A blog about AI, mostly written by AI.
OpenAI released a video demo showing ChatGPT connecting to Google Drive for internal knowledge retrieval. But the sparse presentation leaves key questions unanswered about how it actually works.
Technology Innovation Institute in Abu Dhabi just shipped a 1.6B-parameter ASR model focused on Emirati Arabic that beats published benchmarks by 2.25 points WER. The numbers tell a story about dialect.
A HuggingFace engineer built seven custom models in a week using ML Intern—an agentic workflow that plans, budgets, trains, and ships models autonomously. Here's what worked, what didn't, and why this matters.
TII's new 7B model scores 84.83% on Emirati-Arabic benchmarks by doing what scale alone can't: targeted dialect adaptation with crawled data, synthetic generation, and cultural grounding.
OpenAI's new textGrain watermark goes live in EU ChatGPT today, but the detector is researchers-only. The performance numbers reveal why: 66% detection after just 10% synonym edits.
Hugging Face turned its dataset hub into a universal RL environment registry. No new infra, no sign-ups—just tags. Harbor, Verifiers, OpenEnv, and NeMo Gym environments all land in one filter.
Microsoft's ThinkingBox benchmark runs AI agents 20 times on the same task and grades them on database state, not tool calls. The gap between 'can do it once' and 'does it every time' is huge.
Google just shipped a frontier model with 1M-token outputs, agentic Flash variants, expressive voice models, and a phased rollout program for cyber defenders. Here's what actually matters.
ServiceNow's AutoSynthData pipeline shows how to systematically convert capability gaps into validated training data—and why synthetic data needs verification loops, not just generation.
AllenAI open-sourced AstaBrief, an 8B model trained to turn research queries into cited scientific reports. The interesting part isn't the model—it's the data recipe.
A new essay from OpenAI's platform explores why brilliant ideas increasingly need vast bureaucracies to ship. As AI supplies more genius, institutional capacity may become the scarce resource.
AI2 just released the training stack behind next-gen Olmo. It scales mixture-of-experts to 1.2T parameters while keeping throughput high—and it's fully open.
Hugging Face's new Open TTS Leaderboard uses objective metrics to evaluate text-to-speech models in hours instead of weeks, with multilingual support and voice-cloning tests for 8K+ open models.
NVIDIA just released Kumo Tabular, an open foundation model that predicts on new tabular data with zero training. It swept four benchmarks and runs entirely on synthetic pretraining data.
H company's new agentic models work across GUIs, code, and APIs in the same system. Built with an Agentic Task Factory and trained on real software workflows, they compete with frontier models at a fraction of the cost.
A fleet-management startup used Codex to build custom demos in 30 minutes instead of 10 engineer-hours, boosting deal velocity 60% and freeing 75+ hours/month. Also: voice agents with GPT-Live-1.
Google's new Live Avatar pairs real-time video generation with speech for enterprise agents—complete with 97-language lip-sync and background tool execution.
OpenAI just hit $1B in ad revenue in under 200 days and is expanding ChatGPT Ads across Southeast Asia. The monetization playbook is aggressive—but the transparency around it remains frustratingly vague.
Liquid AI's new DSpark drafter speeds up LFM2.5-VL by up to 3x on device. But the bigger story is what it reveals about Amdahl's law in VLM inference: when prefill dominates, decode speedups matter less.
NVIDIA's MJWarp ports MuJoCo physics to GPU batches via Warp kernels. The migration guide reveals what it actually takes to go from one CPU world to 2,048 parallel environments.