اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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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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📈 تحلیل کانال تلگرام prompt 🤖 AI News
کانال prompt 🤖 AI News (@prompt) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 11 543 مشترک است و جایگاه 10 831 را در دسته فناوری و برنامهها دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 11 543 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 683 و در ۲۴ ساعت گذشته برابر 45 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 98.51% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 11.61% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 11 371 بازدید دریافت میکند. در اولین روز معمولاً 1 340 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 8 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند openai, reasoning, gemini, gpu, math تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendil”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
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| 01 ژوئن | +34 |
پستهای کانال
⚡️ 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.
| 2 | ⚡️ 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 | 2 613 |
| 3 | 🤖 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. | 2 430 |
| 4 | ⚡️ 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 | 2 353 |
| 5 | ⚡️ 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. | 2 611 |
| 6 | 🧠 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." | 4 341 |
| 7 | ⚡️ 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. | 4 258 |
| 8 | ⚡️ 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. | 4 135 |
| 9 | 🚨🔥 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. | 3 967 |
| 10 | ⚡️ 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. | 3 883 |
| 11 | ⚡️ 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. | 3 894 |
| 12 | 🤖 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. | 3 798 |
| 13 | 🤖 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. | 8 311 |
| 14 | ⚡️ 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. | 7 869 |
| 15 | 🔐 1Password keeps your secrets out of Codex's brain entirely
1Password released an MCP server for OpenAI Codex that pulls credentials from vaults at runtime, then discards them. Mounted, used, gone.
Credentials never appear in code, terminals, or the model's context window. That last part matters most.
Bottom line: Just-in-time credentials are the new seatbelt for AI coding agents. | 14 748 |
| 16 | ⚡️ Bigger quant beats aggressive compression on Qwen3.6 35B
ByteShape benchmarked NTP vs MTP GGUFs across 4090, 5090, 4080, 5060 Ti, plus Intel, Ryzen, and Raspberry Pi 5.
MTP gives GPUs a 20-40% gen-speed boost. CPUs? It just adds overhead. Stick to NTP on CPU.
Bottom line: lower bpw is not automatically better. Largest quant that fits often won. | 13 717 |
| 17 | ⚡️ AMD's local AI box hits $3,999. It was $1,700 last summer.
AMD's Ryzen AI Halo workstation opens pre-orders next month at $3,999 with 128GB LPDDR5X. Same chip, same memory, same story. Memory prices exploding since launch are now fully baked into the consumer price.
For $4k you're one click away from an NVIDIA DGX Spark. Tough sell.
Source | 12 213 |
| 18 | 🇨🇳⚡️ Ex-Samsung chip chief says China will pop the DDR5 price bubble by H2 2027
Kye-hyun Kyung, former head of Samsung's DS Group, says Chinese firms investing aggressively in memory production could flood supply and push prices down in the second half of next year.
CXMT leads the charge. Kyung cites data projecting global production capacity at 6 million wafers/month by H2 2027.
Catch: he warns that if Big Tech cuts AI budgets, the industry could flip straight into severe oversupply.
Source | 12 562 |
| 19 | ⚖️🤖 Musk sues OpenAI for "stealing a charity." Jury: you waited too long.
The AP reports that a federal jury in Oakland unanimously tossed Elon Musk's lawsuit against Sam Altman and OpenAI. The jury found Musk had three years to sue and simply did not file on time. Judge Gonzalez Rogers immediately adopted the verdict and dismissed all claims.
90 minutes of deliberation. Three weeks of trial, private emails, Brockman's diaries, Satya Nadella on the stand. Didn't matter. The merits never got touched.
By ruling on timing, the jury dodged the actual question: whether donors to nonprofit AI labs have standing to challenge for-profit conversions under charitable trust law. That question is still live. Musk's team says they're appealing.
Bottom line: OpenAI heads toward its trillion-dollar IPO with the biggest legal threat dead on arrival, and accountability for the nonprofit-to-profit pivot remains untested. | 10 346 |
| 20 | ⚖️🤖 Musk's OpenAI lawsuit dies in under two hours. Jury says: too late.
A federal jury found that Musk waited too long to bring his case against Altman, Brockman, and OpenAI, and TechCrunch reports it ended on statute-of-limitations grounds after a three-week trial. The verdict was unanimous.
Musk's team had wanted the court to force OpenAI and Microsoft to disgorge up to $134 billion in gains and remove Altman and Brockman from leadership. None of that happens. One major threat to OpenAI, a possible forced restructuring, is now off the table ahead of its reported IPO.
Musk's lead counsel responded to the verdict with one word: "Appeal." Good luck with that. The jury sidestepped the core questions about breach of charitable trust entirely, meaning an appeal has to argue the judge got the limitations instructions wrong, not that Altman actually did anything.
Bottom line: Musk filed too late, and nine jurors needed less time than a lunch break to agree on it. | 0 |
