Frontier post-training recipe review with Finbarr Timbers
"Interview" #18
Latest AI/ML News
30926 matching items
Bye bye Fable
another benchmark for real swe work
Your AI bill is a tax on scale
Subscribe • Previous Issues The Hybrid AI Stack Is Coming for the Pricing Power of OpenAI and Anthropic OpenAI and Anthropic are going public while still capturing much of the money spent on foundation-model usage. But deployment patterns are starting to tell a more complicated story. Companies are building hybrid model portfolios, using proprietary models where convenience, Continue reading "Your AI bill is a tax on scale" The post Your AI bill is a tax on scale appeared first on Gradient Flow .
Morocco launches Rally AI Future Lab to accelerate innovation
1,000 researchers and developers gather in Merzouga for inaugural five-day sprint
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.
Tokenomics: AI’s New Design Constraint
The Cost Reality of Running AI at Scale Budget shock is already happening. Multiple major players have pulled back on AI features or subscriptions due to unexpectedly high token costs. Amazon removed its token leaderboard and Microsoft cancelled Claude Code subscriptions. These are early signals that the deploy-everywhere approach is hitting hard financial limits, not Continue reading "Tokenomics: AI’s New Design Constraint" The post Tokenomics: AI’s New Design Constraint appeared first on Gradient Flow .
😺 Google sued the people spamming your phone
PLUS: Apple hid ChatGPT/Claude Siri switching in iOS 27 beta.
Emergency Pod: Claude Fable
Chris McGuire on a chaotic two weeks in AI policy
If context is king, architecture is the castle…
Recorded live at the AI Agent Conference, Ryan sits down with Apollo GraphQL CEO Matt DeBergalis to discuss how enterprises can leverage GraphQL and MCP as a structured semantic architecture to feed clean data to autonomous agents, safeguard internal microservices against unprecedented "east-west" data exfiltration risks, and rein in skyrocketing token spend by explicitly querying only the exact context required.…
Autoregressive Models: Predicting the Future Using the Past
Autoregressive models are one of the most important ideas in time series forecasting and sequence modeling. The name may sound technical at first, but the concept is surprisingly intuitive. An autoregressive model predicts the next value by looking at previous values. That is the core idea. For example, tomorrow’s temperature may depend on the temperatures […] The post Autoregressive Models: Predicting the Future Using the Past appeared first on Analytics Vidhya .
MERICS Data Insight: EU-China trade
MERICS Data Insight: EU-China trade H.Seidl Tue, 06/16/2026 - 09:27 Comment Jun 16, 2026 1 min read MERICS Data Insight: EU-China trade In this edition of MERICS Data Insights, MERICS Visiting Fellow Esther Goreichy looks at the European Union's trade deficit with China. She finds that the deficit is widening despite the bloc's trade defense measures. Author(s) Esther Goreichy Visiting Fellow Author(s) Esther Goreichy Visiting Fellow Related content about EU-China “We need to wean ourselves off the ‘China drug’” (Zeit) External publication Jul 22, 2026 There is still a best-case scenario for VW—but even that “involves immense costs” (Welt) External publication Jul 20, 2026 China in 26: Europe-China relations + Russian-Chinese military cooperation + new economic data Podcast Jul 17, 2026 Related content about Trade and Investment If Europe wants to use its trade leverage with China, now is the time Comment Jul 24, 2026 China's economy in Q2: Domestic woes dampen growth as imbalances intensify Tracker Jul 23, 2026 MERICS Data Insight: Chinese export prices Comment Jul 06, 2026
Nigeria unveils plans for world’s first 'National AI Trust' in London
Minister Tijani sets out governance vision to guide Nigeria’s AI transformation
Uncertainty Estimation vs Oversampling
I am currently doing some work with a fraud detection dataset as part of a research to leverage uncertainty to improve neural networks ensemble of experts' results. Firstly I had to take the dataset's training data and split it into 6 different domains (5 in-distribution and 1 ood). The goal is to train 5 different experts on different fraud patterns and then compute uncertainty of each expert (using Monte-Carlo dropout) and the uncertainty of the ensemble of experts. When we talk about fraud detection we expect the data to be heavily imbalance favoring the non-fraud class. In this case is 1:90. Given this it makes sense to use oversampling when training the neural networks and so I did. I made a sampler which increases the ratio to 1:10, not by creating new transactions (Because due to the data split, some domains have very few transactions to get reliable simulated transactions with oversampling) but by having the fraudulent transactions seen x amount of times more to increase the ratio. Now, with this, I'm having a problem with the uncertainty signal. In a perfect scenario, fraudulent transactions would be more uncertain than legit ones, but due to the oversampling the experts seem to be more uncertain about the legit transactions than the fraudulent ones. I have tried different percentages of oversampling and only without oversampling the uncertainty signal is correct, but then the overall predictive results underperform. So what should I do? Should I try different metho…
AI in Science
This series draws from Ranjit Singh's ethnographic research on how AI systems are being taken up in scientific practice. The post AI in Science appeared first on Data & Society .
India’s Rs 1 lakh crore R&D fund: speed was the signal, but Rs 5 lakh crore is the point
The post India’s Rs 1 lakh crore R&D fund: speed was the signal, but Rs 5 lakh crore is the point appeared first on The Ken .
[AINews] Satya on Loopcraft: Building Frontier Ecosystems
a quiet day lets us report on Satya's hit essay
When safety speaks 20 languages: Day in life of foreign workers at building site with AI as assistant
Builders deploy AI translators to deliver real-time instructions as foreign nationals make up a growing share of the construction workforce.
Identifying the AI Development Workforce
Existing measures of the AI workforce often group together a broad set of AI-related roles with varying skill requirements. This report focuses on the AI development workforce and describes our methodology for identifying AI development jobs and estimating AI development employment. It presents initial findings on the size and share of AI development jobs in the U.S. labor market. Regional breakdowns of AI development jobs are available in PATHWISE , CSET's emerging technology talent tracking tool. The post Identifying the AI Development Workforce appeared first on Center for Security and Emerging Technology .
Synthetic document finetuning for instilling positive traits
This is the fifth in a series of informal research updates from the Google DeepMind Language Model Interpretability team, in interpretability and adjacent areas. The fourth post can be found here . Thanks to Chloe Li for feedback on this post! TLDR: Via adapting the methods of Marks et al and Li et al , we train Gemini 3 Flash to have certain traits/values by midtraining it on documents about how Gemini has those properties, followed by finetuning it on synthetic chat data where it demonstrates those properties. The chat finetuning is effective for instilling the traits robustly, working OOD. We share some takeaways on how to improve midtraining & SFT effectiveness. Introduction This work closely follows Li et al (model spec midtraining, or MSM), who show that by training a model on synthetic documents before chat finetuning starts, they can shape how the model generalizes. Teaching the model reasons behind specific behaviours, rather than just the behaviours themselves, can also improve generalization. Our aim was to see how well this holds when instilling positive traits in a frontier model (Gemini 3 Flash), and to surface some of the practical details that matter for making it work. Our motivation is deep alignment : we want to train principles into the model which guide behaviour even in highly OOD behaviours. Our MVP pipeline used a "traits document" (a short bullet-pointed list of positive traits we wanted the model to exhibit) as our universe context, with a checkpoin…
The hidden cost of code that nobody touches
Every engineering org has the files nobody wants to open. Here's what that actually costs.
Sourcegraph MCP server and a cheaper model beat a Mythos-class model alone
On nine CodeScaleBench tasks designed to evaluate agent effectiveness in large codebases, Claude Sonnet 4.6 with the Sourcegraph MCP server outscored Fable 5, winning six of nine at roughly half the cost for each point of quality.
Memory at the Edge: On-Device Vector Search with Qdrant Edge
On its first day in an unfamiliar house, a home robot has to build memory as it goes: which rooms it has covered, where it last saw the car keys, whether the kitchen looks different now than it did this morning. And it has to answer those questions itself, where it stands, because the network isn’t always there, and it’s too slow to wait on even when it is. That memory has a concrete shape. As the robot moves, it turns what its camera sees into vectors and writes them to a store it carries onboard. To make a decision, it queries that store for the nearest matches to what it is looking at, filtered by where or when it saw them. Capture, embed, search, decide, and the loop runs entirely on the robot, in milliseconds, with no trip to a server. The engine underneath it is Qdrant Edge : the same Qdrant vector search engine, running in-process as an embedded library instead of behind an API.
The hidden cost of code that nobody touches
Every engineering org has the files nobody wants to open. Here's what that actually costs.
Sourcegraph MCP server and a cheaper model beat a Mythos-class model alone
On nine CodeScaleBench tasks designed to evaluate agent effectiveness in large codebases, Claude Sonnet 4.6 with the Sourcegraph MCP server outscored Fable 5, winning six of nine at roughly half the cost for each point of quality.
Algorithm–hardware co-design of neuromorphic networks with dual memory pathways
Nature Machine Intelligence, Published online: 16 June 2026; doi:10.1038/s42256-026-01255-3 Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficiency gains while using 40–60% fewer parameters than state-of-the-art implementations.
Predicting model behavior before release by simulating deployment
OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.
Build and test iOS apps without leaving Codex
More of the iOS app loop, now inside Codex. The Build iOS Apps plugin lets Codex view and test your iOS app in the in-app browser, open SwiftUI previews, and hot reload edits without leaving Codex. Shoutout to the open source projects behind this: • Serve-sim powers the streaming simulator by @Baconbrix https://github.com/EvanBacon/serve-sim • SnapshotPreviews extracts SwiftUI previews by Sentry https://github.com/getsentry/SnapshotPreviews
Powering Physical AI applications with LeRobot/ROS on Jetson
In this session we will focus on how to bring VLM/VLA models to power real-world physical AI applications. We will focus on how to utilize SOTA of VLM (gemma 4) and or GR00T model for performing different pick and place tasks and orchestrate the outputs to control the robots using ROS 2 framework. You will learn how to bring vision-language models into real-world physical AI applications — from model selection to robot control. We'll cover: Choosing the right model for robotics — learn when to use a state-of-the-art VLM like Gemma 4 versus a specialized model like NVIDIA GR00T, and how runtime, throughput, and task requirements shape that decision. VLMs and VLAs in action — see how vision-language and vision-language-action models are applied to real manipulation tasks like pick and place, and what makes them viable for physical AI. Connecting model outputs to robot control — understand how to orchestrate model outputs through the ROS 2 framework to drive real robot behavior. Hands-on hardware demo — walk through a live example using the SO-101 or reBot Arm, putting everything together from model inference to physical actuation.
Local Agents on Jetson: OpenClaw, NemoClaw, and AI You Can Build Into Daily Life
This session moves from running a local model to running a local autonomous agent. OpenClaw is a fully local AI assistant that runs on Jetson and connects to chat workflows, browser-based tools, and multi-step tasks. NemoClaw extends this with sandboxing, onboarding, inference routing, and policy controls for safer and more structured agent deployments. We'll show what changes when an AI system can take actions, use tools, and run privately on your own hardware — 24/7, at home, on the edge. Use cases include building dynamic browser-based games, prototyping smart computer vision apps, and running long research tasks without a cloud dependency. You will learn how to move from running a local model to running a fully local autonomous agent on NVIDIA Jetson. We'll cover: Building a local assistant with OpenClaw — extend the Episode 1 baseline into a full local assistant architecture that connects to chat workflows, browser-based tools, and multi-step tasks — running privately on your own hardware, 24/7. NVIDIA Orin Nano vs. AGX Orin vs. Thor — compare hardware paths side by side so you can make the right choice for your deployment constraints and performance needs. Why tool-calling models matter — see what changes when an AI system can take actions, use tools, and run autonomously, and what breaks when your model can't do it reliably. Safer local agents with NemoClaw — go further with sandboxing, onboarding, inference routing, and policy controls that make local agent deploymen…
Local GenAI on Jetson: OSS models using different inferencing frameworks: Ollama, llama.cpp, & vLLM
This opening session builds the foundation for running popular OSS models such as Gemma, Qwen directly on Jetson — no cloud required. We cover when to use Ollama for rapid local prototyping versus vLLM for higher-throughput serving, show how the same workflow applies to both power different OSS models, and walk through the real decisions behind model choice, containers, quantization, and performance tuning on edge hardware. We close with a teaser of OpenClaw and a bonus take-home challenge to kick off community building. You will learn how to deploy open-source AI models on NVIDIA Jetson — no cloud required, from first launch to production-ready serving. We'll cover: Getting models running on NVIDIA Jetson — spin up popular OSS models (open-source large language models (LLMs) like Gemma and Qwen (LLMs and VLMs) using Ollama or vLLM on Jetson hardware and verify they're working end-to-end. Choosing the right inference engine — understand the practical tradeoffs between Ollama for rapid local prototyping, vLLM for higher-throughput serving, and llama.cpp, so you can pick the right tool for your use case. NVIDIA Jetson-specific serving strategies — walk through the real decisions behind model choice, containers, and performance tuning tailored for Orin and Thor, including what works, what doesn't, and why. Performance fundamentals — get introduced to quantization and speculative decoding: what they are, how they work, and when to reach for them on edge hardware. Real-world appl…
U.S. Semiconductors and China’s AI Military Ambitions
CSET’s Sam Bresnick shared his expert perspective in an op-ed published by Perry World House. In his piece, he argues that relaxing U.S. restrictions on advanced AI semiconductor exports to China would undermine long-term U.S. technological advantage. The post U.S. Semiconductors and China’s AI Military Ambitions appeared first on Center for Security and Emerging Technology .
Coupon subset collector problem - expected draws to get n from s
In Adler and Ross 'Coupon subset collector problem' 2001, the following formula (7) is stated: Assuming I have a constant subset size > 1, can I get the expected number of draws to get the last n from s coupons by using formula (7) and simply summing from n. For example, if there are s=10 distinct coupons, if I want to find how many draws to collect the last n=5 distinct types, and each draw has m=3 coupons, can I sum from j=6 to s=10 to get the correct answer. Thanks in advance.
GitHub Copilot CLI for Beginners: Overview of common slash commands
GitHub Copilot CLI for Beginners: Learn how to use slash commands to control your terminal AI agent. The post GitHub Copilot CLI for Beginners: Overview of common slash commands appeared first on The GitHub Blog .
Elon Musk Sold Investors The Future. Now SpaceX Has To Build It.
SpaceX is trading up on its first full day on the public markets. Here's what it has to do to maintain the momentum.
Accelerating researchers and developers building multilingual AI with a new open dataset
A new repository-level dataset, published on GitHub under CC0-1.0, helps researchers and developers discover multilingual developer content across READMEs, issues, and pull requests. The post Accelerating researchers and developers building multilingual AI with a new open dataset appeared first on The GitHub Blog .
Block & Build: AI vs. Workers with Alexandra Mateescu and Aiha Nguyen
The post Block & Build: AI vs. Workers with Alexandra Mateescu and Aiha Nguyen appeared first on Data & Society .
Yocto Project on Jetson: From Community Project to Official NVIDIA Support
Starting with JetPack 7.2, NVIDIA officially supports the Yocto Project on Jetson. But the story began years earlier with meta-tegra, the community project created and maintained by Matt Madison that brought Yocto Project to Jetson and became a trusted foundation for embedded Linux developers. Join Matt Madison and NVIDIA experts as they discuss the origins of meta-tegra, the journey from community-driven project to official NVIDIA support, and what this milestone means for developers building products with Jetson. What you'll learn: - The origin of meta-tegra and the problem it was built to solve - What NVIDIA's commitment to OE4T means in practice for the codebase and community - Whether the Yocto Project is right for your Jetson product—and how to get started Join us live, bring your questions, and hear the story directly from the people who built it.
OpenSearch vs LanceDB for Vector Search: Query Cost and Infrastructure
Choosing a vector database usually comes down to a tradeoff between a full search service and an in-process library. This post showcases benchmarks that compare OpenSearch and LanceDB on the COCO 2017 images embedded with SigLIP. We measure ingestion throughput, query cost, storage layout, and overall infra cost.
One agent, two trace destinations: Arize AX + Databricks Unity Catalog
Send one OpenTelemetry trace stream to both Arize AX and Databricks Unity Catalog so engineers can debug agents in Arize while data teams analyze the same spans in governed lakehouse storage. The post One agent, two trace destinations: Arize AX + Databricks Unity Catalog appeared first on Arize AI .
MLCommons Releases MLPerf Mobile v6.0 with New Generative AI Benchmarks for On-Device LLMs
Test LLM inference natively on mobile devices with new standardized benchmarks and expanded NPU acceleration. The post MLCommons Releases MLPerf Mobile v6.0 with New Generative AI Benchmarks for On-Device LLMs appeared first on MLCommons .
📈 Data to start your week
AI & job cuts; China’s nuclear lead; GLP-1s & cancer++
Growing the Cloudflare AI team with talent from Ensemble AI
Cloudflare is deepening our investment in AI with the addition of team members from Ensemble AI, focusing on machine learning infrastructure and efficiency.
AI Agent Tool Design: What Works and What Doesn’t
Most <a href="https://www.
Import AI 461: "Alignment is not on track"; FrontierCode; and synthetic research interns
Where are your agents right now?
😺 42 states just subpoenaed OpenAI
PLUS: Anthropic raced to DC to save its banned AI
OII researchers head to FAccT 2026
Researchers and DPhil students from the Oxford Internet Institute are set to attend the Association of Computing Machinery (ACM) Conference on Fairness, Accountability and Transparency (FAccT) in Montréal, from 25-28 June 2026.