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very other LLM framework Seriously. The way they implement LLM objects is painful. So I wrote my own. Super easy way to do calling. That's all they do - for Anthropic, OpenAI, and Google. Based on our telemetry, these account for 95% of use cases. Same interface. Full control over caching, serialization, provider quirks. No magic. No surprises. It's so much easier to do caching and implement messages yourself. Completely model-agnostic. You're not locked into one provider. You just decide. Ephem

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written about in Beyond Standard LLMs, for instance, text diffusion models are an interesting approach. Right now, they fall into the category of experimental research models, but Google shared that they will release a Gemini Diffusion model. It won’t rival their state-of-the-art offerings in modeling quality, but it will be really fast and attractive for tasks with low-latency requirements (e.g., code completion). Also, two weeks ago, the open-weight LLaDA 2.0 models dropped. The largest one, a

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and draws conclusions from that. Amazon knows that if you bought X, you might buy Y, because it's seen what lots of people buy and saw that if they buy X, they're likely to buy Y. Google's dominance of search is based in part on seeing what people search for, and what they click on and what they search for next. Of course, this is a network effect, and comes with a cold start problem that’s a barrier to entry. How do you provide those recommendations, suggestions and connections before you have

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erected from scratch in 122 days. With roughly 200,000 H100/H200s and 30,000 GB200 NVL72, it remains, today, the largest fully operational, single-coherent cluster (setting apart Google, master of multi-datacenter-training). However, Colossus 1’s 300 MW looks modest next to the Gigawatt-scale clusters under construction by OpenAI, Meta and Anthropic. Their hyperscaler partners are happy to leverage their balance sheet and win the market by throwing dollars at it. Was xAI’s prowess a one-time wo

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terms of performance or architecture, Nvidia evolves its products along another dimension. Let’s discuss how widespread adoption of Rubin CPX GPU could impact competing solutions. Google TPU The TPU’s 3D Torus scale-up network provides a unique advantage in that it allows a maximum pod size of up to 9,216 TPUs. This is the largest world size in the industry, yet it offers some of the lowest scale-up networking cost per accelerator. This enables a very broad array of parallelism schemes that othe

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ng pieces in the race for intelligence. Beijing plans for the long term and knows it must secure its own domestic compute destiny. There is an irony: in the 2010s China kicked out Google to enforce its Great Firewall and foster its domestic industry, this time the US government is withholding hardware technology so they cannot seize the lead in AI. We believe that at China’s core, they want to control not only its internet and AI, but the hardware that supports it. From silicon to tokens, China

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sers. Gemini and other leading AIs add hundreds of millions more. In my posts, I often focus on the advances that AI is making (for example, in the past few weeks, both OpenAI and Google AIs chatbots got gold medals in the International Math Olympiad), but that obscures a broader shift that's been building: we're entering an era of Mass Intelligence, where powerful AI is becoming as accessible as a Google search. Until recently, free users of these systems (the overwhelming majority) had access

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large-scale training runs done yet on GB200 NVL72 as software continues to mature and reliability challenges are worked through. This means that Nvidia’s H100 and H200 as well as Google TPUs remain the only GPUs that are today being successfully used to complete frontier-scale training. As it stands today, even the most advanced operators at frontier labs and CSPs are not yet able to carry out mega training runs on the GB200 NVL72. With that said, every new architecture naturally requires time

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How ChatGPT will monetize free users, Router is the Release, AIs will serve Ads, Google's moat eroded?, The shift of purchasing intent queries To many power users (Pro and Plus), GPT5 was a disappointing release. But with closer inspection, the real release is focused on the vast majority of ChatGPT’s users, which is the 700m+ free userbase that is growing rapidly. Power users should be disappointed; this release wasn’t for them. The real consumer opportunity for OpenAI lies with the largest use

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e company is going from GPU-poor to GPU-filthy-rich on a per researcher basis. Meta GenAI 1.0: AI Incrementalism Compared to pure-play AI labs like OpenAI, companies like Meta and Google have followed an “AI Incrementalism” strategy by enhancing existing products with better recommendation systems and GenAI to improve ad targeting, content tagging, and internal tools. This has paid off handsomely in financial results, allowing Meta to shrug off Apple’s attempts at stopping them from tracking use

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t set with 500 tasks, you run each task independently, average the task metrics, and get an overall metric. But in reality, you solve tasks sequentially rather than in parallel. A Google SWE solves google3 issues increasingly better as she gets more familiar with the repo, but a SWE agent solves many issues in the same repo without gaining such familiarity. We obviously need long-term memory methods (and there are), but academia does not have the proper benchmarks to justify the need, or even th

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Apple TV+, are baffling in their pointlessness. However, they are all very effective in getting cash from Apple users (and, of course, at least $20bn of this is TAC payments from Google to make it the default in Safari, for now). Meanwhile, since both the Watch and AirPods on one side and the services on the other are all essentially about the attach rate to iPhone users, you could group them together as one big upsell, which suggests a different chart: half of Apple’s revenue is the iPhone and

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are AI to interns). Statista, meanwhile, aggregates other people’s data, makes sure it ranks highly in SEO, and then tries to get you to register or pay to see the result. I think Google should ban this company from the index, but even if you disagree, saying this is the source is like saying the source is ‘a Google search result’. Again, this is an intern-level issue. Setting that aside, though, let’s dig some more, and look at one number - Japan. Deep Research says that the Japanese smartphone

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-01-17-ai-models-dangerous-before-public-deployment/}} , year = {2025} , month = {01} , } Many frontier AI safety policies from scaling labs (e.g. OpenAI’s Preparedness Framework, Google DeepMind’s Frontier Safety Framework, etc.), as well as past work by third party evaluators including UK AISI , Apollo Research , and METR , focus on pre-deployment testing – ensuring that the AI model is safe and that the lab has sufficient security before the lab deploys the model to the public. Such pre-deplo

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izations for H100, with support for every NVIDIA GPU from V100 and newer; AMD GPUs: Support for MI200, MI300, and Radeon RX 7900 series - with rapidly growing adoption for MI300X; Google TPUs: Support for TPU v4, v5p, v5e, and the latest v6e; AWS Inferentia and Trainium: Supports for trn1/inf2 instances; Intel Gaudi (HPU) and GPU (XPU): Leveraging Intel GPU and Gaudi architectures for AI workloads; CPUs: Featuring support for a growing list of ISAs - x86, ARM, and PowerPC vLLM's hardware compati

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there been much growth in the active users (as opposed to the vaguely curious) in the last 9-12 months, as shown in a bunch of similar surveys? The most revealing - possibly - is Google Trends, which must always be used with caution, but which seems to show a correlation with school holidays. There are a couple of ways that you could answer this. It does take time to change your habits and ways of thinking around an entirely new kind of tool (remember when we printed out emails?). We can be cer

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s is precisely what is already happening in AI – it’s why you can use state of the art generative AI not just at low cost but even for free today in the form of Microsoft Bing and Google Bard – and it is what will continue to happen. Not because such vendors are foolish or generous but precisely because they are greedy – they want to maximize the size of their market, which maximizes their profits. So what happens is the opposite of technology driving centralization of wealth – individual custom

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