Absolutely nobody likes the hiring process. Not the managers hiring, not the recruitment people, and certainly not the candidates.

Tech interviews are one of the worst parts of the process and are pretty much universally hated by the people taking them. We’ve all heard stories of people being asked comp sci questions about O(n) efficiency, only to connect APIs with basic middleware in their day job.

AI straight-up kills hackerrank. AI also significantly reduces the effectiveness of comp sci fundamentals and the coding interview as they are today. Architectural interviews are likely safe for a few years yet. As AI gets better, how can we do better interviews. — Read More

AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming. 

Adding to the confusion is that AI models are often promoted based on industry benchmarks. But these technical metrics often reveal little about how real people and companies actually use them. 

To cut through the noise, TechCrunch has compiled an overview of the most advanced AI models released since 2024, with details on how to use them and what they’re best for. — Read More

With the release of Deep Research tools by the likes of OpenAI, Google, and most recently, Perplexity (cheapest option by far and reasonably well tested against the others), concerns about job safety and displacement due to AI are growing.

But we know history repeats, so does history support these fears? And if so, what skills will be necessary to survive in an AI world?

We’ll discuss the impact timelines previous industrial revolutions had on the economy, examine the most recent research on adoption and productivity from one top University and one top AI lab, understand what it means to be an ‘AI Human,’ finally, giving you the best mental model to analyze whether AI will take your job. — Read More

And Compute is the Upper Bound for Achievable Intelligence

I have lost count of how many times I have been asked about DeepSeek over the past week — specifically, whether it signals the obsolescence of high-performance AI compute or, by extension, the beginning of the end for NVIDIA.

The answer is “No.” — but if you still need more than one word, here is why. — Read More

Channeling his inner Steve Jobs, OpenAI CEO Sam Altman revealed plans on Wednesday to drastically simplify the company’s product lineup, merging its scattered collection of AI models into a single unified system.

… Echoing Jobs’s famous catchphrase, Altman tweeted, “We want AI to ‘just work’ for you; we realize how complicated our model and product offerings have gotten.” — Read More

“When did you feel the AGI?”

This is a question that has been floating around AI circles for a while, and it’s a hard one to answer for two reasons. First, what is AGI, and second, “feel” is a bit like obscenity: as Supreme Court Justice Potter Stewart famously said in Jacobellis v. Ohio, “I know it when I see it.”

I gave my definition of AGI in AI’s Uneven Arrival: …My definition of AGI is that it can be ammunition, i.e. it can be given a task and trusted to complete it at a good-enough rate (my definition of Artificial Super Intelligence (ASI) is the ability to come up with the tasks in the first place).

The “feel” part of that question is a more recent discovery: DeepResearch from OpenAI feels like AGI; I just got a new employee for the shockingly low price of $200/month. — Read More

AI researchers at Stanford and the University of Washington were able to train an AI “reasoning” model for under $50 in cloud compute credits, according to a new research paper released last Friday.

The model, known as s1, performs similarly to cutting-edge reasoning models, such as OpenAI’s o1 and DeepSeek’s R1, on tests measuring math and coding abilities. The s1 model is available on GitHub, along with the data and code used to train it. — Read More

Last week, news spread that a Chinese AI company, DeepSeek, had built a cutting-edge chatbot at a fraction of the cost of its American competitors. It sent the stock prices of American tech companies plummeting.

But Microsoft CEO Satya Nadella put a happy spin on the whole episode, citing a 160-year-old economics concept to suggest that this was good news.

“Jevons paradox strikes again!” Nadella wrote on social media, sharing the concept’s Wikipedia page. “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.” — Read More

We live in interesting times. On Monday morning, tech stocks plunged on investor shock and awe over DeepSeek, a Chinese AI company that has built — I’m leaving out a lot of details — an open-source large language model (LLM) that performs competitively with name brands like ChatGPT at a fraction of the computing cost.

Meanwhile, two stories got buried in the avalanche of activity by President Trump last week. Trump rescinded a Biden executive order on AI safety. And he announced Stargate, a nine-figure AI joint venture aimed at entrenching American AI competitiveness, which has triggered a feud between Elon Musk and Sam Altman, the frenemy cofounders of OpenAI.

These stories will have far bigger geopolitical implications than, say, Musk’s choice of hand gestures. They may even mark an inflection point where the world has decided to charge forward with AI at full speed, for better or worse. — Read More

Millimeter-wave and terahertz integrated circuits and chips are expected to serve as the backbone for future wireless networks and high resolution sensing. However, design of these integrated circuits and chips can be quite complex, requiring years of human expertise, careful tailoring of hand crafted circuit topologies and co-design with parameterized and pre-selected templates of electromagnetic structures. These structures (radiative and non-radiative, single-port and multi-ports) are subsequently optimized through ad-hoc methods and parameter sweeps. Such bottom-up approaches with pre-selected regular topologies also fundamentally limit the design space. Here, we demonstrate a universal inverse design approach for arbitrary-shaped complex multi-port electromagnetic structures with designer radiative and scattering properties, co-designed with active circuits. To allow such universalization, we employ deep learning based models, and demonstrate synthesis with several examples of complex mm-Wave passive structures and end-to-end integrated mm-Wave broadband circuits. The presented inverse design methodology, that produces the designs in minutes, can be transformative in opening up a new, previously inaccessible design space. — Read More

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