AI/ML news, top picks, and generated innovation digests.
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One of the best parts of my job is discovering lesser-known cities and exploring their tech and startup ecosystems. Often, it's the places that attract the least attention that leave the biggest impre...
Anthropic launched a beta version of its Claude Tag feature for Enterprise and Team tiers, shifting its chat model into shared Slack channels. Moving away from traditional isolated chat boxes, users pull the artificial intelligence model into active group threads by typing @Claude. The integration allows any team member in the channel to delegate a task, review […] The post Anthropic drops ‘workplace AI agents’ directly inside Slack appeared first on AI News .
UK-based startup LabCycle has received £180,000 in funding from the British Design Fund (BDF) to support the development of what it calls the ‘world’s first’ AutoDecon system, designed to safely recyc...
A new working paper by OII researchers argues that EU technology policy should hold AI companies to account for actual public wellbeing, not just reduced risk.
AI farming startup funding to drive Africa and Middle East expansion
Claude finally gets a Slackbot upgrade
NVIDIA has released the full Nemotron 3 open model family — Ultra, Super, Nano, and Nano Omni. This office hours session covers each model in the series, and any questions you have about Nemotron 3 in general — what it's built for, when to use it, and what's available in open weights, training datasets, and fine-tuning recipes. What we'll cover: - Nemotron 3 Ultra — 550B MoE frontier reasoning model for long-running autonomous agents: 5x faster inference, up to 30% lower cost, hybrid Mamba-Transformer architecture, and MOPD training for consistent performance across agent harnesses - Nemotron 3 Super — mid-range 120B model targeting enterprise applications that need strong reasoning for multi-agent applications - Nemotron 3 Nano — 30B MoE with 3B active parameters, built for high-volume execution, highly accurate sub-agent accomplishing targeted tasks - Nemotron 3 Nano Omni — multimodal (text, image, audio, video) model purpose-built for targeted specialized agentic tasks - Open weights, training datasets, and fine-tuning recipes — what's available across the family and how to customize for your domain Building with or evaluating the Nemotron 3 family? Bring your questions — whether you're choosing between models, fine-tuning for your domain, or deploying at scale, the team will answer them live.
An exclusive survey of AI workers in Kenya reveals how automated management affects their livelihoods. Unions and advocacy groups are beginning to fight back.
OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.
Ministry of Education's new scholarship plan guarantees jobs after graduation
Novel algorithms and community benchmarking efforts are reshaping how researchers search for advantage in quantum optimization.
The post This Bengaluru startup isn’t building faster chips than Nvidia. It’s building cheaper AI appeared first on The Ken .
Between 2022 and 2025, full-time job vacancies for university graduates in Hong Kong took a deep dive, dropping from 80,000 to 31,000, while junior administrative positions, which are highly susceptible to AI’s encroachment, witnessed a 90 per cent drop.
Listen now | Middle East AI News Minute - 24-Jun-26
Anthropic PBC today unveiled a new version of its chatbot Claude that lives inside Slack, where it operates like a virtual employee. It’s called Claude Tag, and it’s designed to work across entire organizations, helping multiple employees complete tasks for related projects. It builds on existing agentic artificial intelligence tools offered by Anthropic, including Claude Code […] The post Anthropic debuts Claude Tag, a more capable AI teammate that lives within Slack appeared first on SiliconANGLE .
I'm working on a face mask detection project using MobileNetV2 transfer learning for binary classification (with_mask vs without_mask). My dataset has a significant class imbalance: With mask: 685 images (80.3%) Without mask: 168 images (19.7%) Total: 853 images My questions: Which approach is generally more effective for this level of imbalance (80/20 split)?
Upbound Inc. today released Modelplane, a new open-source tool for managing artificial intelligence inference clusters. San Francisco-based Upbound is backed by $69 million from Alphabet Inc.’s GV fund, Intel Capital and others. It’s best known as the creator of Crossplane, an open-source infrastructure management engine. It’s an upgraded version of the Kubernetes control plane, a […] The post Upbound open-sources Modelplane to optimize inference clusters appeared first on SiliconANGLE .
AI agents place new demands on inference infrastructure. Unlike a single chatbot response, an agentic workflow can involve many LLM calls, tool calls, long context windows, and repeated cache reuse across a task. NVIDIA Blackwell is designed to handle these production-scale agent workloads with high throughput, low latency, and improved energy efficiency. This livestream explains how NVIDIA Blackwell helps developers scale AI agents in production, using AgentPerf results as one example of its performance on real-world coding-agent workloads. We’ll also cover how NVIDIA Dynamo adds software-level optimizations for routing, scheduling, and KV cache management. What you’ll learn: Why AI agents require different infrastructure than standard chat applications. How NVIDIA Blackwell improves throughput and efficiency for concurrent agent workloads. What AgentPerf results show about Blackwell performance on realistic agentic coding tasks. How Dynamo optimizes inference with agent-aware routing, scheduling, and KV cache reuse. What developers should consider when deploying AI agents at production scale.
Building AI systems at scale is demanding, requiring low-latency inference, fast vector search, strong GPU price-performance and infrastructure that can grow without multiplying operational complexity. NVIDIA’s latest work with Amazon Web Services (AWS) addresses each of those constraints. Across Amazon OpenSearch and Amazon EC2, NVIDIA AI infrastructure is giving enterprises more practical paths to deploy […]
The bottleneck in most large-scale migrations is not the engineers or the plan. It is that the tools cannot see the whole codebase.
On June 11th, 2026, over 350 developers, researchers, and engineers came together at The Midway in San Francisco for Vector Space Day , our first event of its kind in the United States and our first major gathering in San Francisco. This was a single day, single stage, across three tracks: Agents and Memory, Search and Retrieval, and Edge and Robotics. Hosted by our MC for the day, Adam Chan , who kept the energy flowing from opening keynotes to the final hackathon reveal.
How Modal and Decagon worked together to cut inference latency - and you can too.
Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$ and $D$.
Artificial intelligence played a prominent role at this week’s Bio International Convention in San Diego, the largest biotech event with vendors spanning the full ecosystem of companies in this industry. Today in a special address, Kimberly Powell (pictured), vice president and general manager of healthcare and life sciences at Nvidia Corp., made the case that agentic AI […] The post Nvidia bets on agentic AI to turbocharge biotech discovery appeared first on SiliconANGLE .
Orderful Inc., a startup using artificial intelligence to make supply chains more efficient, today announced that it has raised $35 million in funding. The Series C round was led by Koch Disruptive Technologies with participation from NewRoad Capital. It brings Orderful’s total outside funding to $85 million. Retailers regularly exchange documents with their suppliers and […] The post Orderful nabs $35M to streamline supply chain data management appeared first on SiliconANGLE .
I'm cleaning commercial product data, and even tho I got high accuracy for SKUs, I've reached the limit of regex madness. Data string can contain one or multiple products —with arbitrary separator, which can also occur in normal text—, sometimes their description, model, dimentions, SKU and other info to be ignored. I decided to keep regexes for the easy matches, and give AI a shot for the complex ones, starting with " fastino/gliner2-multi-v1 ", a zero-shot NER (also classification, structured and relation extraction). The multi variant groks Portuguese too, which is nice (data is from Brasil). Promising, but produces too much fluff. I tried GPT, which was the opposite: good precision, but low recall. Simplifying I tried to split multi-product strings first. GLiNER2 was not useful, GPT pretty good with commas, which is most of the cases, but seems overkill for such a simple task; I would like to run it locally, or even on a VPS, so I probably should train a model, but the many options are overwhelming, so it's high time I ask for advice: 1. Which models are recommended to train locally (6G VRAM) or with an accesible price that would extract product name, model, description/dimentions and SKU with good accuracy from a mess like this? I'm still leaning towards GLiNER2 over the xBERTs, nevermind spaCy (I've got a bunch of regexes already, but data is too messy for it?), but I'm going blind. 2. In a single pass, or should I split multiple products first? With a different model?…
Release: datasette 1.0a35 I'll write more about this one soon, but it's a big release. Three highlights from the release notes: New "Create table" interface in the database actions menu, backed by the / /-/create JSON API . It can define columns, primary keys, custom column types, NOT NULL constraints, literal defaults, expression defaults and single-column foreign keys. ( #2787 ) New "Alter table" table action and / / /-/alter JSON API for changing existing tables: add, rename, reorder and drop columns; change column types, defaults, NOT NULL constraints, primary keys and foreign keys; and rename the table. The alter table dialog also includes a "Drop table" button. ( #2788 ) New Template context documentation listing the variables available to custom templates for Datasette's core pages. Variables documented there are treated as a stable API for custom templates until Datasette 2.0. The documentation is generated from dataclass definitions next to the view code, with tests that compare the documented fields against the actual contexts rendered by the database, table, query and row pages. ( #1510 , #2127 , #1477 , #2803 ) Here's a rough video demo I made of the new create/alter table feature as part of reviewing the PR : Tags: datasette
The conversations at last week’s AWS Summit NYC 2026 showed that AI evolution is entering a new phase. From physical robots tackling labor shortages to agentic systems reshaping enterprise operations, the focus is shifting from experimentation to practical deployment. TheCUBE’s host, Gemma Allen, captured candid discussions with Amazon Web Services Inc. executives, partners and customers who are turning […] The post 9 ways AI is reshaping enterprise operations: Key insights from AWS Summit NYC appeared first on SiliconANGLE .
Na contribuição à Tomada de Subsídios da ANPD sobre o Guia Orientativo de Fornecedores de Produtos ou Serviços de Tecnologia da Informação, a organização defende que a proteção de crianças e adolescentes seja considerada desde a concepção dos produtos e serviços digitais. O post Data Privacy Brasil defende deveres de prevenção desde o desenho dos serviços digitais no ECA Digital apareceu primeiro em Data Privacy Brasil Research .
With the AI factory becoming a key focus in enterprise IT, hybrid architecture has become equally important as organizations seek to generate workloads on-premises, in the cloud and at the edge. This is why enterprises are increasingly looking toward major players such as Dell Technologies Inc. and Advanced Micro Devices Inc. for production-scale deployment in […] The post Dell/AMD partnership: Three insights you may have missed from theCUBE’s coverage of Dell Technologies World appeared first on SiliconANGLE .
Estudo do Observatório IA nas Eleições mostra como cinco ferramentas de inteligência artificial responderam a perguntas sobre pré-candidaturas, propostas políticas e perfis de eleitores no período pré-eleitoral. O post Observatório IA nas Eleições lança relatório sobre chatbots apareceu primeiro em Data Privacy Brasil Research .
Authors: Ahmed Radwan, Shaina Raza An AI system that summarizes a customer-service call, supports learning from a medical consultation, or reviews a video interview must do more than recognize objects […] The post When AI watches and listens: Introducing SONIC-O1 for real-world audio-video understanding appeared first on Vector Institute for Artificial Intelligence .
Tool: OPFS + Pyodide test harness I've been pondering if Datasette Lite - the Python Datasette application run entirely in the browser using Pyodide and WebAssembly - might be able to edit persistent SQLite files stored on the user's computer. That's what OFPS (Origin Private File System) is for, so I had Claude Code for web build me this playground UI to try it out in different browsers. Tags: browsers , pyodide , datasette-lite
We asked the ChatGPT Futures Class of 2026 what they hope AI helps people do next. Their answers came back to access, education, connection, and possibility: AI that helps close gaps instead of widening them, gives more people the tools to learn and build, and creates more room for the human parts of life.
Researchers show that serving AI models with llm-d can boost inference speeds by up to 5 times and double throughput — all while using heterogeneous GPUs.
Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .
The post Rethinking cloud operations with agentic observability appeared first on Source .
Doctoral student’s project devises an autonomous airspace coordination system built around a real-time simulation and validation technology. The post Smart drones, safe skies: Student’s system tests, coordinates drone fleets appeared first on Cornell AI Initiative .
This article breaks down each essential math discipline, explains its role in data science, and maps out an efficient learning path you can start today.
Raising children appears to keep the brain young, potentially acting as a buffer against cognitive decline and Alzheimer’s
GPT-5 Pro helped solve a 3-year-old immunology mystery, offering insights into T cell behavior. The breakthrough could support cancer and autoimmune research.
Separating transactional databases from analytical systems was, until recently, considered good architecture. Now, as enterprises adopt AI agents that continuously read, reason over, and act on business data, data warehouse and database vendors are increasingly deciding that separation has become a liability. Just weeks after Databricks unveiled its Lakehouse Transaction and Analytical Processing (LTAP) offering based on Neon Postgres to bring operational (OLTP) and analytical (OLAP) processing closer together, EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database service with the same intent. Both vendors are responding to the same pressure of enabling AI agents for enterprises to operate on fresh operational data without waiting for pipelines and replicas, but EDB argues its approach starts from a fundamentally different place. “Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase,” said Max Romanenko , chief engineering officer at EDB, while “we’re building from the operational layer with Postgres , which is where enterprises already run their most critical workloads, and expanding from there.” In contrast to Databricks’ lakehouse-centric LTAP, EDB keeps Postgres as the operational source of truth and uses Apache Iceberg as a shared catalog layer connecting Postgres with ClickHouse , WarehousePG, and Spark compute engines, Romanenko said. In this way, operationa…
This post shows you how to build a conversational protein research assistant that combines three capabilities: Natural language query parsing to extract structured search parameters, vector similarity search over protein embeddings using a specialized language model and ai-generated scientific summaries of search results.