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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
БольшеСтрана не указанаТехнологии и приложения9 979
📈 Аналитический обзор Telegram-канала prompt 🤖 AI News
Канал prompt 🤖 AI News (@prompt) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 12 486 подписчиков, занимая 9 979 место в категории Технологии и приложения.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 12 486 подписчиков.
Согласно последним данным от 26 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 688, а за последние 24 часа — 27, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 114.63%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 14 341 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 15.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как openai, reasoning, gemini, gpu, math.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendil”
Благодаря высокой частоте обновлений (последние данные получены 27 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
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Посты канала
🤖 Google's Gemma team is taking orders
Someone at Google just asked the community what they want in the next Gemma. And the wishlist is basically: a big MoE (100B-ish, fitting in 64GB RAM), configurable reasoning effort, audio input, and lighter guardrails.
Wild to watch a frontier lab crowdsource its roadmap in public.
| 2 | ⚡️ AI labs are buying rare books by the pallet, shredding them, and a court just said that's fine
Anthropic's "Project Panama" paid tens of millions to scan books for Claude's training data, then destroy the physical copies. Spine cut off, pages fed through a high-speed scanner. Done.
A federal judge ruled it's fair use. Now every lab has a legal template.
Some of those books had almost no surviving copies. They're gone. | 360 |
| 3 | ⚡️ Hugging Face CEO just publicly asked OpenAI for $100M
Posted the actual message himself, "in the spirit of transparency." Bold.
OpenAI's been dropping $100M+ checks across its ecosystem lately. Whether this one lands says a lot about how open-source fits into their plans. | 832 |
| 4 | ⚡️ Every big tech giant now backs open-weight AI. Except Anthropic.
Sundar Pichai just signed on for Google, pointing to Gemma as proof they walk the talk.
That's Google, Meta, Microsoft, and OpenAI all on the same side. Anthropic's standing alone, pre-IPO, while open-source models close the gap fast.
Rough timing. | 911 |
| 5 | 🤖 China may lock down its AI models. Yes, even the open-weight ones.
Beijing's been meeting with Alibaba and ByteDance about cutting off foreign access to their top models. Both open-source and closed-source versions are on the table.
China won global goodwill by giving its models away, and devs everywhere built on cheap Chinese weights as an alternative to pricey US APIs. That tap could close.
Both sides are now doing this. Beijing, like the U.S., is treating cutting-edge AI as a critical national asset that needs controls. Source | 14 827 |
| 6 | ⚡️ China's got 7 AI chip makers shipping H100-class silicon. Most IPO'd in the last 6 months.
Moore Threads, MetaX, Biren, and Enflame are already being called China's "four little dragons," each gunning for Nvidia's spot in AI accelerators. Moore Threads popped ~425% on day one. MetaX climbed ~693%.
Biren alone jumped 76% on its Hong Kong debut after raising $717M, with retail demand subscribed over 2,300 times. Baidu's chip unit Kunlunxin just filed too.
US export controls were supposed to slow this down. They kind of did the opposite. | 21 737 |
| 7 | https://fablewatch.com/ | 21 591 |
| 8 |  Нет текста... | 22 916 |
| 9 | 🚨🔥 US gov forces Anthropic to kill Fable 5 and Mythos 5 for everyone
Export control directive landed at 5:21pm ET, targeting any foreign national access. Net effect: both models killed for all customers.
Anthropic's pushback is sharp: the government's only evidence is a "narrow, non-universal jailbreak" that amounts to asking the model to read a codebase and fix bugs. Defenders do that daily.
First time a leading AI company has taken a publicly deployed model offline due to federal intervention. Won't be the last.
Source | 21 746 |
| 10 | 🤖 Moonshot drops Kimi K2.7 Code, open weights and all
It's a coding-focused model built on K2.6, with the headline trick being ~30% fewer reasoning tokens on equivalent tasks.
Benchmarks are framed almost entirely as gains over its own predecessor. No SWE-Bench Pro numbers against Fable 5 or GPT-5.5. "Kimi Code Bench v2" is an eval only Moonshot runs. Honest? Sure. Comparable? Not really.
Weights are free under the Modified MIT license. While peers quietly go closed, Moonshot keeps shipping open.
Source | 18 723 |
| 11 | ⚡️ MiniMax drops M3: frontier coding open weights, $20M commercial free pass
MiniMax M3 is the first open-weight model to combine frontier coding, 1M-token context, and native multimodal capabilities in one architecture. API pricing starts at $0.30/M input tokens vs. Claude Opus 4.7's $5.00, making it 15x+ cheaper.
The licensing is where it gets interesting. Commercial use is free until your product clears $20M/yr revenue. Under that? Just send an email. M2.7 shipped under a commercially restricted license, so this is a real shift.
Source | 15 885 |
| 12 | ⚡️ Claude Fable 5 is live. Mythos power, guardrails on.
Anthropic just dropped Fable 5, its first Mythos-class model for the public, with hard blocks on cybersecurity and biology responses.
Those queries fall back to Opus 4.8. Early data shows 95%+ of sessions run fully on Fable's own answers, so the fallback is rare.
It'll cost you: $10/M input, $50/M output tokens. Twice the price of Opus 4.8. Max plan gets a free window first, then it's pay-per-token. Enjoy the trial. | 14 791 |
| 13 | ⚡️ Bezos just bet $500M that the brain beats bigger models
His startup Flourish is putting real neurons under the microscope hunting for the brain's "core algorithm" instead of scaling transformers.
The pitch: Cortex AI runs at 20-50 watts vs. the megawatts today's AI burns. A fruit fly's neural net is already 10x more efficient than a transformer.
Honestly, "two-pager to $500M" is a sentence. Source | 12 977 |
| 14 | 🤖 Gemini 3.1 knows the most, executes the least
Best-in-class for abstract reasoning and scientific knowledge. And yet Claude Sonnet leads by a wide margin on economically valuable tasks like financial modeling and research.
Google optimized for breadth and algorithmic creativity. OpenAI engineered for terminal execution and agentic loops. Two very different bets on what "capable" means.
Knows everything. Does the least with it. | 11 047 |
| 15 | ⚡️ Xiaomi hits 1,000+ tps on a 1T model using commodity GPUs
MiMo-V2.5-Pro UltraSpeed cracks 1,000 tokens/s on a trillion-parameter model from a single standard 8-GPU node. No custom silicon, no Groq, no Cerebras.
The industry usually leans on specialized hardware for speeds like this. Xiaomi's bet was model-system codesign on commodity GPUs instead. FP4 quant on MoE experts + speculative decoding did the heavy lifting.
The hardware is still vague (what "commodity" means here matters a lot). But the direction is real.
Source | 10 084 |
| 16 | ⚡️ 120 tok/s on a 12GB GPU. Gemma 4 12B just got silly fast.
Pair the new QAT checkpoint with MTP speculative decoding in llama.cpp and you're hitting 120 tokens/s on a single consumer card. QAT minimizes quality loss by simulating quantization during training, so you're not trading brains for speed.
The dedicated MTP draft model enables significantly faster inference with no quality loss. The PR landed in llama.cpp mainline the same day it was shared.
Beats Qwen 35B MoE on throughput, fits in 12GB. That's a lot of model for a gaming GPU. | 9 240 |
| 17 | 🧠 Asimov saw the cognitive offloading problem coming. In 1956.
The calculator panic of the 1980s is back, just wearing an LLM costume. Same fear: kids outsource thinking to machines and lose the baseline skills that make harder thinking possible.
But LLMs are scarier. A calculator can't write your essay, reason through your argument, or code your app. The surface area of offloadable cognition is basically everything now.
Asimov's Multivac stories kept asking what humans do when machines know more. Turns out the answer wasn't "thrive." It was "forget how to think." | 9 114 |
| 18 | ⚡️ Unitree G1 hauls a load up stairs. Humanoid hardware is quietly lapping the software.
The G1 carries over 40kg while walking and can climb stairs up to 40cm high. It's already the best-selling humanoid on the market, with 1,000+ units shipped.
The robots are ready. The AGI to pilot them isn't. Hardware won't be the bottleneck. | 8 928 |
| 19 | ⚡️ Local AI agent built from scratch, not from vibes
OpenLumara is a hand-written local agent framework: tiny system prompt, extreme token efficiency, everything modular and optional. Basically the opposite of every bloated cloud agent SDK.
The sharp insight from people running it: fewer tools = better reasoning. Give a small model 2-3 focused tools and it stays sharp. Dump the whole toolbox on it and you get context rot. | 8 887 |
| 20 | 🚨🔥 GOP calls the anti-data-center movement a Chinese psy-op. The FBI might investigate.
Republican lawmakers are demanding the FBI probe whether rising anti-AI sentiment is a foreign influence op run by China. But the reports they're citing don't establish direct coordination. They point to funding relationships and "overlapping messaging."
One AEI fellow put it bluntly: "Pretending AI anxiety is fake... is the surest path to failure."
Real concerns (energy bills, water, noise) don't need Beijing to exist. | 10 838 |
