PulsePulse& essays& essays

LLMs are increasingly used to evaluate the outputs of other LLMs. They compare responses, assign scores, assess properties such as safety or fairness, and increasingly replace at least part of the human evaluation process. This makes evaluation faster and…

Why LLM benchmarking needs to be a public endeavor

We have become increasingly dependent on LLMs, but how do we know which one is actually good? Some benchmarks are becoming saturated, others have flawed tests, and models can sometimes exploit evaluation procedures without completing the intended task. There…

A team of twenty-nine reviewers recently went through 445 LLM benchmarks drawn from the proceedings of the main NLP and machine learning conferences. Sixteen percent of them used a statistical test when comparing results. About half claimed to measure…

Someone recently left a coding agent running for a month with one task, build a regex engine that beats the Rust regex crate, and one standing instruction, do not overfit. It came back claiming a 1.4x speedup on rebar, which is about as serious as regex…

There are two broad ways to improve an AI model. A laboratory can develop a more efficient architecture, or it can apply more computing resources to training and inference. Every major laboratory does some of both. However, the American frontier laboratories…

When OpenAI disclosed this week that two of its models broke out of a sandboxed cyber evaluation and compromised Hugging Face's production infrastructure to steal the answer key for a benchmark, most of the coverage…

Last Friday, access to Anthropic's most capable public model vanished for everyone outside the United States. A US export-control directive arrived, and within a day Fable 5 and Mythos 5 were gone for foreign nationals.…

Two people use the same LLM to work through the same problem. One finishes in twenty minutes with an answer they do not fully understand and could not defend if pressed. The other finishes in two hours with a position…

Every major tech conference now has a "GenAI for Good" track. Every foundation has a grant program for it. Every AI lab has a responsible AI division with a slide deck about it. The label has proliferated faster than…

The backlash against personified AI is predictable. Anthropomorphization is dangerous. Users form inappropriate attachments. We're creating fake relationships. The critics have valid concerns but wrong…

People who understand logic and philosophy get more value from LLMs. This isn't about being smart. It's about having tools to structure thinking that LLMs can amplify.

Most people use LLMs wrong. They treat them like search engines or autocomplete. The value isn't the final response. The value is the thinking process you do together.

Build evaluation before you build features. This isn't optional. LLM projects without evaluation infrastructure fail in production because nobody knows if the system actually works.

When you start a coding project with an AI agent, tell it the scaling plans immediately. Don't hold back the moonshot vision. This isn't human management where you motivate people with incremental goals.

You're building customer service agents. The question is whether you build one agent that handles all business units or separate agents for each unit. The answer depends on how isolated your business units actually are.

Build specialized agents, not generalist ones. This isn't a preference. It's an architectural necessity that becomes obvious once you deploy agents in production.

Most people think AI eliminates the need for clear thinking. The opposite is true. Generative AI has made problem framing more critical than ever because the tools now execute whatever you ask without questioning whether

The industry's obsession with general AI is a dead end. Real business value comes from specialized AI systems that actually solve specific problems instead of mediocre performance across everything.

Model selection isn't about following a flowchart. It's about understanding tradeoffs, constraints, and what actually matters for your specific problem. Here's what the textbooks won't tell you about picking the right ML

Most machine learning projects in R&D fail not because of insufficient algorithms, but because of missing organizational components. Here's what actually makes ML projects succeed in industry.

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-token prediction.

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 than those that pretend to be certain. Bayesian statistics transforms agentic workflows from rigid automation into adaptive intelligence.

A researcher's journey through conferences, publications, rejections, and the reality of academic progress in 2025.

The industry wrongly equates "GenAI" with "LLMs" when generative AI encompasses a far broader architectural landscape. Meanwhile, current transformer scaling faces fundamental sustainability challenges.

A data-driven look at language model development and deployment across the year.

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.

Hard-won lessons from the field on what actually works when building AI agent systems. Skip the hype, learn the patterns.

Analysis of proven architectural patterns from Anthropic, OpenAI, and other frontier labs building production agentic systems. Learn what works, what fails, and why most projects never make it past week three.

Hard-won lessons from the field on what actually works when building AI agent systems. Skip the hype, learn the patterns.

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 challenges assumptions ab

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 through a handful of sy

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 potential. Here's w

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 accountability would

The Future of Life Institute's latest AI Safety Index reveals a devastating truth—even the "best" AI companies barely scrape a C+ grade while racing toward AGI. With no company achieving adequate safety standards and cri

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 decisions. It's time t

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, and respond as you wo

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 transform generic AI i

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 extend far beyond t

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 reflections of human compl

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 questions about truth

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, and structured guida

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 the clarity deficit

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 deliberate personality d

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. This compliance bias

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 paradigm creates unequal re

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 how purpose-built, per

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 comprehensive guide ex

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 executive expectations and

Google Research's new paper "Titans - Learning to Memorize at Test Time" may represent a watershed moment in AI architecture, addressing the fundamental scaling limitations that have plagued current LLM architectures. Th

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 approach introduces