TLDR 2026-01-27

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Apple's new AirTag, simply dubbed 'the new AirTag', features a new Bluetooth chip that offers substantial improvements. It can work with the Precision Finding feature in the Find My app to find items up to 50% farther away. The speaker is also 50% louder. The new AirTag doesn't introduce any major new features, only updates to the device's accuracy, volume, and range.

The Microsoft Azure Maia 200 is a new in-house AI server chip designed for inferencing AI models with ludicrous speeds. It is the most efficient inference system Microsoft has ever deployed. The chip gives 30% more performance per dollar and 50% higher TDP than the first-gen Maia 100. It can hit up to 10 petaflops of FP4 compute, three times higher than Amazon's Trainium3.

Tesla robotaxis navigate using an array of cameras, while Waymos uses LIDAR technology along with a suite of complementary sensors. Tesla challenged convention, but it has quietly reintroduced radar, and it is possible it may bring in other modalities. The paths for the two companies seem likely to converge in the future. This means that instead of debating whether cameras or LIDAR is better, regulators should be asking whether robotaxis in general are as safe as human drivers.

Before we simulate human brains, we first need to demonstrate mastery at the sub-million-neuron-brain level. Achieving this goal will depend primarily on more and better data, which is bottlenecked by funding and the achievement of some key technological breakthroughs. It will soon be feasible thanks to a couple of recent advances. We are nearly capable of overcoming such bottlenecks at the scale of insect-sized organisms.

If you can't see how traffic moves, you can't truly calculate cost or detect latency issues — especially if you use dozens of services. In this eBook, Datadog & AWS Solutions Architects provide 6 steps to diagramming your cloud architecture at the infra and traffic levels. Download your copy

AI agent capabilities crossed a threshold at the end of last year. This has caused a phase shift in software engineering. The intelligence part is a bit ahead of integration, so there is a need for new organizational workflows and processes. This year will be a high-energy year as the industry digests the new capability.

37signals' engineering strategy results in good code. The company stays small, cuts scope ruthlessly, and hires well. Its strategy creates a flywheel where developers maintain less code, allowing them to apply more fixup/review cycles, creating a cleaner code base, which helps to reduce headcount. Most companies can't adopt the strategy because they think more features equals more money.

Today's personalized pricing is different. The average person probably doesn't even know that haggling is an option or the factors that contribute to personalized pricing. In the future, people will cultivate their digital representations so they can get the lowest prices and best treatment. This access could even be sold to others who aren't so careful.

AI models are now beginning to make progress in solving unsolved mathematical problems. It is so good that strong engineers are now handing their coding to AI. Similar improvements are occurring across biological science, finance, physics, and a variety of agentic tasks. If progress continues, it will only be a few years before AI is better than humans at essentially everything. This could result in a serious civilizational challenge that will require decisive and careful action.

Starship Flight 12 will launch in six weeks.

The only way to actually meet demand in 2028 and beyond is to have competition in the foundry space.

Replacing SaaS with AI would cost more and deliver a lower-quality product in almost all scenarios, so it makes no sense from an economic or product perspective.

This post is a review of a blog post written by Jeff Dean and Sanjay Ghemawat, the power duo who arguably made Google what it is today.

The Holy Grail of software development is full self-improvement, and the key to building this is a system that can autonomously generate bug reports and understand features that users will want.

Claude makes problems that used to take hours take minutes, automating every tedious task away.

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