Power usage by AI and data center systems in the U.S. is extraordinary by any measure. The International Energy Agency estimates U.S. AI and data centers used about 415 terawatt hours of power in 2024—more than 10% of that year’s nationwide energy output—and it’s expected to double by 2030.
Seeking to head off this unsustainable path of power consumption, researchers at the School of Engineering have developed a proof-of-concept for efficient AI systems that could use 100 times less energy than current ones, while at the same time providing more accurate results on tasks.
The approach developed in the laboratory of Matthias Scheutz, Karol Family Applied Technology Professor, uses neuro-symbolic AI—a combination of conventional neural network AI with symbolic reasoning similar to the way humans break down tasks and concepts into steps and categories. — Read More
Read the Paper
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10 Most Important AI Concepts You Should Understand Before You Start Building AI
A beginner-friendly guide for developers who want to actually understand what they are building.
… There are numerous terms:
LLM, agents, vector databases, tokens, embeddings, RAG, and fine-tuning
Additionally, the majority of tutorials skip over the basics and start building chatbots right away.
The truth is simple:
AI becomes much easier once you understand the core concepts. — Read More
The Roadmap to Mastering Agentic AI Design Patterns
Most agentic AI systems are built pattern by pattern, decision by decision, without any governing framework for how the agent should reason, act, recover from errors, or hand off work to other agents. Without structure, agent behavior is hard to predict, harder to debug, and nearly impossible to improve systematically. The problem compounds in multi-step workflows, where a bad decision early in a run affects every step that follows.
Agentic design patterns are reusable approaches for recurring problems in agentic system design. They help establish how an agent reasons before acting, how it evaluates its own outputs, how it selects and calls tools, how multiple agents divide responsibility, and when a human needs to be in the loop. Choosing the right pattern for a given task is what makes agent behavior predictable, debuggable, and composable as requirements grow.
This article offers a practical roadmap to understanding agentic AI design patterns. It explains why pattern selection is an architectural decision and then works through the core agentic design patterns used in production today. For each, it covers when the pattern fits, what trade-offs it carries, and how patterns layer together in real systems. — Read More
The golden rules of agent-first product engineering
Companies building for agents often treat them as a bolt-on feature.
This is a mistake.
Agents today are more like a new form factor – an interaction layer that sits between your product and your users.
That means you need to build for agents as a primary surface, not an afterthought.
… We learned this the hard way and overhauled our AI architecture two times in the last year. Now, our agent and MCP have 6K+ daily active users.
Here are the golden rules of agent-first product engineering we learned along the way.
1. Let agents do everything users can
2. Meet agents at their level of abstraction
3. Front-load universal context
4. Writing skills is a human skill
5. Treat agents like real users
Read More
Research-Driven Agents: What Happens When Your Agent Reads Before It Codes
Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.
Coding agents generate better optimizations when they read papers and study competing projects before touching code. We added a literature search phase to the autoresearch / pi-autoresearch loop, pointed it at llama.cpp with 4 cloud VMs, and in ~3 hours it produced 5 optimizations that made flash attention text generation +15% faster on x86 and +5% faster on ARM (TinyLlama 1.1B). The full setup works with any project that has a benchmark and test suite. — Read More
Anthropic loses appeals court bid to pause supply chain risk label
A three-judge panel at the D.C. Circuit Court of Appeals on Wednesday rejected a request by the artificial intelligence startup Anthropic to pause the government’s designation of the company as a supply chain risk.
The decision leaves in place at least part of the Defense Department’s official designation of Anthropic’s products as risks to national security. The label — never before applied to an American company — blocks contractors who work with the Pentagon from using Anthropic’s AI models on DOD contracts. — Read More
Mythos, the AI too powerful to be released?
In what’s probably the AI news of the week, month, and even the year, Anthropic has announced a model they are too scared to release. Yes, that’s literally the headline.
In other words, we have been introduced (sort of) to what many believe is a total step change in AI capabilities. And as you can guess, the story is making rounds, and for good reason.
The reason behind the non-release?
This model could allegedly break the Internet and basically every piece of software it’s exposed to.
So, is the world as we know it about to change, or is this the ultimate marketing stunt? — Read More
PentAGI: Penetration testing Artificial General Intelligence
PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. The project is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. — Read More
Patterns for Reducing Friction in AI-Assisted Development
The practices that make human pair programming effective—onboarding, structured design discussion, shared standards—apply equally to working with AI coding assistants. I propose five patterns that bring this collaborative scaffolding to AI-assisted development, shifting the experience from correcting a tool to collaborating with a capable teammate.
PATTERNS
Knowledge Priming
Design-First Collaboration
Context Anchoring
Encoding Team Standards
Feedback Flywheel
— Read More
Claude Managed Agents: get to production 10x faster
Today, we’re launching Claude Managed Agents, a suite of composable APIs for building and deploying cloud-hosted agents at scale.
Until now, building agents meant spending development cycles on secure infrastructure, state management, permissioning, and reworking your agent loops for every model upgrade. Managed Agents pairs an agent harness tuned for performance with production infrastructure to go from prototype to launch in days rather than months.
Whether you’re building single-task runners or complex multi-agent pipelines, you can focus on the user experience, not the operational overhead. — Read More
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