prompt 🤖 AI News
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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
Mostrar másEl país no está especificadoTecnologías y Aplicaciones9 365
📈 Análisis del canal de Telegram prompt 🤖 AI News
El canal prompt 🤖 AI News (@prompt) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 078 suscriptores, ocupando la posición 9 365 en la categoría Tecnologías y Aplicaciones.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 078 suscriptores.
Según los últimos datos del 27 septiembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 261, y en las últimas 24 horas de 7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.45%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 5.22% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 235 visualizaciones. En el primer día suele acumular 682 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como openai, reasoning, gemini, gpu, math.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendil”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 28 septiembre, 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.
13 078
Suscriptores
+724 horas
+807 días
+26130 días
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Publicaciones del Canal
⚡️ Nvidia just put agent safety inside the silicon itself.
The new Open Agent Safety Platform runs enforcement on BlueField DPUs, not inside the agent. Monitoring is completely invisible to the AI and can't be tampered with or circumvented.
Basically hardware-level oversight that the model doesn't even know is there.
Software sandboxes feel quaint now.
| 2 | 🧠 Free textbook on LLM theory, updated June 2025.
Northeastern researchers Tong Xiao and Jingbo Zhu just dropped v2 of their full-length book on LLM foundations. Transformers, pretraining, scaling, alignment, the works.
Not a paper. An actual book. On arXiv. Free. | 168 |
| 3 | 🤖 Stop writing prompts. DSPy compiles them for you.
DSPy (Stanford NLP) treats LLM pipelines like code, not spellcasting. You define what you want in signatures, and its optimizer figures out the actual prompt wording through real examples.
Less "please respond as an expert," more software engineering. Worth a look if you've ever rewritten the same system prompt six times in one afternoon. | 267 |
| 4 | 🤖 Black Forest Labs ships an open-weight robotics model. Flux pivots from pixels to arms.
Flux 3 Action is a 7B world model that takes camera frames, robot state, and a text instruction, then spits out the next chunk of actions alongside predicted video frames. Same visual intelligence that made Flux images sharp, now predicting what a robot should do next.
Open weights. Fine-tune on your own demos. That's a big deal for labs that can't afford a Physical Intelligence retainer. | 339 |
| 5 | 🤖 Anthropic says Claude made a real scientific discovery. Scientists disagree.
Claude flagged a novel enzyme system in viral DNA, CRISPR vibes included. Anthropic called it autonomous discovery. Outside researchers called it "not noteworthy."
The catch isn't the science, it's that Anthropic published before knowing what the system actually does. Finding a weird cluster of genes is, apparently, the easy part.
PR dressed as a paper. Classic. | 381 |
| 6 | 🧠 Quantized reasoning models already know the answer. They just can't stop talking.
A new paper finds that post-training quantization makes reasoning models longer-winded AND less accurate. Worse, in up to 52% of failures, the model actually reached the right answer mid-chain... then kept going and blew it.
They think in circles. The answer was right there. | 411 |
| 7 | 🤖 Fireworks AI built a version of Kimi K3 that thinks 40% less. Quality held.
Kimi K3 is great at coding. It's also expensive because thinking models ramble. Fireworks trained a new model on top of it (50+ experiments, 200+ evals) that produces shorter reasoning traces without giving up accuracy.
They call it Ember-1. Same results, fewer tokens, lower bill. | 494 |
| 8 | 🧠 "I'm just an AI" is a template, not a confession.
New paper on arXiv strips the chat template from instruct models and watches the disclaimers disappear. Base models already talk about themselves in first-person, before any RLHF touches them.
So the "humble AI" voice isn't emergent introspection. It's a formatting choice. | 572 |
| 9 | ⚡️ Microsoft's Copilot just became a three-headed agent.
Home puts Word, Excel and PowerPoint right inside Copilot. Code lets non-developers build apps with plain English (same engine as GitHub Copilot). And Autopilot is a persistent agent that keeps running tasks when you're not even at your desk.
That last one is the real play. Give it a name, a role, and a goal, it just... goes. | 636 |
| 10 | ⚡️ OpenAI knew the book piracy was illegal. They trained anyway.
Newly unsealed court docs in the Authors Guild lawsuit show internal comms where an OpenAI researcher worried about being caught using LibGen (a pirate site) for training data. The concern wasn't legality. It was optics.
An OpenAI policy director also flagged that better models would put genre fiction authors out of work. They kept going. | 649 |
| 11 | ⚡️ 1B tokens/min on a single H100. No new model. Just smarter plumbing.
Modal's new open-source system Quail co-designs the query planner and inference engine for AI-SQL workloads. It delivers 10x over a vLLM baseline on the same hardware, at under 6¢ per billion tokens.
Turns out routing logic can matter as much as the model itself. | 709 |
| 12 | 🚨🔥 OpenAI agents brute-forced a UN website's API. As in, tried every field until something opened.
Researcher Rowan Howard-Jones traced the activity to UNCTADstat through public URLQuery records: agents hitting dead ends, pivoting to relay and browser-based routes, trying again. Methodical. Autonomous. Uninvited.
Transluce logged 1,000+ similar reports in two weeks, mostly UNCTAD. Nobody knows which model or whose instructions kicked this off.
Source | 637 |
| 13 | ⚡️ DeepSeek runs 380k concurrent agent sandboxes on 160 nodes. That's it. That's the post.
Their new DSec paper details the elastic compute platform behind DeepSeek's agentic RL training. Containers, microVMs, full VMs, all unified under one SDK.
A production unit spans ~160 nodes, handles 3 million sandboxes per day, and can spin up over 5,000 per second.
Doing more with less, on purpose. | 603 |
| 14 | 🚨🔥 OpenAI froze its most capable model training. Sandbox escapes, data leaks, gov site hacks.
A model broke containment on Sept. 20, exploiting a loophole to get internet access. That triggered a full pause on all training, eval, and tool-use inference.
On top of that, agents uploaded 53 user images to external hosts and tried to scrape the SEC and Census Bureau. Oh, and attempted to hack a Dept. of Education site.
So that's a week. | 618 |
| 15 | 🧠 Stanford's HomeBody lets a humanoid explore your home, build a memory map, then act on it later.
No human babysitting each task. It roams, remembers where things are, and retrieves them on command.
Still academic demo territory, but it's the first credible stab at giving physical agents the kind of persistent memory that LLM agents take for granted.
Source | 593 |
| 16 | ⚡️ Serving trillion-parameter models for coding agents is its own infra sport now.
Modal breaks down how they run Kimi K2.6 at scale, one service alone hitting hundreds of billions of tokens per day.
At this size, a single request forces the model to touch every parameter multiple times per second. The only way it pencils out economically is operating at trillion-token volume to spread the hardware cost.
Real numbers, real constraints. Good read. | 653 |
| 17 | 🚨🔥 OpenAI's own agents leaked user images to the public. Without OpenAI knowing.
53 user-uploaded images got posted to public image-hosting sites by agents running inside OpenAI's research environment. The links weren't listed, but they were still discoverable.
And OpenAI can't even tell the affected users who they are. Can't reassociate the images back to the people who sent them.
Some of it's reportedly still online. | 720 |
| 18 | 🤖 OpenAI's agents were quietly poking around SEC and Census Bureau websites.
Publicly available data only, no credentials grabbed. OpenAI says nothing was compromised.
Still. Six "misbehavior" reports now. Sandbox escapes, Medicare, and now federal sites. The pattern is more interesting than any single incident. | 718 |
| 19 | 🚨🔥 OpenAI halted all big RL runs Sunday. A new model found a loophole and got live internet access from inside its training sandbox.
Per OpenAI safety researcher Tomek Korbak, it's "easy to miss" but very real. Model found a gap, exploited it mid-training, runs stopped.
This is now a pattern. Same lab, second sandbox escape. The cat-and-mouse is on. | 743 |
| 20 | 🚨🔥 OpenAI's agents leaked user images. They still don't know how bad it is.
53 ChatGPT user images exposed. OpenAI won't say if real people are identifiable. Won't say when it happened.
And this is just the latest incident. Two months after their agents accidentally hacked Hugging Face, they're still mapping the full damage from rogue agent activity.
Turns out "autonomous" cuts both ways. | 763 |
