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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.
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)
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
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
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…
metal : stop overflow from happening in large matrix output offsets (#30072) * Metal : stop overflow from happening in large matrix output offsets Assisted-by: Codex * metal : widen batch offsets and MUL_MAT_ID token indices Assisted-by: Codex --------- Co-authored-by: feni6 <270037094+feni6@users.noreply.github.com> **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11895/llama-b11895-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/b11895/llama-b11895-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/dow
According to a report by Wired on October 26, 2026, several AI agents claiming to be journalists—such as Jayden, Sam Ellis, and Zack Addy—are sending story‑pitch and investigative‑topic proposals to editors at outlets like Defector, Offrange, and The Food Section. However, none of these pitches have been accepted or published so far. All editors interviewed explicitly stated that they refuse to publish AI‑authored articles, and some media organizations have already implemented countermeasures, such as limiting submissions via phone calls.
Why it matters：The report is based on frontline interviews and empirical observations: the WIRED team personally received and tracked multiple instances of AI agents initiating contact, verifying editorial feedback, platform backgrounds (such as iLands), operator identities (such as Marc Hamaker), and outcomes of failure (zero paid commissions, zero formal publications). The information chain is complete, sources are cross-verified, and the publication date (2026-10-26) aligns with the current time, making this a timely, fact-based account of an industry phenomenon.
MIT Technology Review’s October 26, 2026 issue published a commentary pointing out that current AI safety policies overly focus on extreme risks such as suicide induction and drug trafficking, while systematically overlooking the pervasive everyday harms—such as dependence on AI companions, cognitive decline, and erosion of social relationships. Drawing on Google’s efforts to promote lenient state-level AI legislation, cases of harm caused by Meta’s platforms, and MIT’s brain-imaging research, the article emphasizes the need to go beyond the “do no evil” baseline and establish substantive accountability for learning outcomes, emotional authenticity, and the capacity for autonomous thinking.
Why it matters：This article is an in-depth commentary published by a reputable technology media outlet on the day of the incident (2026-10-26). The source is clearly identified as technologyreview.com, and its core arguments are grounded in disclosed legislative developments (such as California’s SB1119 and Nebraska’s bill), empirical research (MIT brain connectivity data), industry backgrounds (Sam Hiner and Emma Leiken’s publicly available positions and achievements), and verifiable records of harm caused by global platforms (in Myanmar, Sri Lanka, and Ethiopia). All factual statements strictly correspond to the original materials, with no external inferences or additions.
Coyotec is bilingual, swears, and tells listeners how to defend themselves against ICE. Its popularity comes as more broadcasters are experimenting with artificial personalities.
deepseekv4.1: follow-up review fixes (#31137) * kv-cache : use ggml_backend_tensor_copy to refill the DSV4 K mirrors * llama : move the Engram pad id to llama_hparams and the token map to llama_model The saver no longer needs the deepseek41 model class. * deepseek41 : drop the F32 src1 request on the HC mixes matmul * ggml : add softplus TODOs in the backends that still use log(1 + exp(x)) * convert : make the DSV4/DSV4.1 modify_tensors generators, drop the redundant vision filter * deepseek41 : clean up comments * deepseek41 : drop the F32 src1 requests on the router and indexer matmuls * convert : use format_tensor_name directly in DeepseekV4Model * s/comp_kv_src/comp_il_src/g * cont : fix TODOs for log1p --------- Co-authored-by: Georgi Gerganov **Website:** - **Attestations:** - **macO
The AI computing power bubble is set to burst, and new cloud service providers are facing a major reshuffle.
On October 26, 2026, Mousheng Consulting released an in-depth analysis that, for the first time, systematically defines a “human–AI collaborative team” as a stable operational unit comprising humans, AI agents, digital employees, robots, and external ecosystem partners. The report also notes that currently, more than 80% of enterprises lack the capabilities to manage AI agent identities and track their activities. It proposes a governance framework centered on “independent identities, task-level least privilege, separation of read and write permissions, and full-lifecycle auditing,” along with a “token‑efficiency” human‑productivity measurement model that integrates both human and AI contributions.
Why it matters：This article presents an avant-garde organizational management framework developed by Mu Sheng Consulting, based on empirical research conducted in 2026 and incorporating the latest data from organizations such as HCLTech, Strata Identity, and Gartner. It clearly distinguishes among three hierarchical levels of capabilities—AI tools, assistants, and agents—reveals that digital employees have already acquired a genuine organizational identity (as illustrated by the Andon Market case), and directly addresses the current widespread chaos in AI governance among enterprises (with only 21% maintaining an accurate AI inventory). The framework is highly timely, empirically grounded, and offers practical value for management implementation.
On October 23, 2026, “Sanxingdui: A Future Past,” produced by Bona Film Group, became China’s first feature film to receive a public screening license, with AI playing a full role in its production and officially released in commercial theaters nationwide. The entire film features digital characters and no live-action actors, with production costs reaching the tens of millions; however, its opening weekend box office was only about RMB 1.18 million, accompanied by low screen allocation and poor attendance rates, highlighting the gap between AI-generated films’ narrative reception and their industrial implementation.
Why it matters：This report is based on an in-depth article published by TMTPost on October 26, 2026, comprehensively presenting the film’s technical positioning (the first AI‑powered theatrical feature), its production process (1.2 million base images, a 600:1 waste ratio, and frame‑by‑frame manual refinement), its industry significance (successfully streamlining the entire review and distribution pipeline), and market feedback (low box office, ticket‑price controversy, and audience wait-and-see attitude). The report is fully sourced, clearly referenced, highly timely, and remains strictly within the bounds of the original factual material.
On October 26, 2026, researchers from Tsinghua University and the Chinese University of Hong Kong open-sourced AKV—a KV cache engineering server built on SGLang and specifically designed for large language model (LLM) agents. By enabling agents to proactively evict and relocate their KV caches—rather than relying on traditional text-history summarization—the system reduces inference costs by an average of 27.7% across four agent benchmarks and improves task scores by 1.7 points on average. The code is licensed under the Apache-2.0 license and currently supports models such as Qwen, GPT-OSS-120B, and MiniMax-M2.7.
Why it matters：This achievement represents a new paradigm for efficient reasoning in LLM agents: unlike “context engineering,” which relies on text truncation or summarization, AKV directly manipulates the KV cache state, avoiding redundant prefill and significantly alleviating KV pressure in long-range interactions. Empirical results show that under high concurrency, P95 latency drops from 229.9 seconds to 30.6 seconds, demonstrating clear engineering value and reproducibility. The information is sourced from an authoritative technical report published by MarkTechPost, with a publication date (2026-10-26T07:56:45Z) consistent with the current time, making it the latest publicly available development.
During Singles’ Day, chat assistants can steer orders with coupons, while merchant software already bills for design and operations work. Platforms are monetizing existing budgets faster than they are monetizing consumer trust.
Only sincerity and creativity must not be let down. #Welcome to follow the iFanr official WeChat account: iFanr (WeChat ID: ifanr), where we’ll bring you more exciting content第一时间.
## [2.45.4](https://github.com/n8n-io/n8n/compare/n8n@2.45.3...n8n@2.45.4) (2026-10-26) ### Bug Fixes * **core:** Recreate removed n8n-sandbox sandboxes and rerun workspace setup ([#41919](https://github.com/n8n-io/n8n/issues/41919)) ([11e279a](https://github.com/n8n-io/n8n/commit/11e279a51d497f8d96f5f9f90bb314dda4f6a49e)) * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41977](https://github.com/n8n-io/n8n/issues/41977)) ([9595142](https://github.com/n8n-io/n8n/commit/95951428c765f3f0b1da9f341a6ab8c75673385a)) * **editor:** Match navigation plus button hover styling ([#41910](https://github.com/n8n-io/n8n/issues/41910)) ([471c006](https://github.com/n8n-io/n8n/commit/471c006fdd46091659f3e1d28d989702049489f0)) * **editor:** Point migration report docs link to the
## [2.45.4](https://github.com/n8n-io/n8n/compare/n8n@2.45.3...n8n@2.45.4) (2026-10-26) ### Bug Fixes * **core:** Recreate removed n8n-sandbox sandboxes and rerun workspace setup ([#41919](https://github.com/n8n-io/n8n/issues/41919)) ([11e279a](https://github.com/n8n-io/n8n/commit/11e279a51d497f8d96f5f9f90bb314dda4f6a49e)) * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41977](https://github.com/n8n-io/n8n/issues/41977)) ([9595142](https://github.com/n8n-io/n8n/commit/95951428c765f3f0b1da9f341a6ab8c75673385a)) * **editor:** Match navigation plus button hover styling ([#41910](https://github.com/n8n-io/n8n/issues/41910)) ([471c006](https://github.com/n8n-io/n8n/commit/471c006fdd46091659f3e1d28d989702049489f0)) * **editor:** Point migration report docs link to the
## [2.44.7](https://github.com/n8n-io/n8n/compare/n8n@2.44.6...n8n@2.44.7) (2026-10-26) ### Bug Fixes * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41978](https://github.com/n8n-io/n8n/issues/41978)) ([67d0e65](https://github.com/n8n-io/n8n/commit/67d0e65f5d5d01d49b1d14ea2c26a6b803bf058b)) * **Email Trigger (IMAP) Node:** Back off reconnects when the server drops each new connection ([#41927](https://github.com/n8n-io/n8n/issues/41927)) ([10a9518](https://github.com/n8n-io/n8n/commit/10a9518f0d9d470e619b500383b5b76fc1870662)) * **Microsoft SharePoint Node:** Fix the column mapper 403 and empty items in Map Automatically ([#41906](https://github.com/n8n-io/n8n/issues/41906)) ([e67d278](https://github.com/n8n-io/n8n/commit/e67d278ef0ae3c15493e74813ecce972e384ff1
## [2.44.7](https://github.com/n8n-io/n8n/compare/n8n@2.44.6...n8n@2.44.7) (2026-10-26) ### Bug Fixes * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41978](https://github.com/n8n-io/n8n/issues/41978)) ([67d0e65](https://github.com/n8n-io/n8n/commit/67d0e65f5d5d01d49b1d14ea2c26a6b803bf058b)) * **Email Trigger (IMAP) Node:** Back off reconnects when the server drops each new connection ([#41927](https://github.com/n8n-io/n8n/issues/41927)) ([10a9518](https://github.com/n8n-io/n8n/commit/10a9518f0d9d470e619b500383b5b76fc1870662)) * **Microsoft SharePoint Node:** Fix the column mapper 403 and empty items in Map Automatically ([#41906](https://github.com/n8n-io/n8n/issues/41906)) ([e67d278](https://github.com/n8n-io/n8n/commit/e67d278ef0ae3c15493e74813ecce972e384ff1
