Tencent

微信这次开源的视觉模型，有点东西啊！🤔 WeVisOne-VL 只有 4B 参数，却能识字、识别物体、精准抠图，连头发丝都不放过。 官方评测中，部分指标甚至超过 8B、9B 模型。 最香的是，模型够小，还能本地部署。 以后处理和前任的合照，终于不用上传云端了。🤣 Tencent/WeVisOne-VL

we gave our AI an eye for detail. 👀 Meet WeVisOne-VL, an open-source 2B/4B model from our Weixin Vision team. It finds and counts objects, reads text, and makes precise masks and cutouts—no separate SAM needed. Not in the image? "None." Tencent/WeVisOne-VL

Serving WeVisOne-VL-4B: two engines, two copies of the weights, one chess piece apart Before you put WeVisOne-VL-4B behind an API, decide which engine writes the answer. The authors' package runs the model two ways: through vLLM, a server built for throughput, or through plain Hugging Face Transformers as the reference. That choice sets your memory, your latency and, on one test image, whether the count was right. The model comes from the WeChat Vision team at Tencent, on top of Alibaba's Qwen3-VL-4B-Instruct. One 4B checkpoint does the work of a detector, a segmenter and a matting tool. It boxes what you name, counts, points, reads text with its position, outlines and cuts out. We deployed it on our own cloud and sent the same 14 requests to each setup. Tables are in the full WeVisOne-VL-4B test with every measurement. Why the fast path holds the model twice Language models write tokens, word pieces produced one at a time. This one also has position tokens: four make a box, two a point. For outlines and cut-outs it writes a marker token after each box. A separate cutter of 9.6 million parameters (mask_decoder.pt) then reads the model's internal state at that marker and paints the pixels. vLLM hands back the text, not those internal states. So with backend="vllm" the package also loads the full Transformers model and replays the answer through it for the cutter. At half precision the weights weigh about 10 GB, and this path keeps two copies. On an RTX 4090 it peaked at 23.0 GiB of 24 GB, against 12.3 GiB for Transformers alone. The default settings do not start on 24 GB With the package's defaults on a 4090, vLLM refuses to boot. It reserves room for ten images and ten videos per prompt, and that leaves nothing for the KV cache, the scratch memory where a model keeps what it has already read and written: Encoder cache will be initialized with a budget of 16384 tokens Available KV cache memory: -0.87 GiB ValueError: No available memory for the cache blocks Our custom backend, the small Python program our platform runs on the GPU machine instead of a standard model server, wraps vllm.LLM with one image per prompt, no video, a shorter context (the most tokens one request may hold) and at most four requests in flight. Giving vLLM 0.5 of the card next to the second copy made the big portrait cut-out run out of memory. At 0.465 with a 4096-token context, the cache came up short. At 0.465 with 3072 tokens, all 14 requests passed. Thin margin: measure peak memory with your own image sizes. Same greedy decoding, different answers On the authors' chess photo (16 white pieces), vLLM said 15 in 1.35 seconds, missing the knight on g5. Transformers said 16 in 3.87 seconds. !Side by side, WeVisOne-VL-4B counting white chess pieces with two engines on an RTX 4090. Left, vLLM: 15 in 1.35 seconds, the white knight on g5 marked as missed. Right, Transformers: 16 in 3.87 seconds, the knight boxed Both runs used greedy decoding, meaning the model always takes its single most likely next token, so no dice are rolled. Most likely the two programs round their maths a little differently, and a box on the edge tipped over. They also split on a street sign (29 words against 21) and on hanging pots (19 against 20). One wrong count out of 14 says nothing about a rate. It does say: run your evaluation set on the engine you will ship. What to measure before you ship - The first request after a start. On vLLM, a detection that runs in 1.9 seconds once warm took 14.5 to 15.5 seconds the first time. Fire a throwaway request at boot. - Peak memory per task, not per model. The cutter asks for about 0.8 GB at once, at any photo size. With the 4090 capped like a strict 12 GB card, the text jobs (boxes, counts, points, reading) still passed. Outlines and cut-outs all failed, even on a 640-pixel photo. - Response size. The platform dropped results above 8 MB, and the caller waited for a 504, the HTTP gateway-timeout error. The backend now fits a 5.2 MB budget (JPEG overlay, 1280 px cut-out). The platform side, where the client waits out the full timeout, is open. The reference engine is the safer default The smallest card that passed all 14 requests was a real RTX 5080 with 16 GB, on Transformers only, peaking at 12.8 GiB. Against the 4090's fast engine it took roughly double the time on the big cut-out (2.1 seconds for a 1620 by 1080 portrait, against 1.0) and up to four times as long on object jobs. The backend picks vLLM on cards with 22 GiB or more in total, Transformers below. Release 0.1.3 makes the tested values the defaults, but has not itself been run on a GPU. 0.1.0 to 0.1.2 were, with the values passed by hand. The 12 GB and 8 GB figures are caps applied on the 4090, not real smaller cards. We sent one request at a time, so concurrency is untested. The authors claim the lead on over 30 benchmarks (55.3 against GroundingDINO's 38.8 on finding named objects). We did not rerun them; we judged outputs by eye and counted the chessboard by hand. The WeVisOne-VL-4B deployment write-up on inferencekey.com has the timing table, the VRAM ladder down to an 8 GB cap with the 2B model, the environment variables and each problem's status. The code is in the SDK example for WeVisOne-VL-4B. Credit: Tencent's WeChat Vision team built the model, the code package and the model card. Qwen3-VL underneath is Alibaba's. The vLLM and Transformers projects make both engines, and the AI Search channel's news video put the model on our radar. Our part was deploying it, measuring it and writing the backend. Read the complete WeVisOne-VL-4B results, tables and setup before you size your card.

Tencent open-sourced WeVisCut, a 9B agent that turns a narration script into a multi-minute edited video 🎬 It plans shots, finds footage, edits, and fixes its own mistakes. Apache 2.0. Tencent/WeVisCut #AI #OpenSource #VideoEditing

Tencent's WeVisCut: a 9B agent that turns a script into a multi-minute edited video by itself: shots, voiceover, footage, music, then checks and fixes its own cut. On par with Claude Opus 5 at ~33x fewer tokens. Apache 2.0. tencent.github.io Tencent/WeVisCut t.co

How to Run Tencent WeVisOne-VL-4B Locally on 8 GB VRAM (Ollama Drops the Masks) Tencent's WeVisOne-VL-4B picked up 5,986 Hugging Face downloads last month, and I suspect a lot of those people expected another small chat-about-your-image model. It's a perception model instead: boxes, points, counts, text locations, segmentation masks and alpha mattes, all from one 4B checkpoint built on Qwen3-VL. You can run WeVisOne-VL-4B locally on an 8 GB GPU with a Q4_K_M GGUF in llama.cpp, and the Hub offers a one-line Ollama pull too. You just lose the masks when you do. I went through the model card, the GitHub README, the config and the GGUF repos. This post covers what the model is, Tencent's numbers against Qwen3-VL-8B (and the baseline they left out), VRAM per format, and the two ways to run it. What WeVisOne-VL-4B actually is The model card says it comes from WeChat Vision at Tencent and is built on Qwen3-VL. The config.json confirms it: Qwen3VLForConditionalGeneration, 36 text layers, hidden size 2560, a 24-layer ViT with patch size 16. That's the Qwen3-VL-4B layout with a 152,680-token vocabulary. The extra tokens are the trick. Coordinates are emitted as <0> to <1000> (normalized boxes and points), and every predicted box in dense tasks is followed by an <OBJ> token (ID 152678). A separate 9.6M-parameter pixel decoder, shipped as mask_decoder.pt, reads those <OBJ> positions plus multi-level ViT features and produces masks and stride-1 alpha mattes, so there's no SAM in the loop. The task list from the GitHub repo: detection, referring, point, count_box / count_point, ocr / ocr_grounding, segmentation, matting. Other facts worth knowing before you download: - License: Apache-2.0 for Tencent-authored code; third-party components keep their own licenses. - Context: max_position_embeddings is 262144, same as the Qwen3-VL base. Tencent's evals cap images at 1,960 visual tokens, so context isn't the constraint here. - Single-turn, single image. The pixel decoder rejects video grids, and the model returns structured output without chain-of-thought. - It answers None when the thing you asked for isn't in the image, which matters more than it sounds (see the benchmarks). - There's also a 2B sibling. Paper is arXiv:2610.31329, submitted 22 Oct 2026. WeVisOne-VL-4B vs Qwen3-VL benchmarks, and who's missing All numbers below are from the GitHub README. Tencent compares against Qwen3-VL-8B, Qwen3.5-9B, Rex-Omni-3B, LocateAnything-3B and a few specialists. | Task (metric) | Qwen3-VL-8B | Qwen3.5-9B | Rex-Omni-3B | LocateAnything-3B | WeVisOne-VL-4B | |---|---|---|---|---|---| | LVIS detection (F1) | 42.9 | 43.7 | 46.9 | 50.7 | 55.3 | | Dense200 detection (F1) | 13.8 | 18.0 | 58.3 | 58.7 | 71.3 | | RefCOCOg test (F1) | 74.9 | 74.6 | 74.3 | 77.6 | 81.8 | | WeRef rejection rate | 18.3 | 70.7 | 43.3 | 73.0 | 96.4 | | PixMo-Count acc | n/a | 80.4 | 71.9 | 82.8 | 89.6 | | TotalText OCR (F1) | 35.4 | n/a | 40.6 | 43.3 | 52.0 | | SROIE OCR (F1) | 43.3 | n/a | 44.8 | 40.3 | 68.1 | On matting (MAD ×10³, lower is better), WeVisOne-VL-4B scores 9.8 on Tencent's WeRefMat referring benchmark against 22.4 for a Qwen3.5-9B + SAM2-Matting pipeline. On ReasonSeg val it gets 71.2 gIoU vs 65.0 for a fine-tuned LISA-13B. The rejection numbers are the ones I care about. Qwen3-VL-8B on the WeRef set rejects absent targets 18.3% of the time, which means it draws a box around something that isn't there most of the time. At 96.4% you can put the detector in a pipeline; at 18.3% you end up babysitting it. One caveat: WeRef-Bench and WeRefMat are Tencent's own benchmarks. The 2B sibling is closer than you'd expect: 53.3 on LVIS, 92.9% WeRef rejection, 88.1 on PixMo-Count. If the 4B doesn't fit your card at bf16, the 2B through the official package is the one to try before you give up on masks. I do have complaints about the comparison. Qwen3-VL-4B isn't in the tables. This model is a 4B built on Qwen3-VL, and Tencent compares it with the 8B. I'd like to know how much of the gain comes from the fine-tuning and how much from the base, and the one baseline that would tell us is missing. There's no Gemma and no general VLM benchmark either: no DocVQA, no OCRBench, no MMMU, no chart QA. That fits what the model is for, but it leaves the "vs Qwen VL and Gemma" question unanswered for general image chat, and nobody has published those numbers yet. People are already questioning the baseline selection. Discussion #3 on the model page asks how the baselines were selected, pointing out that the GroundingPI paper reports additional baselines and higher scores on some overlapping benchmarks, and how the model compares with frontier proprietary models. No reply at the time of writing. How much VRAM it needs: bf16 vs Q8_0 vs Q4_K_M Tencent publishes no VRAM figures. The table below is estimated from file sizes. These are weight sizes only; activations and KV cache come on top. | Format | Weights | Vision projector | Total on disk | 8 GB card? | |---|---|---|---|---| | f16 GGUF | 8.84 GB | 0.84 GB (f16 mmproj) | ~9.7 GB | No | | Q8_0 GGUF | 4.70 GB | 0.45 GB (Q8_0 mmproj) | ~5.2 GB | Yes | | Q4_K_M GGUF | 2.72 GB | 0.45 GB | ~3.2 GB | Easily | Sizes are the actual file sizes in mradermacher/WeVisOne-VL-4B-GGUF; the repo's README table rounds them up (2.8 GB for Q4_K_M, 0.6 GB for the Q8_0 mmproj). The Hub lists the original checkpoint at 5B params in BF16, so the official bf16 path is roughly 10 GB of weights. That doesn't fit on an 8 GB card without offloading. There is one measured data point, from the PALLER-NOVUS GGUF repo: in a 20-query pilot on an RTX 2060 6 GB (context 4096, 1,024 to 1,960 image tokens), 4B Q4_K_M peaked at 4.38 GiB of whole-device VRAM at 4.17 s per request. The 4B F16 GGUF had to spill onto the CPU on that card and took about 60 s per query. My guess is that 12 GB is the realistic floor for the Tencent package at bf16, but nobody has published a measured number yet. Run it with the official package (the only way to get masks) The Tencent package is what loads mask_decoder.pt. Install it from the repo: git clone https://github.com/Tencent/WeVisOne-VL.git && cd WeVisOne-VL conda create -n wevisone-vl python=3.10 -y && conda activate wevisone-vl pip install -r requirements.txt && pip install -v -e . from PIL import Image from wevisone_vl import WeVisOneVL from wevisone_vl.visualization import save_result model = WeVisOneVL("tencent/WeVisOne-VL-4B", backend="transformers") # or backend="vllm" image = Image.open("demo/examples/images/seabreeze.jpg").convert("RGB") result = model.predict(image, task="matting", query="person") save_result("outputs/matting", image, result) That's the README example. Swap task for detection (with a list of categories as query), ocr, count_box and so on. The README also lists options for device ("auto", "cuda:0", "npu:0", "cpu"), dtype, min_pixels / max_pixels and the usual decoding arguments like max_new_tokens and temperature. If you're short on memory, lower max_pixels before anything else; fewer visual tokens means less activation memory. There's a Gradio demo too: CUDA_VISIBLE_DEVICES=0 python demo/app.py --model tencent/WeVisOne-VL-4B Add --backend vllm to run it on vLLM instead of transformers. The Hub's "Use this model" menu also offers a plain vllm serve "tencent/WeVisOne-VL-4B" and an SGLang launch command. Since the architecture is stock qwen3_vl, those serve the language model only: you get coordinate tokens back, parse them yourself, and never see a mask. The Hub's transformers snippet for the model asks "What animal is on the candy?" That's the generic image-text-to-text example, so don't judge the model by free-form chat; it was trained for structured outputs. GGUF in Ollama and llama.cpp: boxes yes, masks no There are three community GGUF repos for the 4B so far: mradermacher, prithivMLmods and PALLER-NOVUS, which ships 2B and 4B in one repo. The Hub's "Use this model" menu on the mradermacher repo gives: llama-server -hf mradermacher/WeVisOne-VL-4B-GGUF:Q4_K_M ollama run hf.co/mradermacher/WeVisOne-VL-4B-GGUF:Q4_K_M The problem is that a GGUF is the language model plus an mmproj vision projector. The pixel decoder is a separate PyTorch file, and neither llama.cpp nor Ollama knows what to do with an <OBJ> token. So on an 8 GB card you should get detection, referring boxes, pointing, counting and OCR grounding as coordinate text, but not segmentation or matting. PALLER-NOVUS says the same: putting mask_decoder.pt next to a GGUF doesn't give you pixel output. Their Q4_K_M runs did return structured boxes on a pinned, patched llama.cpp build (they say the patch carries changes for another model and adds no WeVisOne capabilities, but stock upstream is untested), launched with --special so the coordinate tokens survive decoding. I haven't seen anyone confirm that Ollama's hf.co pull picks up the separate mmproj for this architecture, so test image input there before you build on it. There are no imatrix quants yet either; mradermacher only has static ones so far. Is it the vision model to run on an 8 GB card? For grounding work, I think yes. If your local pipeline is GroundingDINO for boxes plus SAM for masks, or a general VLM you keep asking for coordinates, this is the first small model I'd test as a replacement, mostly because of the rejection rate. Q4_K_M plus its projector at ~3.2 GB leaves plenty of room. For "read this screenshot and tell me what the error says", I wouldn't switch. The OCR benchmarks here measure finding and transcribing text regions with boxes, not answering questions about a document. Stay on whatever general Qwen3-VL build you already use until someone posts DocVQA or OCRBench numbers for this one. And if you want masks on 8 GB, you're waiting on a quantized path for the official package, or on someone wiring the pixel decoder into llama.cpp. Which small vision model do you actually use for OCR or screenshots locally right now, and would you trust a 4B model's boxes without a human checking them? Sources - tencent/WeVisOne-VL-4B model card and config.json - Tencent/WeVisOne-VL on GitHub (benchmark tables, install, tasks) - WeVisOne-VL paper, arXiv:2610.31329 - mradermacher/WeVisOne-VL-4B-GGUF - prithivMLmods/tencent-WeVisOne-VL-4B-GGUF - PALLER-NOVUS/WeVisOne-VL-GGUF (RTX 2060 pilot in BENCHMARKS.md) - Discussion #3: Questions on Baseline Selection

9 'Free' AI & Dev Repos: Only 5 Are Actually Free A viral reel lists nine 'free' GitHub repos that replace paid software and implies you'll save hundreds a month. All nine are real, but 'free' needs an asterisk, so here's the honest sort (stars and prices at time of filming). Genuinely free and self-hosted: Cap (replaces Loom), Chatwoot (Intercom), OpenProject (Asana, Community Edition), OpenWhispr (Wispr Flow), and VoiceStudio (ElevenLabs-style voice cloning that runs locally). Download, run, done. Free app, but you still pay someone: OpenSEO replaces the Semrush subscription but needs a paid DataForSEO API key (pay-as-you-go, $50 minimum top-up), so you pay per lookup; and open-higgsfield is a free UI that calls Higgsfield's own API and spends credits on your paid Higgsfield account. Read the licence first: the fully-free Calendly self-host is the Cal.diy fork, whose README says personal, non-production use only (commercial Cal.com is open-core); and WeKnora is a local document-Q&A (RAG) tool that can read your Notion, not a Notion-workspace replacement. And every one means you run the server, updates, backups and security yourself. The reel's on-screen savings total only counts five of the nine tools and doesn't even add up, so treat savings as 'could', not 'will'. Still, five solid freebies is a genuinely good haul. Sources (checked 22 Oct 2026): Cap (replaces Loom): CapSoftware/Cap Chatwoot (Intercom): chatwoot/chatwoot OpenProject (Asana): opf/openproject OpenWhispr (Wispr Flow): OpenWhispr/openwhispr VoiceStudio (ElevenLabs): debpalash/VoiceStudio OpenSEO (Semrush): every-app/open-seo open-higgsfield (Higgsfield): wide-trace/open-higgsfield WeKnora (RAG / reads Notion): Tencent/WeKnora Cal.diy (Calendly, MIT self-host): calcom/cal.diy Which of the five would you self-host? Independent channel. Not sponsored. Retold in our own words and visuals. #AI #OpenSource #SelfHosted #shorts Made with AI-generated visuals and voice.

One Git Repo to Sync 19 AI Coding Tools Across Your Team Your team’s AI setups are drifting apart again. TeamAI CLI, open sourced by Tencent, keeps every skill, rule, agent and MCP config in one Git repo. When a team runs Claude Code, Codex and Cursor side by side, personal configs scatter across home directories and slowly diverge. The fix is simple: an admin creates an empty repo, everyone runs init once, and every new session pulls the latest state automatically. No central server needed. One setup covers nineteen AI coding tools, with Claude Code, Codex, CodeBuddy and WorkBuddy supporting all twelve asset types. Distribution splits across Role, Project, Tag and Scope, so subgroups only receive what fits them. Under the hood it is TypeScript with tree-sitter code graphs, plus recall that lets agents read team lessons before starting work. In five and a half months it earned 5.4k stars from seventy six contributors across eighty one npm releases, a steady cadence for multi tool teams of five or more. Source: Tencent/teamai-cli #TeamAI #AICoding #OpenSource

9 open source Alternatives: Free Code Is Not Free Everything #shorts Nine open-source alternatives, with the catches that subscription-replacement videos often skip: API usage, hosting, hardware and commercial license terms. This is a source-checked overview, not a claim that every tool is installed or that you will save a fixed amount. Original pixel-art Wyatt animation, original script and locally generated transition sounds. AI-generated Kore narration. No reference footage, sprite, creator likeness, music or cloned voice used. YouTube altered/synthetic-content disclosure: Yes. Checked27October2026. Project terms may change. Local models and third-party dependencies can have their own licenses. Cal.diy is currently marked for personal, non-production use. OpenSEO's real DataForSEO data is paid usage after limited trial credit. OpenHiggsfield requires a platform key; it is not verified unlimited free generation. Cap's distributed desktop app pricing charges for commercial rights; this is distinct from its source-code license. No affiliation or endorsement. SOURCES OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio VoiceStudio terms/editions: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Community Edition: openproject.org Chatwoot: chatwoot/chatwoot Cal community repo: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: CapSoftware/Cap Cap pricing: cap.so [de-DE] 9 Open-Source-Alternativen: Freier Code ist nicht kostenlos #shorts Neun Open-Source-Alternativen, die die Haken aufzeigen, die Videos zu Abo-Alternativen oft auslassen: API-Nutzung, Hosting, Hardware und kommerzielle Lizenzbedingungen. Dies ist eine geprüfte Übersicht, keine Garantie für die Installation aller Tools oder eine feste Ersparnis. Originale Pixel-Art-Animation von Wyatt, Originalskript und lokal generierte Übergangssounds. KI-generierte Kore-Erzählung. Es wurden keine Referenzaufnahmen, Sprites, Abbildungen der Urheber, Musik oder geklonte Stimmen verwendet. Offenlegung veränderter/synthetischer Inhalte von YouTube: Ja. Geprüft am 27. Oktober 2026. Die Projektbedingungen können sich ändern. Lokale Modelle und Abhängigkeiten von Drittanbietern können eigene Lizenzen haben. Cal.diy ist derzeit für den privaten, nicht-kommerziellen Gebrauch gekennzeichnet. Die echten DataForSEO-Daten von OpenSEO sind nach einer begrenzten Testphase kostenpflichtig. OpenHiggsfield benötigt einen Plattformschlüssel; die unbegrenzte kostenlose Generierung ist nicht garantiert. Die Preise für die Desktop-App von Cap beinhalten die kommerziellen Nutzungsrechte; diese sind von der Quellcode-Lizenz zu unterscheiden. Keine Verbindung oder Empfehlung. QUELLEN OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio VoiceStudio Nutzungsbedingungen/Editionen: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Community Edition: openproject.org Chatwoot: chatwoot/chatwoot Cal Community-Repository: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: CapSoftware/Cap Preisinformationen: cap.so [es-US] 9 alternativas de código abierto: Código gratis no significa gratis para todo #shorts Nueve alternativas de código abierto, con los detalles que suelen omitir los vídeos que reemplazan las suscripciones: uso de la API, alojamiento, hardware y términos de la licencia comercial. Esta es una descripción general verificada, no una garantía de que todas las herramientas estén instaladas ni de que se obtenga un ahorro fijo. Animación original de Wyatt en pixel art, guion original y sonidos de transición generados localmente. Narración de Kore generada por IA. No se utilizaron imágenes de referencia, sprites, imágenes del creador, música ni voces clonadas. Aviso de YouTube sobre contenido alterado/sintético: Sí. Verificado el 27 de octubre de 2026. Los términos del proyecto pueden cambiar. Los modelos locales y las dependencias de terceros pueden tener sus propias licencias. Cal.diy está marcado actualmente para uso personal, no comercial. Los datos reales de DataForSEO de OpenSEO son de pago tras un período de prueba limitado. OpenHiggsfield requiere una clave de plataforma; no se garantiza la generación gratuita ilimitada. El precio de la aplicación de escritorio distribuida de Cap cobra por los derechos comerciales; esto es independiente de su licencia de código fuente. Sin afiliación ni respaldo. FUENTES OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio Términos y condiciones de VoiceStudio: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Edición Comunitaria: openproject.org Chatwoot: chatwoot/chatwoot Repositorio comunitario de Cal: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: CapSoftware/Cap Precios de Cap: cap.so [fr-FR] 9 alternatives open source : le code gratuit n'est pas gratuit partout #shorts Neuf alternatives open source, avec les points importants souvent omis dans les vidéos de remplacement d'abonnements : utilisation de l'API, hébergement, matériel et conditions de licence commerciale. Il s'agit d'un aperçu vérifié du code source, et non d'une garantie d'installation de tous les outils ou d'économies fixes. Animation originale en pixel art de Wyatt, script original et sons de transition générés localement. Narration Kore générée par IA. Aucune image de référence, aucun sprite, aucune image du créateur, aucune musique ni voix clonée n'ont été utilisés. Divulgation du contenu modifié/synthétique sur YouTube : Oui. Vérifié le 27 octobre 2026. Les conditions du projet peuvent changer. Les modèles locaux et les dépendances tierces peuvent avoir leurs propres licences. Cal.diy est actuellement indiqué pour un usage personnel et non commercial. L'utilisation des données réelles DataForSEO d'OpenSEO est payante après une période d'essai limitée. OpenHiggsfield nécessite une clé de plateforme ; la génération gratuite illimitée n'est pas vérifiée. L'application de bureau distribuée de Cap facture les droits commerciaux ; cela est distinct de sa licence de code source. Aucune affiliation ni approbation. SOURCES OpenWhispr : openwhispr/openwhispr VoiceStudio : debpalash/VoiceStudio Conditions d’utilisation et éditions de VoiceStudio : voicestudio.sh WeKnora : Tencent/WeKnora OpenProject : opf/openproject Édition communautaire : openproject.org Chatwoot : chatwoot/chatwoot Dépôt communautaire de Cal : calcom/cal.com OpenHiggsfield : wide-trace/open-higgsfield OpenSEO : every-app/open-seo DataForSEO : dataforseo.com Cap : CapSoftware/Cap Tarifs : cap.so [hi] 9 ओपन सोर्स विकल्प: फ्री कोड का मतलब सब कुछ मुफ्त नहीं #shorts नौ ओपन-सोर्स विकल्प, जिनमें वे सभी शर्तें शामिल हैं जिन्हें अक्सर सब्सक्रिप्शन-रिप्लेसमेंट वीडियो अनदेखा कर देते हैं: API उपयोग, होस्टिंग, हार्डवेयर और वाणिज्यिक लाइसेंस की शर्तें। यह स्रोत-जांच पर आधारित एक विस्तृत अवलोकन है, यह दावा नहीं है कि हर टूल इंस्टॉल किया गया है या आप एक निश्चित राशि बचा लेंगे। मूल पिक्सेल-आर्ट वायट एनिमेशन, मूल स्क्रिप्ट और स्थानीय रूप से उत्पन्न ट्रांज़िशन साउंड। AI-जनरेटेड कोर नैरेटर। कोई संदर्भ फुटेज, स्प्राइट, निर्माता की छवि, संगीत या क्लोन की गई आवाज का उपयोग नहीं किया गया है। YouTube द्वारा परिवर्तित/सिंथेटिक सामग्री का खुलासा: हाँ। 27 अक्टूबर 2026 को जांचा गया। प्रोजेक्ट की शर्तें बदल सकती हैं। स्थानीय मॉडल और तृतीय-पक्ष निर्भरताओं के अपने लाइसेंस हो सकते हैं। Cal.diy वर्तमान में व्यक्तिगत, गैर-उत्पादन उपयोग के लिए चिह्नित है। OpenSEO का वास्तविक DataForSEO डेटा सीमित परीक्षण क्रेडिट के बाद सशुल्क उपयोग के लिए उपलब्ध है। OpenHiggsfield के लिए एक प्लेटफ़ॉर्म कुंजी की आवश्यकता होती है; यह असीमित निःशुल्क उत्पादन के लिए सत्यापित नहीं है। Cap के वितरित डेस्कटॉप ऐप मूल्य निर्धारण में वाणिज्यिक अधिकारों के लिए शुल्क लिया जाता है; यह इसके स्रोत-कोड लाइसेंस से अलग है। कोई संबद्धता या समर्थन नहीं। स्रोत OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio VoiceStudio नियम और शर्तें/संस्करण: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject कम्युनिटी एडिशन: openproject.org Chatwoot: chatwoot/chatwoot Cal कम्युनिटी रेपो: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com कैप: CapSoftware/Cap कैप प्राइसिंग: cap.so [id] 9 Alternatif Open Source: Kode Gratis Tidak Berarti Gratis Segalanya #shorts Sembilan alternatif sumber terbuka, dengan catatan yang sering diabaikan oleh video pengganti berlangganan: penggunaan API, hosting, perangkat keras, dan ketentuan lisensi komersial. Ini adalah tinjauan yang telah diperiksa sumbernya, bukan klaim bahwa setiap alat telah diinstal atau bahwa Anda akan menghemat sejumlah uang tertentu. Animasi Wyatt pixel-art asli, skrip asli, dan suara transisi yang dihasilkan secara lokal. Narasi Kore yang dihasilkan AI. Tidak ada cuplikan referensi, sprite, kemiripan kreator, musik, atau suara kloning yang digunakan. Pengungkapan konten yang diubah/sintetis YouTube: Ya. Diperiksa 27 Oktober 2026. Ketentuan proyek dapat berubah. Model lokal dan dependensi pihak ketiga dapat memiliki lisensi sendiri. Cal.diy saat ini ditandai untuk penggunaan pribadi, bukan produksi. Data DataForSEO asli dari OpenSEO berbayar setelah kredit uji coba terbatas. OpenHiggsfield memerlukan kunci platform; ini tidak diverifikasi sebagai generasi gratis tanpa batas. Harga aplikasi desktop terdistribusi Cap mengenakan biaya untuk hak komersial; ini berbeda dari lisensi kode sumbernya. Tidak ada afiliasi atau dukungan. SUMBER OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio Ketentuan/Edisi VoiceStudio: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Edisi Komunitas: openproject.org Chatwoot: chatwoot/chatwoot Repositori komunitas Cal: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: CapSoftware/Cap Harga Cap: cap.so [it] 9 alternative open source: il codice libero non è gratuito in tutto #shorts Nove alternative open-source, con tutti i dettagli che i video che promettono di sostituire gli abbonamenti spesso omettono: utilizzo delle API, hosting, hardware e termini di licenza commerciale. Questa è una panoramica verificata a partire dalle fonti, non una garanzia che ogni strumento sia installato o che si possa risparmiare una somma fissa. Animazione originale in pixel art di Wyatt, sceneggiatura originale e suoni di transizione generati localmente. Narrazione di Kore generata dall'IA. Nessun filmato di riferimento, sprite, immagine del creatore, musica o voce clonata utilizzati. Dichiarazione di YouTube sui contenuti modificati/sintetici: Sì. Verificato il 27 ottobre 2026. I termini del progetto potrebbero subire variazioni. Modelli locali e dipendenze di terze parti possono avere licenze proprie. Cal.diy è attualmente contrassegnato per uso personale e non produttivo. I dati reali di DataForSEO di OpenSEO sono a pagamento dopo un periodo di prova limitato. OpenHiggsfield richiede una chiave di piattaforma; non è verificata la generazione gratuita illimitata. Il prezzo dell'app desktop distribuita di Cap prevede l'utilizzo dei diritti commerciali; questo è distinto dalla licenza del codice sorgente. Nessuna affiliazione o approvazione. FONTI OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio Termini/edizioni di VoiceStudio: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Community Edition: openproject.org Chatwoot: chatwoot/chatwoot Repository della community di Cal: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: Italiano: CapSoftware/Cap Prezzi di Cap: cap.so [pt-BR] 9 Alternativas de código aberto: código gratuito não é tudo de graça #shorts Nove alternativas de código aberto, com as ressalvas que os vídeos de substituição de assinaturas geralmente omitem: uso de API, hospedagem, hardware e termos de licença comercial. Esta é uma visão geral verificada a partir do código-fonte, não uma afirmação de que todas as ferramentas estão instaladas ou que você economizará uma quantia fixa. Animação original em pixel art de Wyatt, roteiro original e sons de transição gerados localmente. Narração de Kore gerada por IA. Nenhuma filmagem de referência, sprite, imagem do criador, música ou voz clonada foi usada. Divulgação de conteúdo alterado/sintético do YouTube: Sim. Verificado em 27 de outubro de 2026. Os termos do projeto podem mudar. Modelos locais e dependências de terceiros podem ter suas próprias licenças. O Cal.diy está atualmente marcado para uso pessoal, não comercial. Os dados reais do DataForSEO do OpenSEO são de uso pago após um período de teste limitado. O OpenHiggsfield requer uma chave de plataforma; não há verificação de geração gratuita ilimitada. O preço do aplicativo de desktop distribuído do Cap cobra por direitos comerciais; isso é distinto de sua licença de código-fonte. Sem afiliação ou endosso. FONTES OpenWhispr: openwhispr/openwhispr VoiceStudio: debpalash/VoiceStudio Termos/edições do VoiceStudio: voicestudio.sh WeKnora: Tencent/WeKnora OpenProject: opf/openproject Community Edition: openproject.org Chatwoot: chatwoot/chatwoot Repositório da comunidade Cal: calcom/cal.com OpenHiggsfield: wide-trace/open-higgsfield OpenSEO: every-app/open-seo DataForSEO: dataforseo.com Cap: CapSoftware/Cap Cap Preços: cap.so

Six routes to a team knowledge base | 6 open-source choices Knowledge bases need more than chatbots. Six options, with different use cases and adoption checks. RAGFlow, AnythingLLM, Haystack, MaxKB, WeKnora, Onyx. Explore the original cards: channel profile → AI Open Source Radar → Collections. Choose the collection for this task and inspect the project cards. useaistation.com Sources checked 23 Oct 2026. This is a project-selection guide, not hands-on testing or a live ranking. These are different choices, not a verified integrated stack. Software licenses, model weights, hardware and external service costs must be checked separately. Community/core and enterprise features may differ. Developer disclosure: we build AI Open Source Radar, not the featured projects. Narration: Microsoft free Aria neural voice. Project and License sources (observed branch references, subject to change): RAGFlow: infiniflow/ragflow License: raw.githubusercontent.com AnythingLLM: Mintplex-Labs/anything-llm License: raw.githubusercontent.com Haystack: deepset-ai/haystack License: raw.githubusercontent.com MaxKB: 1Panel-dev/MaxKB License: raw.githubusercontent.com WeKnora: Tencent/WeKnora License: raw.githubusercontent.com Onyx: onyx-dot-app/onyx License: raw.githubusercontent.com #OpenSource #AITools #RAG