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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
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Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ10 483
π ΠΠ½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ Telegram-ΠΊΠ°Π½Π°Π»Π° prompt π€ AI News
ΠΠ°Π½Π°Π» prompt π€ AI News (@prompt) ΡΠ·ΡΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ° ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΠΈΠ²Π½ΡΠΌ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠΌ. Π‘Π΅ΠΉΡΠ°Ρ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅Ρ 11 945 ΠΏΠΎΠ΄ΠΏΠΈΡΡΠΈΠΊΠΎΠ², Π·Π°Π½ΠΈΠΌΠ°Ρ 10 483 ΠΌΠ΅ΡΡΠΎ Π² ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ.
π ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π°ΡΠ΄ΠΈΡΠΎΡΠΈΠΈ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ°
Π‘ ΠΌΠΎΠΌΠ΅Π½ΡΠ° ΡΠΎΠ·Π΄Π°Π½ΠΈΡ Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ ΠΏΡΠΎΠ΅ΠΊΡ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ ΡΡΡΠ΅ΠΌΠΈΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠΎΡΡ, ΡΠΎΠ±ΡΠ°Π² Π°ΡΠ΄ΠΈΡΠΎΡΠΈΡ ΠΈΠ· 11 945 ΠΏΠΎΠ΄ΠΏΠΈΡΡΠΈΠΊΠΎΠ².
Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠΌ Π΄Π°Π½Π½ΡΠΌ ΠΎΡ 01 ΠΈΡΠ»Ρ, 2026, ΠΊΠ°Π½Π°Π» ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ ΡΡΠ°Π±ΠΈΠ»ΡΠ½ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ. ΠΠ° ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ 30 Π΄Π½Π΅ΠΉ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠΈΡΠ»Π° ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ² ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 615, Π° Π·Π° ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ 24 ΡΠ°ΡΠ° β 14, ΠΏΡΠΈ ΡΡΠΎΠΌ ΠΎΠ±ΡΠΈΠΉ ΠΎΡ Π²Π°Ρ ΠΎΡΡΠ°ΡΡΡΡ Π²ΡΡΠΎΠΊΠΈΠΌ.
- Π‘ΡΠ°ΡΡΡ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ: ΠΠ΅ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½
- Π£ΡΠΎΠ²Π΅Π½Ρ Π²ΠΎΠ²Π»Π΅ΡΡΠ½Π½ΠΎΡΡΠΈ (ER): Π‘ΡΠ΅Π΄Π½ΠΈΠΉ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π²ΠΎΠ²Π»Π΅ΡΡΠ½Π½ΠΎΡΡΠΈ Π°ΡΠ΄ΠΈΡΠΎΡΠΈΠΈ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 98.70%. Π ΠΏΠ΅ΡΠ²ΡΠ΅ 24 ΡΠ°ΡΠ° ΠΏΠΎΡΠ»Π΅ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ ΠΊΠΎΠ½ΡΠ΅Π½Ρ ΠΎΠ±ΡΡΠ½ΠΎ Π½Π°Π±ΠΈΡΠ°Π΅Ρ 10.95% ΡΠ΅Π°ΠΊΡΠΈΠΉ ΠΎΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΡΠΈΡΠ»Π° ΠΏΠΎΠ΄ΠΏΠΈΡΡΠΈΠΊΠΎΠ².
- ΠΡ Π²Π°Ρ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ: Π ΡΡΠ΅Π΄Π½Π΅ΠΌ ΠΊΠ°ΠΆΠ΄ΡΠΉ ΠΏΠΎΡΡ ΠΏΠΎΠ»ΡΡΠ°Π΅Ρ 11 769 ΠΏΡΠΎΡΠΌΠΎΡΡΠΎΠ². Π ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΠ΅ΡΠ²ΡΡ ΡΡΡΠΎΠΊ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡ Π½Π°Π±ΠΈΡΠ°Π΅Ρ 1 306 ΠΏΡΠΎΡΠΌΠΎΡΡΠΎΠ².
- Π Π΅Π°ΠΊΡΠΈΠΈ ΠΈ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ: ΠΡΠ΄ΠΈΡΠΎΡΠΈΡ Π°ΠΊΡΠΈΠ²Π½ΠΎ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΈΠ²Π°Π΅Ρ ΠΊΠΎΠ½ΡΠ΅Π½Ρ: ΡΡΠ΅Π΄Π½Π΅Π΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ΅Π°ΠΊΡΠΈΠΉ Π½Π° ΠΎΠ΄ΠΈΠ½ ΠΏΠΎΡΡ β 8.
- Π’Π΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡ: ΠΠΎΠ½ΡΠ΅Π½Ρ ΡΠΎΡΡΠ΅Π΄ΠΎΡΠΎΡΠ΅Π½ Π½Π° ΠΊΠ»ΡΡΠ΅Π²ΡΡ ΡΠ΅ΠΌΠ°Ρ , ΡΠ°ΠΊΠΈΡ ΠΊΠ°ΠΊ openai, reasoning, gemini, gpu, math.
π ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ½Π°Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ°
ΠΠ²ΡΠΎΡ ΠΎΠΏΠΈΡΡΠ²Π°Π΅Ρ ΡΠ΅ΡΡΡΡ ΠΊΠ°ΠΊ ΠΏΠ»ΠΎΡΠ°Π΄ΠΊΡ Π΄Π»Ρ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΌΠ½Π΅Π½ΠΈΡ:
βWelcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendilβ
ΠΠ»Π°Π³ΠΎΠ΄Π°ΡΡ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠ°ΡΡΠΎΡΠ΅ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠΉ (ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ 02 ΠΈΡΠ»Ρ, 2026) ΠΊΠ°Π½Π°Π» ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΈΠ²Π°Π΅Ρ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΈ Π²ΡΡΠΎΠΊΠΈΠΉ ΡΡΠΎΠ²Π΅Π½Ρ ΠΎΡ Π²Π°ΡΠ° ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. ΠΠ½Π°Π»ΠΈΡΠΈΠΊΠ° ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ Π°ΡΠ΄ΠΈΡΠΎΡΠΈΡ Π°ΠΊΡΠΈΠ²Π½ΠΎ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΡΠ΅Ρ Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠΎΠΌ, ΡΡΠΎ Π΄Π΅Π»Π°Π΅Ρ Π΅Π³ΠΎ Π²Π°ΠΆΠ½ΠΎΠΉ ΡΠΎΡΠΊΠΎΠΉ Π²Π»ΠΈΡΠ½ΠΈΡ Π² ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ.
11 945
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11 950
β‘οΈ 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.
11 950
π¨π₯ 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
11 950
π€ 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
11 950
β‘οΈ 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
11 950
β‘οΈ 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.
11 950
β‘οΈ 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
11 950
π€ 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.
11 950
β‘οΈ 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
11 950
β‘οΈ 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.
11 950
π§ 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."
11 950
β‘οΈ 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.
11 950
β‘οΈ 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.
11 950
π¨π₯ 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.
11 950
β‘οΈ 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.
11 950
β‘οΈ 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.
11 950
π€ 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.
11 950
π€ 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.
11 950
β‘οΈ Beijing's Booster Robotics is quietly becoming the platform that runs robot soccer
Each team runs fully autonomously, driven by AI, with zero human intervention. The slapstick hides real tech.
Booster's T1 robots trot, tackle, shoot, and defend entirely on their own. Teams bolt their own AI models on top of Booster's hardware, making it the Android of humanoid robotics.
Bottom line: China isn't just building robots. It's building the platform everyone else runs on.
