MemTensor

MemT 1.0 is launched: Towards Memory-Native Evolution. On October 20th, we are officially releasing and open-sourcing the memory-native foundation model series, MemT 1.0. MemT 1.0 integrates persistent, updatable internal memory into model computation, so that historical information and task experience can be retained and recalled in subsequent related tasks. Our goal is to make "remember, update, and apply experience" a capability the model can learn. This release includes MemT-1.0-Flash and MemT-1.0-Nano, with 288B and 36B total parameters and 17B and 3B active parameters, respectively. Across 11 memory benchmarks, the two models achieve average scores of 50.6 and 47.0, ranking first and second among the parametric memory methods evaluated in this assessment — all under conditions where the query context does not provide the original history text. !Image description Figure: Memory performance of MemT 1.0 across benchmarks 01 Memory-Native: From Information Retention to a Learnable Capability Foundation models are internalizing capabilities such as reasoning and multimodality into themselves; memory is another critical capability. It enables a model to carry forward prior information and experience, accumulating and using them across sustained interactions. Earlier, Metis introduced memory foundation models, exploring memory as a native capability of the model. MemT 1.0 builds on this foundation with an upgrade: native memory modules are added to selected Transformer blocks, so that the original attention and memory attention run in parallel, jointly supporting information storage and use through dynamic local memory and trainable hyper memory. !Image description Figure: The memory-native architecture of MemT 1.0 Textual memory methods provide the original history or retrieved evidence at query time; MemT 1.0 instead leverages previously written information through internal memory states, without requiring the raw history text to be provided again. The key innovation is teaching the model how to retain, update, and read memory. During online interaction, the backbone and hyper-memory weights remain fixed; what changes is the dynamic memory state maintained independently for each interaction trajectory. Generation in the current turn reads existing memory; once the interaction completes, the input and answer are written back to memory for use in subsequent interactions. 02 The Memory Mechanism: How It Updates, How It Scales The memory mechanism of MemT 1.0 is built around three key pillars. - Token-wise stable updates A stable update mechanism based on Gated DeltaNet-2 updates memory token by token during the storage process after each interaction, merging new information into existing memory through erase, write, and forget mechanisms. - Mixture-of-Storage for capacity scaling Mixture-of-Storage (MoS) expands the native memory module into multiple memory experts, distributing information through independent storage and utilization routing. MoS employs dense routing in which all experts participate, providing a mechanism for expanding memory capacity. In this main evaluation, each memory layer uses a single expert; multi-expert scaling is examined separately in ablation experiments. !Image description Figure: Mixture-of-Storage (MoS) - Two-stage training Continual pretraining builds language modeling capability that relies on native memory, followed by post-training that learns to complete tasks based on memory. The report constructs a 34B-token training corpus, comprising 20B tokens for continual pretraining and 14B tokens for post-training. Post-training adopts a "context–query–answer" format, in which the student model learns to reproduce the full-context teacher's answering behavior using only its memory state. 03 Measured Performance: It Remembers, and It Stays Reliable A memory-native model must not only make good use of historical information, but also maintain its original capabilities under memory noise. We evaluated MemT 1.0's actual performance along two dimensions: memory capability and general capability. Memory capability: can it use history well without the source text? The experiments span 11 memory benchmarks, evaluating information retention and retrieval across different history lengths, dynamic updates and management, and personalized understanding. History is written into memory in chronological order; the model then answers related questions. Memory is strictly isolated across different users and histories. With no original history text in the query context, MemT-1.0-Flash and MemT-1.0-Nano score 50.6 and 47.0 on average, ranking first and second among the parametric memory methods in this evaluation — 12.5 and 8.9 points above the best average of other parametric memory methods, respectively. The two models have different strengths: Flash performs better on memory operations, personalized understanding, dialogue recall, and implicit learning; Nano scores higher on PerMemSafe and Memora. The strongest textual memory systems still hold an overall advantage, particularly in long-history tests. Ensuring that important details survive continuous updates and are accurately read back when needed remains a key direction for memory-native models. !Image description *Memory capability evaluation across 11 benchmarks. Benchmarks are grouped by interaction trajectory length. DS denotes DeepSeek-V4-Flash-0731; QW denotes Qwen3.6-35B-A3B. In the original table, bold and underline denote the best and second-best results within each memory category, respectively. General capability: can it hold up against irrelevant memory? Models must also cope with interference from irrelevant memory. We write irrelevant information into memory before evaluation to simulate interference during sustained interaction, then compare against the corresponding backbone models under the same noise setting across four dimensions — knowledge and scientific reasoning, instruction following, long-context understanding, and code generation — on six benchmarks. Flash's average score is roughly on par with its backbone, while Nano drops by 0.9 points. First, instruction following: can the model accurately execute the current request? In the table, IFE refers to IFEval, which tests whether a model complies with explicit requirements such as specified formats, length limits, and required keywords — matters that determine whether an answer can be put directly to work. Under irrelevant information, Flash improves 0.9 points over its backbone to 93.9; Nano improves 3.3 points to 92.6, showing that both models still execute current instructions well in this test. !Image description *General capability evaluation under the noise setting. Values for MemT 1.0 denote score differences relative to the corresponding backbone under the same setting. Second, the practical programming everyone cares about: can generated code actually get the job done? BCB-H refers to BigCodeBench-Hard, which requires the model to understand complex requirements, combine software libraries, and generate code that passes execution tests — testing whether the model can turn programming knowledge into runnable solutions. In this test, Flash and Nano each improve 2.0 points over their backbones, reaching 34.4 and 27.7, respectively. However, another code test, LiveCodeBench v6, drops by 0.7 and 5.8 points, indicating that different types of programming tasks are affected differently, and a single improvement should not be extrapolated into an overall gain in programming capability. Long-text understanding asks: faced with a large body of material, can the model sort out the information and answer questions? LB refers to LongBench v2, which covers long documents, multi-document settings, long conversations, and code repositories, testing the model's comprehension and reasoning over long-form material. Under irrelevant information, Flash improves 0.2 points over its backbone to 64.4, roughly on par overall, while Nano drops 2.0 points. The retention of historical information in internal memory and the comprehension of current long-form material need to be examined separately. These results show that memory capability and general capability can largely coexist, but some tasks still incur performance loss. Keeping models reliable while they continuously accumulate information is the direction memory-native models need to keep optimizing. 04 Putting Experience to Work: How Memory Changes What Happens Next MemT 1.0 explores a new path for growing foundation model capabilities: beyond parameters, data, and context, effective experience is retained and put to work in subsequent tasks. In a Coding Agent, operational experience — methods for reproducing problems, failure modes, and verification workflows — is retrieved, written into the model's internal memory, and then guides subsequent actions, without continuously occupying task context in textual form. The current experiments use an externally constructed experience library and apply task-specific post-training on top of Nano. We compared no experience, textual experience, and memory-native approaches on 500 tasks from SWE-bench Verified and 300 tasks from SWE-bench Multilingual covering nine programming languages, running each task independently three times. - On Verified, with experience provided at task start, Nano raises the average success rate from 66.07% to 69.33%, versus 68.87% with textual experience. When the execution framework triggers experience writing at key breakpoints, the average success rate reaches 69.73%, and the proportion of tasks succeeding all three times rises from 54.00% to 62.80%, an improvement of 8.80 percentage points. - On Multilingual, the framework-triggered setting achieves an average success rate of 63.56%, higher than 60.00% without experience and 63.00% with textual experience; the proportion of tasks succeeding all three times rises from 45.33% to 55.33%. The gains mainly appear in average success rate and consistency across repeated runs; the proportion of tasks succeeding at least once did not consistently improve. !Image description *Agentic coding performance on SWE-bench Verified and SWE-bench Multilingual. In the original table, bold and underline denote the best and second-best results within each benchmark and experience-setting combination. 05 Storage and Runtime Efficiency: Persistent Accumulation with a Controllable State With a fixed memory configuration, new information updates the existing matrix, and the persistent state size does not keep growing with history. In the main evaluation configuration, Nano's FP32 memory state is 40 MiB per interaction trajectory, and Flash's is 1,280 MiB, excluding shared model weights and the current turn's KV cache. In the efficiency evaluation on BEAM history, at a 128K-token history length, Nano achieves roughly 1.86x end-to-end inference speed compared with the full-context approach, with a persistent state about 1/66 of its size; at a 256K history, end-to-end speed is about 3.99x. After history preparation, across the evaluated range from 512 to 256K tokens, Nano's average query first-token latency is approximately 104–112 ms, remaining essentially stable. End-to-end latency includes history preparation and the generation of a fixed 128-token answer; query first-token latency excludes history preparation. !Image description *Efficiency evaluation results: end-to-end latency and query first-token latency. !Image description *Persistent storage footprint per interaction trajectory. These advantages mainly appear in long-history scenarios; with short histories, Nano is usually slower. A fixed state also means limited information capacity: total storage still grows with the number of sessions and the configured capacity. The efficiency experiments above should not be directly equated with lower costs on programming tasks — experience writing and additional verification still incur overhead. 06 Limitations and Next Steps We see that fixed-capacity memory in memory-native models must strike a balance between preserving early details and accommodating new information, and long-term fidelity of precise information remains a challenge. Current experience application still relies on external experience construction and retrieval; a complete loop of autonomously acquiring, verifying, and continuously integrating experience has not yet formed. Next, we will improve long-term memory, expand memory capacity, and reduce the cost of memory updates. We will also further explore autonomous experience acquisition and continuous cross-task memory integration, gradually moving toward agents that learn from interaction through memory-native evolution. Memory-native capability provides a foundation for continual learning and recursive self-improvement (RSI); feedback, verification, and update mechanisms still need to evolve together. 07 Learn More Parameters set where a model's capability starts; memory opens up the space for intelligence to grow. Developers and researchers are welcome to explore the MemT 1.0 models and the possibilities of memory-native capability. Hugging Face: huggingface.co GitHub: MemTensor/MemT-1.0

@Astronaut_1216 出处：@Astronaut_1216 的实测系列 封号恢复帖 x.com 跨工具找项目 x.com 记忆系统介绍 x.com Memmy 仓库 MemTensor/memmy-agent

Choosing a MemOS Integration for Your AI Agent Imagine a customer returning three days after a replacement was approved. They want to know when it will ship, and they have a new delivery address. The agent handling this conversation needs the earlier approval, the updated address, and the current shipping status from the order system. That gives you a useful starting point for choosing a memory system. You can follow a specific piece of information from the conversation where it first appeared to the task that needs it later. We built MemOS to help applications carry context across conversations. You can connect through a managed API, run the open-source service on your infrastructure, or add memory to a supported agent through a plugin. The right starting point depends on what you’re building and how you want to operate it. Choose where the integration will run For an application your team is developing, the first decision is whether to use our hosted service or manage the deployment yourself. If you’re adding memory to an existing agent environment, a plugin handles the connection between the agent and its memory. |What you’re building|Where to start|What your team manages| |---|---|---| |A service running on your infrastructure|MemOS open-source framework|The service, storage dependencies, model configuration, and operations| |An OpenClaw or DeepSeek Harness setup using cloud memory|MemOS Cloud Plugin|The agent environment and plugin configuration| |An OpenClaw, Hermes Agent, or DeepSeek Harness setup with local memory storage|MemOS Local Plugin|The local runtime, model connections, and backups| With MemOS Cloud, we operate the memory service. Self-hosting gives you control over the code and infrastructure; our reference Docker deployment runs the MemOS API alongside Neo4j and Qdrant. The plugins connect memory to the agent’s normal execution. MemOS Cloud Plugin recalls relevant memories before a run and saves new messages afterward. MemOS Local Plugin maintains SQLite storage on the machine and combines vector and lexical retrieval. It also turns execution experience into reusable policies and Skills, with a Memory Viewer for inspecting the results. Its local configuration includes model connections and optional team sharing, which you should review when deciding where data will be processed. Once you’ve chosen a path, test the workflow you actually need. The examples below use MemOS Cloud’s interfaces. Decide what the next conversation needs to remember In the replacement case, the customer’s notification preference belongs with their personal history. The company’s replacement policy belongs in shared business knowledge. A procedure for checking an order and opening a replacement ticket may be useful across many customers. Our customer service example separates these into user memories, Agent Skills, and a policy knowledge base. That lets the agent retrieve customer context alongside general procedures while continuing to consult the order and ticket systems for current business state. MemOS Cloud provides different memory categories for these purposes. Facts and preferences capture customer context. Event Memory preserves details about what happened, while Skills and Tool Memory capture reusable task experience. Profiles organize information into predefined fields when your application needs a structured view of a user. The setup varies by category. A Profile requires a template bound to the user. Tool Memory needs tool-call information in the input, and a knowledge base needs uploaded content. Use allow_memory_view to choose which categories may be generated from a write. During development, inspect what the system extracts from a few representative conversations. “Please text me when it ships” is a preference. “If I move next month, I’ll need to change my address” is conditional. Your test should check whether that condition survives extraction. For a closer look, our memory extraction endpoint returns factual and preference candidates without writing them to the memory store. It supports synchronous extraction from text conversations, so your application can inspect the results or apply its own checks. After using the full write pipeline, inspect the stored records as well. Make the returning customer easy to recognize When the customer comes back through another channel, use the same user_id. Your application should derive that identifier from its authenticated customer record. MemOS Cloud’s project and user scopes determine which personal memories a request searches. The API Key selects the project, and user_id identifies the customer within it. Keep the API Key on your server and connect each request to your application’s access rules. If several agents serve the same customer, you can tag memories with agent_id and filter by the agent that produced them. An agent can also have its own independent memory, for example to accumulate reusable support Skills. Enable Create Independent Memory for Agent in the project settings for that behavior; it is disabled by default. A new conversation can still use the customer’s earlier memories. The conversation_id supplies conversational context and can influence relevance, but it does not automatically restrict retrieval to that session. To find the information needed for the next response, our Search Memory API combines keyword and vector retrieval with ranking, filtering, and deduplication. You can select memory categories with include_memory_view, set the number of results with memory_limit_number, and narrow the search using filter. In this case, inspect whether the search returns the replacement approval and relevant delivery details. Then check the context your application actually sends to the model. Our application integration workflow shows where recalled memories are added to the prompt. Handle the address change Information that was accurate on Monday may need updating on Thursday. MemOS Cloud’s time-aware memory handling supports changing states and historical questions, so current information and earlier versions can serve different requests. When importing older conversations, provide chat_time to preserve when the information was recorded. You also need a way to fix mistakes. If your support team knows which record is wrong, it can update the memory by ID, changing its content, title, or status. If the customer gives a correction in conversation, your backend can submit it through natural-language feedback with the relevant user and conversation context. Follow the operation through to completion. Cloud writes use asynchronous processing by default. Use the returned task_id to check get/status; a completed task includes a summary of memory changes. Then retrieve the affected information again and confirm that the next response uses the updated address. For the shipment itself, your application should still verify the address and order status through the business system before taking action. Be specific about deletion If the customer asks to remove their history, decide which records the request covers. Our delete/memoryendpoint accepts one of three targets per call: specific memory_ids, a user_id, or an agent_id. Each operation stays within the API Key’s project. Agent-wide deletion requires independent Agent memory to be enabled. That endpoint does not accept a conversation ID or knowledge base ID as a deletion target. Knowledge Base files have a separate deletion endpoint that accepts file_ids. After deleting, repeat the relevant searches. Include any copies your application keeps in caches, exports, or logs when defining the full deletion workflow. Try the whole case before rolling it out Use the same replacement scenario throughout your pilot. A few variations will tell you more about the integration than a collection of unrelated prompts. 1. Return in a new session. Record the approval, then open another conversation for the same customer. Check whether the agent can continue the case without asking for information already provided. 2. Change a detail. Update the delivery address or switch the notification preference from email to text. Inspect the stored change and the next response. 3. Include a conditional statement. Try “If I move to Seattle, I’ll need to update my address.” Check that a possible move has not become a confirmed address change. 4. Use another customer account. Repeat the question as a different user. Inspect the request’s identity mapping and make sure the original customer’s personal information is absent. 5. Remove the test records. Delete the intended memories and search again. If the workflow uses Knowledge Base files, test their removal separately. Track how long new information takes to become retrievable, along with p95 latency, token usage, and time spent investigating failures. Compare task outcomes with and without memory: whether the agent gives the correct next step, how often the customer has to repeat themselves, and whether corrections carry into later conversations. Keep a short record of the configuration, results, and issues still to resolve. That record should make it clear why the chosen deployment fits your application. Our Cloud quick start walks through the first write and retrieval. If you’re running the service yourself, begin with the self-hosting instructions. Bring one complete customer case through the integration before expanding to more workflows.

MemTensor released MemT-1.0, open models with memory built into the computation: they recall past chats without re-reading the old text, and at 128K history the memory state is ~66x smaller than a KV cache (company's own evals). MemTensor/MemT-1.0 huggingface.co t.co

중국 MemTensor가 기억을 모델 계산 안에 넣은 오픈 모델 MemT-1.0을 공개했다. 지난 대화 원문 없이 내부 기억으로 과거를 쓰고, 128K 이력 저장 공간은 KV 캐시보다 약 66배 작다(자체 평가). MemTensor/MemT-1.0 t.co

GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 This is GitHub Trending Today #55. 35 trending open-source projects on GitHub right now. Paid channel subscription: youtube.com Text version link: githubawesome.com 00:00 - Introduction 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Thank you so much for watching. Please like the video and subscribe to our channel. #github #ai #opensource Notice: The content was created with the assistance of AI and may contain inaccuracies or errors. ▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee link.githubawesome.com [ar] GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 هذا هو موضوع GitHub الرائج اليوم #55. 35 مشروعًا مفتوح المصدر رائجًا على GitHub الآن. الاشتراك في القناة المدفوعة: youtube.com رابط النسخة النصية: githubawesome.com 00:00 - مقدمة 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv شكرًا جزيلًا على المشاهدة. يُرجى الإعجاب بالفيديو والاشتراك في قناتنا. #github #ai #opensource ملاحظة: تم إنشاء هذا المحتوى بمساعدة الذكاء الاصطناعي، وقد يحتوي على بعض الأخطاء أو عدم الدقة. ▬▬▬▬▬▬ 👋 تواصل معي 👋 ▬▬▬▬▬▬ ➡ لينك تري: link.githubawesome.com ➡ ادعمني بشراء قهوة: link.githubawesome.com [bn] গিটহাব ট্রেন্ডিং টুডে #৫৫: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 এটি আজকের গিটহাব ট্রেন্ডিং #৫৫। এই মুহূর্তে গিটহাবে ৩৫টি ট্রেন্ডিং ওপেন-সোর্স প্রজেক্ট রয়েছে। পেইড চ্যানেল সাবস্ক্রিপশন: youtube.com টেক্সট ভার্সনের লিঙ্ক: githubawesome.com ০০:০০ - ভূমিকা ০০:১১ - free-my-tv simbasjungles/free-my-tv ০০:৩৬ - pele obsidianmd/pele ০১:০১ - SPACE perplexityai/SPACE ০১:২৮ - ascii-sharks stackedlol/ascii-sharks ০১:৫২ - harp dominikmartn/harp ০২:১৭ - EAGLE-4 eagle-hq/EAGLE-4 ০২:৪১ - ascii-magic-art kailashsr38/ascii-magic-art ০৩:০৬ - ElyOffice ELY-Open-Source/ElyOffice ০৩:২৮ - DeepInfer deepseek-ai/DeepInfer ০৩:৫৪ - zero-breach interplains/zero-breach ০৪:২০ - editonny soumatheusgomes/editonny ০৪:৪৬ - ari nichochar/ari ০৫:১১ - tv-detox Ansub/tv-detox ০৫:৩৯ - fx0 vercel-labs/fx0 ০৬:০৫ - pandacode aicodeking/pandacode ০৬:৩২ - scrawl kitlangton/scrawl 06:58 - অনডাইন wevm/ondine 07:26 - ট্রেডার-স্কিল Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - টুইনডেস্ক InukaAsith/TwinDesk 08:40 - সুপারলাইট iha2/superlite 09:07 - গ্রামমাই zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - পেপারলিট Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parth01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - ড্র্যাগশিফ্ট llamaboi55/DragShift ১৪:৩৯ - সলিডসিভি (solidcv) solidsf-inc/solidcv ভিডিওটি দেখার জন্য আপনাকে অসংখ্য ধন্যবাদ। অনুগ্রহ করে ভিডিওটি লাইক করুন এবং আমাদের চ্যানেলটি সাবস্ক্রাইব করুন। #github #ai #opensource বিজ্ঞপ্তি: এই কন্টেন্টটি এআই (AI)-এর সহায়তায় তৈরি করা হয়েছে এবং এতে ভুল বা ত্রুটি থাকতে পারে। ▬▬▬▬▬▬ 👋 আমার সাথে যোগাযোগ করুন 👋 ▬▬▬▬▬▬ ➡ লিংকট্রি (LinkTree): link.githubawesome.com ➡ বাই মি এ কফি (Buy Me A Coffee): link.githubawesome.com [de-DE] GitHub Trending heute #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Dies ist GitHub Trending Today #55. 35 aktuell angesagte Open-Source-Projekte auf GitHub. Abonnement des kostenpflichtigen Kanals: youtube.com Link zur Textversion: githubawesome.com 00:00 – Einleitung 00:11 – free-my-tv simbasjungles/free-my-tv 00:36 – pele obsidianmd/pele 01:01 – SPACE perplexityai/SPACE 01:28 – ascii-sharks stackedlol/ascii-sharks 01:52 – harp dominikmartn/harp 02:17 – EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 – nanoui justrach/nanoui 13:49 – nsh longbridge/nsh 14:15 – DragShift llamaboi55/DragShift 14:39 – solidcv solidsf-inc/solidcv Vielen Dank fürs Zuschauen! Gebt dem Video ein Like und abonniert unseren Kanal. #github #ai #opensource Hinweis: Dieser Inhalt wurde mithilfe von KI erstellt und kann Ungenauigkeiten oder Fehler enthalten. ▬▬▬▬▬▬ 👋 Kontakt 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Unterstütze mich mit einer Spende: link.githubawesome.com [es-US] GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Esta es la sección de Tendencias de GitHub de hoy #55. 35 proyectos de código abierto que son tendencia en GitHub en este momento. Suscripción de pago al canal: youtube.com Enlace a la versión de texto: githubawesome.com 00:00 - Introducción 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-piensa raullenchai/esp32-piensa 12:09 - kividb kividbio/kividb 12:35 - esfinge parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - Arrastrar cambio llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Muchas gracias por ver el video. Dale me gusta y suscríbete a nuestro canal. #github #ai #opensource Aviso: Este contenido se creó con la ayuda de IA y puede contener imprecisiones o errores. ▬▬▬▬▬▬ 👋 Contáctame 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Invítame a un café: link.githubawesome.com [fr-FR] GitHub Trending Today n° 55 : free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Voici la 55e tendance du jour sur GitHub. Découvrez 35 projets open source populaires sur GitHub en ce moment. Abonnement payant à la chaîne : youtube.com Lien vers la version texte : github.com/github-trending-today-55 00:00 - Introduction 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10h51 - kapitsa nace-ai/kapitsa 11h19 - PaperLeet Hongyang-Du/PaperLeet 11h41 - esp32-thinks raullenchai/esp32-thinks 12h09 - kividb kividbio/kividb 12h35 - sphinx parthh01/sphinx 12h57 - oma-cast typecraft-dev/oma-cast 13h23 - nanoui justrach/nanoui 13h49 - nsh longbridge/nsh 14h15 - DragShift llamaboi55/DragShift 14:39 - SolidCV solidsf-inc/solidcv Merci beaucoup d'avoir regardé cette vidéo. N'hésitez pas à la liker et à vous abonner à notre chaîne. #github #ai #opensource Avertissement : Ce contenu a été créé avec l'aide d'une IA et peut contenir des inexactitudes ou des erreurs. ▬▬▬▬▬▬ 👋 Me contacter 👋 ▬▬▬▬▬▬ ➡ LinkTree : link.githubewesome.com ➡ Offrez-moi un café : link.githubewesome.com [hi] GitHub ट्रेंडिंग टुडे #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 यह GitHub ट्रेंडिंग टुडे #55 है। GitHub पर इस समय ट्रेंडिंग 35 ओपन-सोर्स प्रोजेक्ट हैं। चैनल की सशुल्क सदस्यता: youtube.com टेक्स्ट संस्करण का लिंक: githubawesome.com 00:00 - परिचय 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - पांडाकोड aicodeking/pandacode 06:32 - स्क्रॉल kitlangton/scrawl 06:58 - ओन्डाइन wevm/ondine 07:26 - ट्रेडर-स्किल Jakeschincariol/trader-skill 07:49 - रा-जित indexable-inc/ra-jit 08:14 - ट्विनडेस्क InukaAsith/TwinDesk 08:40 - सुपरलाइट iha2/superlite 09:07 - ग्रामाई zilvestro/grammai 09:31 - रुवियो salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv वीडियो देखने के लिए बहुत-बहुत धन्यवाद। कृपया वीडियो को लाइक करें और हमारे चैनल को सब्सक्राइब करें। #github #ai #opensource सूचना: यह सामग्री AI की सहायता से बनाई गई है और इसमें कुछ त्रुटियाँ या अशुद्धियाँ हो सकती हैं। ▬▬▬▬▬▬ 👋 मुझसे संपर्क करें 👋 ▬▬▬▬▬▬ ➡ लिंक ट्री: link.githubawesome.com ➡ मुझे कॉफ़ी पिलाएँ: link.githubawesome.com [id] GitHub Trending Hari Ini #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Ini adalah GitHub Trending Today #55. 35 proyek open-source yang sedang tren di GitHub saat ini. Langganan saluran berbayar: youtube.com Tautan versi teks: githubawesome.com 00:00 - Pendahuluan 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - keterampilan pedagang Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlit iha2/superlite 09:07 - tata bahasa zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - Kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Terima kasih Terima kasih banyak telah menonton. Silakan sukai video ini dan berlangganan saluran kami. #github #ai #opensource Peringatan: Konten ini dibuat dengan bantuan AI dan mungkin mengandung ketidakakuratan atau kesalahan. ▬▬▬▬▬▬ 👋 Hubungi saya 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Dukung saya dengan membeli kopi: link.githubawesome.com [it] GitHub di tendenza oggi #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Ecco la lista dei progetti di tendenza di GitHub di oggi n. 55. 35 progetti open source di tendenza su GitHub in questo momento. Abbonamento a pagamento al canale: youtube.com Link alla versione testuale: githubawesome.com 00:00 - Introduzione 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 Italiano: eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 Italiano: vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio Italiano: salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast Italiano: typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Grazie mille per aver guardato il video. Metti mi piace e iscriviti al nostro canale. #github #ai #opensource Nota: il contenuto è stato creato con l'aiuto dell'IA e potrebbe contenere imprecisioni o errori. ▬▬▬▬▬▬ 👋 Contattami 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Offrimi un caffè link.githubawesome.com [iw] GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 זהו #55, מגזין GitHub Trending Today. 35 פרויקטים בקוד פתוח פופולריים ב-GitHub כרגע. מנוי לערוץ בתשלום: youtube.com קישור לגרסת טקסט: githubawesome.com 00:00 - מבוא 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - שרבוט kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - סופרלייט iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - ספינקס parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv תודה רבה שצפיתם. אנא עשו לייק לסרטון והירשמו לערוץ שלנו. #github #ai #opensource הערה: התוכן נוצר בעזרת בינה מלאכותית ועשוי להכיל אי דיוקים או שגיאות. ▬▬▬▬▬▬ 👋 צרו איתי קשר 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ תקנו לי קפה link.githubawesome.com [ja] GitHub Trending Today #55: free-my-tv、pele、SPACE、ascii-sharks、harp、DeepInfer、ElyOffice、fx0 こちらはGitHubトレンドランキング第55位です。現在GitHubで注目を集めているオープンソースプロジェクト35件をご紹介します。 有料チャンネル登録：youtube.com テキスト版リンク：githubawesome.com 00:00 - はじめに 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv ご視聴いただきありがとうございます。ぜひ動画に「いいね！」をして、チャンネル登録をお願いします。 #github #ai #opensource 注意：このコンテンツはAIの支援を受けて作成されたため、不正確な情報や誤りが含まれている可能性があります。 ▬▬▬▬▬▬ 👋 お問い合わせ 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee link.githubawesome.com [ko] GitHub 트렌딩 투데이 #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 오늘의 GitHub 인기 트렌드 #55입니다. 현재 GitHub에서 가장 인기 있는 오픈소스 프로젝트 35개를 소개합니다. 유료 채널 구독: youtube.com 텍스트 버전 링크: githubwesome.com 00:00 - 소개 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv 시청해 주셔서 감사합니다. 영상에 좋아요를 누르고 채널 구독도 부탁드립니다. #github #ai #opensource 주의: 본 콘텐츠는 AI를 이용하여 제작되었으며, 부정확하거나 오류가 포함될 수 있습니다. ▬▬▬▬▬▬ 👋 연락처 👋 ▬▬▬▬▬▬ ➡ 링크트리: link.githubawesome.com ➡ 커피 한 잔 사주세요: link.githubawesome.com [ml] GitHub ട്രെൻഡിംഗ് ടുഡേ #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 ഇതാണ് GitHub ട്രെൻഡിംഗ് ടുഡേ #55. GitHub-ൽ ഇപ്പോൾ ട്രെൻഡിംഗ് ആയ 35 ഓപ്പൺ സോഴ്‌സ് പ്രോജക്ടുകൾ. പണമടച്ചുള്ള ചാനൽ സബ്‌സ്‌ക്രിപ്‌ഷൻ: youtube.com ടെക്‌സ്റ്റ് പതിപ്പ് ലിങ്ക്: githubawesome.com 00:00 - ആമുഖം 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - സീറോ-ബ്രീച്ച് interplains/zero-breach 04:20 - എഡിറ്റോണി soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - പാണ്ടകോഡ് aicodeking/pandacode 06:32 - സ്‌ക്രാൾ kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - ട്രേഡർ-സ്കിൽ Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - ട്വിൻഡെസ്ക് InukaAsith/TwinDesk 08:40 - സൂപ്പർലൈറ്റ് iha2/superlite 09:07 - ഗ്രാമൈ zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - പേപ്പർലീറ്റ് Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - സ്ഫിങ്ക്സ് parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - ഡ്രാഗ്ഷിഫ്റ്റ് llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv കണ്ടതിന് വളരെ നന്ദി. ദയവായി വീഡിയോ ലൈക്ക് ചെയ്യുകയും ഞങ്ങളുടെ ചാനൽ സബ്സ്ക്രൈബ് ചെയ്യുകയും ചെയ്യുക. #github #ai #opensource ശ്രദ്ധിക്കുക: ഉള്ളടക്കം AI യുടെ സഹായത്തോടെ സൃഷ്ടിച്ചതാണ്, അതിൽ കൃത്യതയില്ലായ്മകളോ പിശകുകളോ അടങ്ങിയിരിക്കാം. ▬▬▬▬▬▬ 👋 എന്നെ ബന്ധപ്പെടുക 👋 ▬▬▬▬▬▬ ➡ ലിങ്ക്ട്രീ: link.githubawesome.com ➡ എനിക്ക് ഒരു കോഫി വാങ്ങൂ link.githubawesome.com [nl-NL] GitHub Trending Vandaag #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Dit is GitHub Trending Today #55. 35 trending open-sourceprojecten op GitHub op dit moment. Betaald kanaalabonnement: youtube.com Link naar tekstversie: githubawesome.com 00:00 - Introductie 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Hartelijk dank voor het kijken. Geef de video een like en abonneer je op ons kanaal. #github #ai #opensource Let op: De inhoud is gemaakt met behulp van AI en kan onnauwkeurigheden of fouten bevatten. ▬▬▬▬▬▬ 👋 Neem contact met me op 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Koop me een koffie: link.githubawesome.com [pa] GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 ਇਹ ਅੱਜ GitHub ਟ੍ਰੈਂਡਿੰਗ #55 ਹੈ। GitHub 'ਤੇ ਇਸ ਵੇਲੇ 35 ਟ੍ਰੈਂਡਿੰਗ ਓਪਨ-ਸੋਰਸ ਪ੍ਰੋਜੈਕਟ। ਭੁਗਤਾਨਸ਼ੁਦਾ ਚੈਨਲ ਗਾਹਕੀ: youtube.com ਟੈਕਸਟ ਵਰਜਨ ਲਿੰਕ: githubawesome.com 00:00 - ਜਾਣ-ਪਛਾਣ 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - ਜ਼ੀਰੋ-ਬ੍ਰੀਚ interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - ਸਕ੍ਰੌਲ kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - ਵਪਾਰੀ-ਹੁਨਰ Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - ਸੁਪਰਲਾਈਟ iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - ਪੇਪਰਲੀਟ Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - ਨੈਨੋਈ justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - ਡਰੈਗ ਸ਼ਿਫਟ llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv ਦੇਖਣ ਲਈ ਤੁਹਾਡਾ ਬਹੁਤ ਧੰਨਵਾਦ। ਕਿਰਪਾ ਕਰਕੇ ਵੀਡੀਓ ਨੂੰ ਪਸੰਦ ਕਰੋ ਅਤੇ ਸਾਡੇ ਚੈਨਲ ਨੂੰ ਸਬਸਕ੍ਰਾਈਬ ਕਰੋ। #github #ai #opensource ਨੋਟਿਸ: ਸਮੱਗਰੀ AI ਦੀ ਸਹਾਇਤਾ ਨਾਲ ਬਣਾਈ ਗਈ ਸੀ ਅਤੇ ਇਸ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਗਲਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ▬▬▬▬▬▬ 👋 ਮੇਰੇ ਨਾਲ ਸੰਪਰਕ ਕਰੋ 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Buy Me A Coffee link.githubawesome.com [pl] GitHub Trending Today nr 55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Oto GitHub Trending Today #55. 35 najpopularniejszych projektów open source na GitHubie. Płatna subskrypcja kanału: youtube.com Link do wersji tekstowej: githubwesome.com 00:00 - Wprowadzenie 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Dziękujemy za obejrzenie. Polub film i zasubskrybuj nasz kanał. #github #ai #opensource Uwaga: Treść została stworzona przy pomocy sztucznej inteligencji i może zawierać nieścisłości lub błędy. ▬▬▬▬▬▬ 👋 Skontaktuj się ze mną 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Kup mi kawę link.githubawesome.com [pt-BR] GitHub Trending Today #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Esta é a lista de Tendências do GitHub de hoje, número 55. 35 projetos de código aberto em alta no GitHub neste momento. Assinatura paga do canal: youtube.com Link para a versão em texto: githubawesome.com 00:00 - Introdução 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 – rabisco kitlangton/scrawl 06:58 - ondina wevm/ondine 07:26 - habilidade do trader Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 – superlite iha2/superlite 09:07 - gramática zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Muito obrigado por assistir. Por favor, curta o vídeo e inscreva-se no nosso canal. #github #ai #opensource Aviso: O conteúdo foi criado com o auxílio de IA e pode conter imprecisões ou erros. ▬▬▬▬▬▬ 👋 Contato 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Compre-me um café: link.githubawesome.com [ru] GitHub Trending Today №55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 Это 55-й по популярности проект в трендах GitHub на данный момент. 35 самых популярных проектов с открытым исходным кодом на GitHub прямо сейчас. Платная подписка на канал: youtube.com Ссылка на текстовую версию: githubawesome.com 00:00 - Введение 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - trader-skill Jakeschincariol/trader-skill 07:49 - ra-jit indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - superlite iha2/superlite 09:07 - grammai zilvestro/grammai 09:31 - ruvio salihcantekin/ruvio 09:59 - MemT-1.0 MemTensor/MemT-1.0 10:25 - dumbtv humblemuzzu/dumbtv 10:51 - kapitsa nace-ai/kapitsa 11:19 - PaperLeet Hongyang-Du/PaperLeet 11:41 - esp32-thinks raullenchai/esp32-thinks 12:09 - kividb kividbio/kividb 12:35 - sphinx parthh01/sphinx 12:57 - oma-cast typecraft-dev/oma-cast 13:23 - nanoui justrach/nanoui 13:49 - nsh longbridge/nsh 14:15 - DragShift llamaboi55/DragShift 14:39 - solidcv solidsf-inc/solidcv Большое спасибо за просмотр. Пожалуйста, поставьте лайк видео и подпишитесь на наш канал. #github #ai #opensource Примечание: Контент создан с помощью ИИ и может содержать неточности или ошибки. ▬▬▬▬▬▬ 👋 Свяжитесь со мной 👋 ▬▬▬▬▬▬ ➡ LinkTree: link.githubawesome.com ➡ Поддержите меня, купив мне кофе: link.githubawesome.com [ta] GitHub டிரெண்டிங் டுடே #55: free-my-tv, pele, SPACE, ascii-sharks, harp, DeepInfer, ElyOffice, fx0 இது GitHub-இன் இன்றைய டிரெண்டிங் #55. தற்போது GitHub-இல் பிரபலமாக உள்ள 35 ஓப்பன்-சோர்ஸ் திட்டங்கள். கட்டணச் சேனல் சந்தா: youtube.com உரை வடிவ இணைப்பு: githubawesome.com 00:00 - அறிமுகம் 00:11 - free-my-tv simbasjungles/free-my-tv 00:36 - pele obsidianmd/pele 01:01 - SPACE perplexityai/SPACE 01:28 - ascii-sharks stackedlol/ascii-sharks 01:52 - harp dominikmartn/harp 02:17 - EAGLE-4 eagle-hq/EAGLE-4 02:41 - ascii-magic-art kailashsr38/ascii-magic-art 03:06 - ElyOffice ELY-Open-Source/ElyOffice 03:28 - DeepInfer deepseek-ai/DeepInfer 03:54 - zero-breach interplains/zero-breach 04:20 - editonny soumatheusgomes/editonny 04:46 - ari nichochar/ari 05:11 - tv-detox Ansub/tv-detox 05:39 - fx0 vercel-labs/fx0 06:05 - pandacode aicodeking/pandacode 06:32 - scrawl kitlangton/scrawl 06:58 - ondine wevm/ondine 07:26 - வர்த்தகர்-திறன் Jakeschincariol/trader-skill 07:49 - ரா-ஜித் indexable-inc/ra-jit 08:14 - TwinDesk InukaAsith/TwinDesk 08:40 - சூப்பர்லைட் iha2/superlite 09:07 - கிராமாய் zilvestro/grammai 09:31 - ருவியோ salihcantekin/ruvio 09:59 - MemT-1.0 git

Grok Bot 做视频初体验，后悔了！后悔没早体验！ 提示词就一句话：给开源项目MemTensor/MemT-1.0%E5%81%9A%E4%B8%80%E4%B8%AA%E5%AE%A3%E4%BC%A0%E8%A7%86%E9%A2%91%EF%BC%8C%E8%A6%81%E6%B1%82%E4%B8%93%E4%B8%9A%E6%9C%89%E6%B7%B1%E5%BA%A6%EF%BC%8C%E5%B0%BD%E9%87%8F%E5%A4%9A%E5%B1%95%E7%8E%B0%E9%A1%B9%E7%9B%AE%E5%86%85%E5%AE%B9%E3%80%82 整体没有任何干预，直出，人声部分用了开源tts~ t.co

[Paper] MemT-1.0: Towards Memory-Native Evolution >Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To this end, we introduce MemT-1.0, a new architecture of memory foundation models that internalize memory as part of model computation. Building on Metis, MemT-1.0 employs Gated DeltaNet-2 with a stable token-level memory update mechanism within each turn. We further introduce Mixture-of-Storage to scale native memory capacity. Following a deliberate backbone preparation, we develop a two-stage training pipeline consisting of continued pre-training and post-training, supported by 34B tokens of training data and efficient training and inference infrastructure. We release a model family comprising MemT-1.0-Nano and MemT-1.0-Flash, and conduct a comprehensive evaluation of memory and general capabilities. The results show that they outperform the parametric memory baselines on average and remain competitive with retrieval-augmented methods, while largely preserving general capabilities. At a history length of 128K tokens, MemT-1.0-Nano achieves approximately 1.86× end-to-end inference speedup and a 66× reduction in persistent storage relative to full context KV cache. By internalizing retrieved procedural experience, MemT-1.0 improves task success on agentic tasks, providing a practical step toward memory-native evolution. - arXiv : arxiv.org - PDF : arxiv.org - GitHub : MemTensor/MemT-1.0 - HuggingFace : huggingface.co

Топ-10 GitHub за 3 дня: aerial-tv и ascii-sharks Обзор: топ-10 новых репозиториев GitHub за 18–21 октября 2026 — новые проекты GitHub и open source, которые быстрее всех набрали звёзды. В выпуске: pengchujin/aerial-tv — лаунчер в стиле tvOS для Android TV без рекламы; stackedlol/ascii-sharks — акулы и рыбы из ASCII-символов на canvas; eagle-hq/EAGLE-4 — ускорение вывода больших языковых моделей. 1. pengchujin/aerial-tv — Лаунчер в стиле tvOS для Android TV: без рекламы, для телевизоров Xiaomi и TCL. (Kotlin, +1097 ★) 2. stackedlol/ascii-sharks — Акулы и рыбы, нарисованные ASCII-символами на canvas, на TypeScript. (TypeScript, +605 ★) 3. Hugo-Dz/spritefusion-sprite-normals — Генерирует карты нормалей для пиксель-арт спрайтов, листов и GIF. (Rust, +403 ★) 4. eagle-hq/EAGLE-4 — Код запуска EAGLE-4 — ускорение вывода больших языковых моделей. (Python, +401 ★) 5. dominikmartn/harp — Неофициальный форк терминала Warp: боковая панель для агентов Claude Code и Codex. (Rust, +370 ★) 6. wevm/ondine — Компилирует диаграммы Mermaid в SVG на Rust ещё при сборке сайта. (Rust, +304 ★) 7. typecraft-dev/oma-cast — Лаунчер в стиле Raycast для Omarchy: приложения и все действия меню в одном поиске. (JavaScript, +297 ★) 8. ip-please/ip-please.github.io — Пиксельная игра в духе Papers, Please: инспектор ловит тех, кто выдаёт ��ебя за американцев. (JavaScript, +280 ★) 9. MemTensor/MemT-1.0 — Языковые модели с встроенной памятью: Nano на 36B и Flash на 288B параметров. (Python, +224 ★) 10. opentrap/opentrap — Бесплатная замена Burp Suite: перехватывающий прокси для проверки веб-приложений. (Java, +197 ★) Источник: GitHub Search API, репозитории созданы с 2026-10-18, отсортированы по звёздам. Лидер: pengchujin/aerial-tv Остальные: stackedlol/ascii-sharks, Hugo-Dz/spritefusion-sprite-normals, eagle-hq/EAGLE-4, dominikmartn/harp, wevm/ondine, typecraft-dev/oma-cast, ip-please/ip-please.github.io, MemTensor/MemT-1.0, opentrap/opentrap #GitHub #opensource #программирование Каждые 3 дня — 10 новых open-source проектов GitHub, быстрее всех набравших звёзды. Подписывайся, чтобы не пропустить следующий выпуск.

found memmy poking through new repos tonight. the bit I liked: it doesn't wait for the agent to decide to save a memory. it reads the session files Claude Code and Codex already write to disk and merges them into one local store. haven't run it. MemTensor/memmy-agent t.co