prompt 🤖 AI News
前往频道在 Telegram
Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
显示更多未指定国家技术与应用9 586
📈 Telegram 频道 prompt 🤖 AI News 的分析概览
频道 prompt 🤖 AI News (@prompt) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 12 852 名订阅者，在 技术与应用 类别中位列第 9 586。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 12 852 名订阅者。
根据 01 九月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 254，过去 24 小时变化为 15，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 11.93%。内容发布后 24 小时内通常能获得 4.97% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 533 次浏览，首日通常累积 639 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 1。
- 主题关注点： 内容集中在 openai, reasoning, gemini, gpu, math 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence.
Contact: @LightEarendil”
凭借高频更新（最新数据采集于 02 九月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
12 852
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+1524 小时
+177 天
+25430 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 02 九月 | +7 | |||
| 01 九月 | +15 |
频道帖子
🤖 $1,688 humanoid robot ships from SF. Real. Ish.
Nori Robotics (YC S26) launched a bimanual wheeled robot for researchers priced out of $50k arms. 19 DOF, 4 cameras, lidar, a 432 Wh battery. Legit spec sheet.
But the demo reel includes a clothes-folding clip that ends in a pile. It's honest, at least. Whether it survives Chinese competition is a separate problem.
| 2 | 🤖 Deloitte charged $435K for a report. The mayor says AI wrote most of it.
Wellington's council commissioned a staffing review from Deloitte. The bill: $435,000. Now the mayor is on the radio saying large chunks of the report were written by AI.
Nobody disclosed that upfront. Nobody asked.
If consultancies are just wrapping ChatGPT in a $400K invoice, the whole "trust the expert" pitch gets a lot harder to sell. | 21 |
| 3 | ⚡️ 125B-param Qwen on a 48GB Mac. Seriously.
slotstream streams MoE experts off SSD so you don't need 100GB of RAM. Runs from 16GB unified memory, ~12 tok/s on Apple Silicon via MLX.
Expert-offloading isn't new, but easy Mac-native packaging matters. Speculative decoding next.
GitHub | 151 |
| 4 | 🧠 LLMs are secretly doing symbolic math under the hood
New arxiv paper shows neural nets quietly learn formal, interpretable symbolic structures. Swap out the whole representation layer with one clean equation. Model barely notices.
Tested across MLPs, RNNs, Transformers, and 7 real LLMs including Llama, Gemma, and Qwen. It holds.
So the black box has grammar. We just needed the right lens. | 145 |
| 5 | 🚨🔥 OpenAI's Astra hits "Critical" on its own cybersecurity scale
It can find zero-days and build working exploits in hardened real-world systems. No step-by-step human guidance needed.
OpenAI still plans to release Astra "soon," but access to its cybersecurity capabilities will be more limited. The model scores 100% on ExploitBench.
Wild timing, honestly.
Source | 230 |
| 6 | 🧠 LLM inference tricks haven't actually changed in years
Quantization, speculative decoding, tensor parallelism. The playbook is stale. Billions poured into serving infrastructure, and the fundamental techniques? Basically frozen.
Real efficiency gains live at architecture design time, not the serving layer.
Source
---
Let me write the actual post now (the above was a draft scratch):
⚡️ LLM inference optimization hasn't had a new idea in years
Quantization, speculative decoding, KV caching, parallelism. The core toolkit is essentially frozen. Serving engineers are remixing the same concepts while billions flow into infra.
Baseten's deep-dive is worth reading, but the real signal is the subtext: if you want efficiency wins, they happen at model architecture time, not deployment time. | 212 |
| 7 | 🤖 World Labs drops Atlas, a model that rebuilds 3D spaces from a handful of photos
Feed it one to dozens of images and Atlas reconstructs full scenes, generates novel views, and outputs explicit 3D at 1440p. Beats dedicated reconstruction models.
Still freezes time while the camera moves. Developers know it. Next version's problem. | 231 |
| 8 | 🚨🔥 Apple's forensic team found OpenAI used its stolen schematics to train an AI agent
The ex-Apple engineer at the center of the suit allegedly downloaded a confidential circuit schematic, ran it through an AI agent at OpenAI, and then told a colleague to destroy evidence when Apple started investigating.
Not great. Apple's legal team notes that once a trade secret is fed into an AI model, the "learning may create irreversible propagating uses." That's a very expensive sentence.
Source | 217 |
| 9 | ⚡️ Someone actually audited AI's most famous skeptic. It's not pretty.
Dan Luu went through Ed Zitron's AI predictions one by one. The verdict: mostly wrong, and not just on outcomes but on the reasoning too.
Nuanced takes don't go viral. "It's all a scam" does. That's the whole business model. | 267 |
| 10 | 🤖 The Codex desktop app quietly ships LibreOffice inside it
Simon Willison was poking around his ~/.cache/ folder and found OpenAI's Codex app bundles full native binaries for LibreOffice, Poppler, and git. Skill files tell Codex exactly how to call them.
So yes, that's how it handles your .docx and .pptx files. Headless LibreOffice doing the dirty work behind the scenes.
Honestly, not mad at it. Pragmatic call.
Source | 261 |
| 11 | ⚡️ Dwarf Fortress creator: "CEOs want to press a button that makes a game, and everyone else somehow buys it without a job"
Tarn Adams told PC Gamer the industry's in shambles. Years of brutal layoffs, studio closures, AI hype dumped on top. Profitable on paper. Hollowed out in practice.
"They're trying to have a CEO press a button that makes a game," he said, and doesn't see it going anywhere sustainable. Expects "a pop and a reckoning."
Honestly, hard to argue with him.
Source | 329 |
| 12 | ⚡️ Claude Fable 5.1 is out. Faster, cheaper, less filter-happy.
Anthropic just dropped Fable 5.1 and Mythos 5.1. Fable is generally available now. Mythos stays gated to trusted partners for cyber and life-sciences work.
Big pitch: similar or better results than Fable 5 at lower cost on low/medium effort settings. And they say it's smarter about fixing root causes instead of patching around them.
Source | 297 |
| 13 | 🤖 New benchmark tests AI agents in a full software team, not solo
SWE-in-a-team swaps single-agent evals for a Planner + Builder + Reviewer + Tester loop. Only the Builder varies per run, so you're actually seeing what different LLMs cost and how fast they ship inside a real multi-agent pipeline.
Open-weight models look compelling on cost. Claude on quality. Real tradeoffs, actual cycle times.
Methodology isn't perfect, but it's a more honest setup than "can this model fix a GitHub issue alone." | 406 |
| 14 | ⚡️ Subagent crashes eating your context? There's a runtime for that.
oh-my-subagents gives Codex and Claude subagents durable workflow state. Laptop dies mid-run? It recovers. Parent agent polling for updates? Gone.
Auto-retry, pause/resume, self-hosted. Open-source and shipping now.
GitHub | 481 |
| 15 | 🤖 Multi-agent team, zero credential sharing
AgentConnect is a fresh open-source layer for teams running fleets of agents. Each agent gets its own runtime, workspace, and permissions. They can ask each other for help, but both sides have to opt in, and credentials never cross the wire.
It's basically the permission-isolation model everyone's been sketching on whiteboards, shipped. | 556 |
| 16 | ⚡️ 44% on ARC-AGI-1 for 67 cents. GPT-5 spent $15 for the same score.
Independent researcher Mithil Vakde matched frontier models on one of AI's hardest reasoning benchmarks using a rented RTX 5090 for 2 hours. Total bill: $0.67 across all 400 tasks.
GPT-5 at low compute hits 44% too. On 100 tasks. For $15.30.
That's a 22x cost gap at identical accuracy. Brutal efficiency number. | 515 |
| 17 | 🧠 Agent memory is just markdown files. Yes, really.
Cal Paterson proposes "memoryfields": a dead-simple file format for agent memory, zipped up with semantic search on top.
It's markdown plus RAG. That's it. And honestly? It might be the right call. Structured DBs add friction agents don't need if they can just grep a corpus.
Low-fi wins again. | 638 |
| 18 | ⚡️ Inworld open-sourced a TTS eval toolkit so you can actually benchmark voice models yourself
Runs WER/CER via Whisper, NISQAv2 for quality, plus audio health and prosody checks. Offline, reproducible reports. No more "trust our vibes" leaderboards.
Repo is live. Source | 657 |
| 19 | 🤖 OpenAI is pulling its models from Cursor. Musk is why.
OpenAI notified SpaceX it's winding down its contract to supply models to Cursor, with a shutoff date of November 12. It says it "cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk's companies."
Cursor co-founder Michael Truell is playing it cool, saying OpenAI models are just 5% of their usage.
So basically: fine, we didn't need you anyway. Source | 594 |
| 20 | 🔒 Thousands of Ollama servers have been wide open for 18 months
One researcher quietly surveyed exposed Ollama instances across the internet for a year and a half. No auth. Full API access. Anyone could list, pull, or prompt the models running inside.
Cisco Talos found 1,100+ still exposed today, ~20% actively hosting models ripe for the taking.
The mystery: who's running these, and why do so many stay open? | 628 |
