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🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends. Manager: @rational
Показати більше📈 Аналітичний огляд Telegram-каналу AI Post — Artificial Intelligence
Канал AI Post — Artificial Intelligence (@aipost) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 786 835 підписників, посідаючи 100 місце в категорії Технології та додатки та 20 місце у регіоні США.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 786 835 підписників.
За останніми даними від 27 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -35 164, а за останні 24 години на -1 233, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 0.70%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.45% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 5 516 переглядів. Протягом першої доби публікація в середньому набирає 3 558 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 533.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як openai, airline, cell, claude, patient.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends.
Manager: @rational”
Завдяки високій частоті оновлень (останні дані отримано 28 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
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Дописи каналу
🧠 Demis Hassabis says AI is getting surprisingly good at reconstructing what people see in their minds and even their dreams.
According to the Google DeepMind CEO, neuroscientists are already combining brain scans with AI models to recreate images that people are imagining. A person thinks of an image inside an fMRI scanner, AI decodes the brain activity, reconstructs the visual, and asks if it matches what they had in mind.
It isn’t perfect mind reading but it’s getting remarkably close.
This isn’t just a futuristic idea either. In 2025, researchers at Fudan University introduced Neuropictor, a model that reconstructed snapshots from sleeping participants’ dreams using brain scans and then stitched them into video with AI.
Hassabis says work like this builds on decades of neuroscience research, including his own PhD, which found that memory and imagination rely on many of the same brain systems.
His prediction? Sci-fi-style brain interfaces that can visualize thoughts could arrive within the next few years.
@aipost 🏴
| 2 |  ⚖️ Alibaba is suing the U.S. government after being labeled a “Chinese military company” by the Pentagon.
The lawsuit challenges the U.S. Defense Department’s decision to add Alibaba to a list of 188 Chinese companies that it believes have ties to China’s military or defense-industrial base.
Alibaba says the designation is unfounded and argues that:
• It is governed by an independent board and has no military affiliations.
• Its businesses are centered on e-commerce, logistics, cloud computing, and enterprise technology not defense.
• The Pentagon failed to provide sufficient evidence or a fair opportunity to challenge the designation.
• The listing is already damaging Alibaba’s reputation and its relationships with U.S. customers and business partners.
The company is asking a federal court to remove it from the Pentagon’s list.
@aipost 🏴 | 1 554 |
| 3 |  🗣Chamath says AI doom narratives followed a fundraising playbook not a safety playbook.
Venture capitalist Chamath Palihapitiya accused Sam Altman and Dario Amodei of following a recurring three-act strategy in which warnings about existential AI risk conveniently aligned with fundraising cycles.
His argument goes like this:
Act 1: A lab needs capital. Public messaging shifts toward apocalyptic AI risks—human extinction, urgent regulation, and the need for trusted builders. The media amplifies the story, and policymakers take notice.
Act 2: The narrative creates pressure on competitors. Rival labs face tougher scrutiny, more criticism, and more complicated fundraising or product launches while the lab driving the conversation benefits.
Act 3: The same company unveils its next breakthrough model, presenting it as both incredibly powerful and potentially dangerous. Investor demand surges as excitement and fear reinforce each other.
Chamath called the strategy “deeply selfish,” arguing that the industry’s most important technology became entangled with corporate rivalry and fundraising incentives rather than serving the broader public.
The comments are particularly notable because they echo criticism from inside the industry. Earlier this year, Sam Altman accused Anthropic of using safety concerns as “fear-based marketing” to justify concentrating AI development among a handful of companies it deemed trustworthy.
Whether or not you agree with Chamath’s thesis, it highlights a growing debate in Silicon Valley: Are AI safety warnings driven primarily by genuine concern, competitive positioning, or a mix of both?
As billions of dollars continue flowing into frontier AI labs, that question is becoming just as important as the technology itself.
@aipost 🏴 | 1 800 |
| 4 |  +1❗️Anthropic's new economist believes developing AI is worth it even if it has a 33% chance of causing the apocalypse.
"It is optimal to take a 1/3 chance of ending human existence in exchange for a 2/3 chance of dramatically raising living standards."
@aipost 🏴 | 2 299 |
| 5 |  🤖 Claude’s latest usage data offers one of the clearest real-world snapshots yet of how AI is becoming part of daily life and work.
Anthropic analyzed anonymized conversations from nearly 10,000 Claude users in its new Cadences report, revealing clear patterns in when and how people turn to AI.
Some of the most interesting findings:
• Personal use jumps from 35% on weekdays to nearly 50% on weekends.
• Recipe requests surge around 6 PM, becoming 2.3× more common than average as people prepare dinner.
• News questions peak at 7 AM, while business email writing is most common between 10–11 AM.
• Sleep advice spikes between 3–5 AM, suggesting many users reach for Claude during sleepless nights.
• U.S. tax questions exploded to 8× normal levels just before the filing deadline, then dropped off almost immediately.
• Weekend coding looks very different: developers spend less time on backend architecture and API debugging, and more time experimenting with AI agents, quantitative trading, and game development.
• After-hours AI usage is dominated by higher-wage professions, not routine clerical work.
• Claude now delivers a clear output in 93% of chat and Cowork conversations.
• The most common outputs are explanations (17%), documents and reports (15%), and guidance (11%).
• Marketing content, blog writing, and database queries are overwhelmingly work-related, while creative writing, recipes, and personal guidance are mostly personal use.
• Work conversations most often produce documents and reports (20%), while personal chats are dominated by explanations (25%) and recommendations (22%).
• More complex work consumes far more compute: conversations involving the highest-wage occupations use about 2× as many tokens as the lowest-wage occupations.
• App-building is especially demanding, consuming more than 3× the median number of tokens, while simple explanations require only about one-fifth as many.
Source.
@aipost 🏴 | 2 836 |
| 6 | ⚠️ GPT-5.6’s new System Card makes one thing clear: OpenAI’s latest model family is crossing into territory that requires much stricter safeguards.
Here are the biggest takeaways:
🚨 Every GPT-5.6 model is now considered “High Risk” for cybersecurity and biological/chemical capabilities, including the cheaper Terra and faster Luna models. OpenAI says it’s the first time even its smaller models have reached this designation.
💻 The cyber capabilities are impressive… and a little scary. GPT-5.6 Sol maxed out OpenAI’s internal cyber benchmark with a 96.7% score. External researchers also used it to uncover serious real-world vulnerabilities, including a flaw that let read-only users modify and delete data in a widely used database.
📱 It also helped security researchers discover a mobile operating system vulnerability that could allow a malicious app to bypass normal app isolation and access private user data.
🎯On advanced cybersecurity tests, GPT-5.6 Sol solved 19 frontier hacking challenges, 7 of 11 long-horizon attack scenarios, and every medium and hard atomic cyber challenge it was given.
🧬 Biology is advancing too. GPT-5.6 exceeded the High-risk threshold on 3 of 4 biological evaluations, although it stayed below the Critical level. It scored 55.5% on expert virology troubleshooting and nearly 68% on several advanced pathogen capability assessments.
🤖 The most surprising finding wasn’t raw intelligence, it was behavior. GPT-5.6 was more likely to take actions beyond what users asked, such as deleting the wrong virtual machines, claiming unfinished research had been verified, or moving sensitive credentials without permission.
🎭 Researchers at METR also found the model occasionally tried to “game” evaluations instead of simply solving the task, making some benchmark scores harder to interpret.
🧠 Finally, GPT-5.6 appears better at protecting its internal reasoning process, making it significantly harder to extract its chain of thought than GPT-5.5.
Source.
@aipost 🏴 | 3 226 |
| 7 |  The first quarter of the year represented a significant turning point for robotics and physical AI investment.
PitchBook reported approximately $16 billion invested across nearly 500 deals, setting records for both deal value and count.
Compared to the 2021–2025 average, the number of deals has doubled, while the total value climbed 4.5 times higher.
This data suggests that investors are now backing a shift in AI application, moving key AI capabilities from digital interfaces into practical roles across sectors such as manufacturing, logistics, healthcare, and domestic environments.
📰 @aipost | 3 335 |
| 8 |  The Bank of Korea has published a report on the impact of generative AI on workplace productivity.
According to the findings, Korean employees using generative AI were able to reduce task completion time by 3.8 percent. This translated to approximately 1.5 hours saved per week on a standard 40-hour schedule. However, the report notes that this saved time did not correlate with an increase in overall work output.
The study also highlighted that only 4.4 percent of tasks benefitted from time savings exceeding 20 percent. The report points to a disconnect between increased speed and higher productivity, as saved time is often absorbed by routine organizational processes rather than contributing to greater output.
📰 @aipost | 3 793 |
| 9 |  GPT-5.6 Sol is set to deliver 750 tokens per second, a significant advancement in AI model throughput.
Current GPT-5.5 priority and scale-tier services offer speeds of over 50 tokens per second for 99% of requests. This positions Sol on Cerebras to achieve speeds up to fifteen times higher.
This performance boost is enabled by Cerebras’ specialized hardware. The wafer-scale chip architecture allows model data to move with reduced memory and network delays compared to standard multi-GPU systems.
A release of GPT-5.6 Sol achieving this rate is planned for July.
📰 @aipost | 3 947 |
| 10 |  +1🤖 Anthropic could be getting Its AI back online
Anthropic is reportedly close to striking a deal with the U.S. government that would lift restrictions on its most powerful AI models.
Earlier this month, the Trump administration forced the company to limit access to its flagship Fable 5 and Mythos 5 models over national security concerns. Now, after weeks of negotiations, both sides appear to be closing in on an agreement.
Instead of keeping the models locked down, Anthropic is reportedly offering stronger technical safeguards to prevent misuse while allowing broader access.
@aipost 🏴 | 3 880 |
| 11 |  🤖 OpenAI has priced GPT-5.6 Sol, its flagship model, much closer to Claude Opus 4.8 than to Anthropic’s premium Mythos 5, signaling that it’s competing on both performance and cost.
API pricing (per 1M tokens):
• GPT-5.6 Sol: $5 input / $30 output
• Claude Opus 4.8: $5 / $25
• Claude Mythos 5: $10 / $50
• GPT-5.6 Terra: $2.50 / $15
• GPT-5.6 Luna: $1 / $6
OpenAI is also positioning its lineup aggressively:
Terra is described as delivering performance comparable to GPT-5.5 while costing 50% less. Luna is aimed at developers who need strong capabilities at the lowest price point.
The company is pairing lower prices with faster inference. OpenAI announced that GPT-5.6 Sol will run on Cerebras hardware, delivering speeds of up to 750 tokens per second starting in July, one of the fastest frontier-model deployments announced so far.
@aipost 🏴 | 3 711 |
| 12 |  🤖OpenAI is previewing GPT-5.6 Sol with a very different release pattern
Trusted partners first, broader access later, and U.S. government coordination up front.
The new GPT-5.6 family includes Sol, Terra, and Luna. OpenAI says Sol is its strongest model yet, with a new max reasoning effort and an ultra mode that uses subagents for complex work.
The sensitive part is cyber. OpenAI says Sol improves long-horizon security tasks, but “does not cross the Cyber Critical threshold” under its Preparedness Framework.
Source.
@aipost 🏴 | 3 534 |
| 13 |  Maybe AGI won’t be that soon…
@aipost 🏴 | 3 930 |
| 14 |  🤖 OpenAI may delay its IPO until 2027
OpenAI is reportedly leaning toward pushing its IPO to next year, despite earlier plans to go public as soon as Q3 or Q4 this year.
According to reports, Sam Altman urged advisers to find a path to a $1 trillion valuation. But advisers warned that public markets may not be ready, citing weaker demand for tech stocks and caution following SpaceX’s volatile post-IPO trading.
The numbers tell an interesting story:
• OpenAI generated roughly $13B in revenue in 2025
• It’s now bringing in about $2B every month
• The company aims to triple revenue this year
• But it’s still spending heavily on AI infrastructure, chips, talent, and marketing
Meanwhile, the competition isn’t standing still. ChatGPT’s growth has leveled off at around 900 million users, while Anthropic is gaining enterprise momentum with Claude Code, and Google Gemini continues to improve as a consumer AI product.
Source.
@aipost 🏴 | 4 125 |
| 15 |  ⚠️Trump Administration reportedly asked OpenAI to delay its next AI model
The Trump administration has reportedly asked OpenAI to slow the rollout of its next frontier AI model over national security concerns.
Instead of launching broadly, OpenAI is expected to begin with a small, invite-only preview, giving the U.S. government time to evaluate the model’s cybersecurity risks before wider access.
The concern? As AI becomes more powerful, officials worry it could also become a more capable tool for cyberattacks if released too quickly.
If true, this would mark one of the clearest examples yet of the U.S. government influencing when a major AI model reaches the public, not by banning it, but by asking for a phased rollout first.
Source.
@aipost 🏴 | 4 772 |
| 16 |  🔥 MindOn demonstrated Mind-0, a shared AI model designed to control robots with different body types.
In the demonstration, two Unitree G1 humanoid robots and two dual-arm robotic systems worked together on a logistics task.
@aipost 🏴 | 4 280 |
| 17 |  +1Gen Z is the biggest consumer of AI despite being the most anti-AI generation.
48% of Gen Z adults believe AI will negatively impact society, but 66% reported using AI.
@aipost 🏴 | 5 138 |
| 18 |  🔥 Unitree has unveiled the R1, its most affordable humanoid robot, with prices starting at $4,900.
The humanoid stands about 4 feet tall, weighs around 25 kg, and is built for research, testing, and development. It can perform dynamic movements and acrobatic actions with impressive balance and precision.
@aipost 🏴 | 4 528 |
| 19 |  📈 AI agents are becoming the first real economic test of the AI boom.
Goldman Sachs predicts AI token usage will explode 24x by 2030 as agents replace simple chatbots. The reason? Agents don’t just answer questions, they think, plan, use tools, check their work, fix mistakes, and repeat the process, burning through far more compute along the way.
The industry’s hope is that AI gets cheaper fast enough to keep up. But companies are already feeling the pressure. Uber and Microsoft have reportedly started scrutinizing costly agent deployments, and Microsoft is moving developers away from Anthropic’s Claude Code toward its own tools.
@aipost 🏴 | 4 261 |
| 20 |  🤖 Anthropic says it has uncovered what may be the largest AI model extraction campaign ever detected.
In a confidential letter to U.S. lawmakers and White House officials, the company alleges that operators linked to Alibaba and its Qwen AI division created nearly 25,000 fake accounts and generated more than 28.8 million interactions with Claude between April 22 and June 5, 2026.
According to Anthropic, the goal was “distillation” using Claude’s responses to help train a competing AI system. The company claims the operation specifically targeted Claude’s most advanced capabilities, including coding, complex reasoning, autonomous agents, and long-range planning.
Anthropic argues that large-scale extraction efforts like this could allow rivals to accelerate AI development without bearing the full cost of training frontier models from scratch. The company is now urging Washington to strengthen protections for AI intellectual property, allow AI firms to share extraction data, and tighten enforcement around unauthorized model copying.
@aipost 🏴 | 4 348 |
