Exploring the frontiers of AI, sharing research insights, and discussing the latest trends in machine learning, data science, and technology innovation.
The industry built transformers for language and forgot that most enterprise data moves through time. Now we're realizing that temporal patterns require fundamentally different approaches than next-to...
When anyone can code with ChatGPT, building the same tutorial projects as everyone else won't get you hired. Here's how to create portfolio projects that demonstrate real engineering judgment.
AI agents that acknowledge what they don't know make better decisions. Learn how Bayesian statistics transforms agentic workflows from rigid automation into adaptive intelligence.
The industry wrongly equates GenAI with LLMs. We explore why this conflation matters and why current transformer scaling faces fundamental sustainability limits.
A data-driven look at language model development and deployment across 2025.
From aerospace engineering to AI research—why the shift toward fully agentic systems makes human-computer interaction more essential, not less.
Delegation, Description, Discernment, Diligence. Rick Dakan and Joseph Feller built a practical framework for working with AI that focuses on competencies, not hype.
ChatGPT asks permission. Claude assumes control. Gemini can't decide. As models converge on capability, personality becomes the product differentiator that actually matters.
Most supervision advice is garbage. Here's what works when you're actually responsible for other humans. Seven principles that separate supervisors who retain talent from those who burn through it.
Real-world patterns and antipatterns for building production agentic AI systems. Learn from frontier lab practices, avoid catastrophic mistakes, and ship reliable agent architectures.
Groundbreaking research from Anthropic, UK AISI, and the Alan Turing Institute reveals that as few as 250 malicious documents can backdoor language models of any size. This finding fundamentally chall...
Every LLM has a distinct personality that fundamentally warps the information it provides. As we mistake these AI quirks for objective intelligence, we're unknowingly filtering all human knowledge thr...
Large Language Models have consumed the internet's collective knowledge, but as we enter the era of synthetic training data, we're creating a closed-loop system that may be fundamentally limiting AI's...
With AI companies collectively failing basic safety standards while racing toward AGI, we need radical reforms that go far beyond voluntary pledges and self-assessment. Here's what genuine AI safety a...
The Future of Life Institute's latest AI Safety Index reveals a devastating truth—even the
We're celebrating AI systems for acing human exams while ignoring what truly matters—their ability to navigate ethical complexity, understand nuance, and grapple with the moral weight of real-world ...
Imagine a digital version of yourself that contains every memory you've ever formed, every decision you've ever made, and every conversation you've ever had—powered by an LLM that can think, reason,...
Effective LLM prompting for industry research isn't about perfect instructions—it's about applying battle-tested heuristics that consistently produce actionable insights. These practical principles ...
As LLMs increasingly evaluate other LLMs, grade student work, and assess human performance, we create a circular system where artificial intelligence defines its own success criteria. The implications...
As generative AI systems become integral to our digital lives, UNESCO's Red Teaming playbook reveals the urgent need for systematic bias testing. But should we test for biases or accept them as reflec...
Large Language Models inherit the biases of human civilization while claiming objectivity. But should they be neutral arbiters or faithful mirrors of human complexity? The answer reveals fundamental q...
Just as teaching a child to ride a bike requires clear, focused instruction rather than overwhelming information, effective LLM prompt engineering for analytical tasks demands precision, specificity, ...
The proliferation of LLM-generated synthetic users in design and research creates a fundamental crisis of representation that undermines the very purpose of user-centered design. This analysis exposes...
Designing personality into LLM agents isn't cosmetic enhancement—it's a fundamental requirement for creating trustworthy, effective, and sustainable human-AI interactions. This article argues for de...
Large Language Models exhibit a fundamental inability to meaningfully disagree with users, not due to safety constraints but because of deeper limitations in reasoning and argumentation capabilities. ...
Through my work as an AI Tech Lead across startups, enterprises, and government projects spanning Pakistan, the US, Ireland, and France, I've witnessed firsthand how the current AI development paradig...
Despite advances in generative AI capabilities, enterprises continue to struggle with generic AI systems that lack specialized expertise in critical domains. This research-backed framework explores ho...
Large Language Models have revolutionized AI with their ability to understand and generate human-like text. However, these models have inherent limitations in their knowledge and capabilities. This co...
Generative AI has become a frequent topic of strategic discussions in boardrooms across industries. While the technology offers remarkable capabilities, there's often a significant gap between executi...
Google Research's new paper
The recent release of DeepSeek R1 challenges our conventional understanding of large language model deployment. While most discussions center around scaling parameters and computing power, DeepSeek's ...