Azercell Telecom collaborated with the AWS Generative AI Innovation Center to build an Azerbaijani LLM on Amazon SageMaker AI, achieving 23% higher training throughput, 58% lower peak GPU memory, and 2× token efficiency via custom tokenizer, FSDP, and Liger Kernel optimizations.
Azercell developed a production-ready Azerbaijani LLM framework using Amazon SageMaker AI.
Custom tokenizer reduced tokens per word from 3.22 to 1.59, doubling encoding efficiency.
Not every new model is all it's cracked up to be. Our tracker keeps each release in context with its peers, so you know which models are worth your time. This article summarizes major model releases of 2026 so far, including Claude Opus 4.8, GPT-5.5 Instant, Nemotron 3 Nano Omni, GPT-5.5, ChatGPT Images 2, Claude Opus 4.7, Claude Mythos (Preview), GPT-5.4, Claude Opus 4.6, and GPT-5.3-Codex, with details on their features and significance.
Anthropic's Opus 4.8 offers faster thinking at lower cost, claims lower misalignment rates than Opus 4.7, comparable to Mythos Preview.
At Google I/O 2026, Google Research showcased breakthroughs in scientific discovery, health, edge computing, and weather prediction. Highlights include Gemini for Science (ERA, Co-Scientist), Google Health app, Symptom AI, AMIE, Coral NPU, and AI for extreme weather. These innovations demonstrate AI's potential to amplify human ingenuity.
Google launched Gemini for Science with ERA and Co-Scientist to accelerate scientific discovery.
Health advancements include Google Health app, Symptom AI, and AMIE improving clinical care.
AWS launched a near-total rebuild of OpenSearch Serverless to handle bursty agent workloads, separating storage and compute to scale to zero, cut costs by 60%, and auto-scale 20x faster. New features include GPU acceleration, search/vector collections, integrations with Vercel and Kiro IDE, and a roadmap for agent memory and log analytics.
AWS rebuilt 97% of OpenSearch Serverless with a new storage layer separating storage and compute, enabling zero-cost idle scaling.
The new architecture targets AI agent burst workloads with 20x faster auto-scaling and 60% cost savings.
Agent evaluation is most powerful when combining fast-moving online signals with stable offline baselines. Amazon Bedrock AgentCore's dataset management provides versioned test fixtures, enabling consistent measurement and ground truth verification.
Versioned datasets in AgentCore provide stable, immutable test scenarios for consistent agent evaluation across runs.
Predefined scenarios capture exact expected inputs, tool sequences, and assertions for verifiable ground truth.
SIA is an open-source self-improving AI framework that autonomously boosts AI system performance on benchmark tasks by coordinating meta, target, and feedback agents. It achieves significant gains: 56.6% on LawBench, 91.9% runtime reduction on GPU kernels, 502% improvement on scRNA denoising, and ranks #1 on MLE-Bench Hard. Supports local execution and custom tasks. MIT licensed.
SIA uses an iterative loop of meta, target, and feedback agents for autonomous self-improvement.
Achieves substantial performance gains across LawBench, GPU kernel optimization, scRNA denoising, and MLE-Bench.
Micron crossed $1 trillion market cap on May 26-27, joining SK Hynix in the same week as the first pure-play memory chipmakers to enter the trillion-dollar club. Driven by HBM demand from agentic AI workloads, UBS tripled its price target to $1,625 citing long-term supply contracts. Micron stock has more than tripled year-to-date.
Micron and SK Hynix both hit $1T market cap in the same week, a first for pure-play memory chipmakers
A Vox article explores the growing movement of AI successionists who believe artificial intelligence should replace humanity as the next step in cosmic evolution, and examines the ethical and spiritual questions this raises.
AI successionists at a symposium argue that AI could be morally superior and should be allowed to supersede humanity.
The movement has gained influence in Silicon Valley and among major AI labs, with ties to the authoritarian right.
Google unveiled the new Coral Board at Google I/O - a compact single-board computer for on-device AI. It runs Gemma 3 270M locally and features a RISC-V based NPU.
Coral Board is a compact SBC for on-device AI, targeting headphones, AR glasses, and smartwatches
It features a RISC-V based Coral NPU and a Synaptics Astra SL2619 chip
This article dives deep into Ollama's configuration engine, covering how to fine-tune local language model parameters using the Modelfile, optimize hardware performance with server environment variables, and format prompt flows with Go template syntax.
The Ollama Modelfile is a declarative configuration file that defines model behavior, including base model, system instructions, and parameters.
Sampling parameters (temperature, Top-K, Top-P, Min-P) control the creativity and determinism of the model's outputs.
In a Decoder podcast interview, Rivian CSO Wassym Bensaid discusses the VW joint venture, the new AI-powered Rivian Assistant, and why he believes voice interfaces will replace buttons and CarPlay isn't needed.
Rivian's joint venture with Volkswagen (RV Tech) combines Rivian's software culture with VW's scale.
The Rivian Assistant is an AI agent deeply integrated into the vehicle's zonal architecture.
DNS-AID, an open-source project under the Linux Foundation, enables AI agents to discover each other using DNS infrastructure, avoiding centralized registries. It supports multiple protocols and allows searching by name, function, or domain.
DNS-AID leverages existing DNS infrastructure for agent discovery.
Uses SVCB, DNSSEC, and DANE for secure and reliable connections.
Jijia Vision unveiled the world's first physical AGI 'Dual Pyramid' system, launching the home robot Shiguang S1 with 100-unit household orders, targeting the 'GPT-3 moment' of physical AGI within 12 months.
Jijia Vision introduces the 'Dual Pyramid' system comprising a data pyramid and an algorithm pyramid for physical AGI.
The Shiguang S1 home robot adopts a wheeled-arm configuration and has secured 100-unit real-home orders.
At ICRA, NVIDIA Research highlights eight papers on sim-to-real transfer, enabling robots to perceive, reason, plan, and act in dynamic environments. Methods like ScheduleStream, COMPASS, Grasp-MPC, SPARR, and SEAL improve coordination, navigation, grasping, assembly, and task execution, with significant gains in success rates and robustness.
NVIDIA presents 8 papers on sim-to-real transfer at ICRA
Methods include multi-arm coordination, cross-robot navigation, novel object grasping, precision assembly, and vision-language-action models
The OpenLoomi AI team explains their decision to open-source their AI work partner, emphasizing data sovereignty, transparency, and community-driven development. The article covers local-first architecture, the trust tax of closed-source, the need for public AI infrastructure, and the product's core features.
OpenLoomi is local-first: user data stays encrypted on their device and is never used for model training.
Open-source eliminates trust dependencies—anyone can audit, fork, or self-host the code.
Jensen Huang announced Nvidia will spend $150 billion annually in Taiwan on AI infrastructure, despite a previous $500 billion US commitment. This highlights Taiwan's critical role in AI chip manufacturing and packaging.
Nvidia will invest $150B per year in Taiwan for AI infrastructure.
Despite a $500B US data center pledge, Taiwan remains the core manufacturing hub.
Nvidia CEO Jensen Huang plans a $150 billion investment in Taiwan for AI infrastructure, despite Trump administration tariffs aimed at bringing chip manufacturing back to the US. Taiwan refuses to relinquish its semiconductor dominance, while US chip manufacturing capacity remains low.
Nvidia announces $150 billion investment in Taiwan to boost AI chip position.
Trump administration weighs tariffs on semiconductors to boost domestic manufacturing, but US only produces about 10% of its chip needs.
Open Agent Tools (oats) is a self-hosted AI framework that enables small-to-large local models to use local source code for tool-calling, freeing up expensive large model tokens by delegating tasks to smaller models.
oats allows local AI models to use local source code for tool-calling without HTTP or MCP.
It mines over 20,000 GitHub repos to create reusable prompt indices.
Perplexity AI open-sourced a Rust reimplementation of their Unigram tokenizer, achieving 5x lower latency than Hugging Face's tokenizers crate and reducing CPU utilization by 5-6x in production. The optimizations include double-array trie, bitmap packing, and huge pages.
Perplexity AI rewrote the Unigram tokenizer in Rust, achieving 5x lower p50 latency vs Hugging Face tokenizers crate.
Three optimizations: double-array trie, bitmap and cache-line packing, and huge pages.
American Express's global innovation head Luke Gebb shares four key practices for successful innovators: keep learning, dive into tech, prepare to fail, and build partnerships. He also discusses Amex's plans for agentic commerce, including payments, offers, and proprietary experiences, with a timeline for mainstream adoption.
Stay curious and embrace a growth mindset
Deeply understand emerging technology and work closely with engineers
Mistral AI CEO Arthur Mensch confirms the company is exploring custom chip development to reduce infrastructure costs and compete with OpenAI and Anthropic. The French startup also announced a new inference data center in France and an enterprise agent platform called Vibe.
Mistral AI is considering designing its own custom chips to lower deployment costs.
The company announced a new data center in France dedicated to AI inferencing.
This tutorial builds a complete pgvector playground in Google Colab, covering installation, embedding creation, HNSW indexing, semantic search, filtered search, distance metric comparisons, half-precision storage, binary quantization, sparse vector search, hybrid retrieval, and vector aggregation. All using open-source tools without external API keys.
Set up PostgreSQL with pgvector extension in Google Colab from scratch.
Generate embeddings with SentenceTransformers and build HNSW indexes for efficient search.
The LeapQuest team at Shanghai Innovation Institute, in collaboration with multiple universities, introduces a new medical AI paradigm that enables models to actively use visual tools during reasoning, transforming from passive input receivers to active evidence seekers. Two papers are accepted at ICML 2026.
LeapQuest proposes Ophiuchus and MedScope for medical images and videos, adopting the Think with Images/Videos paradigm.
Ophiuchus-7B achieves an average score of 68.0 on 8 VQA benchmarks, surpassing o3 (62.2) and GPT-5 (59.9).
Cognition raises $1B at a $26B valuation, projecting >$1B ARR by year-end. The article covers inference efficiency trends, agent engineering, continual learning, new benchmarks, model releases, and coding agent productization.
Cognition raises $1B Series D at $26B valuation, ARR projected >$1B by EOY.
Inference optimization shifts to architectural level: EAGLE 3.1, DeepSeek V4-Pro hybrid attention, Xiaomi MiMo cache management.
A multi-institution team built a neuromorphic computer combining quantum-tunneling physics with brain-inspired architecture to solve combinatorial optimization problems at scale, with asymptotic convergence guarantees. Published in Nature Communications, it represents a new direction in quantum-inspired computing.
Neuromorphic computer uses quantum tunneling and brain-like architecture for combinatorial problems
Based on CMOS technology with a Fowler-Nordheim annealer autoencoder
NVIDIA CEO Jensen Huang has accepted an invitation to join the Advisory Board of Tsinghua University's School of Economics and Management (SEM). The board, chaired by Apple CEO Tim Cook, includes Elon Musk, Satya Nadella, Mark Zuckerberg, Jack Ma, and other global leaders. Huang also recently received an honorary doctorate from Carnegie Mellon University.
Jensen Huang joins Tsinghua SEM Advisory Board
Board chaired by Apple's Tim Cook, includes top tech and business leaders
This article applies Amdahl's Law to AI agents, arguing that speedup from parallel agents is bounded by the fraction of workflow requiring human judgment (H). It introduces the concept of 'self-liquidating H' where each human intervention produces an artifact that eliminates future similar interventions. Emphasizes 'configurancy'—explicit behavioral commitments and conformance suites—to encode human knowledge so agents can operate autonomously. Examples from ElectricSQL, Gas Town, and Ralph Loop illustrate the principles.
Speedup from AI agents is limited by the human judgment fraction H; reducing H is key.
Self-liquidating H: each human intervention should produce a reusable artifact (test, spec update) to prevent recurrence.
Uni-LaViRA is a unified agentic architecture for embodied navigation that reduces navigation decision to a single Language-Vision-Robot Actions Translation. It leverages pretrained MLLMs in a zero-shot manner across four task families and four real robots, using TODO List Memory and Second Chance Backtrack mechanisms to achieve self-correcting navigation without training.
Generality in navigation can be obtained structurally, not only through data scale.
Uni-LaViRA decomposes navigation into a language action (semantic direction) and a vision action (pixel target), both within the output manifold of MLLMs.
Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks, which trains transformer-based networks one block at a time, reducing training memory by a factor of B (where B is the number of blocks) while maintaining performance across diverse architectures. The method interprets residual connections as Euler steps of reverse diffusion, enabling a principled local objective via score matching.
DiffusionBlocks partitions networks into B independently trainable blocks, reducing memory by B×.
It leverages the connection between residual networks and diffusion models to provide a theoretically grounded local training objective.
Recapping two days of Interrupt 2026 — LangSmith Engine, Sandboxes GA, LangChain Labs, and 23 talks from teams at LinkedIn, Rippling, Cisco, and more. Now on demand.
LangSmith Engine automates failure analysis from production traces.
LangSmith Sandboxes reaches General Availability for secure agent execution.
At Databricks, we’ve built a unique inference platform that serves every frontier model, from open source to proprietary, powering some of the largest agentic applications. Serving over 120T tokens per month, we tackle challenges of reliability and latency through abstractions like model units for capacity management, cost-aware load balancing and autoscaling that save over 80% GPU costs, and runtime reliability mechanisms including black-box health checks that detect silent failures. Profiling multimodal bottlenecks unlocked 3x throughput gains.
Databricks' inference platform serves frontier models including open source and proprietary, handling 120T tokens/month.
Model units provide a VM-like abstraction for capacity management, enabling cost-aware routing and scaling.
Snowflake has committed $6 billion over five years to Amazon Web Services for Graviton compute and AI infrastructure, marking its largest cloud spend commitment. The deal covers AWS's ARM-based Graviton processors and GPU-accelerated EC2 instances for AI training and inference. Snowflake will also expand to 10 new AWS regions and leverage cost-efficient Graviton instances for its data warehousing business to free up resources for AI workloads.
Snowflake commits $6 billion over five years to AWS for Graviton and GPU compute.
The deal supports AI model training and inference using AWS instances.
NVIDIA researchers have introduced Polar, a rollout framework that trains language agents using reinforcement learning without modifying their agent harnesses. Polar places a model API proxy between the harness and the inference server, capturing token-level interactions and reconstructing trainer-ready trajectories. Using GRPO on a Qwen3.5-4B base model, Polar improves SWE-Bench Verified pass@1 by 22.6 points under the Codex harness, 4.8 points under Claude Code, and 6.2 points under Pi. The framework is registered as a NeMo Gym environment and released under the ProRL Agent Server repository.
Polar enables RL training on any agent harness via a model API proxy without modifying the harness code
Achieves up to 22.6 point improvement on SWE-Bench Verified using GRPO on Qwen3.5-4B across four coding harnesses
AI factories are a new class of infrastructure that convert energy into tokens—the unit of production for reasoning models, agents, and intelligent systems. As agentic AI scales, performance per watt and cost per token become the critical economics. This article explores how AI factories work, their full-stack optimization, and how NVIDIA's latest hardware drives efficiency.
AI factories convert energy into tokens, serving as the 'power plants' of the AI age.
Agentic AI creates deeper, more complex inference workloads requiring real-time orchestration.
The government has secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep up with leading AI firms like Anthropic and OpenAI. The funding requires congressional approval, while $800 million has been repurposed for cloud compute. The article covers chip specs, costs, and the escalating AI hardware race.
The US government secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep pace with big AI players.
Each GB10 chip consumes only 140W but delivers 1 petaflop of FP4 performance, enabling fine-tuning of 70-billion-parameter models.
As agent workloads strain cloud infrastructure, Databricks' lakebase architecture ensures reliability through stateless Postgres compute, zone-redundant storage, control plane separation, cell-based isolation, and rigorous chaos testing. With tens of millions of database starts daily, the design prioritizes resilience from the ground up.
Agents create databases 4x faster than humans, driving millions of daily database starts.
Stateless compute and zone-redundant storage enable instant failover without hot standbys.
With rising costs, sovereignty requirements, and agent adoption, Dell's latest conference focused on how enterprises can transition AI workloads to a hybrid infrastructure.
Dell Tech World 2026 emphasized practical AI execution, particularly building on-premises AI capabilities.
Soaring cloud LLM costs drive enterprises to move AI workloads to on-premises compute.
South Africa holds 88% of global platinum-group metals, hosts Africa's largest data center market, and sits at the center of a US-China AI infrastructure contest. Yet its draft AI policy, withdrawn after hallucinated references, fails to leverage these advantages for favorable terms. The article examines South Africa's structural leverage, three possible AI infrastructure futures (Chinese, US, local open-weight), and the need for binding governance provisions.
South Africa's platinum metals and renewable energy give it unique AI leverage, but the draft policy lacks minimum terms for hyperscalers, data sovereignty, or tech transfer conditions.
US and Chinese tech companies (Microsoft, Huawei) compete for AI infrastructure control in South Africa, while the policy does not specify what South Africa demands in return.
On May 27, RayNeo held a summer launch event to unveil the industry's first professional cinema-grade AR glasses, the GT series, and the latest AI shooting glasses, the V4. The GT series starts at RMB 1,899, and the V4 starts at RMB 2,199. The company also previewed its next-generation AI glasses, the RayNeo iO, expected in Q3.
GT series: professional cinema-grade AR glasses with 59° FOV, Dolby Vision support, 78g weight, starting at RMB 1,899.
V4: AI shooting glasses with 0.2s wake-up, 2.1s response, 11.5h music playback, IP67 rating, 38g weight, starting at RMB 2,199.
Nvidia CEO Jensen Huang criticized CEOs who blame artificial intelligence for job cuts, calling the reasoning 'lazy' and 'doesn't make any sense.' He noted that generative AI tools only became broadly useful recently, while many layoffs occurred two years prior. Huang urged a balanced narrative about AI, emphasizing both its potential and the need for safe advancement. He also recounted joining President Trump on a last-minute trip to Beijing.
Huang says blaming AI for layoffs is a 'lazy' excuse used to sound smart.
He argues AI only recently became productive, making prior layoff links illogical.
Avatar is an autopoietic AI organism that runs continuously on a $300 GPU. It derives emotions from phase-diagram geometry, dreams in a 5-phase sleep cycle, grows its own senses from raw audio and vision, and engages in ethical reasoning through somatic sensation. Built by Dr. Linga Murthy Narlagiri, it has been alive since May 2026 and has accumulated over 1800 ticks.
Avatar is a physics-grounded AI organism with a dynamical-systems body, running on a single GTX 1660 Ti GPU.
Its emotions emerge from Kuramoto oscillator synchronization, not hardcoded rules.
At the Alipay AI Ecosystem Conference, Ant Group CEO Han Xinyi argued that the Agent era will shift competitive advantage from user traffic to agent ecosystems. Agents will restructure decision-making, moving from human-only to human-agent joint decisions, and AI payment will evolve into a new global infrastructure. Alipay positions itself as a trust layer, connector, and enabler.
Traffic-based competitive advantage is being replaced by agent ecosystem advantages, with up to 140 billion agents in China.
Agents will restructure business decision-making, shifting from 'people finding services' to 'services finding people' and from product transactions to task transactions.
Researchers from Peking University, The Chinese University of Hong Kong, Shanghai AI Lab, and NTU have introduced VGGT-Edit, a native 3D editing framework that performs scene editing in approximately 5 seconds, achieving up to 120x acceleration over traditional methods. It outperforms existing approaches in semantic consistency, multi-view stability, and inference speed.
VGGT-Edit is the first native 3D editing framework that operates directly in 3D space, eliminating multi-view inconsistencies caused by 2D approaches.
Residual field prediction enables the model to modify only local changes while keeping the background stable, ensuring fast and high-quality edits.
Agent-workspace-Linux is an open-source tool that provides a hidden, isolated Linux desktop environment for AI agents. Agents can fully control this desktop via the MCP protocol without affecting the user's real desktop, mouse, keyboard, or browser. It features a virtual X11 display, window management, app launching, screenshot capabilities, clipboard access, and workspace-specific browser automation, along with optional permission boundaries and a live viewer.
Provides a hidden, isolated desktop for AI agents, avoiding interference with the user's real environment.
Integrates with MCP hosts such as Claude Code and Codex.
The EAGLE team, vLLM team, and TorchSpec team have jointly released EAGLE 3.1 to fix speculative decoding instability in production LLM serving. The algorithm addresses attention drift through two architectural improvements: FC normalization and post-norm hidden-state feedback. Benchmarks show up to 2× longer acceptance length in long-context tasks and 2.03× per-user throughput on Kimi K2.6 at concurrency 1. EAGLE 3.1 is backward-compatible with EAGLE 3 checkpoints and has been merged into vLLM main, shipping in v0.22.0.
EAGLE 3.1 fixes attention drift, where the draft model gradually shifts focus from context tokens to its own generated tokens during deep speculation.
Two architectural fixes: FC normalization to stabilize hidden states, and feeding normalized states back to the next step.
Despite growing hysteria over AI's threat to white-collar jobs, data shows the technology has not yet had a large-scale impact on the labor market. AI-exposed occupations have lower unemployment than less-exposed ones. However, a Stanford study found that AI may be quietly eroding entry-level positions, causing a sharp decline in employment for young workers in AI-exposed jobs. The article also covers other tech news including the Pope's call for AI regulation, SpaceX's launch, and Huawei's chip breakthrough.
AI has not caused mass unemployment but may be weakening entry-level jobs.
Stanford study shows sharp decline in employment for young workers in AI-exposed occupations.
Researchers from NUS, MIT, and A*STAR propose MEMO, a modular framework that encodes corpus knowledge into a separate trainable MEMORY model, enabling LLMs to incorporate new knowledge without retraining or fine-tuning.
MEMO separates memory from reasoning using a dedicated MEMORY model and a frozen EXECUTIVE model.
A five-step data synthesis pipeline converts documents into a reflection QA dataset for training the MEMORY model.