NeuralZone | AI Apps
@NeuralZone – Curated, hand-picked AI tools and services that are actually useful. Buy ads: https://telega.io/c/NeuralZone contact us via @photofixer
Mostrar más📈 Análisis del canal de Telegram NeuralZone | AI Apps
El canal NeuralZone | AI Apps (@neuralzone) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 402 634 suscriptores, ocupando la posición 213 en la categoría Tecnologías y Aplicaciones y el puesto 251 en la región Internacional.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 402 634 suscriptores.
Según los últimos datos del 12 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -9 637, y en las últimas 24 horas de -491, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.99%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.67% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 28 152 visualizaciones. En el primer día suele acumular 6 712 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 27.
- Intereses temáticos: El contenido se centra en temas clave como defi, neuralzone, oddin.ai, edwin, ido.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“@NeuralZone – Curated, hand-picked AI tools and services that are actually useful.
Buy ads: https://telega.io/c/NeuralZone
contact us via @photofixer”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 13 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
"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 ptsThe 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.
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
