Tech Crunch™
Your ultimate source for the latest tech news, trends, and innovations. Stay informed and ahead of the curve with in-depth coverage of startups, gadgets, AI, cybersecurity, and more. 📥: @Rasbrook
Show more📈 Analytical overview of Telegram channel Tech Crunch™
Channel Tech Crunch™ (@techcrunchtg) in the English language segment is an active participant. Currently, the community unites 11 545 subscribers, ranking 10 378 in the Technologies & Applications category and 55 284 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 11 545 subscribers.
According to the latest data from 15 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -280 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.75%. Within the first 24 hours after publication, content typically collects 0.70% reactions from the total number of subscribers.
- Post reach: On average, each post receives 318 views. Within the first day, a publication typically gains 81 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as iran, european, techtip, mixer, economy.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Your ultimate source for the latest tech news, trends, and innovations. Stay informed and ahead of the curve with in-depth coverage of startups, gadgets, AI, cybersecurity, and more.
📥: @Rasbrook”
Thanks to the high frequency of updates (latest data received on 16 September, 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.
"answer like a struggling student" wasn't enough to make them play the role convincingly.
✏️ Gemini 3.1 Flash Lite, Claude Haiku 4.5, and GPT-5.4-mini were given 379 algebra problems and assigned five different student personas. Yet they failed at the task: whether the model was an "A student" or a "poor student," its accuracy stayed between 96.8% and 100%.
The issue is that the model already knows the correct answer and struggles to suppress its own capabilities. So the authors split the task into two stages: first, a separate algorithm models what a student knows and where they are likely to make mistakes; then, an LLM explains the student's answer. This produced more plausible performance differences: the "near-expert" student scored 85.2% accuracy, the average student 57.8%, and the struggling student 44.1%.
👨 The authors acknowledge that they have not yet compared their virtual "students" with real ones. So for now, this is more a method for teaching AI to make believable mistakes than an accurate model of how humans learn.
🔥 — yes, AI can be better than a human
😱 — no, it's just "special effects"
@TechCrunchTg 🌐