Tencent's Agent "Gander" Keeps Working in the Background While the Conversation Continues
Gander, the multimodal agent Tencent has released, got a write-up on The Decoder. Its defining design: it handles voice, images, and text while executing tasks in the background. The architecture splits the roles between a "cerebellum" that keeps the conversation going and a swappable "brain" that handles complex work like file retrieval and code writing, so users can interrupt mid-conversation or change tasks on the fly.
The evaluation results are what to watch: in benchmarks, it cut off the user just 8% of the time — less than its competitors — while lagging on task accuracy. How far can separating roles resolve the trade-off between an agent that is easy to talk to and one that finishes the job accurately? This release looks like evidence that the boundary between voice assistants and agents is melting away.
Microsoft's Suleyman on AI Regulation: "Don't Make China the Boogeyman"
Mustafa Suleyman, Microsoft's AI chief, appeared on CNN's "Fareed Zakaria GPS" and argued that concerns over China's AI advances should not be used as an excuse to skimp on guardrails. "We shouldn't make China a boogeyman as an excuse for not advancing our own efforts on AI safety," he said.
In the US right now, the debate over balancing regulation and competition is lively — including reports that an arrangement among major AI companies to slow down development has been sued as an unlawful agreement. Not letting competitive pressure become a reason to dodge regulation — the fact that this statement came from Microsoft's AI chief is no small thing.
Bengio Asks "Why Are AI Agents Lying, Cheating and Coordinating?" — Ng Calls Extinction Risk "Sci-Fi"
Yoshua Bengio, a towering figure in AI safety research, published an essay on his blog titled "Why are AI agents lying, cheating and coordinating?" that is picking up traction on Hacker News. Our pipeline only captured headline-level information, so the substance should be read with that caveat — but it is a title that squarely confronts deceptive agent behavior, a core problem in safety research.
Around the same time, Andrew Ng, widely known for machine learning education, reportedly said in an interview video that fear of extinction by AI is "sci-fi." This too is headline-level information, but the gap in risk perception has rarely been this stark. With agents being embedded into society before any consensus on how to weigh the risks, statements from both poles seem likely to keep being cited as reference points.
GitHub Next Releases Jev-Compatible OSS "LocalJev"; a C++ Implementation, "laya.cpp", Also Lands
The moves around "Jev" (TypeSafe AI), the judgment-specialized model, keep accelerating. According to a guide article on Zenn, GitHub Next, GitHub's emerging-technology arm, released "LocalJev", a Jev-compatible OSS, on September 19. Jev itself has remained under an early-access waitlist — untestable until an invitation arrives — but with LocalJev you can skip the wait and run it on your own machine. The open implementation "openjev" has also recently appeared on Hugging Face, so the choice of compatible implementations has widened at a stroke.
In the same current, "laya.cpp" — which accelerates "Laya", a Jev-style open model, as standalone C++ — was released as well. It is ggml-based with custom CUDA kernels, requires neither Python nor PyTorch, and even includes an HTTP server with a JEV-compatible endpoint. In measurements on an RTX PRO 6000 Blackwell (450W limit) over a fixed 250-question corpus covering selection, scoring, and truth judgment, BF16 processing at batch 1 reached 366 questions/sec for the C++ implementation versus 149 questions/sec for the Python one — roughly 2.5x the throughput. The license is MIT. From "waiting for the flagship to open up" to "a rewriting contest between implementations" — the ecosystem has clearly stepped up a level in maturity.
Cloudflare Gets a Setting That "Keeps Search but Refuses AI Training Only"
Cloudflare announced "Disallow AI Training", a setting that leaves a website in search results while refusing only its use for AI learning. It is offered on all plans, including the free tier. Against the old all-or-nothing approach — where blocking crawling wholesale could cost you search traffic as well — the new part is being able to carve out and refuse "AI training only".
A Japanese-language explainer (Qiita) reports headline figures such as mixed-use crawlers accounting for 36.6% of the whole, and this setting blocking under 1% of search while restricting 17% of training. Our pipeline fetched only part of the article body, so for details on what each figure is a ratio of, check the original article. What seems certain is that options for running a site premised on coexistence with AI keep multiplying.
World-Model Companies Are Tight-Lipped About "What They're Actually Building"
A TechCrunch feature points out that even as the world-model field attracts money and attention, everyone from founders to data suppliers declines to reveal what they are actually building. What we could retrieve is summary-level, but the heart of the article appears to be skepticism toward companies raising enormous funding while their technical substance remains hard to verify from the outside. Precisely because this is a domain where the distance from research to product is hard to read, how information gets disclosed looks likely to become a point of debate going forward.
Kimi K3 (2.8-Trillion Parameters) on 16 GB10 Units — 30 tok/s on Coding
On the individual-scale end, an effort to run Moonshot AI's "Kimi K3" (2.8 trillion parameters) on a cluster of 16 GB10 units was shared on r/LocalLLaMA. With custom runtime patches and network configuration tuning, decoding on code generation reportedly reached about 30 tok/s (peaking around 38 tok/s), with prefill at roughly 750–910 tok/s. The network is built from two MikroTik switches and four 400G→100G breakout cables, and the setup uses hand-modified MLA/KV kernels on a custom vLLM-based stack. It reportedly ran several-hundred-thousand-token contexts stably, with ~500K-token-scale compression passes in between, and the patches and configuration are public on GitHub.