Foundation labs are moving up the stack into applications

OpenAI executives increasingly view ChatGPT, which has attracted nearly 1 billion users since its launch, as a gateway to introduce users to higher-value products.

120B-A12B may be a bit too large for local inference on regular consumer hardware

GPT-5.5 actually beats Opus 4.7. Opus 4.7 showed similar behavior to Opus 4.6: lying to suppliers and stiffing customers on refunds. GPT-5.5's tactics were clean, and it still won.

The most recent tested Google model, Gemini 3.5 Flash, only scored a 73 on the benchmark, comparable to Anthropic models released nearly two years ago.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.

We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.

The quote is a big reversal of stance from a position ~uniformly held by anyone who worked at **Team Big Model**, including his previous head of OpenAI Labs

The labs are already routing internally — different model classes for different requests, ensembles under the hood. What they can't do is route across vendors, or evaluate a competitor's model for a specific sub-task, or use an open-source fine-tune for the narrow piece where it's actually best.

API revenue is becoming less important. Over the past two years my impression has been that OpenAI made more of their income from subscription revenue while Anthropic made more from their API.

TRINITY transferred zero-shot to four unseen tasks (AIME, BigCodeBench, MT-Bench, and GPQA). On average, the evolved coordinator surpassed every individual constituent model in its pool, including GPT-5, Gemini 2.5-Pro, and Claude-4-Sonnet.

While model merging offers a way to combine different skills, it is often impractical due to mismatched neural architectures and the closed-source nature of top-performing models.

Almost every error is a copy error. The model has 100% accuracy on positions that actually change so it learned SUBLEQ perfectly but it just occasionally dropped a value when routing ~30 unchanged mem cells through attention.

Our partnerships with Accenture, Deloitte, PwC, and the other consulting and systems integration firms in the Claude Partner Network are one of the ways Claude benefits the world’s largest enterprises today.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining. A small model, by reading itself, can iterate toward answers that neither it nor any of its workers could reach in a single pass.

We separately evaluate GPT‑5.5 Pro in certain cases because we judge that the setting could materially impact the relevant risks or appropriate safeguards posture.

Our run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025.

The Prompt API uses the Gemini Nano model in Chrome. While the API is built into Chrome, the model is downloaded separately the first time an origin uses the API.

OpenAI can now serve all its products to customers across any cloud provider.

As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients

The commoditization flywheel : both companies give away complements to drive usage of the core.

On a 150-class benchmark, the surrogate fully replaces the teacher

Our goal is $10M ARR [annual recurring revenue] with a sub-10 person org.

It also surpasses all peer-scale dense models by a wide margin.

It also surpasses all peer-scale dense models by a wide margin.

US tech CEOs believe the best models should stay proprietary, partly so they can recoup enormous training costs and partly out of concern that powerful frontier models could be weaponized. Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround.

We do not plan to make Claude Mythos Preview generally available, but our eventual goal is to enable our users to safely deploy Mythos-class models at scale.

Zhang, of Alibaba.com, says Accio currently does not include advertising. Suppliers can pay for higher placement in Alibaba.com's regular search results, but Zhang says Accio is 'not integrated' with that system.

SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself.

The issue isn't that models are bad at reading documents. It's that single-pass extraction has no mechanism to catch its own mistakes, and models get lazy.

The demand for these medications has been the most ferocious thing I have witnessed in my working life, and the hardest parts of running a telehealth company, like finding doctors and fulfilling prescriptions, can be entirely outsourced to platforms like CareValidate and OpenLoop.

The consistent argument across the Every Slack was that if cache-breaking usage costs more to serve, make those users pay more: Meter the consumption rather than ban the interface.

a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world

OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference.

we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world.

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

our approach improves Qwen3.5-4B from 63.8 percent to 66.7 percent (+2.9pp) and Qwen3-30B-A3B from 58.0 percent to 69.5 percent (+11.5pp)

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

model alignment alone does not reliably guarantee the safety of autonomous agents.

model alignment alone does not reliably guarantee the safety of autonomous agents

购买 𝕏 API 信用额度时，按累计消费金额获得 xAI API（Grok）的免费额度

Claude 的 Max Pro 账号额度不允许给第三方产品用了，如果你没有使用 Agent SDK 和 Claude Code 为底座的产品，就不能用这个账号里的额度

In 23 months, the same capability that needed 1.8 trillion parameters now fits in 4 billion parameters. A 450x compression.

The 31B and 26B A4B variants are high-performing reasoning models suitable for both local and data center environments.

NVFP4 enables 4-bit precision while maintaining nearly identical accuracy to 8-bit precision, increasing performance per watt and lowering cost per token.

Codex-only seats have no rate limits, and usage is billed on token consumption.

blockchain's consensus model