prompt 🤖 AI News
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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
Show moreThe country is not specifiedTechnologies & Applications10 048
📈 Analytical overview of Telegram channel prompt 🤖 AI News
Channel prompt 🤖 AI News (@prompt) in the English language segment is an active participant. Currently, the community unites 12 441 subscribers, ranking 10 048 in the Technologies & Applications category.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 12 441 subscribers.
According to the latest data from 22 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 685 over the last 30 days and by 15 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 91.85%. Within the first 24 hours after publication, content typically collects 9.91% reactions from the total number of subscribers.
- Post reach: On average, each post receives 11 422 views. Within the first day, a publication typically gains 1 232 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 11.
- Thematic interests: Content is focused on key topics such as openai, reasoning, gemini, gpu, math.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendil”
Thanks to the high frequency of updates (latest data received on 23 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
12 441
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Channel Posts
🤖 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
| 2 | ⚡️ 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. | 22 411 |
| 3 | https://fablewatch.com/ | 22 241 |
| 4 |  No text... | 22 916 |
| 5 | 🚨🔥 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 |
| 6 | 🤖 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 |
| 7 | ⚡️ 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 |
| 8 | ⚡️ 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 |
| 9 | ⚡️ 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 |
| 10 | 🤖 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 |
| 11 | ⚡️ 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 |
| 12 | ⚡️ 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 |
| 13 | 🧠 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 |
| 14 | ⚡️ 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 |
| 15 | ⚡️ 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 |
| 16 | 🚨🔥 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 |
| 17 | ⚡️ Google is renting $920M/month of compute from SpaceX
110,000 Nvidia GPUs, CPUs, memory, the whole stack, Oct 2026 through June 2029.
Google is already the world's largest single owner of AI compute, but demand still caught them short. Anthropic just signed a similar deal at $1.25B/month for the Colossus 1 cluster.
xAI built those data centers to run Grok. Now it's renting them to its biggest rivals. Honestly, not a bad pivot. | 7 942 |
| 18 | ⚡️ Anthropic's Mythos hits 52x on training code. Humans top out at 4x.
Anthropic runs the same test every release: optimize training code. Skilled human, 4-8 hours, gets 4x. Claude Opus 4 averaged 3x in May 2025. Mythos Preview hit 52x by April 2026.
Anthropic frames it as a possible path to recursive self-improvement, but admits Claude still hasn't shown the research taste to pick which problems matter.
Narrow task, lab conditions, unreleased model. Still a brutal trendline. | 9 293 |
| 19 | 🤖 Canada wants its own supercomputer. No AWS required.
Canada's "AI for All" strategy calls for a public AI supercomputer and investment in sovereign, Canadian-owned compute and cloud infrastructure. That's the government treating compute as national infrastructure, not a vendor contract.
Canada's current AI data centre and cloud setup is "largely foreign-owned," and sovereign compute capacity is "nascent" with "significant investment" needed to cut reliance on foreign providers. So yeah, the stakes are real.
Carney launched the plan Thursday. Big ambitions, thin details so far. | 8 627 |
| 20 | 🤖 The "Cannes AI film" didn't actually screen at Cannes
Higgsfield AI's Hell Grind, a 95-minute generative sci-fi feature built by 15 people in 14 days for under $500k, was pitched as a Cannes debut. The festival itself went out of its way to say otherwise.
Higgsfield held screenings at a private industry event and a commercial cinema in the town of Cannes, not a Festival de Cannes venue. The CEO still called it a Cannes premiere on LinkedIn. So.
80% of that $500k went straight into AI compute costs, which is the one genuinely interesting data point here. | 10 638 |
