TLDR 2026-02-24

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Anthropic claims that DeepSeek, Moonshot, and MiniMax used approximately 24,000 fraudulent accounts to train their own chatbots. The process of distillation, where data from one AI system is used to train another, is common in the AI industry. However, Anthropic's terms forbid the practice and also do not allow its technologies to be used in China. Anthropic has called on government officials and other AI companies to help prevent Chinese companies from distilling American models.

SpaceX, OpenAI, and Anthropic are set to IPO at an unprecedented scale. Typical IPOs offer 15% to 25% of their shares to public markets, which creates enough liquidity for price discovery while allowing founders and early investors to maintain control. At standard float percentages, these three companies need to raise $432 billion to $576 billion from public markets in a single quarter. The entire US IPO market raised $469 billion between 2016 and 2025. It is likely these companies will debut with smaller floats.

The biggest projects in geothermal started construction around 18 months ago. The industry executed well enough to get a shot at true commercial-scale deployments. The next few years will determine if the wave of projects can lead to the success metric of '10% of electricity generation'. While the first version of the technology seems possible, subsequent versions and improvements that lead to scalability will be much more challenging. The leading companies are still going strong, which is promising.

Attention has started turning to using AI to probe the possibility of digitizing smell. Researchers hope that the ability to program smell will illuminate the many mysteries of olfaction. Digital scent could have many practical and quite profitable applications. This article looks at the state of the technology and introduces the various possible avenues for research.

Give your agents the understanding they need to generate reliable code, reviews, and answers. Unblocked builds context from your team's code, PR history, conversations, documentation, planning tools, and runtime signals. It surfaces the insights that matter so AI outputs reflect how your system actually works.

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The line between technical and non-technical work is being redrawn. The winners will be the ones who think like builders. Non-technical people should try to take on more 'engineering' tasks as AI makes tools more powerful and accessible. Engineers should use the same tools to turn themselves into full-stack shipping machines. Companies are investing billions to make engineers more powerful, so take advantage of that investment.

Coding agents have dramatically dropped the cost of producing code. The ability to run parallel agents has compounded this effect. However, delivering good code still has a cost. Developers still need to ensure that the produced code is good code. The challenge is to develop new personal and organizational habits to respond to the new reality of agentic engineering.

A recently published paper called 'The 2028 Global Intelligence Crisis' explores a hypothetical world in which there is an aggressive AI build-out and adoption of AI agents. In this world, unemployment is high despite elevated nominal growth and productivity, and the stock market tumbles due to credit stress from companies laid low by AI metastasizing. It illustrates how AI becoming good could leave many industries vulnerable. This post provides a summary of the paper along with a link to the original.

Many jobs in tech aren't that rewarding anymore. AI has made it even worse. However, leaving your current job without a plan right now isn't a good idea because every CEO wants to get leaner. Companies want to increase AI use rather than hire more people, so the scales are weighted against employees. Use the time in your current job to learn and grow more until you have a better place to go to.

12 months of real engineering data reveal what actually changes with AI adoption — speed, stability, and measurable outcomes. Download the practical, data-backed AI measurement guide for engineering leaders.

AI tools allow startups to run leaner than ever, spawning new benchmarks like 'revenue-per-employee'.

Experts correctly disregard bad sources in their fields of expertise, but trust those same sources on other topics.

Every good model understands that 'use red/green TDD' means to write automated tests, confirm they fail, then iterate on the implementation until the tests pass.

Two Microsoft executives have written a paper arguing that senior software engineers must mentor junior developers to prevent AI from hollowing out the profession's future skills base.

Hiring by Big Tech has risen sharply in India in recent months.

As the first engineer on TLDR's new Applied AI team, you'll build AI agents and composable Claude Skills to let non-technical teammates create their own AI workflows. Learn more.

Dictionary compression can shrink YouTube JavaScript download size for returning desktop users up to 90% compared to existing best-practice compression.