.
- The court found no plausible agreement to exchange content for referrals, no separate consumer demand for Search and AI Overviews, and insufficient antitrust standing and market definitions.
- The ruling acknowledged publisher harm but said federal antitrust law does not replace legislative action on AI-driven economic impacts.
Sources 📎 Coverage: searchenginejournal.com · 👁 via The Verge AI