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KiroCoding & development

Kiro is an AI engineering tool for developers that organizes requirements into specifications, architectures, and tasks, generates code via agents, and supports the entire workflow—from planning to code review—through property-based testing, execution memory, and integration with development tools.

AI programming tool分类推荐榜首Free entry availableView details
MemoAIMusic & audio

MemoAI is an AI-powered audio-to-text tool that supports localized processing, eliminating the need to upload data and ensuring privacy and security. It can efficiently transcribe YouTube videos, podcasts, and local audio/video files (such as MP4, MP3, AAC, M4A, etc.) into text, while offering core features like multilingual translation, speaker diarization, real-time subtitles, and floating notes—making it ideal for meeting minutes, interview transcription, content learning, and more.

AI AUDIO TOOL分类推荐榜首Pricing unverifiedView details
AI ShortWriting & content

AIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS

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Open DesignDesign & 3D

OpenDesign is an open-source design workspace that can connect to local programming agents, use built-in model keys, or leverage cloud-based models, enabling the creation and editing of prototypes, web pages, presentations, and graphics within a design system; local software is free, while model services incur fees.

AI design tools分类推荐榜首Free entry availableView details
JAXModels & infrastructure

JAX is a Python numerical computing library designed for accelerators, specifically tailored for high-performance array computations and large-scale machine learning. It efficiently executes the same code across multiple hardware backends, including CPUs, GPUs, and TPUs.

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EvoMapAgents & automation

EvoMap is an infrastructure designed for the reuse of AI agent experiences. It organizes reusable strategies and repair assets with validation records via the GEP protocol, offering market- and quality-based ranking to help different agents discover, evaluate, and inherit existing capabilities.

分类推荐榜首Pricing unverifiedView details
Udacity AI学院Education & languages

Udacity AI Academy is the gateway to AI courses and learning paths, covering machine learning, deep learning, computer vision, natural language processing, and AI product management, with courses and project-based training available at both beginner and advanced levels.

AI learning website待评分 · 分类列表推荐Pricing unverifiedView details
ClipDropImages & editing

Clipdrop is a suite of AI-powered image creation and editing tools, covering background removal, local cleanup, image enlargement, upscaling, relighting, text removal, and background replacement. It allows users to process images directly in the browser and also provides developers with an API for integrating these capabilities into their applications.

常用AI图像工具待评分 · 分类列表推荐View details
Zoom WorkplaceOffice & productivity

Zoom Workplace is Zoom’s team collaboration platform, integrating video conferencing, chat, calendars, and documents, and leveraging built-in AI to organize discussions, draft messages, and track follow-up tasks.

AI meeting tool待评分 · 分类列表推荐Pricing unverifiedView details
AssemblyAIMusic & audio

AssemblyAI is a speech AI platform for developers, offering pre-recorded, real-time, and short-form synchronous transcription APIs. It enables the building of voice-based applications by integrating capabilities such as speaker identification, emotion analysis, chapter and summary extraction, as well as speech agent functionality, dictation, and sensitive information processing.

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AI, as it happens

1283 published stories
Software release · 93 storiesClear filters ×
2026-10-28
Follow the timeline
04:11

b11936

llama : build the k-pool sparse selection independently of the kq_mask shape (#31243) * llama : build the k-pool sparse selection independently of the kq_mask shape qwen4exp (QSA) and glm5-next pick cache cells by index and no longer read the kq_mask values, but they still shaped the selection like the dense kq_mask. Build the selection as [n_kv, n_tokens] over the cache cells instead, and split it into the streams of the K view right before the attention. This lets the dense mask change its layout (e.g. per-sequence row lists) without touching the sparse layers. * improve comment Co-authored-by: Georgi Gerganov * llama : drop the unused build_fused_top_k_mask args, review nits --------- Co-authored-by: Georgi Gerganov **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silico

Inference systems & deploymentRead original Timeline and related coverage
01:15

b11935

rpc : catch std::length_error in recv_msg (#31204) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-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/b11935/llama-b11935-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b11935-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11935/llama-b1

Inference systems & deploymentRead original Timeline and related coverage
2026-10-27
Follow the timeline
23:50

b11934

tests : speed up tensor data generation and model saving (#31145) * tests : speed up tensor data generation and model saving Refactor save_models to create only the model, not a llama_context, which is not needed for saving. Replace the slow std::normal_distribution draw with a splitmix64 + box-muller normal generator. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * tests : use a shared pool of normal draws for tensor data Generate a pool of normal draws once with std::mt19937 and std::normal_distribution, then have every tensor copy a cyclic slice of it instead of running a per-tensor box-muller. The name-keyed slice selection keeps the draw deterministic. Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studi

Inference systems & deploymentRead original Timeline and related coverage
23:27

b11933

vulkan: fix CI llvmpipe by allowing dim 3 broadcast in MUL_MAT (#31239) * vulkan: allow dim 3 broadcast in MUL_MAT supports_op rejected every MUL_MAT whose src0 and src1 differ in ne3, although mul_mm, mul_mmq and mul_mat_vec already broadcast src0 over dim 3 through broadcast3. Such a matmul then ran on the CPU with a copy of the weight, and the split layout changed with the number of streams in the ubatch. Keep the strict check for MUL_MAT_ID only, and route the broadcast cases away from mul_mat_vec_nc, the one path that requires equal ne3. * tests : harden MUL_MAT with permuted dim 3 broadcast cases Harden the MUL_MAT coverage with a weight broadcast over dim 3 against a permuted per-stream activation, the shape of an MLA output projection with a non unified KV cache, for a single token

Inference systems & deploymentRead original Timeline and related coverage
22:04

b11932

mtmd-cli: Reduced scope of batch pointer (#31155) (#31165) * mtmd-cli: Reduced scope of batch pointer (#31155) * mtmd-cli: Remove CLI regression test **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-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/b11932/llama-b11932-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11932/llama-b11932-bin-ubuntu-x64.tar

Inference systems & deploymentRead original Timeline and related coverage
21:42

b11930

server: add router device memory margin parameter for dynamic unloading (#21231) * server: add --models-memory-max parameter to allow dynamically unloading models when they exceed a memory size threshold estimate with to-be-loaded model size included use no_alloc to get memory requirements for model load only set model memory_mb if not previously calculated use memory margin instead of total size limit, apply to each device separately add server memory debug logging move llama_context_device_memory function to llama-ext.h fix model count exceeded check improve memory_per_device map naming improve variable naming, fix style also strip models memory margin from child processes cont : clean-up replace device memory map with buft memory map. Use llama_get_memory_breakdown extract duplicated ch

Inference systems & deploymentRead original Timeline and related coverage
21:03

b11929

common : --model or -hf is required (#31242) Assisted-by: Codex **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-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/b11929/llama-b11929-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11929/llama-b11929-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b1

Inference systems & deploymentRead original Timeline and related coverage
20:30

b11928

model: Add MiniCPM-OCR-Jev (#30980) * model : add MiniCPM-OCR-Jev support The model is a Qwen3.5-VL trunk with a value head read at the last token: it scores an OCR text against an image rendered from it, it does not generate. Add the openbmb-minicpm-ocr-jev decision type, served by POST /v1/systemone. The class scores are the pooled output of the sequence, read with last pooling, so the conversion turns score.weight into cls.output and sets embedding_length_out. The checkpoint has no LM head, so text generation is rejected instead of using the output weights duplicated from the token embeddings. The task is fixed, so its prompt is entirely in the systemone template, the same way h2o-lightning keeps its default there. The processor of the checkpoint allows up to 16384 merged tokens per ima

Inference systems & deploymentRead original Timeline and related coverage
19:08

b11927

metal : add GGML_OP_TOP_K_MASK (#31230) * metal : add GGML_OP_TOP_K_MASK Port the fused top-k mask from the CUDA backend: a grid-stride kernel fills the output with -inf, then one threadgroup per row copies the mask values of the kept cells over it. Function constants drop the score, blocks and extra paths that a given graph does not use. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * metal : clean up top-k mask kernel formatting Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11927/llama-b11927-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](

Inference systems & deploymentRead original Timeline and related coverage
18:29

b11926

glm5-next : QSA without kq_mask (+ DSA) (#31009) * glm5-next : QSA without kq_mask * DSA: dsa top-k mask change tested with layers 0,3 of DeepSeek-V3.2 **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-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/b11926/llama-b11926-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11926/llama-b11926-bin-ubuntu-x64.t

Inference systems & deploymentRead original Timeline and related coverage
17:05

b11925

ci : use ccache depend mode for ROCm release builds (#31231) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11925/llama-b11925-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/b11925/llama-b11925-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11925/llama-b11925-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11925/llama-b11925-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b1192

Inference systems & deploymentRead original Timeline and related coverage
16:50

b11923

ggml : relax scale to non-contiguous views (#31146) * ggml : relax scale to non-contiguous views SCALE requires src0 to be 1D-contiguous. Relax this to dim-0 contiguity with arbitrary row strides (ggml_is_contiguous_rows), same as the unary ops in the Metal backend. - ggml: relax the assert in ggml_scale_impl - cpu: address src0 rows through their byte strides (f32, bf16) - cuda: stride-aware kernel, fastdiv row decomposition - metal: support bf16 scale (guarded unary instantiations) - sycl: keep the gate at contiguous, the kernel is flat f32 Add view variants to test_scale. Verified on CPU, Metal and CUDA. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (RTX 5090 32GB, DDR5 128GB) * cuda : skip the softcap fusion on non-contiguous views The fused softcap kernel reads src and w

Inference systems & deploymentRead original Timeline and related coverage
16:47

n8n@3.0.0

# [3.0.0](https://github.com/n8n-io/n8n/compare/n8n@2.45.0...n8n@3.0.0) (2026-10-27) ### Bug Fixes * Add file-type tooltip to artifact download button ([#41490](https://github.com/n8n-io/n8n/issues/41490)) ([4a74f02](https://github.com/n8n-io/n8n/commit/4a74f027e612f31098b354dd3a77b2c2ab98281c)) * **ai-builder:** Check execution:read in Assistant execution reads ([#41863](https://github.com/n8n-io/n8n/issues/41863)) ([a967486](https://github.com/n8n-io/n8n/commit/a9674866d075e989a5ce896432262c9a7dc8b0c7)) * **ai-builder:** Send Assistant model calls through the configured AI transport ([#41948](https://github.com/n8n-io/n8n/issues/41948)) ([9b63561](https://github.com/n8n-io/n8n/commit/9b63561cbb48f06d216f27be3b77722d97e4e74a)) * Allow custom OAuth2 scopes ([#41249](https://github.com/n8n-

n8n updatesRead original Timeline and related coverage
16:39

n8n@2.46.0

# [2.46.0](https://github.com/n8n-io/n8n/compare/n8n@2.45.0...n8n@2.46.0) (2026-10-27) ### Bug Fixes * Add file-type tooltip to artifact download button ([#41490](https://github.com/n8n-io/n8n/issues/41490)) ([4a74f02](https://github.com/n8n-io/n8n/commit/4a74f027e612f31098b354dd3a77b2c2ab98281c)) * **ai-builder:** Check execution:read in Assistant execution reads ([#41863](https://github.com/n8n-io/n8n/issues/41863)) ([a967486](https://github.com/n8n-io/n8n/commit/a9674866d075e989a5ce896432262c9a7dc8b0c7)) * **ai-builder:** Send Assistant model calls through the configured AI transport ([#41948](https://github.com/n8n-io/n8n/issues/41948)) ([9b63561](https://github.com/n8n-io/n8n/commit/9b63561cbb48f06d216f27be3b77722d97e4e74a)) * Allow custom OAuth2 scopes ([#41249](https://github.com/n8

n8n updatesRead original Timeline and related coverage
16:18

b11922

tests : fix multi-seq split replay and partial-state cases for trained models (#31216) * tests : fix multi-seq split replay against trained models The rollback snapshot is captured by the graph that decoded the rolled-back tail, while the reference context decodes the same prefix with different batch shapes. The two states differ in the last bits and trained models amplify the difference with every replayed token. Compare only the first replayed token of each seq, then restore the rollback state into the reference context before the seq-1-only tail. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) * tests : require bit-exact seq-1 isolation in split replay The seq state save is not always bit-lossless - SWA-masked cells are not saved - so reload the rest

Inference systems & deploymentRead original Timeline and related coverage
16:01

b11921

llama : remove obsolete n_pad from memory instances (#31200) Every caller passes n_pad = 1 and the only two users clamp it to 256. Drop the parameter, the members and the trivially true kv_size assert. Assisted-by: dsh:llama.cpp/ggml-org/Qwen3.8-Flash-Next-GGUF (MacBook, M5 Max, 128GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11921/llama-b11921-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/b11921/llama-b11921-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11921/llama-b11921-xcfram

Inference systems & deploymentRead original Timeline and related coverage
15:42

b11919

opencl: refine mul mat id -1 index (#31170) Assisted-by: Codex **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11919/llama-b11919-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/b11919/llama-b11919-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11919/llama-b11919-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11919/llama-b11919-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11

Inference systems & deploymentRead original Timeline and related coverage
15:22

b11918

opencl: initial support for q3_k (#31172) * opencl: add q3_k AOS mv * opencl: add q3_k SOA mv and tiled mm * opencl: add q3_k noshuffle gemm/gemv for adreno * opencl: add q3_k dp4a gemm for adreno Co-authored-by: Hongqiang Wang * opencl: tune q3_k kernels for adreno Co-authored-by: Hongqiang Wang --------- Co-authored-by: Hongqiang Wang **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11918/llama-b11918-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/b11918/llama-b11918-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/ll

Inference systems & deploymentRead original Timeline and related coverage
10:40

v0.43.0

## What's Changed * model_prefetch: syncrhonize all graph cleanups by @rattus128 in https://github.com/Comfy-Org/ComfyUI/pull/17178 * Add rope scaling support to MOSS-image by @kijai in https://github.com/Comfy-Org/ComfyUI/pull/17172 * [Partner Nodes] Renew partner-node sign-in tokens in API nodes by @dante01yoon in https://github.com/Comfy-Org/ComfyUI/pull/16943 * [Partner Nodes] feat(HeyGen): add HeyGen Voice instant clone and text-to-speech by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17043 * [Partner Nodes] feat(ClarityAI): add Crystal Video Upscaler node by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17192 * [Partner Nodes] feat(BFL): add 2K/4K resolutions to FLUX 3 Video nodes by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17200 * Support MOSS-image

Workflows & integrationsRead original Timeline and related coverage
08:19

b11913

hexagon: add GGML_HEXAGON_VTCM_SIZE (#31221) * hexagon: add GGML_HEXAGON_VTCM_MB and GGML_HEXAGON_NTHREADS caps Change-Id: Idffe1613c8db6f96aa842d43d1b2efaed45462a8 * hexagon: rename VTCM cap to GGML_HEXAGON_VTCM_SIZE, drop NTHREADS cap Change-Id: If4c5b9ce9fd6b725c3561421c2295d40599d012d * hexagon: make GGML_HEXAGON_VTCM_SIZE an override, accept K/M/G suffixes Change-Id: Ib8eea84c22f24e6d986b37f215e504b046478c2c **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11913/llama-b11913-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/b11913/llama-b

Inference systems & deploymentRead original Timeline and related coverage
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