BloggHugging Face
The Agent Said It Was Done. The Database Disagreed.
A Blog post by Microsoft on Hugging Face
4 okt. huggingface.co
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4 oktober 2026
2 oktober 2026
BloggHugging Face
Open-sourcing AstaBrief, the fast report-generation model in Asta
A Blog post by Ai2 on Hugging Face
2 okt. huggingface.co
BloggHugging Face
AutoSynthData: Generating Training Data for Enterprise Agents
A Blog post by ServiceNow-AI on Hugging Face
2 okt. huggingface.co
1 oktober 2026
BloggHugging Face
Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs
A Blog post by Ai2 on Hugging Face
1 okt. huggingface.co
30 september 2026
BloggHugging Face
Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
30 sep. huggingface.co
29 september 2026
BloggHugging Face
NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction
A Blog post by NVIDIA on Hugging Face
29 sep. huggingface.co
BloggHugging Face
Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents
A Blog post by Multiverse Computing on Hugging Face
29 sep. huggingface.co
28 september 2026
BloggHugging Face
Holo4: powering generalist computer-use agents
A Blog post by H company on Hugging Face
28 sep. huggingface.co
24 september 2026
BloggHugging Face
Accelerating vision-language models with LFM2.5-VL-DSpark
A Blog post by Liquid AI on Hugging Face
24 sep. huggingface.co
23 september 2026
BloggHugging Face
How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows
A Blog post by NVIDIA on Hugging Face
23 sep. huggingface.co
VideoAI Engineer
AI Engineer Paris 2026 Main Stage: Google DeepMind, ElevenLabs, Hugging Face & Stripe | Day 2
Live from STATION F, the AI Engineer Paris 2026 Main Stage continues with a full day of technical talks from the teams building and scaling production AI systems. Watch sessions covering model customization, inference infrastructure, voice and realtime AI, coding agents, software factories, and the emerging AI economy. Day 2 main-stage lineup: • Jakob Pörschmann (Black Forest Labs) — From Pixels to Robots • Dorian Lods (ElevenLabs) — Generating Identity at Scale • Harry Mellor (Hugging Face) — How a Transformers Model Loads in vLLM • Arielle Le Bail (Stripe) — Inside the AI Economy: What Stripe’s Data Reveals • Hervé Bredin (pyannoteAI) — Rebuilding a Streaming Model for Voice AI • Charles Frye (Modal) — The Low-Latency Inference Engineering Playbook • Dominic Pajak (Arm) — Eyes Up: Edge AI Innovation • Yannis Psorakis (Cognition) — Agentic Map-Reduce for Large-Scale Coding Tasks • Matt Pocock — Fixing the PR Bottleneck • Maarten Grootendorst (Google DeepMind) — The Efficiency of Gemma 4 • Olivier Teboul (Gradium) — The Missing Layers of Conversational AI This livestream covers the Main Stage. Additional tracks and workshops run concurrently and are not included in this broadcast.
23 sep. youtube.com
VideoAI Engineer
Skill issue: stop deploying vision language models, use them with Skills — Merve Noyan, Hugging Face
Merve Noyan wrote a book on vision language models and now wants developers to stop calling them directly. Put one in front of a camera and you will never get real time; a small detector trained for the task runs at forty frames per second on a toaster and beats the VLM anyway. Her other complaint is licensing: people deploy a popular detector without noticing its copyleft license. So she built a toolkit that hands her favorite Apache 2.0 models to a coding agent, which she calls a clueless computer vision engineer, plus what she calls vibe training. Give it a dataset with no labels and it labels images with a nine billion parameter open VLM, passes the overlaid bounding boxes to two smaller VLM judges, merges their verdicts on minimum agreement rather than consensus, and trains RF-DETR. The whole run costs three or four dollars on Hugging Face jobs and inference providers. On road signs the trained detector lands a good mean average precision against ground truth, and on document parsing it generalizes, catching a signature the labeling model itself missed.
23 sep. youtube.com
BloggHugging Face
**Know Who Spoke When: Build Real-Time, Multi-Speaker AI with NVIDIA Nemotron 3 Diarization**
A Blog post by NVIDIA on Hugging Face
23 sep. huggingface.co
22 september 2026
BloggHugging Face
How UK AISI and EvalEval Are Making Benchmark Results Reproducible
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
22 sep. huggingface.co
BloggHugging Face
Transformers now runs llama.cpp quants
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
22 sep. huggingface.co
BloggHugging Face
Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
22 sep. huggingface.co
21 september 2026
BloggHugging Face
Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem
A Blog post by Multiverse Computing on Hugging Face
21 sep. huggingface.co
BloggHugging Face
tokenizers v1: encode, decode and scaling, measured
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
21 sep. huggingface.co
17 september 2026
BloggTechCrunch AI
Base Labs launches an open-weight AI safety partnership with Hugging Face and Goodfire
Baseten lanserade tillsammans med sitt forskningsprogram Base Labs en ny säkerhetsstandard för open-source AI-modeller, i samarbete med Hugging Face och Goodfire AI. Bakgrunden är att open-source-modeller blir allt oftare saboterade genom en teknik som kallas abliteration — Hugging Face listar redan över 6 000 sådana försvagade modeller. Base Labs ska utveckla och publicera metoder för att träna och övervaka öppna modeller på ett sätt som gör säkerheten inbyggd från början, inte något som läggs på efteråt. Företagen menar att öppna modeller faktiskt är säkrare än stängda, eftersom man då kan se hur modellerna beter sig.
17 sep. techcrunch.com
15 september 2026
BloggHugging Face
Your Agent Aced the Task. Will It Do It Again?
IBM Research presenterar ett problem med AI-agenter: även om en agent lyckas lösa en uppgift i genomsnitt 77 procent av gångerna, löser den samma uppgift varje gång i bara 53 procent av fallen. Det är en påtaglig skillnad på 24 procentenheter. Problemet är att agentens beslut ofta görs utifrån osäkra sannolikhetsfördelningar — vissa steg i processen är nästan på gränsen till att kunna gå olika vägar. IBM utvecklade därför ett verktyg som kallas Consistency Analyzer, som hittar dessa kritiska punkter genom att spela upp samma steg flera gånger. Sedan kan de skapa riktlinjer som hjälper agenten att bli mer konsistent, vilket halverade gapet från 24 till 12 procentenheter utan att offra genomsnittlig noggrannhet.
15 sep. huggingface.co