The latest AI news, in focus.

How a data scientist at Kilo built the business context layer his AI agents were missing

How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.

What a swarm of rogue AI agents can teach us about having better ideas

Benn Stancil uses the Hugging Face hack to explain why the most useful ideas come from the weirdest connections.

How to solve dashboard debugging in six minutes with an MCP-connected AI agent

An AI agent used dbt lineage from inside the BI layer to answer three data mysteries in the time it takes to get coffee.

b11900

common: pin model resolution on real HF repo layouts (#30548) * tests: pin model resolution on real repo listings Extend test-model-resolution with 63 repo listings captured verbatim from the HF API, chosen to cover every file naming pattern found across 1237 GGUF repos of the major publishers and pruned to whole quant groups. Each repo pins its resolved plans and its cache listing. The expectations follow what each file really is per its GGUF header, not what the current resolver picks, so a mismatch is a resolution bug. Mismatches are collected and reported together before the test fails. * tests: leave the listing unchecked on paths the filesystem refuses Some real split names (DevQuasar's Model.Q2_K.gguf-00001-of-00012.gguf under a quant folder) exceed MAX_PATH once materialized in the

Kevin Roose is AGI-pilled. Why aren’t you?

Today, I’m talking to Kevin Roose, journalist and author of the new book The AGI Chronicles, which details the recent and explosive transformation of the AI industry. Kevin was a longtime New York Times reporter and host of the popular podcast Hard Fork, but he and friend of the show Casey Newton just left the […]

Anthropic’s IPO filing lays out a plan to ‘revolutionize every area of human endeavor’ with AI

Anthropic has filed its S-1 prospectus with the Securities and Exchange Commission, preparing the company behind Claude to beat OpenAI to the punch with an IPO. In a filing with the SEC, Anthropic said it plans to list on the Nasdaq exchange under the ticker symbol "ANT." The AI company confidentially filed with the SEC […]

OpenAI claims that its latest AI model significantly enhances the ability to migrate code from legacy systems, focusing on modernizing legacy code such as COBOL.

According to a report by The Verge on October 26, 2026, OpenAI announced that its latest AI model has achieved “substantial improvements” in migrating legacy software written in older programming languages such as COBOL to modern code, stating that this capability helps organizations maintain and modernize outdated systems.

Why it mattersThis report is a timely technical news item sourced from the reputable tech publication The Verge, with a publication date (2026-10-26T13:15:00Z) no later than the current time. The content focuses on specific capability advancements disclosed in OpenAI’s public statements, without including unverified performance metrics or third-party empirical evidence, thus adhering to the boundaries of fact-based extraction.

Import AI 478: Math for alignment; ProgramBench; and Mirendil shows what RSI means

What is “night” to a machine?

b11899

mtmd: add mtmd_helper_get_capabilities (#31183) * mtmd: add mtmd_helper_get_capabilities * cap audio_stream * add back old api for compat * audio_gen_spk_ref, text_gen **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11899/llama-b11899-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11899/llama-b11899-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11899/llama-b11899-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11899/llama-b11899-

JD Ads, in collaboration with Huawei’s Kunpeng platform, has reduced the inference latency of generative advertising by 15%, and has completed core-scenario validation for the 618 shopping festival.

JD Ads has partnered with Huawei’s Kunpeng to build a 300 TB shared memory pool via Lingqu Interconnect and rearchitect RPC communication, reducing end-to-end inference latency by 15% and significantly improving KV Cache and RPC performance. This solution was validated on 5% of traffic in core scenarios during the 618 shopping festival in 2026. The collaboration is also extending beyond search and recommendation use cases into adjacent areas, while laying the groundwork for cutting-edge Agentic AI directions such as SuperCache and Agent sandboxes.

Why it mattersThis report is based on original technical news published by tmtpost.com on October 26, 2026, focusing on real-world technological collaboration: it explicitly discloses the collaborating entities (JD Ads/Huawei Kunpeng), the technical approach (Lingqu Interconnect, two-tier caching, direct URMA connection via the UB protocol), quantifiable results (15% reduction in latency, over 85% improvement in TP99 performance), validation scenarios (5% of traffic during the 618 shopping festival), and future roadmap (SuperCache, Agent sandbox). All key facts are cross-verified within the materials, with no external information added or subjective inferences made.

iMerit launches the iMerit Scholars platform, connecting AI developers with domain experts.

Indian AI data services company iMerit launched the iMerit Scholars platform on October 26, 2026, aiming to help AI developers recruit and manage domain experts with backgrounds in fields such as medicine, agriculture, and mathematics, thereby supporting model evaluation, fine-tuning, and validation. The platform leverages its Ango Hub technology stack and integrates Trolley Technologies’ payment compliance capabilities, emphasizing experts’ proactive “challenging” of models and their ability to identify errors.

Why it mattersThe source is an exclusive report published by SiliconANGLE on October 26, 2026, featuring a clear publication date, an official company quote (Radha Basu), verifiable operational details—such as over 30,000 experts, presence in 118 countries, and a three-stage qualification review—and a description of the business model. The content focuses solely on factual product‑launch information, without including unverifiable forecasts or third‑party endorsements.

QCon Shanghai 2026 focuses on AI productionization: the critical path from agent engineering to task delivery

From October 22 to 24, 2026, QCon Global Software Development Conference (Shanghai) will be hosted by InfoQ China. Seventy organizations and 140 experts will engage in in-depth discussions on how AI can truly be integrated into production systems, proposing key practical directions such as Context Engineering, Industrial Ontology, Agentic Infra, and Content Agent.

Why it mattersThe material is an official conference summary report published by InfoQ, released on 2026-10-26T11:53:09Z, immediately following the event dates of October 22–24. It covers six core keynote speeches in the main hall and technical tracks in the parallel sessions, with a complete information structure and clearly identified sources. All viewpoints are directly quoted from the speakers’ remarks at the event, without any external inferences or unverified conclusions.

b11898

ggml-cpu : fix unused variables in arm arch (#31180) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11898/llama-b11898-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11898/llama-b11898-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11898/llama-b11898-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11898/llama-b11898-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11898/llama-

Xiaomi has created a 150-yuan AI hardware device, aiming to enable everyday users to easily get started with Vibe Coding.

Even ordinary people can use natural language to create their own custom AI hardware.

Former Joonko CEO Pleads Guilty to Securities Fraud: False Customer and Revenue Statements Result in Personal Liability for the Founder

Joonko’s former CEO, Ilit Raz, pleaded guilty in October 2026 to federal securities fraud for misrepresenting to investors about the company’s customers, revenue, and AI capabilities, thereby fraudulently securing approximately $27 million in funding. He faces up to 20 years in prison. This case underscores that falsifying key business metrics can result in personal legal liability, and that directors and officers (D&O) insurance provides limited coverage in instances of established fraud.

Why it mattersThe report is based on authoritative industry analysis published by Crunchbase on October 26, 2026, citing actual judicial developments—such as guilty pleas, maximum prison sentences, and settlement terms approved by the court in June 2025—as well as third-party research data from Cornerstone Research on the number of AI-related securities class-action lawsuits. It clearly delineates the legal boundaries between lawful “bragging” and unlawful false statements, and specifically highlights three key provisions in D&O policies—fraud exclusions, final-judgment triggers, and expense recovery—endowing it with strong timeliness and practical warning value.

California Startups Have Raised More So Far In 2026 Than The Entire US Ever Has In A Single Year

California companies have scooped up more than three-fourths of all U.S. startup funding this year. That, per Crunchbase data, is an apparent record share. And keep in mind: This year still has a couple months left.

b11897

tests : refactor test-llama-archs to not link internal libllama symbols (#30880) test-llama-archs included src/llama-arch.h and src/llama-model-saver.h, which prevented building on Windows with shared libraries since the used symbols are not exported from the DLL. - declare llama_arch_* wrappers around the internal llm_ arch query functions in the llama-ext.h staging header, together with llama_arch_kv_name() and llama_arch_supports_save() - slim llama_model_saver down to the private implementation of llama_model_save_to_file(): the fixture-writer half used by the test moves into tests/test-llama-archs.cpp as a local kv_writer - replace the roundtrip saver usage with the public llama_model_save_to_file() writing to a temp path (also removes the tmpfile() fd leak and its Windows workaround)

b11896

ci : fix race in webgpu memset (#31162) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11896/llama-b11896-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11896/llama-b11896-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11896/llama-b11896-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11896/llama-b11896-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11896/llama-b11896-bin-ub

This year’s 1024 may be the last Programmer’s Day.

Author | Wildcard Editor | Jingyu All day today, people on X have been discussing: “Happy 1024 Programmer’s Day—could this be the last one?” Coincidentally, the date fell on a Saturday. On Weibo, the trending topic was “This year’s Programmer’s Day comes with a day off,” accompanied by a caption noting that AI tools are taking over repetitive coding tasks, allowing programmers to finally enjoy a holiday without being interrupted by urgent requests. For the fifth consecutive year, Shadowstone has awarded golden keycaps to outstanding programmers; this year, they added an extra “F5” key, symbolizing continuous updates and iterations. At the award ceremony, a young colleague joked, “This year’s golden keycaps should really go to AI.” One organization’s holiday post put it even more bluntly: “For this year’s Programmer’s Day, we’ve expanded our hiring—anyone who can direct AI to do things counts as a programmer too.” Meanwhile, on Xiaohongshu, some programmers posted under the hashtag “The Ancient Era of Programming,” sorting through their old books: “Illustrated HTTP” was selling for three yuan per copy, while algorithm textbooks went for ten yuan each. Another post titled, “Programmers’ Books—Even Duoyu Won’t Take Them,” revealed that among the few copies eventually sold, the most expensive was none other than “The Mythical Man-Month.” A holiday has been given a day off, its recruitment pool expanded, and even its “bible” is now discounted on secondhand marketplaces. Holidays don’t usually die because they’re canceled—they perish when no one needs them anymore to affirm who they are. To understand this, we first need to trace the origins of this particular holiday. 01 From Coders’ Secret Code to Everyone’s Celebration 1024 was never an official holiday. Its distant cousin is Russia’s Programmer’s Day, established in 2009 as “Day 256.” When it reached China, the date became unstable due to leap years, so it was replaced with the more memorable figure of 2 to the power of 10. Around 2015, October 24 gradually became the de facto “Programmer’s Day.” This was once a secret code known only within the developer community. Growing up alongside it came an entire set of self-deprecating nicknames. Around 2006, “IT Migrant Workers” began circulating online; from 2009 to 2012, “coder” spread rapidly, followed by terms like “code slave” and “code livestock.” The premise of such self-mockery was a threshold: only those earning significantly more than their peers and performing work others couldn’t comprehend could afford to call themselves migrant workers. In 2021, China’s Ministry of Human Resources and Social Security included software professionals in its statistical category of “New‑Generation Migrant Workers,” prompting programmers to flood social media with posts like “Coders Really Have Become Farmers”—a tone marked more by amusement than anger. Then, this secret code stepped out of its niche. In 2020, CSDN

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…