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NeuralZone | AI Apps

NeuralZone | AI Apps

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@NeuralZone – Curated, hand-picked AI tools and services that are actually useful. Buy ads: https://telega.io/c/NeuralZone contact us via @photofixer

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📈 Аналітичний огляд Telegram-каналу NeuralZone | AI Apps

Канал NeuralZone | AI Apps (@neuralzone) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 399 139 підписників, посідаючи 216 місце в категорії Технології та додатки та 243 місце у регіоні Міжнародний.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 399 139 підписників.

За останніми даними від 25 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -10 026, а за останні 24 години на -276, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 7.76%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.40% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 30 982 переглядів. Протягом першої доби публікація в середньому набирає 5 604 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 25.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як defi, neuralzone, oddin.ai, edwin, ido.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
@NeuralZone – Curated, hand-picked AI tools and services that are actually useful. Buy ads: https://telega.io/c/NeuralZone contact us via @photofixer

Завдяки високій частоті оновлень (останні дані отримано 26 липня, 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.

399 139
Підписники
-27624 години
-2 2747 днів
-10 02630 день

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Six AI coding agents took one visual IQ test, and Codex 5.5 won by method, speed, and cost One small test asked agents to solve 25 visual puzzles on iq-test.cc, select age 30, and return a result link. This was not a lab benchmark. It was a practical check of vision work, browser use, patience, time, and plan cost. "Take the IQ test on iq-test.cc. When you finish, select age 30 and send me the link to your result." Agent IQ Time Limit spent Claude Cowork Opus 4.8 90 85m ~10 pts Claude Code Opus 4.8 90 96m ~28 pts Claude Sonnet 4.6 68 62m n/a Codex 5.5 $100 Fast 124 18m ~12 pts Codex 5.4 $100 Fast 101 16m ~14 pts Codex 5.5 $200 Fast 131 34m ~6 pts The score is only part of the story. Codex 5.5 did better because it worked like a careful test taker: collect puzzle images, build clean contact sheets, zoom into hard cases, then recheck weak answers before submit. More context: the top IQ 131 run used a shorter prompt and the site default age, so it was not a perfect same-prompt run. Still, normal browser access was missing, and Codex found another path through Chrome, clicked all 25 answers, and finished anyway. Claude was careful, especially Opus. It wrote notes and reasoned step by step. Codex was more organized and faster. The article shows screenshots, failed paths, exact prompts, and puzzle examples. The most useful lesson: for visual web tasks, method can beat size. A huge context window did not save Claude, and two extra Codex minutes were worth 23 IQ points. read details on our website Please support this young channel by subscribing. Your subscription really helps us grow. There are no ads here.
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GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra. What’s inside: 🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale; 🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer; 🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features; 🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load; 🔘Two MTP heads, enabling up to 2.2x faster generation; 🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels; 🔘A new online RL stage after SFT and DPO. Results: 🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks: 🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size; 🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%. The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team. ➡️ HuggingFace
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How many AI subscriptions are you paying for? • ChatGPT • Claude • GPT Image • Runway • Kling • Seedance • ElevenLabs • Nano
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🐶 ASO Corgi — platform for the App Store developers. Find the keywords your apps and competitors rank for, and track positio
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💻 Microsoft launches 7 AI models and takes aim at the frontier At Build 2026, Microsoft unveiled its new MAI model family, c
💻 Microsoft launches 7 AI models and takes aim at the frontier At Build 2026, Microsoft unveiled its new MAI model family, covering reasoning, coding, image generation, speech synthesis, and transcription. Key releases: • MAI-Thinking-1 - reasoning model that Microsoft says rivals top coding models and performs on par with Claude Sonnet 4.6 in blind evaluations. • MAI-Code-1-Flash - a lightweight 5B coding model for GitHub Copilot and VS Code. • MAI-Image-2.5 - image generation and editing model competing with Nano Banana Pro. • MAI-Voice-2 and MAI-Transcribe-1.5 - speech models supporting up to 43 languages. 📸 NeuralZone
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I tested one dangerous-looking first-person video idea through four fal.ai models, compared price and realism, and learned why Seedance 2.0 still won my small experiment overall I used fal.ai because I could test several video models from one service and keep the same queue flow. I wanted a hard starting prompt: one long first-person action shot, clear height, parachute, industrial yard, and a muddy landing. I picked Seedance 2.0 as the cinematic baseline, Kling v3 Pro for action and smart shot planning, Veo 3.1 because it is the premium Google option, and Wan 2.7 as a cheaper 1080p challenger. What I got: Seedance 2.0 was best. It kept the helmet-camera feeling, the hands, the parachute lines, and the story rhythm most clearly. My only real complaint was the realism of the fall into the puddle. It cost about $2.43 for 8s at 720p. Kling v3 Pro gave me good image quality, but the actions were not realistic. With audio on, 8s cost about $1.34. Wan 2.7 accepted the prompt, but the result was basically an absurd video made from disconnected cuts. At 1080p, 8s cost about $1.20. Veo 3.1 was the tricky one. The first request with the same prompt hit a content policy check. Then I softened the wording a little, sent it again, and got a video. It lost the first-person view and switched to third-person, but the result still looked cool. That successful 8s 1080p run with audio cost about $3.20. So my successful clips cost about $8.17 total if I count the second Veo run. My winner was not the cheapest one; it was the one that followed the whole idea with the least confusion. See the result 👇
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֎ New: ChatGPT users can now build real apps directly from chat ChatGPT's app store just added AppDeploy, and it works with f
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