Demystifying AI agents: going beyond the buzzwords
"Agent" is the most overused word in AI right now. But strip away the hype and what are you actually working with? Adobe principal scientist Deepak Pai breaks down the real building blocks of agentic systems and when they're worth reaching for.
The ‘together tech’ wave might be the most intriguing startup bet of 2026
While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction. Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going vir...
Using DSPy to automatically create, evaluate, and optimize your prompts
The post Automate Writing Your LLM Prompts appeared first on Towards Data Science.
3 SpaCy Tricks for Efficient Text Processing & Entity Recognition
In this article, we will explore three essential spaCy tricks that every developer should have in their toolkit to maximize processing speed and customize entity recognition.
I Let an AI Agent Run 40 Experiments While I Slept
I set up an AI agent on a rented GPU, pointed it at a training script, and went to bed. By morning it had run 40 experiments, improved validation loss by 5.9%, and cut memory usage from 44 GB to 17 GB. It also spent four hours chasing a bug that a linter introduced behind […]
NVIDIA AI Releases Dynamo Snapshot: A CRIU-Based Fast Startup System for AI Inference on Kubernetes
NVIDIA Dynamo Snapshot checkpoints and restores vLLM inference workers on Kubernetes using CRIU and cuda-checkpoint tools.
The post NVIDIA AI Releases Dynamo Snapshot: A CRIU-Based Fast Startup System for AI Inference on Kubernetes appeared first on MarkTechPost.
Perplexity AI Introduces Hybrid Local-Server Inference Orchestrator for Personal Computer: Automatic On-Device and Cloud Task Routing
Perplexity AI announces a hybrid local-server inference orchestrator for Personal Computer, automatically routing AI tasks between on-device and cloud models.
The post Perplexity AI Introduces Hybrid Local-Server Inference Orchestrator for Personal Computer: Automatic On-Device and Cloud Task Routin...
The Meta hack shows there’s more to AI security than Mythos
On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama Wh...
Seoul Purpose: How NVIDIA and South Korea Are Building the Future of AI
Home to cutting-edge sovereign AI infrastructure and robotics innovators, as well as one of the world’s most passionate gaming communities, South Korea is one of the world’s centers of AI. NVIDIA founder and CEO Jensen Huang is in Seoul this week to meet the partners and builders behind that work. S...
Scientists are seriously asking if bees and ChatGPT are conscious
New studies suggest consciousness can't be judged solely by behavior, whether it's a chatbot discussing philosophy or a bee searching for nectar. Researchers are increasingly focusing on the internal mechanisms of brains and computers, concluding that today's AI is likely not conscious while leaving...
The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models
arXiv:2606.05169v1 Announce Type: new
Abstract: We give a stereological theory of LLM benchmark coverage. For any suite with effective dimensionality d_eff, the visible Hausdorff distance between two convex capability profiles consistent with the same scores is bounded by epsilon + C R m^(-1/(d_eff...
ERRORQUAKE: Heavy-Tailed Error Severity Distributions in Open-Weight Large Language Models
arXiv:2606.05170v1 Announce Type: new
Abstract: At matched accuracy, open-weight LLMs differ substantially in the shape of their error severity distribution -- a difference invisible to the scalar error rate. Hallucination benchmarks report a single error count and treat all errors as equivalent, y...
Staged Factorial Screening for Budget-Constrained Micro-Pretraining
arXiv:2606.05186v1 Announce Type: new
Abstract: Budget-constrained micro-pretraining often requires triaging many candidate recipes on a shared accelerator before larger search budgets are spent. We study whether a staged fractional-factorial workflow can recover stable early effect structure in th...
Temporal Preference Concepts and their Functions in a Large Language Model
arXiv:2606.05194v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly being deployed to make decisions that require trading off near-term gains against long-term consequences, yet little is known about how they internally represent or resolve these tradeoffs. In this work, w...
How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
arXiv:2606.05256v1 Announce Type: new
Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated acco...
What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
arXiv:2606.05304v1 Announce Type: new
Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form co...
I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition
arXiv:2606.05316v1 Announce Type: new
Abstract: Multimodal memes are dynamic and often require up to date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowledge of pretrained models that may be incomplete, outdated, or unavailab...
GITCO: Gated Inference-Time Context Optimization in TSFMs
arXiv:2606.05332v1 Announce Type: new
Abstract: Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and silently degrade zero-shot forecast quality. We propose improving TSFM accuracy at inference time by...
Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
arXiv:2606.05334v1 Announce Type: new
Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component ...