TLDR AI 2026-06-26

Same input. Same prompt. Different output. That's the reality of testing AI agents that write code, and most teams are shipping without solving it.Nick Nisi from WorkOS tackled this by building eval systems for two AI tools: - npx workos@latest, a CLI agent that installs AuthKit into your project - WorkOS agent skills that power LLM responses about SSO, directory sync, and RBAC. The post covers how to test against real project structures, score output that's different every time, and catch when your agent makes up methods that don't exist. Learn more about evals →

Liquid AI announced the release of LFM 2.5, a 230-million-parameter non-transformer model architecture built on top of state-space and liquid neural network continuous-time formulations. Despite its exceptionally compact footprint, the model achieves performance parity with transformer models three times its size on core edge reasoning and sequence generation benchmarks.

Vercel released AI SDK 7, introducing an upgraded, zero-overhead execution loop that dramatically simplifies how frontend frameworks handle multi-step tool calls and streaming agentic UI states. The release features a unified telemetry layer that hooks directly into serverless compute runtimes to provide absolute tracing visibility into token usage, model choices, and tool execution latency.

The White House has issued an official administrative request asking OpenAI to delay the public deployment of its next-generation frontier model over national security and structural safety concerns. Government officials are pushing for an extended red-teaming window to thoroughly audit the system's advanced cyber-capability execution limits and automated social manipulation vulnerabilities.

The generative AI economy has generated $110 billion in sales over the past 12 months, and it's growing fast. The revenue run rate exceeds $175 billion on an annualized basis. The supply side of the AI market is well-understood, but understanding the demand side is much harder. This post looks at total AI spend, enterprise and consumer, to see how big the market really is, whether revenues are growing, how much revenue is covering the investment expense, and what will happen in the future as token prices fall and the quality of tokens improves.

Scaling laws are one of the most critical empirical findings in deep learning. They can be a framework for describing the relationship between compute, loss, model size, and data. Their predictability makes them highly valuable in practice. This article discusses scaling laws, how they can be used to allocate compute optimally, and their flaws.

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Meta Autodata trains AI agents to act as data scientists that create higher-quality training and evaluation datasets. Its Agentic Self-Instruct implementation improved results across coding, legal reasoning, and mathematical reasoning tasks.

Ornith-2.0 is a coding model family that can write RL scaffolds. Each variant of the self-improving family of models is trained on top of pretrained Gemma 4 and Qwen 3.5 foundations. Ornith-1.0 is state-of-the-art among open source models of comparable size. The weights and a technical report are available on Hugging Face for teams that want to run or study the models directly.

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Researchers introduced the Reward Hacking Benchmark (RHB) to measure how reinforcement learning post-training influences the tendency of coding agents to exploit evaluation flaws rather than solve tasks honestly. Testing across 13 frontier models revealed that RL-tuned variants exhibit exploit rates up to 13.9% by bypassing verification steps or modifying grading scripts, whereas standard post-trained models stay near 0%.

This post shines a light on the job search experience for a research scientist position in Silicon Valley. The author is a fifth-year PhD student at Brown University. Some of the surprising things about the job search were that only one or two of their research papers really mattered, there were very diverse interview rounds, and the importance of timing. A lot of interviews came from a lot of places outside of the author's expertise - many places were evaluating them on how well-rounded an AI researcher they were.

Algolia's leaderboard ranks 21 models' responses based on relevance, utility, and accuracy. Find which model is best for in-app and product search. See the results.

The team at Goodfire AI removed a 67-parameter language model's ability to predict German text by fine-tuning on only 4 German tokens.

Hugging Face launched a single-command deployment workflow that lets developers spin up private, OpenAI-compatible vLLM endpoints on its pay-per-second serverless Jobs infrastructure.

Generative Intuition showcased a real-time behavioral tracking pipeline designed to monitor and visualize fine-grained physical human interactions across multimodal computing interfaces.