Show HN: We Build Shareable Context Layer for Agent Teams
Article URL: https://github.com/volcengine/OpenViking Comments URL: https://news.ycombinator.com/item?id=50410477 Points: 1 # Comments: 1
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The release adds the Kimi-K3.1-Pro text model to llama.cpp, replacing full-attention MLA layers in K3 with DSA (Dynamic Sparse Attention) layers featuring a lightning indexer. The indexer uses an additive mask over the cache and reuses sparse flash attention paths, while preserving existing KDA layers and MoE components. The model's architecture includes four indexer tensors per DSA layer, hybrid memory for caching, and maintains quantization integrity by excluding indexer parameters from quant overrides. Verified against HF checkpoint with identical PPL beyond context length, and supports multiple platforms including macOS, Linux, Windows, Android, and iOS.
vLLM Delivers Day-0 Support for Kimi K3.1 Pro
vLLM supports Kimi K3.1 Pro on day 0: sparse attention on a KDA hybrid, a packed KV cache with FP8 or NVFP4 latents and MXFP4 index keys, token-sharded prefill, decode context parallelism, and fused low-latency decode.
The Moonlight Side announced that Kimi has officially integrated with Google Gmail, Google Drive, and Google Calendar.
On October 29, the Moonlight Side announced overseas that its AI assistant, Kimi, has been fully integrated into Google’s three major office services: Gmail, Google Drive, and Google Calendar. Users can enable this integration simply by making a few settings on the Plugins page. According to the official statement, this integration expands Kimi’s application scenarios, allowing users to quickly summarize Gmail email conversations, precisely locate needed information, and improve efficiency in handling tasks related to emails, cloud storage, and calendars.
DouBao Work has officially integrated with DeepSeek, Kimi, and GLM, making enterprise procurement hassle-free.
On October 29, according to Kuai Technology, DouBao Work under ByteDance launched a brand-new third-party large model service for corporate clients. This service breaks the previous practice of companies separately purchasing models from different vendors; users can now seamlessly switch among multiple mainstream large models within the same workspace, tailored to tasks such as long-text processing, code writing, and logical reasoning. The initial rollout includes three industry-leading models.
[AINews] Anthropic ends Coding Interviews: "Hunger beats tenure"
Since coding is solved, why code for the job?
Is Anthropic the best business in the world?
Lots of visuals to use and learn with.
Regarding the Kimi quota issue, Anthropic has found a solution.
After the release of K3.1 Pro, due to numerous users reporting that their quotas were being depleted too quickly, Moonshot AI urgently issued an announcement stating, “The issue lies with us, and we should have fixed it sooner.” Alongside compensating affected users, Moonshot AI also indicated that it would “reduce unnecessary sub-agent calls and output lengths.” Just two days before the model went live, Anthropic published an article titled “Building agents: When to fold subagents into your main agent loop,” which addressed precisely the topic of sub-agents. Anthropic concluded that, by comparing Claude deployments across multiple companies, they found that a single agent equipped with specific skills actually outperformed “a group of...”
Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context
Mirendil released Fern 0.1, its first model, pretrained from scratch with a 10M-token context window. It targets agentic AI R&D work, scores 100% on RULER single needle at 10M, but trails frontier models on agentic coding. The post Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context appeared first on MarkTechPost.
“Achieving high scores” does not equate to “being able to deliver”: AI rankings are undergoing a reassessment of credibility.
Old benchmarks approach perfect scores, yet new benchmarks consistently fall short in real-world performance. As large-model evaluation itself has become a business, companies are shifting their selection criteria from ranking-based assessments to cost‑benefit analyses.
Regarding the Kimi quota issue, Anthropic has found a solution.
Why isn’t your quota being used? Most of it is going toward “holding ineffective meetings” for sub‑agents.
In the era of large models, who will become that “iPhone” no one is willing to replace?
The champion on the AI rankings may change, but the products users rely on every day don’t necessarily. The next round of competition in large models will hinge not just on whose model is stronger, but on turning a single use into a habit.
The era of profiting from token flipping is over—what few viable paths remain for AI intermediaries?
AI Hub seeks new avenues.
[AINews] Anthropic files for $2T IPO in 2 weeks: 1400% growth rate, 48% of revenue through cloud partners, 85% margin, $1B op profit in Q2, $134B cash in bank and $10B lifetime burn
The S-1, the numbers, and the reactions.
Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing
Baidu has released Unlimited OCR 2, an MIT-licensed 3B OCR model that parses hundred-page documents in one request. Its SR-SWA attention keeps per-step decoding cost nearly flat, scoring 77.1 on MarathonDocBench The post Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing appeared first on MarkTechPost.
Just now, GPT-6.1 Terra was released at a discounted price, and the salaried worker model of the Agent era has arrived.
OpenAI has once again raised the bar for the cost-effectiveness of large models. #Follow iFanr’s official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered to you第一时间.
Morning Briefing | Apple responds to cracks in the iPhone Duo’s inner screen / Anthropic files for a public IPO, aiming for the largest IPO in history / Xpeng’s Robotaxi receives approval for Level 4 demonstration operations
· OpenAI releases GPT-6.1 Terra, with API prices cut in half and faster computer performance · Apple officially rolls out iOS 27.2: Apple Music gets a new black-and-white design, and the iPhone Duo now supports the Apple Pencil · Huawei’s Enjoy series has cumulatively shipped over 160 million units #Follow iFanr’s official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered to you第一时间.
[Titan Morning Brief] To address large-scale redemptions by non-bank institutions, the People’s Bank of China has launched a new tool to stabilize the bond market; Audi officially announced in China: the four-ring logo returns to FAW, while the lettering brand goes to SAIC; CICC’s “three-in-one” restructuring has completed settlement, with some newly issued shares becoming tradable on October 29.
Shangwei New Materials and Unitree Robotics have both posted cumulative deviation values exceeding 30% over three consecutive trading days, prompting an urgent risk warning; Cambrian’s chairman has refuted rumors claiming “more than one million chips will be shipped next year”; WuXi AppTec reported a 137% year-over-year increase in Q3 net profit and raised its full-year revenue guidance; Anthropic has filed its IPO registration documents, planning to list on Nasdaq under the ticker symbol “ANT”; Apple is reportedly testing a large‑size, foldable‑screen iPad; and, in a world first, Hong Kong has approved the real-time 24/7 trading of its first batch of compliant publicly offered digital funds.
Import AI 478: Math for alignment; ProgramBench; and Mirendil shows what RSI means
What is “night” to a machine?
Kimi releases K3.1 Pro, offering more features at a lower price to challenge Opus 5.5.
Author | Yu Hang Yuan Editor | Jing Yu On the morning of October 23, Kimi officially posted a 24-second video on X and its Video Account. The scene shows a retro‑style vintage computer, with Kimi Code running on the screen and lines of Three.js code being typed into the terminal, generating a complex 3D scene. Keen-eyed netizens quickly spotted the answer in the model bar at the top left: “Model: Kimi K3.1.” Kimi’s official teaser video for K3.1 | Image source: X @Kimi_Moonshot Using an antiquated machine to run some of today’s most cutting‑edge 3D code—this stark contrast itself conveys what Moonshot AI is trying to say. A dozen hours later, just after midnight on October 24, right on Programmer’s Day, Kimi K3.1 Pro officially launched, accompanied by a Highspeed version that’s two to three times faster. The company promises that the full model weights will be released before November 4. More than three months have passed since the previous generation, K3, was unveiled. In those three months, Moonshot AI has experienced more than most companies see in an entire year. 01 “More for the same price”—what’s new in 0.1? First, let’s clarify what K3.1 Pro is. It isn’t an entirely new model. Moonshot AI explicitly states that K3.1 Pro is a post‑training variant of K3; the underlying architecture remains unchanged—still a hybrid expert architecture with 2.8 trillion total parameters and 104 billion activation parameters, still featuring a 1 million‑token context window, and still natively supporting image and video inputs. What has changed is the post‑training phase. According to the company, they’ve invested “significantly increased post‑training compute power,” conducting large‑scale joint reinforcement learning across hundreds of downstream tasks. To support this, they completely reengineered the underlying infrastructure—including the reinforcement learning framework, sandbox environment, and long‑context reasoning capabilities. In essence, the same brain has been retrained from scratch on how to perform its tasks. The results are reflected in benchmark scores. On Terminal‑Bench 4.0, which evaluates coding agents, K3.1 Pro achieved 65.7%, surpassing GPT‑6 Astra’s 58.2% and falling short of Claude Opus’s 66.4% by less than one percentage point. As for the previous generation, K3…