Новости
Заголовки и ссылки на публикации официальных блогов разработчиков, профильных изданий и научных источников. Каталог хранит дату, издателя и ссылку — сам текст остаётся у правообладателя. Связь с компанией ставится по явному упоминанию в заголовке или описании; если упоминания нет, новость видна без привязки.
- Новостей в каталоге
- 342
- За 30 дней
- 258
- Источников (фидов)
- 16
- Свежая публикация
- 10.10.2026
Чаще всего упоминают за месяц: OpenAI · 52 Google DeepMind · 25 Amazon (Nova) · 24 NVIDIA · 24 Anthropic · 20 Meta AI (FAIR) · 12 Microsoft AI · 12 Mistral AI · 7 Alibaba (Qwen / Tongyi Lab) · 2 Сбер (GigaChat) · 1
Найдено: 20 · страница 1 из 1
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Interconnects (Nathan Lambert) издание
I expect rapid progress but not towards general superintelligence
I’ve often been surprised when I hear from top researchers in industry that they think AI will be better than them at their job in a few years, and I didn’t really know why I doubted it.
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Interconnects (Nathan Lambert) издание
The Cyber Risk Discourse is Broken
Open-weights, ideology, and acknowledging trade-offs.
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Interconnects (Nathan Lambert) издание
Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AI
Podcast #19
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Interconnects (Nathan Lambert) издание
The current balance of power in open models
The expanded form of a testimony I prepared for Congress.
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Interconnects (Nathan Lambert) издание
Why I still haven’t bought into true RSI
An “AI moderate’s” view on recent events and the trajectory of frontier models.
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Interconnects (Nathan Lambert) издание
Open-Source AI & Open Models Reading List
How to get up to speed on open models and their implications.
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Interconnects (Nathan Lambert) издание
One resignation turned the embers of AI fear into a wildfire
Some quick notes on a truly weird week.
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Interconnects (Nathan Lambert) издание
When will average people feel AI’s impact?
We’re <5 years into a compounding revolution which could take a century, and how the AI industry should manage this.
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Interconnects (Nathan Lambert) издание
Latest open artifacts (#24): Motif-3, GLM-5.3, Hy4-preview and open model licenses
The open model ecosystem continues to expand in its breadth
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Interconnects (Nathan Lambert) издание
Teaching Everyone to Fish for Tokens
Nvidia wants you building your own model, not buying from Anthropic/OpenAI.
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Interconnects (Nathan Lambert) издание
GLM-5.3: How Chinese labs keep stride with the frontier
Hint: It’s really not a distillation story.
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Interconnects (Nathan Lambert) издание
I wrote an AI textbook — how long until AI can do it better?
Reflections on AI's writing ability and how AI models get more capable.
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Interconnects (Nathan Lambert) издание
5 useful things you'll learn in my new post-training textbook (shipping now!)
After a few long years of finding time to document my lessons from training open models, my post-training book is done!
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Interconnects (Nathan Lambert) издание
Lessons from the hacks
Musings on model alignment, what determines safety, and where we go from here.
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Interconnects (Nathan Lambert) издание
Introducing our Artifacts Hub and Adoption Dashboard
Scaling our curation and measurement of the open ecosystem.
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Interconnects (Nathan Lambert) издание
Latest open artifacts (#23): Laguna S2.1, Inkling, & Kimi K3 show the utility of open models on the Pareto frontier
Capacity to train strong models is proliferating.
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Interconnects (Nathan Lambert) издание
Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next
A podcast with Florian Brand.
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Interconnects (Nathan Lambert) издание
Kimi K3: The open-weights escalation
The global implications on the AI ecosystem.
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Interconnects (Nathan Lambert) издание
6 months to live for open models
The most serious test to date of open source AI’s viability is happening right now.
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Interconnects (Nathan Lambert) издание
Latest open artifacts (#22): Zyphra, Cohere, and Poolside are expanding the breadth of the ecosystem
An assessment of the open ecosystem and the motivations behind releasing models
Источники новостей
Список фидов задан вручную в data/news/sources.yaml: попадают только источники,
где есть машинночитаемая лента (RSS/Atom). У кого ленты нет (например, x.ai, Anthropic, DeepSeek),
новости приходят через издания и агрегаторы — это честнее, чем разбирать чужую разметку.