run-llama

Running agents on Java? You need a typed admin client. LlamaCloud's new SDK lets you manage data sources and jobs via clean interfaces. No string-building REST calls. run-llama/llamacloud-admin-java

Parsing PDFs in TS usually means wrestling with layout. LlamaParse handles the OCR cloud-side, returning clean JSON. run-llama/llama-parse-ts

LLM Orchestration Explained: Routing, Fallbacks, and Production Reliability TLDR - LLM orchestration is the control layer that decides which model does what, in what order, and what happens when something breaks. - It's really three jobs. The model layer picks the model, the pipeline layer builds prompts and tool calls, and the run layer keeps everything durable, limited, and auditable. - Teams use more than one model because of cost, speed, skills, and provider outages. They pick between them with routing, cascading, fallback, or parallel ensembles. - Frameworks, gateways, and workflow engines aren't rivals. They sit at different layers, so you start with whichever one is failing in production. - Unmeshed is the run layer. It handles retries, fallbacks, routing rules, spend limits, and approvals in one place, and it works alongside the tools you already use. Ok so, LLM orchestration. Sounds super fancy, right? It really isn't. Here's the deal. Most teams start with one model call, and it works great. Then they add a second model, then a third, and suddenly nobody knows which one is doing what, or what happens when one of them breaks. That's basically what it's for. It decides who does what, in what order, and what to do when something goes wrong. We're happy to say it's way less scary than it sounds. We'll go through the three jobs people mix up, how routing and fallback work, and where Unmeshed fits. And where it doesn't, because we'll be straight with you about that too. Okay, let's get into it. 1. What Is LLM Orchestration? LLM orchestration is how you coordinate multiple language models, prompts, tools, and data sources into one reliable process. It decides which model handles each step and what context it gets. And it decides what happens when a step fails or costs way too much. !LLM orchestration architecture diagram showing five key decisions in multi-model orchestration: model selection, context management, failure handling, cost control, and human approval workflows for production AI systems. Okay, but what does that actually mean? We figure the easiest way to wrap your head around it is to pretend it's a restaurant kitchen. You've got a bunch of cooks. Some are fast, some are fancy, and some only do desserts. Someone has to decide who makes which dish, what to do when the oven dies, and who stops the kitchen from blowing the whole budget on truffles. That someone is the head chef. And that's basically what orchestration is. In plain terms, it involves five decisions. - Which model handles each step (who cooks the dish) - What the model gets to see and use (the ingredients and tools) - What happens when something breaks (the oven dies) - How much a run is allowed to spend (the truffle budget) - Who has to approve it before it goes out (the taste test) Quick note on what it isn't. It's not a single prompt. It's not only a gateway that passes requests along. And it's not agent orchestration, where the model picks its own next step (we cover that in our guide to agentic orchestration). Today we're only talking about the models. If you want the bigger picture, our guide to AI orchestration covers pipelines, agents, and platforms. This one is just the model side of things. 2. Why Do Production Systems Use More Than One Model? Production systems use more than one model because no single model is best at everything. One might be great at hard reasoning but too slow and pricey for sorting tickets. And every provider has a bad day eventually. !Multi-model LLM orchestration diagram showing why enterprises use multiple AI models for cost optimization, low-latency responses, specialized model capabilities, and provider outage resilience in production AI systems. Okay, but why though? Can't you just pick the best model and call it a day? Hmm, that's what we thought too. Turns out, not really. - Cost: You wouldn't hire a fancy chef to boil an egg. A quick classification doesn't need your priciest model. - Speed: If someone's waiting on the answer, it has to be quick. A nightly report can take its time. - Skills: Some models are just better at code, long documents, or using tools. - Drama: Providers have outages, rate limits, and deprecations, usually at the worst possible time. There's one more sneaky reason. Models get updated, sometimes quietly, and a prompt that worked last month can suddenly act weird. With a backup model ready (and a way to compare the two), that turns into a boring switch. Boring is good. Cost is usually what gets teams moving on this, and routing is one of the best ways to bring it down. We cover the tricks in our guides to cost optimization and token efficiency. 3. What Are the Three Layers of LLM Orchestration? LLM orchestration has three layers. The model layer picks which model answers. The pipeline layer builds prompts, retrieval, and tool calls. And the run layer keeps everything durable, limited, and auditable. A model orchestration layer usually only covers the first one. !LLM orchestration architecture showing three layers: model orchestration with AI gateways, pipeline orchestration with frameworks, and workflow orchestration with durable execution and auditability. Back to the kitchen for a sec. These three layers are basically three different people. The model layer is the waiter who decides which cook gets the order. The pipeline layer is the recipe, so what goes in and in what order. And the run layer is the kitchen manager who keeps track of everything, fixes stuff when it goes wrong, and writes it all down. | Layer | Job | Typical Tools | What Breaks Without It | |---------|---------|---------|---------| | Model | Pick the right model and handle provider failure | Gateways and routers | One outage or price change hits your whole product | | Pipeline | Build prompts, retrieve data, call tools, validate output | Frameworks such as LangChain and LlamaIndex | Prompts and tool calls turn into tangled glue code | | Run | Keep runs durable, limited, approved, and traceable | Workflow engines | A crash loses state, and nobody can explain a result | Most guides only talk about one of these and call it a day. So you end up with a waiter and no manager, or a great recipe and nobody watching the stove. Lowkey, that's where most of the pain comes from. You don't need all three on day one. Start with whichever one hurts. The mistake is thinking the one you have covers the other two. It doesn't. 4. How Does Multi-Model Orchestration Work? Multi-model orchestration works by picking a model for each request. There are four main ways to do it, which are routing, cascading, fallback, and parallel ensembles. Each one trades cost, speed, and quality a bit differently, and most teams end up combining two. Here's the easy version of all four. Routing is like a host seating people at the right table. Cascading is trying the cheap option first and only calling the expensive one if the cheap one flops. !Multi-model LLM orchestration comparison showing AI model routing, cascading model selection, fallback strategies, and parallel ensemble methods for optimizing cost, quality, reliability, and performance. Fallback is the backup plan for when your first option is down. And a parallel ensemble is asking a few cooks to make the same dish and picking the best one (pricey, ngl). | Pattern | How It Works | Best For | Main Risk | |---------|---------|---------|---------| | Routing | A router picks one model before generation based on the request | Mixed workloads with clear easy and hard cases | A weak router sends hard requests to weak models | | Cascading | Run a cheaper model first and escalate if the answer scores low | High volume traffic where most answers are easy | Extra latency when escalation triggers | | Fallback | Switch to another model or provider when a call fails | Reliability against outages and rate limits | Repeated side effects if the retry isn't safe | | Parallel ensemble | Call several models and compare or merge the answers | High stakes decisions | Cost multiplies with every model you add | There's actual research on this too. A paper on routing and cascading for LLMs describes routing as picking one model per query, and cascading as running bigger and bigger models until the answer is good enough. The authors also came up with cascade routing, which mixes both. And the big finding is that it all depends on how well you can tell if an answer is any good. That last part matters. A cascade is only as smart as whatever decides "is this answer ok?" Some teams use a simple rule, some use a validator, and some use another model as the judge, which is kind of a can of worms (our guide to LLM as a judge goes into where bias sneaks in). One warning about fallback. Switching to another model is easy. Doing it safely is the tricky bit. If a retry sends the same email twice or charges a card twice, well, that's a bad day. So limit your retries, and make the steps around the model safe to run twice. 5. What Is an LLM Pipeline, and How Is It Different From an LLM Workflow? An LLM pipeline is a fixed set of steps that turns an input into a model output, like retrieve, prompt, generate, and validate. An LLM workflow wraps those steps with branching, retries, state, and human approval. Still the kitchen. A pipeline is one recipe, with the same steps in the same order every time. A workflow is the whole dinner service, which includes what to do when the oven breaks, when to ask the head chef, and what happens when a customer wants a refund. | Dimension | LLM Pipeline | LLM Workflow | |---------|---------|---------| | Shape | A fixed sequence of steps | Steps plus branches, loops, and waits | | State | Usually lives in memory for one run | Saved between steps so a run can resume | | Failure handling | An error stops the run | Retries, fallbacks, and escalation paths | | Human input | Rare | Approval steps that pause the run | | Typical home | Application code or a framework | A workflow engine | Here's an example. A support ticket comes in. The pipeline classifies it, grabs the account history, writes a reply, and checks the tone. Done. The workflow decides which model writes the reply, retries if the provider times out, sends any big refund to an actual human, and writes down every step along the way. 6. Which Do You Need for LLM Orchestration: a Framework, a Gateway, or a Workflow Engine? You need a framework to build the pipeline, a gateway for routing and provider control, and a workflow engine to run it all durably. Plenty of teams end up needing all three. Start with whichever layer is failing in production. Think of it like this. The framework is your recipe book. The gateway is the delivery driver who finds a route to the model. And the workflow engine is the kitchen manager again. All different jobs. | Tool Type | What It Covers | Examples | What It Doesn't Cover | |---------|---------|---------|---------| | Framework | Prompts, retrieval, tool calls, model interfaces | LangChain, LlamaIndex | Durable runs, spend limits, approvals | | Gateway | Routing, load balancing, retries, fallbacks across providers | LiteLLM | Multi-step business logic and human review | | Workflow engine | State, retries, branching, approvals, audit | Unmeshed | Prompt design and retrieval tuning | LangChain describes itself as a framework for building agents and LLM-powered apps, with a standard interface for models and tools. LlamaIndex started out as a data framework for connecting models to your own data. Its README now says the main focus is document parsing and extraction though, so worth knowing that before you pick it for something else. On the gateway side, the LiteLLM router handles load balancing, retries, cooldowns, and fallbacks across deployments and providers. We go deeper on that in our guide to the LLM gateway. None of these are rivals of a workflow engine. They just sit at different layers, and the best setups use them together. So pick based on what's breaking for you right now. 7. How Does Unmeshed Handle LLM Orchestration? Unmeshed handles the run layer of LLM orchestration. Your model calls become steps in durable workflows. So retries, fallback paths, routing rules, approvals, and spend ceilings all live in one place, with a full run history and no lock-in to any one model. !LLM orchestration workflow diagram showing model routing rules, durable AI model execution, fallback model selection, human approval workflows, audit logging, and production-ready LLM orchestration for enterprise AI systems. In kitchen terms, we're the manager. Here's the rundown. - Durable runs and retries: Model calls run as steps in workflows with retries and saved state, so a crash picks up where it stopped instead of starting over. - Routing rules: A decision engine uses decision tables with first match or all match rules. You can route by request type, size, or customer tier without a pile of branching code. - Fallback paths: When a model step fails, the workflow just branches to another model or a safe default. - Spend and tool limits: Agents are bound to a fixed set of tools, and each one can have a spend ceiling that throttles it or switches it to a cheaper model. See AI agent governance. - Approvals: A human-in-the-loop step pauses a run until a person decides. - Visibility: Every run keeps a full history of steps, inputs, and outputs. For the agent side of things, see agent orchestration. It's model-agnostic too, so you're not stuck with one provider, and it's MCP native. There are SDKs for Java, Go, Python, and TypeScript. Oh, and it comes from the creators of Netflix Conductor, in case that helps. Now, the honest part. We're not a prompt framework, and we're not a gateway. If you love LangChain for building and LiteLLM for reaching providers, keep them! We just run them for you. > Your Models Are Not The Hard Part. > Unmeshed runs model calls as workflow steps with retries, fallback paths, and spend limits, so a provider outage doesn't take your product down. > Build A Fallback 8. What Breaks When You Skip LLM Orchestration? Without LLM orchestration, one provider outage takes your product down. Retries repeat work, spending drifts with nothing to stop it, and nobody can say why a model answered the way it did. These aren't model problems. They're everything around the model. Picture the kitchen with no manager. Orders get lost. Someone burns the sauce twice. Nobody knows what anything cost. Yeah, it's that. - A provider returns errors: No fallback means your feature goes down right along with them. - A retry repeats a side effect: The customer gets two emails, or two refunds. - Spend runs away: A loop or a huge context bill shows up at the end of the month instead of the moment it starts. - A model updates quietly: Output changes, and you've got no record of which model ran which request. - An auditor asks why: And you can't show the inputs, the model, or who approved it. Most of this comes down to keeping state and records. A durable execution layer keeps state across failures, and LLM observability shows you what happened inside each call. Once people depend on the feature, you want both. 9. How Do You Decide What to Build First? Build first whatever is failing in production today. Add fallback if outages hurt. Add routing if cost does. Add a pipeline framework if prompts are tangled, and a workflow engine once retries, approvals, or audit trails turn into manual work. Here's the quick cheat sheet. - Outages or rate limits hurt. Add a gateway with fallback. - The bill is too high. Add routing or a cascade, and check answer quality before and after. - Prompts and tool calls are tangled. Move them into a framework. - Retries, approvals, or audit trails are manual. Add a workflow engine. A good path is one model and a fallback to start. Add routing once cost starts to sting, then a workflow layer when the process around the model gets bigger. And only fix things you've actually seen break, not things you're just nervous about. In a Nutshell Orchestration is really three jobs wearing one name. Choose the model, build the pipeline, and run it durably. Then fix whichever layer is failing first. That order keeps you from buying a tool for a problem you don't have yet. So yeah, that's the whole thing. If outages are what keep you up, start with a gateway and fallback. If the bill is scary, start with routing. If you're drowning in retries and approvals, look at a workflow engine. Not sure which one is you? Check your last outage or your last wild bill. It's usually right there. And the goal, like always, is that a model failing is boring. Not a fire.

LlamaIndex Memory drops your priority=1 memory block first --- title: "LlamaIndex Memory drops your priority=1 memory block first" published: true canonical_url: memorysync.hashnode.dev description: "LlamaIndex Memory truncates priority=1 blocks before priority=2, the reverse of the docs (issue #23090). Status and safe workarounds." tags: ai, python, llamaindex, rag --- Disclosure: I work on MemorySync (I'm Mohammed, the founder). I drafted this page with AI assistance and checked it against the source issue, the linked PR and the MemorySync LlamaIndex guide on Oct 28, 2026. I didn't run any code for this page. The question run-llama/llama_index#23090 reports that when memory blocks plus short-term chat history exceed token_limit, Memory._truncate_memory_blocks drops blocks in ascending priority order. So the block with priority=1 (documented as highest priority) is truncated first, and priority=2 survives. With the docs' own example (FactExtractionMemoryBlock at 1, VectorMemoryBlock at 2), condensed facts go before bulk retrieval. Status (checked 2026-10-28): open, 3 comments. A commenter pointed to PR #23093 ("fix(core): truncate lowest-priority memory blocks first"), but that PR is now closed without being merged, so no fix has landed as far as the issue shows. Workarounds Not run for this page; the issue includes a short repro you can run against your installed version first. - Use priority=0 for the block you can't lose. Per the field's docs, 0 means never truncate; the issue doesn't report that being broken. - Raise token_limit or shrink blocks so truncation doesn't trigger in normal conversations. - Don't rely on the 1-vs-2 order until a fix ships. If you must, test which block survives on your version with the issue's repro and set priorities to match the observed behaviour, then re-check after upgrades. Common questions Is there a fix? Not as of Oct 28, 2026: the issue is open, and PR #23093 was closed without being merged. Which priority is safe? priority=0, documented as never truncated. The issue doesn't report that being broken. Where MemorySync fits Only if you use it already or are evaluating it. The MemorySync LlamaIndex guide documents a MemorySyncMemoryBlock whose atruncate drops its lowest-ranked lines instead of the whole block. That changes what happens to that one block when it's truncated; it doesn't fix the order bug in Memory itself. To see save and recall in your browser first, try the MemorySync playground. Sources - run-llama/llama_index#23090, "[Bug]: Memory truncates highest-priority memory blocks first" (read 2026-10-28) - run-llama/llama_index PR #23093 (closed, not merged; read 2026-10-28) - MemorySync LlamaIndex guide: docs.memorysync.io/guides/llamaindex

@llama_index we checked the spec's repo: tables have 39 rule files, text only 27, so test your tables first run-llama/document_parsing_protocol

The Ultimate Developer Stack: 23 Trending GitHub Repos | 28-10-2026 | Dev Repos Daily cutt.ly 🔥 Nobody Told You About This New AI Repo (Summer2027-Internships) Stay on top of the open source ecosystem with this handpicked selection of trending GitHub repositories. From innovative developer tools to groundbreaking frameworks, these are the projects making waves in the software community right now. October 28, 2026 What is inside this video: - Top 23 trending GitHub repositories ranked by stars gained today - Developer insights and project overviews for every featured repo - AI-narrated walkthrough of each open source project - Direct links to all repositories below for easy access ⏱️ TIMESTAMPS (YouTube Chapters): 00:00 - Intro: Today's High-Impact Tools 00:09 - Trending Leaderboard Overview 00:25 - #1 Summer2027-Internships 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Data-Science-For-Beginners 03:27 - #8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - Outro & Key Takeaways ━━━━━━━━━━━━━━━━━━━━━━━ TODAY'S TOP 23 TRENDING REPOSITORIES: ━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-Internships (⭐ 48.2k • +21 today • Python) Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9k • +161 today • TypeScript) Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • +10 today • Python) The Context Platform for your Data and AI Stack 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • +76 today • TypeScript) 🆙 Upscayl - #1 Free and Open Source AI Image Upscaler for Linux, MacOS and Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • +55 today • HTML) A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • +99 today • Unknown) A curated list of awesome remote jobs and resources. Inspired by vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37.8k • +47 today • Jupyter Notebook) 10 Weeks, 20 Lessons, Data Science for All! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • +84 today • C) Microsoft PowerToys is a collection of utilities that supercharge productivity and customization on Windows 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • +41 today • Vim Script) Vim-fork focused on extensibility and usability 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • +74 today • C#) The Free Software Media System - Server Backend & API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • +23 today • Python) LlamaIndex is the document processing platform for AI 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • +14 today • Go) CLI tool and library for generating a Software Bill of Materials from container images and filesystems 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • +90 today • TypeScript) Playwright is a framework for Web Testing and Automation. It allows testing Chromium, Firefox and WebKit with a single API. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • +64 today • TypeScript) 🔥🔥🔥 Open-source Jira, Linear, Monday, and ClickUp alternative. Plane is a modern project management platform to manage tasks, sprints, docs, and triage. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5k • +17 today • C) Open-source vector similarity search for Postgres 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━ Subscribe to @devrepos for daily GitHub trending repositories, open source roundups, and developer news. New videos every day covering the best open source software and trending projects on GitHub. [ar] أفضل حزمة أدوات للمطورين: 23 مستودعاً رائجاً على GitHub | 28-10-2026 | مستودعات المطورين اليومية cutt.ly 🔥 لم يخبرك أحد عن هذا المستودع الجديد للذكاء الاصطناعي (تدريب صيفي 2027) ابقَ على اطلاع دائم بأحدث التطورات في عالم البرمجيات مفتوحة المصدر مع هذه المجموعة المختارة بعناية من مستودعات GitHub الرائجة. من أدوات تطوير مبتكرة إلى أطر عمل رائدة، هذه هي المشاريع التي تُحدث ضجة في مجتمع البرمجيات حاليًا. ... ٢٨ أكتوبر ٢٠٢٦ محتوى هذا الفيديو: - أفضل ٢٣ مستودعًا رائجًا على GitHub، مُرتبة حسب عدد النجوم المكتسبة اليوم - رؤى المطورين ونظرة عامة على المشاريع لكل مستودع مميز - شرح صوتي مُعزز بالذكاء الاصطناعي لكل مشروع مفتوح المصدر - روابط مباشرة لجميع المستودعات أدناه لسهولة الوصول ⏱️ الفواصل الزمنية (فصول يوتيوب): ٠٠:٠٠ - مقدمة: أدوات اليوم الأكثر تأثيرًا ٠٠:٠٩ - نظرة عامة على قائمة المتصدرين الرائجة ٠٠:٢٥ - #١ تدريب صيفي ٢٠٢٧ ٠٠:٥٤ - #٢ n8n ٠١:٢٤ - #٣ datahub ٠١:٤٥ - #٤ upscayl ٠٢:٠٨ - #٥ free-for-dev ٠٢:٣٣ - #٦ awesome-remote-job ٠٢:٥٩ - #٧ علوم البيانات للمبتدئين ٠٣:٢٧ - #٨ باور تويز 03:50 - #9 نيوفيم 04:10 - #10 جيلي فين 04:33 - #11 لاما إندكس 04:56 - #12 سيفت 05:18 - #13 بلاي رايت 05:46 - #14 بلين 06:09 - #15 بي جي فيكتور 06:28 - #16 أوسوم 06:59 - #17 رانر إيمجز 07:19 - #18 رست ديسك 07:43 - #19 ليزي جيت 08:05 - #20 ترانسفورمرز 08:31 - #21 دايركت توس 09:02 - #22 جيت ليكس 09:22 - #23 أوبن كور ليجاسي باتشر 09:45 - الخاتمة والمفتاح أهم النقاط ━━━━━━━━━━━━━━━━━━━━━━━━ أفضل 23 موقعًا رائجًا اليوم: ━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-Internships (⭐ 48.2 ألف • +21 اليوم • بايثون) فرص تدريب صيفية لعام 2027 في هندسة البرمجيات، وعلوم البيانات، والذكاء الاصطناعي، والتحليل الكمي، وإدارة المنتجات، والأجهزة. يتم تحديثها يوميًا بواسطة Simplify و Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9 ألف • +161 اليوم • TypeScript) منصة أتمتة سير العمل البرمجية العادلة مع إمكانيات ذكاء اصطناعي مدمجة. اجمع بين البناء المرئي والبرمجة المخصصة، مع إمكانية الاستضافة الذاتية أو السحابية، وأكثر من 400 تكامل. منصة أتمتة سير العمل البرمجية العادلة مع إمكانيات الذكاء الاصطناعي المدمجة. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9 ألف • +10 اليوم • بايثون) منصة السياق لبياناتك وتقنيات الذكاء الاصطناعي 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2 ألف • +76 اليوم • تايب سكريبت) 🆙 Upscayl - برنامج مجاني ومفتوح المصدر لتحسين جودة الصور بتقنية الذكاء الاصطناعي، يعمل على أنظمة لينكس وماك أو إس وويندوز. ... #3 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2 ألف • +55 اليوم • HTML) قائمة بعروض SaaS وPaaS وIaaS التي تتضمن مستويات مجانية تهم مطوري DevOps وInfradev 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50 ألف • +99 اليوم • غير معروف) قائمة منتقاة من أفضل الوظائف والموارد عن بُعد. مستوحى من vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 مايكروسوفت/علوم البيانات للمبتدئين (⭐ 37.8 ألف • +47 اليوم • Jupyter Notebook) 10 أسابيع، 20 درسًا، علوم البيانات للجميع! #7 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1 ألف • +84 اليوم • C) مجموعة أدوات Microsoft PowerToys تُعزز الإنتاجية وتُتيح تخصيص نظام Windows. 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2 ألف • +41 اليوم • سكربت Vim) نسخة مُعدّلة من Vim تُركز على قابلية التوسع وسهولة الاستخدام. 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7 ألف • +74 اليوم • C#) نظام الوسائط البرمجية الحرة - خادم خلفي وواجهة برمجة تطبيقات (API) 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ ٥٢٫٧ ألف • +٢٣ اليوم • بايثون) LlamaIndex هي منصة معالجة المستندات للذكاء الاصطناعي 🔗 run-llama/llama_index #١٢ anchore/syft (⭐ ٩٫٨ ألف • +١٤ اليوم • جو) أداة سطر أوامر ومكتبة لإنشاء قائمة مكونات برمجية من صور الحاويات وأنظمة الملفات 🔗 anchore/syft #١٣ microsoft/playwright (⭐ ٩٨٫٧ ألف • +٩٠ اليوم • تايب سكريبت) Playwright هو إطار عمل لاختبار وأتمتة الويب. يتيح اختبار Chromium وFirefox وWebKit باستخدام واجهة برمجة تطبيقات واحدة. #١٣ microsoft/playwright (⭐ ٩٨٫٧ ألف • +٩٠ اليوم • تايب سكريبت) Playwright هو إطار عمل لاختبار وأتمتة الويب. يتيح اختبار Chromium وFirefox وWebKit باستخدام واجهة برمجة تطبيقات واحدة. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6 ألف • +64 اليوم • TypeScript) 🔥🔥🔥 بديل مفتوح المصدر لـ Jira وLinear وMonday وClickUp. Plane منصة حديثة لإدارة المشاريع، تُتيح إدارة المهام، والسباقات، والوثائق، وفرز المهام. Plane 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5 ألف • +17 اليوم • C) بحث مفتوح المصدر عن تشابه المتجهات لـ Postgres 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━ اشترك في @devrepos للاطلاع على أحدث مستودعات GitHub الرائجة، وملخصات المشاريع مفتوحة المصدر، وأخبار المطورين. فيديوهات جديدة يوميًا تغطي أفضل برامج المصادر المفتوحة والمشاريع الرائجة على GitHub. [bn] ডেভেলপারদের জন্য সেরা কালেকশন: ২৩টি ট্রেন্ডিং গিটহাব রিপোজিটরি | ২৮-১০-২০২৬ | ডেভ রিপোস ডেইলি cutt.ly 🔥 এই নতুন এআই রিপোটির কথা আপনাকে কেউ বলেনি (সামার ২০২৭-ইন্টার্নশিপ) ট্রেন্ডিং গিটহাব রিপোজিটরিগুলোর এই বিশেষভাবে নির্বাচিত সংগ্রহের মাধ্যমে ওপেন সোর্স ইকোসিস্টেমের সাথে আপ-টু-ডেট থাকুন। উদ্ভাবনী ডেভেলপার টুলস থেকে শুরু করে যুগান্তকারী ফ্রেমওয়ার্ক পর্যন্ত, এই প্রজেক্টগুলোই এখন সফটওয়্যার কমিউনিটিতে আলোড়ন সৃষ্টি করছে। ২৮শে অক্টোবর, ২০২৬ এই ভিডিওতে যা আছে: আজ অর্জিত স্টারের ভিত্তিতে র‍্যাঙ্ক করা শীর্ষ ২৩টি ট্রেন্ডিং গিটহাব রিপোজিটরি - প্রতিটি ফিচার করা রিপোর জন্য ডেভেলপারদের মতামত এবং প্রজেক্টের সংক্ষিপ্ত বিবরণ - প্রতিটি ওপেন সোর্স প্রজেক্টের AI-বর্ণিত ওয়াকথ্রু - সহজে অ্যাক্সেসের জন্য নিচে সমস্ত রিপোজিটরির সরাসরি লিঙ্ক ⏱️ ​​টাইমস্ট্যাম্প (ইউটিউব চ্যাপ্টার): ০০:০০ - ভূমিকা: আজকের হাই-ইমপ্যাক্ট টুলস ০০:০৯ - ট্রেন্ডিং লিডারবোর্ডের সংক্ষিপ্ত বিবরণ ০০:২৫ - #১ Summer2027-Internships ০০:৫৪ - #২ n8n ০১:২৪ - #৩ datahub ০১:৪৫ - #৪ upscayl ০২:০৮ - #৫ free-for-dev ০২:৩৩ - #৬ awesome-remote-job ০২:৫৯ - #৭ Data-Science-For-Beginners ০৩:২৭ - #৮ পাওয়ারটয়েজ ০৩:৫০ - #৯ নিওভিম ০৪:১০ - #১০ জেলিফিন ০৪:৩৩ - #১১ লামা_ইনডেক্স ০৪:৫৬ - #১২ সাইফট ০৫:১৮ - #১৩ প্লেরাইট ০৫:৪৬ - #১৪ প্লেন ০৬:০৯ - #১৫ পিজিভেক্টর ০৬:২৮ - #১৬ অসাম ০৬:৫৯ - #১৭ রানার-ইমেজেস ০৭:১৯ - #১৮ রাস্টডেস্ক ০৭:৪৩ - #১৯ লেজিগিট ০৮:০৫ - #২০ ট্রান্সফর্মার্স ০৮:৩১ - #২১ ডিরেক্টাস ০৯:০২ - #২২ গিটলিকস ০৯:২২ - #২৩ ওপেনকোর-লেগ্যাসি-প্যাচার ০৯:৪৫ - আউটরো ও কী মূল কথা ━━━━━━━━━━━━━━━━━━━━━━━ আজকের শীর্ষ ২৩টি ট্রেন্ডিং রিপোজিটরি: ━━━━━━━━━━━━━━━━━━━━━━ #১ SimplifyJobs/Summer2027-Internships (⭐ ৪৮.২ হাজার • আজ +২১ • পাইথন) সামার ২০২৭ সফটওয়্যার ইঞ্জিনিয়ারিং, ডেটা সায়েন্স, এআই, কোয়ান্ট, প্রোডাক্ট ম্যানেজমেন্ট, এবং হার্ডওয়্যার ইন্টার্নশিপের পোস্ট। Simplify এবং Pitt CSC দ্বারা প্রতিদিন আপডেট করা হয়। 🔗 SimplifyJobs/Summer2027-Internships #২ n8n-io/n8n (⭐ ২০৮.৯ হাজার • আজ +১৬১ • টাইপস্ক্রিপ্ট) নেটিভ এআই সক্ষমতাসহ ফেয়ার-কোড ওয়ার্কফ্লো অটোমেশন প্ল্যাটফর্ম। ভিজ্যুয়াল বিল্ডিংয়ের সাথে কাস্টম কোড, সেলফ-হোস্ট বা ক্লাউড, ৪০০+ ইন্টিগ্রেশন একত্রিত করুন। 🔗 n8n-io/n8n #৩ datahub-project/datahub (⭐ ১২.৯ হাজার • আজ +১০ • পাইথন) আপনার ডেটা এবং এআই স্ট্যাকের জন্য কনটেক্সট প্ল্যাটফর্ম। 🔗 datahub-project/datahub #৪ upscayl/upscayl (⭐ ৫১.২ হাজার • আজ +৭৬ • টাইপস্ক্রিপ্ট) 🆙 Upscayl - লিনাক্স, ম্যাকওএস এবং উইন্ডোজের জন্য #১ ফ্রি এবং ওপেন সোর্স এআই ইমেজ আপস্কেলার। 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • আজ +55 • HTML) SaaS, PaaS এবং IaaS অফারগুলির একটি তালিকা, যেগুলির ফ্রি টিয়ার রয়েছে এবং যা ডেভঅপ্স ও ইনফ্রাডেভের জন্য আকর্ষণীয়। 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • আজ +99 • অজানা) চমৎকার রিমোট চাকরি এবং রিসোর্সের একটি সংকলিত তালিকা। vinta/awesome-python দ্বারা অনুপ্রাণিত 🔗 lukasz-madon/awesome-remote-job #৭ microsoft/Data-Science-For-Beginners (⭐ ৩৭.৮ হাজার • আজ +৪৭ • জুপিটার নোটবুক) ১০ সপ্তাহ, ২০টি পাঠ, সকলের জন্য ডেটা সায়েন্স! 🔗 microsoft/Data-Science-For-Beginners #৮ microsoft/PowerToys (⭐ ১৪০.১ হাজার • আজ +৮৪ • C) মাইক্রোসফট পাওয়ারটয়েজ হলো ইউটিলিটিগুলোর একটি সংগ্রহ যা উইন্ডোজে উৎপাদনশীলতা এবং কাস্টমাইজেশনকে দারুণভাবে বাড়িয়ে তোলে। 🔗 microsoft/PowerToys #৯ neovim/neovim (⭐ ১০৩.২ হাজার • আজ +৪১ • Vim Script) এক্সটেনসিবিলিটি এবং ব্যবহারযোগ্যতার উপর দৃষ্টি নিবদ্ধ একটি ভিম-ফর্ক। 🔗 neovim/neovim #১০ jellyfin/jellyfin (⭐ ৫৮.৭ হাজার • আজ +৭৪ • C#) ফ্রি সফটওয়্যার মিডিয়া সিস্টেম - সার্ভার ব্যাকএন্ড এবং এপিআই। 🔗 jellyfin/jellyfin #১১ run-llama/llama_index (⭐ ৫২.৭ হাজার • আজ +২৩) • পাইথন) লামা ইনডেক্স (LlamaIndex) হলো এআই (AI)-এর জন্য ডকুমেন্ট প্রসেসিং প্ল্যাটফর্ম 🔗 run-llama/llama_index #১২ অ্যাঙ্কোর/সিফট (⭐ ৯.৮ হাজার • আজ +১৪ • গো) কন্টেইনার ইমেজ এবং ফাইলসিস্টেম থেকে সফটওয়্যার বিল অফ মেটেরিয়ালস (Software Bill of Materials) তৈরি করার জন্য একটি সিএলআই (CLI) টুল এবং লাইব্রেরি 🔗 anchore/syft #১৩ মাইক্রোসফট/প্লেরাইট (⭐ ৯৮.৭ হাজার • আজ +৯০ • টাইপস্ক্রিপ্ট) প্লেরাইট (Playwright) হলো ওয়েব টেস্টিং এবং অটোমেশনের জন্য একটি ফ্রেমওয়ার্ক। এটি একটিমাত্র এপিআই (API) দিয়ে ক্রোমিয়াম (Chromium), ফায়ারফক্স (Firefox) এবং ওয়েবকিট (WebKit) পরীক্ষা করার সুযোগ দেয়। 🔗 microsoft/playwright #১৪ মেকপ্লেন/প্লেন (⭐ ৬১.৬ হাজার • আজ +৬৪ • টাইপস্ক্রিপ্ট) 🔥🔥🔥 ওপেন-সোর্স জিরা (Jira), লিনিয়ার (Linear), মানডে (Monday), এবং ক্লিকআপ (ClickUp)-এর বিকল্প। প্লেন হলো টাস্ক, স্প্রিন্ট, ডক্স এবং ট্রায়েজ পরিচালনার জন্য একটি আধুনিক প্রজেক্ট ম্যানেজমেন্ট প্ল্যাটফর্ম। 🔗 makeplane/plane #15 pgvector/pgvector (⭐ ২৩.৫ হাজার • আজ +১৭ • C) পোস্টগ্রেসের জন্য ওপেন-সোর্স ভেক্টর সিমিলারিটি সার্চ 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━ প্রতিদিনের গিটহাব ট্রেন্ডিং রিপোজিটরি, ওপেন সোর্স রাউন্ডআপ এবং ডেভেলপার নিউজের জন্য @devrepos-এ সাবস্ক্রাইব করুন। প্রতিদিন গিটহাবের সেরা ওপেন সোর্স সফটওয়্যার এবং ট্রেন্ডিং প্রজেক্টগুলো নিয়ে নতুন ভিডিও আসে। [de-DE] Der ultimative Entwickler-Stack: 23 angesagte GitHub-Repos | 28.10.2026 | Dev Repos Daily cutt.ly 🔥 Dieses neue KI-Repository ist noch ein Geheimtipp (Sommer 2027 – Praktika) Bleiben Sie mit dieser handverlesenen Auswahl angesagter GitHub-Repositories immer auf dem Laufenden im Open-Source-Ökosystem. Von innovativen Entwicklertools bis hin zu bahnbrechenden Frameworks – diese Projekte begeistern die Software-Community geradezu. 28. Oktober 2026 Inhalt dieses Videos: - Die 23 beliebtesten GitHub-Repositories, sortiert nach den heute erhaltenen Sternen - Entwickler-Einblicke und Projektübersichten zu jedem vorgestellten Repository - KI-gestützter Rundgang durch jedes Open-Source-Projekt - Direkte Links zu allen Repositories für einfachen Zugriff ⏱️ ZEITSTEMPEL (YouTube-Kapitel): 00:00 - Intro: Die wichtigsten Tools von heute 00:09 - Übersicht der beliebtesten Repositories 00:25 - #1 Sommer 2027 – Praktika 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Data-Science-For-Beginners 03:27 - #8 PowerToys 03:50 – #9 neovim 04:10 – #10 jellyfin 04:33 – #11 llama_index 04:56 – #12 syft 05:18 – #13 playwright 05:46 – #14 plane 06:09 – #15 pgvector 06:28 – #16 awesome 06:59 – #17 runner-images 07:19 – #18 rustdesk 07:43 – #19 lazygit 08:05 – #20 transformers 08:31 – #21 directus 09:02 – #22 gitleaks 09:22 – #23 OpenCore-Legacy-Patcher 09:45 – Abspann & Legende Wichtigste Erkenntnisse ━━━━━━━━━━━━━━━━━━━━━━━ Die 23 beliebtesten Jobportale von heute: ━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Sommer 2027 – Praktika (⭐ 48,2k • +21 heute • Python) Stellenangebote für Praktika im Bereich Softwareentwicklung, Data Science, KI, Quantitative Methoden, Produktmanagement und Hardware im Sommer 2027. Täglich aktualisiert von Simplify und Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208,9k • +161 heute • TypeScript) Fair-Code-Workflow-Automatisierungsplattform mit nativen KI-Funktionen. Visuelles Erstellen mit benutzerdefiniertem Code kombinieren, selbst hosten oder in der Cloud, über 400 Integrationen. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12,9k • +10 heute • Python) Die Kontextplattform für Ihren Daten- und KI-Stack. 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51,2k • +76 heute • TypeScript) 🆙 Upscayl – Nr. 1 kostenloser Open-Source-KI-Bild-Upscaler für Linux, macOS und Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.200 • +55 heute • HTML) Eine Liste von SaaS-, PaaS- und IaaS-Angeboten mit kostenlosen Tarifen, die für DevOps- und Infrastrukturentwickler interessant sind. 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50.000 • +99 heute • Unbekannt) Eine kuratierte Liste mit tollen Remote-Jobs und Ressourcen. Inspiriert von vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37,8k • +47 heute • Jupyter Notebook) 10 Wochen, 20 Lektionen: Data Science für alle! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140,1k • +84 heute • C) Microsoft PowerToys ist eine Sammlung von Hilfsprogrammen, die die Produktivität und Anpassungsmöglichkeiten unter Windows deutlich verbessern. 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103,2k • +41 heute • Vim Script) Vim-Fork mit Fokus auf Erweiterbarkeit und Benutzerfreundlichkeit. 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58,7k • +74 heute • C#) Das Free Software Media System – Server-Backend & API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52,7k • +23 heute • Python) LlamaIndex ist die Dokumentenverarbeitungsplattform für KI. 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9,8k • +14 heute • Go) CLI-Tool und Bibliothek zur Generierung einer Software-Stückliste aus Container-Images und Dateisystemen. 🔗 anchore/syft #13 microsoft/playwright (⭐ 98,7k • +90 heute • TypeScript) Playwright ist ein Framework für Webtests und -automatisierung. Es ermöglicht das Testen von Chromium, Firefox und WebKit mit einer einzigen API. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61,6k • +64 heute • TypeScript) 🔥🔥🔥 Plane ist eine Open-Source-Alternative zu Jira, Linear, Monday und ClickUp. Es handelt sich um eine moderne Projektmanagement-Plattform zur Verwaltung von Aufgaben, Sprints, Dokumenten und Priorisierung. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23,5k • +17 heute • C) Open-Source-Vektorähnlichkeitssuche für PostgreSQL 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━━ Abonniere @devrepos für täglich aktuelle GitHub-Repositories, Open-Source-Übersichten und Entwickler-News. Täglich neue Videos zu den besten Open-Source-Softwareprojekten und Trendprojekten auf GitHub. [es-US] El stack definitivo para desarrolladores: 23 repositorios de GitHub en tendencia | 28/10/2026 | D... cutt.ly 🔥 Nadie te habló de este nuevo repositorio de IA (Prácticas de verano de 2027) Mantente al día con el ecosistema de código abierto con esta selección de repositorios de GitHub que marcan tendencia. Desde herramientas innovadoras para desarrolladores hasta frameworks revolucionarios, estos son los proyectos que están causando sensación en la comunidad de software. 28 de octubre de 2026 Contenido de este video: - Los 23 repositorios de GitHub más populares, clasificados según las estrellas obtenidas hoy. - Información para desarrolladores y descripciones generales de cada repositorio destacado. - Tutorial guiado por IA de cada proyecto de código abierto. - Enlaces directos a todos los repositorios a continuación para un acceso fácil. ⏱️ MARCAS DE TIEMPO (Capítulos de YouTube): 00:00 - Introducción: Herramientas de alto impacto de hoy 00:09 - Resumen de la clasificación de tendencias 00:25 - N.° 1: Prácticas de verano de 2027 00:54 - N.° 2: n8n 01:24 - N.° 3: datahub 01:45 - N.° 4: upscayl 02:08 - N.° 5: free-for-dev 02:33 - N.° 6: awesome-remote-job 02:59 - N.° 7: Ciencia de datos para principiantes 03:27 - N.° 8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - Outro y clave Conclusiones ━━━━━━━━━━━━━━━━━━━━━━━ LOS 23 REPOSITORIOS MÁS POPULARES DE HOY: ━━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-Interships (⭐ 48.2k • +21 hoy • Python) Ofertas de prácticas de verano de 2027 en ingeniería de software, ciencia de datos, IA, análisis cuantitativo, gestión de productos y hardware. Actualizado diariamente por Simplify y Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9k • +161 hoy • TypeScript) Plataforma de automatización de flujos de trabajo con código justo y capacidades de IA nativas. Combina la creación visual con código personalizado, autoalojamiento o en la nube, más de 400 integraciones. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • +10 hoy • Python) La plataforma de contexto para tu pila de datos e IA. 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • +76 hoy • TypeScript) 🆙 Upscayl: el escalador de imágenes con IA gratuito y de código abierto n.° 1 para Linux, macOS y Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • +55 hoy • HTML) Una lista de ofertas de SaaS, PaaS e IaaS con planes gratuitos de interés para devops e infradev. 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • +99 hoy • Desconocido) Una lista seleccionada de excelentes trabajos y recursos remotos. Inspirado en vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Ciencia de Datos para Principiantes (⭐ 37.8k • +47 hoy • Jupyter Notebook) 10 semanas, 20 lecciones: ¡Ciencia de Datos para todos! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • +84 hoy • C) Microsoft PowerToys es una colección de utilidades que potencian la productividad y la personalización en Windows. 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • +41 hoy • Vim Script) Bifurcación de Vim centrada en la extensibilidad y la usabilidad. 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • +74 hoy • C#) Sistema de medios de software libre: backend y API del servidor. 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • +23 hoy • Python) LlamaIndex es la plataforma de procesamiento de documentos para IA. 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • +14 hoy • Go) Herramienta y biblioteca de línea de comandos para generar una lista de materiales de software a partir de imágenes de contenedores y sistemas de archivos. 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • +90 hoy • TypeScript) Playwright es un framework para pruebas y automatización web. Permite probar Chromium, Firefox y WebKit con una única API. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • +64 hoy • TypeScript) 🔥🔥🔥 Alternativa de código abierto a Jira, Linear, Monday y ClickUp. Plane es una plataforma moderna de gestión de proyectos para administrar tareas, sprints, documentación y priorización de tareas. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5k • +17 hoy • C) Búsqueda de similitud vectorial de código abierto para PostgreSQL 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━━ Suscríbete a @devrepos para ver diariamente los repositorios más populares de GitHub, resúmenes de código abierto y noticias para desarrolladores. Nuevos videos cada d... [fr-FR] Le stack ultime du développeur : 23 dépôts GitHub en vogue | 28/10/2026 | Dev Repos Daily cutt.ly 🔥 Personne ne vous a parlé de ce nouveau dépôt d'IA (Stages été 2027) Restez à la pointe de l'écosystème open source grâce à cette sélection exclusive de dépôts GitHub incontournables. Des outils de développement innovants aux frameworks révolutionnaires, découvrez les projets qui font sensation dans la communauté du logiciel en ce moment. 28 octobre 2026 Contenu de cette vidéo : - Les 23 dépôts GitHub les plus populaires, classés par nombre d'étoiles gagnées aujourd'hui - Présentations et analyses de chaque dépôt mis en avant - Visite guidée commentée par une IA de chaque projet open source - Liens directs vers tous les dépôts ci-dessous pour un accès facile ⏱️ HORODATAGE (Chapitres YouTube) : 00:00 - Introduction : Les outils à fort impact du jour 00:09 - Aperçu du classement des tendances 00:25 - N° 1 Summer2027-Internships 00:54 - N° 2 n8n 01:24 - N° 3 datahub 01:45 - N° 4 upscayl 02:08 - N° 5 free-for-dev 02:33 - N° 6 awesome-remote-job 02:59 - N° 7 Data-Science-For-Beginners 03:27 - N° 8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - Conclusion et légende Points clés ━━━━━━━━━━━━━━━━━━━━━━ TOP 23 DES DÉPÔTS LES PLUS CONSULTÉS AUJOURD'HUI : ━━━━━━━━━━━━━━━━━━━━━━━ N° 1 SimplifyJobs/Stages Été 2027 (⭐ 48,2 k • +21 aujourd'hui • Python) Offres de stage Été 2027 en génie logiciel, science des données, IA, analyse quantitative, gestion de produits et matériel informatique. Mise à jour quotidienne par Simplify et Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208,9 k • +161 aujourd'hui • TypeScript) Plateforme d'automatisation des flux de travail FairCode avec des fonctionnalités d'IA natives. Combinez la création visuelle avec du code personnalisé, auto-hébergé ou cloud, plus de 400 intégrations. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12,9 k • +10 aujourd'hui • Python) La plateforme Context pour votre stack de données et d'IA 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51,2 k • +76 aujourd'hui • TypeScript) 🆙 Upscayl - Upscaler d'images IA gratuit et open source n° 1 pour Linux, macOS et Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140,2 k • +55 aujourd'hui • HTML) Liste d'offres SaaS, PaaS et IaaS proposant des versions gratuites intéressantes pour les développeurs DevOps et Infrastructure. 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50 k • +99 aujourd'hui • Inconnu) Sélection d'offres d'emploi et de ressources intéressantes pour le télétravail. Inspiré par vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37,8 k • +47 aujourd'hui • Jupyter Notebook) 10 semaines, 20 leçons : la science des données pour tous ! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140,1 k • +84 aujourd'hui • C) Microsoft PowerToys est une collection d'utilitaires qui optimisent la productivité et la personnalisation sous Windows. 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103,2 k • +41 aujourd'hui • Script Vim) Une version dérivée de Vim axée sur l'extensibilité et la facilité d'utilisation. 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58,7 k • +74 aujourd'hui • C#) Le système de gestion de médias de logiciels libres : serveur et API. 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52,7 k • +23 aujourd'hui • Python) LlamaIndex est une plateforme de traitement de documents pour l'IA. 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9,8 k • +14 aujourd'hui • Go) Outil et bibliothèque en ligne de commande pour générer une nomenclature logicielle à partir d'images de conteneurs et de systèmes de fichiers. 🔗 anchore/syft #13 microsoft/playwright (⭐ 98,7 k • +90 aujourd'hui • TypeScript) Playwright est un framework pour les tests et l'automatisation web. Il permet de tester Chromium, Firefox et WebKit avec une API unique. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61,6 k • +64 aujourd'hui • TypeScript) 🔥🔥🔥 Alternative open source à Jira, Linear, Monday et ClickUp. Plane est une plateforme moderne de gestion de projet permettant de gérer les tâches, les sprints, la documentation et le triage. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23,5 k • +17 aujourd'hui • C) Outil open source de recherche de similarité vectorielle pour Postgres 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━ Abonnez-vous à @devrepos pour découvrir chaque jour les dépôts GitHub les plus ... [hi] बेहतरीन डेवलपर स्टैक: 23 ट्रेंडिंग GitHub रिपोज़ | 28-10-2026 | डेव रिपोज़ डेली cutt.ly 🔥 इस नए AI रिपॉजिटरी के बारे में आपको किसी ने नहीं बताया (ग्रीष्म 2027 - इंटर्नशिप) ओपन सोर्स इकोसिस्टम की नवीनतम जानकारी के लिए GitHub के इन चुनिंदा रिपॉजिटरी का इस्तेमाल करें। इनोवेटिव डेवलपर टूल्स से लेकर क्रांतिकारी फ्रेमवर्क तक, ये वो प्रोजेक्ट हैं जो सॉफ्टवेयर जगत में धूम मचा रहे हैं। 28 अक्टूबर, 2026 इस वीडियो में क्या है: - आज प्राप्त स्टार रेटिंग के आधार पर शीर्ष 23 ट्रेंडिंग GitHub रिपॉजिटरी - प्रत्येक प्रदर्शित रिपॉजिटरी के लिए डेवलपर अंतर्दृष्टि और प्रोजेक्ट अवलोकन - प्रत्येक ओपन सोर्स प्रोजेक्ट का AI-नैरेटेड वॉकथ्रू - आसान पहुँच के लिए नीचे सभी रिपॉजिटरी के सीधे लिंक ⏱️ टाइमस्टैम्प (YouTube अध्याय): 00:00 - परिचय: आज के उच्च-प्रभाव वाले उपकरण 00:09 - ट्रेंडिंग लीडरबोर्ड अवलोकन 00:25 - #1 समर 2027 इंटर्नशिप 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 फ्री-फॉर-डेव 02:33 - #6 शानदार रिमोट जॉब 02:59 - #7 डेटा साइंस फॉर बिगिनर्स 03:27 - #8 पॉवर टॉयज़ 03:50 - #9 नेओविम 04:10 - #10 जेलीफिन 04:33 - #11 लामा_इंडेक्स 04:56 - #12 सिफ्ट 05:18 - #13 प्लेराइट 05:46 - #14 प्लेन 06:09 - #15 पीजीवेक्टर 06:28 - #16 ऑसम 06:59 - #17 रनर-इमेजेज 07:19 - #18 रस्टडेस्क 07:43 - #19 लेज़ीगिट 08:05 - #20 ट्रांसफॉर्मर्स 08:31 - #21 डायरेक्टस 09:02 - #22 गिटलीक्स 09:22 - #23 ओपनकोर-लेगेसी-पैचर 09:45 - आउट्रो और कुंजी मुख्य बिंदु ━━━━━━━━━━━━━━━━━━━━━━━━ आज के शीर्ष 23 ट्रेंडिंग रिपॉजिटरी: ━━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-इंटर्नशिप (⭐ 48.2k • आज +21 • पायथन) ग्रीष्मकालीन 2027 के लिए सॉफ्टवेयर इंजीनियरिंग, डेटा साइंस, एआई, क्वांट, प्रोडक्ट मैनेजमेंट और हार्डवेयर इंटर्नशिप के अवसर। Simplify और Pitt CSC द्वारा प्रतिदिन अपडेट किया जाता है। 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9k • आज +161 • TypeScript) नेटिव AI क्षमताओं वाला फेयर-कोड वर्कफ़्लो ऑटोमेशन प्लेटफ़ॉर्म। विज़ुअल बिल्डिंग को कस्टम कोड के साथ मिलाएं, सेल्फ-होस्ट या क्लाउड का उपयोग करें, 400+ इंटीग्रेशन उपलब्ध हैं। 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • आज +10 • Python) आपके डेटा और AI स्टैक के लिए कॉन्टेक्स्ट प्लेटफ़ॉर्म 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • आज +76 • TypeScript) 🆙 Upscayl - Linux, MacOS और Windows के लिए नंबर 1 मुफ़्त और ओपन सोर्स AI इमेज अपस्केलर। 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • आज +55 • HTML) SaaS, PaaS और IaaS सेवाओं की एक सूची जिनमें DevOps और InfraDevelop के लिए उपयोगी निःशुल्क विकल्प उपलब्ध हैं। 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • आज +99 • अज्ञात) शानदार रिमोट जॉब्स और संसाधनों की एक चुनिंदा सूची। vinta/awesome-python से प्रेरित 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37.8k • आज +47 • Jupyter Notebook) 10 सप्ताह, 20 पाठ, सभी के लिए डेटा साइंस! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • आज +84 • C) Microsoft PowerToys यूटिलिटीज़ का एक संग्रह है जो Windows पर उत्पादकता और अनुकूलन को बढ़ाता है। 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • आज +41 • Vim Script) विस्तारशीलता और उपयोगिता पर केंद्रित Vim-फोर्क 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • आज +74 • C#) फ्री सॉफ्टवेयर मीडिया सिस्टम - सर्वर बैकएंड और API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • आज +23 • Python) LlamaIndex एक AI-आधारित दस्तावेज़ प्रसंस्करण प्लेटफ़ॉर्म है। 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • आज +14 • Go) कंटेनर इमेज और फ़ाइल सिस्टम से सॉफ़्टवेयर बिल ऑफ़ मैटेरियल्स जनरेट करने के लिए एक CLI टूल और लाइब्रेरी। 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • आज +90 • TypeScript) Playwright वेब टेस्टिंग और ऑटोमेशन के लिए एक फ़्रेमवर्क है। यह एक ही API के साथ Chromium, Firefox और WebKit की टेस्टिंग की सुविधा देता है। 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • आज +64 • TypeScript) 🔥🔥🔥 ओपन-सोर्स Jira, Linear, Monday और ClickUp का विकल्प। Plane एक आधुनिक प्रोजेक्ट मैनेजमेंट प्लेटफॉर्म है जिसका उपयोग टास्क, स्प्रिंट, डॉक्स और ट्राइएज को मैनेज करने के लिए किया जाता है। 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5k • आज +17 • C) पोस्टग्रेस के लिए ओपन-सोर्स वेक्टर समानता खोज 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━━ दैनिक GitHub ट्रेंडिंग रिपॉजिटरी, ओपन सोर्स राउंडअप और डेवलपर समाचारों के लिए @devrepos को सब्सक्राइब करें। GitHub पर सर्वश्रेष्ठ ओपन सोर्स सॉफ़्टवेयर और ट्रेंडिंग प्रोजेक्ट्स को कवर करने वाले नए वीडियो हर दिन देखें। [id] Tumpukan Perangkat Lunak Pengembang Terbaik: 23 Repositori GitHub Terpopuler | 28-10-2026 | Dev R... cutt.ly 🔥 Belum Ada yang Memberitahu Anda Tentang Repositori AI Baru Ini (Magang Musim Panas 2027) Tetap terhubung dengan ekosistem open source dengan pilihan repositori GitHub yang sedang tren ini. Dari alat pengembang yang inovatif hingga kerangka kerja yang inovatif, ini adalah proyek-proyek yang sedang membuat gebrakan di komunitas perangkat lunak saat ini. 28 Oktober 2026 Isi video ini: - 23 repositori GitHub terpopuler yang diurutkan berdasarkan bintang yang diperoleh hari ini - Wawasan pengembang dan ikhtisar proyek untuk setiap repositori unggulan - Panduan yang dinarasikan AI untuk setiap proyek sumber terbuka - Tautan langsung ke semua repositori di bawah ini untuk akses mudah ⏱️ CAP WAKTU (Bab YouTube): 00:00 - Pendahuluan: Alat-alat Berdampak Tinggi Hari Ini 00:09 - Ikhtisar Papan Peringkat Terpopuler 00:25 - #1 Magang Musim Panas 2027 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Ilmu Data untuk Pemula 03:27 - #8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - Outro & Key Poin Penting ━━━━━━━━━━━━━━━━━━━━━━━ 23 REPOSITORI YANG SEDANG TREN HARI INI: ━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-Internships (⭐ 48,2 ribu • +21 hari ini • Python) Lowongan magang musim panas 2027 di bidang rekayasa perangkat lunak, ilmu data, AI, kuantitatif, manajemen produk, dan perangkat keras. Diperbarui setiap hari oleh Simplify dan Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208,9 ribu • +161 hari ini • TypeScript) Platform otomatisasi alur kerja fair-code dengan kemampuan AI asli. Gabungkan pembuatan visual dengan kode kustom, hosting sendiri atau cloud, 400+ integrasi. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • +10 hari ini • Python) Platform Konteks untuk Tumpukan Data dan AI Anda 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • +76 hari ini • TypeScript) 🆙 Upscayl - Pengoptimal Gambar AI Gratis dan Sumber Terbuka #1 untuk Linux, MacOS, dan Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • +55 hari ini • HTML) Daftar penawaran SaaS, PaaS, dan IaaS yang memiliki tingkatan gratis yang menarik bagi devops dan infradev 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • +99 hari ini • Tidak diketahui) Daftar pilihan pekerjaan dan sumber daya jarak jauh yang luar biasa. Terinspirasi oleh vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37.8k • +47 hari ini • Jupyter Notebook) 10 Minggu, 20 Pelajaran, Ilmu Data untuk Semua! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • +84 hari ini • C) Microsoft PowerToys adalah kumpulan utilitas yang meningkatkan produktivitas dan kustomisasi di Windows 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • +41 hari ini • Skrip Vim) Vim-fork yang berfokus pada ekstensibilitas dan kegunaan 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • +74 hari ini • C#) Sistem Media Perangkat Lunak Gratis - Backend Server & API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • +23 hari ini • Python) LlamaIndex adalah platform pemrosesan dokumen untuk AI 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • +14 hari ini • Go) Alat dan pustaka CLI untuk menghasilkan Daftar Material Perangkat Lunak dari citra kontainer dan sistem file 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • +90 hari ini • TypeScript) Playwright adalah kerangka kerja untuk Pengujian dan Otomatisasi Web. Ini memungkinkan pengujian Chromium, Firefox, dan WebKit dengan satu API. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • +64 hari ini • TypeScript) 🔥🔥🔥 Alternatif Jira, Linear, Monday, dan ClickUp sumber terbuka. Plane adalah platform manajemen proyek modern untuk mengelola tugas, sprint, dokumen, dan triase. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5k • +17 hari ini • C) Pencarian kesamaan vektor sumber terbuka untuk Postgres 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━ Berlangganan @devrepos untuk repositori GitHub yang sedang tren setiap hari, rangkuman sumber terbuka, dan berita pengembang. Video baru setiap hari yang membahas perangkat lunak sumber terbuka terbaik dan proyek-proyek yang sedang tren di GitHub. [it] Lo stack definitivo per sviluppatori: 23 repository GitHub di tendenza | 28/10/2026 | Dev Repos D... Italiano: cutt.ly 🔥 Nessuno ti ha parlato di questo nuovo repository di IA (Tirocini estivi 2027) Rimani aggiornato sull'ecosistema open source con questa selezione accurata di repository GitHub di tendenza. Da strumenti di sviluppo innovativi a framework rivoluzionari, questi sono i progetti che stanno facendo scalpore nella comunità del software in questo momento. 28 ottobre 2026 Contenuto del video: - I 23 repository GitHub più popolari, classificati in base alle stelle guadagnate oggi - Approfondimenti per sviluppatori e panoramiche dei progetti per ogni repository presentato - Guida interattiva con narrazione AI per ogni progetto open source - Link diretti a tutti i repository qui sotto per un facile accesso ⏱️ INDICE DEI CONTENUTI (Capitoli di YouTube): 00:00 - Introduzione: Strumenti di grande impatto di oggi 00:09 - Panoramica della classifica dei più popolari 00:25 - #1 Summer2027-Internships 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Data-Science-For-Beginners 03:27 - #8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - Outro e Key Punti chiave ━━━━━━━━━━━━━━━━━━━━━━━━ I 23 REPOSITORY PIÙ DI TENDENZA DI OGGI: ━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Summer2027-Internships (⭐ 48.2k • +21 oggi • Python) Offerte di stage per l'estate 2027 in ingegneria del software, data science, IA, analisi quantitativa, gestione del prodotto e hardware. Aggiornato quotidianamente da Simplify e Pitt CSC. #1 SimplifyJobs/Summer2027-Internships (⭐ 48.2k • +21 oggi • Python) Offerte di stage per l'estate 2027 in ingegneria del software, data science, IA, analisi quantitativa, gestione del prodotto e hardware. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9k • +161 oggi • TypeScript) Piattaforma di automazione del flusso di lavoro con codice equo e funzionalità native di IA. Combina la creazione visuale con codice personalizzato, self-hosting o cloud, oltre 400 integrazioni. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • +10 oggi • Python) La piattaforma di contesto per il tuo stack di dati e IA 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • +76 oggi • TypeScript) 🆙 Upscayl - Il miglior strumento gratuito e open source per l'upscaling di immagini basato sull'IA per Linux, MacOS e Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • +55 oggi • HTML) Un elenco di offerte SaaS, PaaS e IaaS con piani gratuiti di interesse per DevOps e InfraDev 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • +99 oggi • Sconosciuto) Un elenco curato di fantastiche offerte di lavoro da remoto e risorse. Ispirato da vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37.8k • +47 oggi • Jupyter Notebook) 10 settimane, 20 lezioni, Data Science per tutti! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • +84 oggi • C) Microsoft PowerToys è una raccolta di utilità che potenziano la produttività e la personalizzazione su Windows 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • +41 oggi • Vim Script) Fork di Vim focalizzato su estensibilità e usabilità 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • +74 oggi • C#) Il sistema multimediale software libero - Backend server e API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • +23 oggi • Python) LlamaIndex è la piattaforma di elaborazione documenti per l'IA 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • +14 oggi • Go) Strumento e libreria CLI per la generazione di una distinta base software da immagini container e filesystem 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • +90 oggi • TypeScript) Playwright è un framework per il test e l'automazione web. Consente di testare Chromium, Firefox e WebKit con un'unica API. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • +64 oggi • TypeScript) 🔥🔥🔥 Alternativa open source a Jira, Linear, Monday e ClickUp. Plane è una moderna piattaforma di gestione progetti per gestire attività, sprint, documentazione e priorità. #14 makeplane/plane (⭐ 61.6k • +64 oggi • TypeScript) 🔥🔥🔥 Un'alternativa open source a Jira, Linear, Monday e ClickUp. Plane è una moderna piattaforma di gestione progetti per gestire attività, sprint, documentazione e priorità. #14 makeplane/plane (⭐ 61.6k • +64 oggi • TypeScript) 🔥🔥🔥 Un'alternativa open source a Jira, Linear, M... [iw] הסטאק האולטימטיבי למפתחים: 23 פרויקטי GitHub חמים | 28/10/2026 | Dev Repos Daily cutt.ly 🔥 אף אחד לא סיפר לכם על מאגר הבינה המלאכותית החדש הזה (קיץ 2027 - התמחויות) הישארו מעודכנים במערכת האקולוגית של קוד פתוח עם מבחר נבחר זה של מאגרי GitHub טרנדיים. מכלי פיתוח חדשניים ועד מסגרות פורצות דרך, אלו הפרויקטים שעושים גלים בקהילת התוכנה כרגע. 28 באוקטובר, 2026 מה יש בסרטון הזה: - 23 מאגרי GitHub הטרנדיים ביותר מדורגים לפי כוכבים שנצברו היום - תובנות מפתחים וסקירות פרויקטים עבור כל מאגר מומלץ - סיור מקיף על כל פרויקט קוד פתוח באמצעות בינה מלאכותית - קישורים ישירים לכל המאגרים למטה לגישה נוחה ⏱️ חותמות זמן (פרקי YouTube): 00:00 - מבוא: הכלים בעלי ההשפעה הגבוהה של היום 00:09 - סקירת טבלת המובילים הטרנדיים 00:25 - #1 קיץ 2027 - התמחויות 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Data-Science-For-Beginners 03:27 - #8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awesome 06:59 - #17 runner-images 07:19 - #18 rustdesk 07:43 - #19 lazygit 08:05 - #20 transformers 08:31 - #21 directus 09:02 - #22 gitleaks 09:22 - #23 OpenCore-Legacy-Patcher 09:45 - סיום וסולם נקודות חשובות ━━━━━━━━━━━━━━━━━━━━━━━ 23 המאגרים הטרנדיים ביותר של היום: ━━━━━━━━━━━━━━━━━━━━━━━ #1 SimplifyJobs/Kitchen2027-Internships (⭐ 48.2k • +21 היום • Python) פרסומי התמחויות בהנדסת תוכנה, מדעי נתונים, בינה מלאכותית, כימות, ניהול מוצר וחומרה בקיץ 2027. מתעדכנים מדי יום על ידי Simplify ו-Pitt CSC. 🔗 SimplifyJobs/Summer2027-Internships #2 n8n-io/n8n (⭐ 208.9k • +161 היום • TypeScript) פלטפורמת אוטומציה של זרימת עבודה בקוד הוגן עם יכולות בינה מלאכותית מקוריות. שלב בנייה ויזואלית עם קוד מותאם אישית, אירוח עצמי או ענן, 400+ אינטגרציות. 🔗 n8n-io/n8n #3 datahub-project/datahub (⭐ 12.9k • +10 היום • Python) פלטפורמת ההקשר עבור מחסנית הנתונים והבינה המלאכותית שלך 🔗 datahub-project/datahub #4 upscayl/upscayl (⭐ 51.2k • +76 היום • TypeScript) 🆙 Upscayl - #1 משדרג תמונות בינה מלאכותית חינמי וקוד פתוח עבור לינוקס, MacOS ו-Windows. 🔗 upscayl/upscayl #5 ripienaar/free-for-dev (⭐ 140.2k • +55 היום • HTML) רשימה של הצעות SaaS, PaaS ו-IaaS שיש להן שכבות חינמיות שמעניינות מפתחי פיתוח ופיתוח אינפרא-אדום 🔗 ripienaar/free-for-dev #6 lukasz-madon/awesome-remote-job (⭐ 50k • +99 היום • לא ידוע) רשימה אוצרת של עבודות ומשאבים מדהימים מרחוק. בהשראת vinta/awesome-python 🔗 lukasz-madon/awesome-remote-job #7 microsoft/Data-Science-For-Beginners (⭐ 37.8k • +47 היום • Jupyter Notebook) 10 שבועות, 20 שיעורים, מדעי נתונים לכולם! 🔗 microsoft/Data-Science-For-Beginners #8 microsoft/PowerToys (⭐ 140.1k • +84 היום • C) Microsoft PowerToys הוא אוסף של כלי עזר שמשפרים את הפרודוקטיביות וההתאמה האישית ב-Windows 🔗 microsoft/PowerToys #9 neovim/neovim (⭐ 103.2k • +41 היום • סקריפט Vim) Vim-fork המתמקד בהרחבה ושימושיות 🔗 neovim/neovim #10 jellyfin/jellyfin (⭐ 58.7k • +74 היום • C#) מערכת המדיה של תוכנה חופשית - שרת Backend ו-API 🔗 jellyfin/jellyfin #11 run-llama/llama_index (⭐ 52.7k • +23 היום • Python) LlamaIndex היא פלטפורמת עיבוד מסמכים עבור בינה מלאכותית 🔗 run-llama/llama_index #12 anchore/syft (⭐ 9.8k • +14 היום • Go) כלי וספרייה של ממשק שורת פקודה (CLI) ליצירת רשימת חומרים של תוכנה מתמונות של מכולות ומערכות קבצים 🔗 anchore/syft #13 microsoft/playwright (⭐ 98.7k • +90 היום • TypeScript) Playwright היא מסגרת לבדיקות אינטרנט ואוטומציה. היא מאפשרת בדיקת Chromium, Firefox ו-WebKit עם API יחיד. 🔗 microsoft/playwright #14 makeplane/plane (⭐ 61.6k • +64 היום • TypeScript) 🔥🔥🔥 אלטרנטיבה בקוד פתוח ל-Jira, Linear, Monday ו-ClickUp. Plane היא פלטפורמת ניהול פרויקטים מודרנית לניהול משימות, ספרינטים, מסמכים וטראז'. 🔗 makeplane/plane #15 pgvector/pgvector (⭐ 23.5k • +17 היום • C) חיפוש דמיון וקטורי בקוד פתוח עבור Postgres 🔗 pgvector/pgvector ━━━━━━━━━━━━━━━━━━━━━━━━ הירשמו ל-@devrepos לקבלת מאגרי GitHub יומיים פופולריים, סיכומים בקוד פתוח וחדשות מפתחים. סרטונים חדשים מדי יום המכסים את תוכנות הקוד הפתוח הטובות ביותר ופרויקטים פופולריים ב-GitHub. [ja] 究極の開発者スタック：GitHubでトレンドのレポジトリ23選 | 2026年10月28日 | 開発者レポジトリ・デイリー cutt.ly 🔥 誰も教えてくれなかった、注目のAIリポジトリ（2027年夏期インターンシップ） 厳選された話題のGitHubリポジトリで、オープンソースエコシステムの最新情報を常に把握しましょう。革新的な開発ツールから画期的なフレームワークまで、ソフトウェアコミュニティで今まさに注目を集めているプロジェクトをご紹介します。 2026年10月28日 この動画の内容： - 本日獲得したスター数でランキングされた、GitHubで話題のリポジトリ上位23件 - 各リポジトリの開発者向け解説とプロジェクト概要 - AIによるナレーション付き、各オープンソースプロジェクトの解説 - すべてのリポジトリへの直接リンク（下記参照） ⏱️ タイムスタンプ（YouTubeチャプター）： 00:00 - はじめに：今日の注目ツール 00:09 - トレンドランキング概要 00:25 - #1 Summer2027-Internships 00:54 - #2 n8n 01:24 - #3 datahub 01:45 - #4 upscayl 02:08 - #5 free-for-dev 02:33 - #6 awesome-remote-job 02:59 - #7 Data-Science-For-Beginners 03:27 - #8 PowerToys 03:50 - #9 neovim 04:10 - #10 jellyfin 04:33 - #11 llama_index 04:56 - #12 syft 05:18 - #13 playwright 05:46 - #14 plane 06:09 - #15 pgvector 06:28 - #16 awe

Open-source AI tool you need to know 📌 ​LlamaIndex is a data framework that connects your private documents, databases, and APIs to LLMs for production RAG. ​🔗 run-llama/llama_index ​Bookmark this for your next AI project! ​#OpenSource #GitHub #AI

Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document complexity in terms of use cases brought on from our customers, that are completely unsolved from a price-performance standpoint. These include trial balances, well logs, canvass binders, site plan reviews, duty reconciliations, loss runs, and more. Most existing extraction benchmarks are saturated by the frontier models (our cheapest modes now score 93%+ on ExtractBench). So there is enormous economic value in building a complex extraction benchmark that reflects this long-tail of knowledge work. We spent a long time curating the most complex elements that we saw from real world extraction use cases: ✅ Document conventions: containing legends or rules where the rest of the document should be interpreted ✅ Density of internal jargon: specific definitions that must be cross-referenced within a doc ✅ Aggregation and reasoning: beyond returning exact values on a page, infer values derived from aggregating multiple sources of information ✅ Drawn relations: complex diagrams like procedures and circuits ✅ Charts inside documents: read precise points within a chart ✅ Long chart sheets: some data series like well logs cram all this data into an extremely long page Within the industries where document extraction is relevant, even small mistakes can have enormous negative impacts. Therefore our headline evaluation metric is based on **perfect documents: the proportion of docs with perfect (100%) value f1 score** * (Besides this we also measure value f1 and grounded f1) It’s an extremely hard task for even the latest frontier models to solve, topping out at ~58% or so in Value F1 score for Codex with Astra - which is quite good but extremely expensive (almost $1 per page!) Our LlamaParse agentic/agentic plus modes are 20-40% and cover the left side of the Pareto frontier, at price ranges that are feasible for full production use. ExtractBenchPro is available today as a website/technical blog, and also on Github and HuggingFace. Check it out! Blog: lnkd.in Website: lnkd.in Hugging Face: lnkd.in Github (is ExtractBench): run-llama/ExtractBench

Code Search RAG: Chunk by Function With tree-sitter, Not by 512 Tokens - Book: RAG Pocket Guide - Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go - My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools - Me: xgabriel.com | GitHub --- The chunk the codebase assistant retrieved for "where does the retry delay get computed?" starts halfway down a function, at an except clause, and has no def line, no class name, nothing but a file path to say where it came from. The model writes a confident answer about a function it never saw the name of. The bug is in the chunker. It cut the file every 512 tokens, the way it cuts prose, and the cut landed in the middle of a function. Code has natural seams at def and class; a token counter ignores them. The fix fits in two small files. Parse Python with tree-sitter, cut at function and class boundaries, carry the file path and the enclosing symbol as metadata, and split the definitions that are too big on statement boundaries. Then score both chunkers on a labelled query set, so you see the difference on your own machine instead of taking my word for it. What a token window does to a function A fixed window has no idea where a function starts. If a function begins at token 470 of a 512-token window, the chunk gets the decorator, the signature and four lines of body. The next chunk gets the rest of the body with no name attached. Ask for "the function that parses the config file" and the retriever has to match either a signature with no logic or logic with no signature. The cAST paper from Carnegie Mellon and Augment Code found the same problem: "Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code." Their AST-based chunker, which splits large syntax nodes and merges small siblings under a size limit, reported a 4.3-point Recall@5 gain on RepoEval retrieval and a 2.67-point Pass@1 gain on SWE-bench generation over the fixed-size baseline. Frameworks have moved the same way. LlamaIndex ships a CodeSplitter that parses with tree-sitter and, in its source, caps chunks at 1,500 characters by default. The cAST authors published astchunk, which measures chunk size in non-whitespace characters and supports Python, Java, C# and TypeScript. Both are fine to use. Writing your own once is how you find out what they are doing for you, and how you tune it when your codebase has 600-line functions. !A source file as a vertical strip with dashed horizontal cut lines slicing straight through function blocks, next to the same file cut cleanly along function boundaries Token windows cut wherever the count runs out. AST chunks cut where the code does. Two chunkers, same token budget Install the pieces. py-tree-sitter 0.26.0 needs Python 3.10 or newer, and the Python grammar ships as its own wheel. pip install tree-sitter tree-sitter-python \ sentence-transformers numpy The embedder is BAAI/bge-small-en-v1.5: a small English model with 384 dimensions and a 512-token max sequence length. Both chunkers get the same budget of 480 tokens, counted with the embedder's own tokenizer, so a short header and the special tokens still fit under 512 and nothing gets silently truncated. That is the fair version of "512-token chunks". First file, chunkers.py, starting with the Chunk type: # chunkers.py from dataclasses import dataclass from typing import Callable import tree_sitter_python as tspython from tree_sitter import Language, Parser PARSER = Parser(Language(tspython.language())) DEFS = {"function_definition", "class_definition"} Count = Callable[[str], int] @dataclass class Chunk: path: str symbol: str # "JSONDecoder.raw_decode", "<module>", or "" start: int # 1-based first line end: int # 1-based last line, inclusive text: str def embed_text(self) -> str: head = f"# {self.path}" if self.symbol: head += f" :: {self.symbol}" return f"{head}\n{self.text}" The baseline comes next. It adds lines while their token count stays under the budget, emits a chunk, then backs up about 64 tokens for the overlap. The BGE tokenizer emits no token for a newline, so summing per line counts the same tokens the AST chunker counts on joined text: # chunkers.py (continued) def fixed_chunks(path: str, src: str, ntok: Count, size: int = 480, overlap: int = 64): lines = src.split("\n") out, i = [], 0 while i < len(lines): j, used = i, 0 while j < len(lines): cost = ntok(lines[j]) if used + cost > size and j > i: break used += cost j += 1 text = "\n".join(lines[i:j]) out.append(Chunk(path, "", i + 1, j, text)) if j >= len(lines): break back, k = 0, j while k > i + 1 and back < overlap: k -= 1 back += ntok(lines[k]) i = k return out The baseline is slightly generous: it snaps to line ends and overlaps about 64 tokens, where a plain token splitter would cut mid-line. Its chunks still get the file path in the header. The only things it lacks are syntax-aware boundaries and the symbol name, which keeps the comparison about exactly those two things. src.split("\n") rather than splitlines() is deliberate. tree-sitter counts rows by \n only, and splitlines() also splits on form feeds, which some older Python files contain. Mixing the two puts every line number after the first form feed off by one. The AST chunker goes in the same file. First the helpers that write chunks: # chunkers.py (continued) def unwrap(node): if node.type == "decorated_definition": return node.child_by_field_name("definition") return node def name_of(defn) -> str: return defn.child_by_field_name("name").text.decode() def ast_chunks(path: str, src: str, ntok: Count, budget: int = 480): lines = src.split("\n") tree = PARSER.parse(src.encode()) out: list[Chunk] = [] def span(a, b): # 0-based rows, inclusive return "\n".join(lines[a:b + 1]) def emit(sym, a, b, head=""): body = span(a, b) if ntok(head + body) <= budget: out.append(Chunk(path, sym, a + 1, b + 1, head + body)) return # one statement is bigger than the budget: window it room = max(budget - ntok(head), 64) for c in fixed_chunks(path, body, ntok, size=room): out.append(Chunk(path, sym, a + c.start, a + c.end, head + c.text)) emit writes one chunk when the span fits and falls back to line windows only when a single statement is bigger than the budget. pack sits on top of it and greedily groups neighbouring statements. It is still inside ast_chunks, so keep the indentation: # ast_chunks, continued def pack(nodes, sym, head=""): group = [] for node in nodes: if group: a = group[0].start_point[0] b = node.end_point[0] if ntok(head + span(a, b)) > budget: emit(sym, a, group[-1].end_point[0], head) group = [] group.append(node) if group: emit(sym, group[0].start_point[0], group[-1].end_point[0], head) The last two helpers decide what a unit is, and the final line starts the walk at the module root: # ast_chunks, continued def define(node, defn, scope): name = name_of(defn) sym = f"{scope}.{name}" if scope else name a, b = node.start_point[0], node.end_point[0] if ntok(span(a, b)) <= budget: emit(sym, a, b) return body = defn.child_by_field_name("body") head = span(a, body.start_point[0] - 1) + "\n" if defn.type == "class_definition": block(body.named_children, sym, head) else: pack(body.named_children, sym, head) def block(nodes, scope, head=""): loose = [] for node in nodes: defn = unwrap(node) if defn.type in DEFS: if loose: pack(loose, scope or "<module>", head) loose = [] define(node, defn, scope) else: loose.append(node) if loose: pack(loose, scope or "<module>", head) block(tree.root_node.named_children, "") return out Walk it top-down. block goes through the statements of a module or a class body. Each definition goes to define, and the statements between definitions (imports, constants, a class docstring) collect into loose and get packed into <module> chunks or chunks named after the class. define emits a definition whole if it fits. Decorators count as part of the definition, because tree-sitter-python wraps them in a decorated_definition whose definition field holds the function or class. The symbol path is the breadcrumb. A method inside JSONDecoder gets JSONDecoder.raw_decode, and embed_text() puts # json/decoder.py :: JSONDecoder.raw_decode on the first line of what gets embedded. A query that says "JSON decoder" now has a literal match even when the method body never says it. When one function is bigger than the budget Function-level chunks break down on the big ones. A 300-line class or a 200-line function does not fit in 480 tokens, and dumping it whole into the embedder does not help. The model card for all-MiniLM-L6-v2 says so directly: "By default, input text longer than 256 word pieces is truncated." Everything past the cutoff is invisible to retrieval, and nothing warns you. The chunker above handles size in three steps. Too-big class: recurse. define calls block on the class body, so every method becomes its own chunk with a Class.method symbol. The class signature becomes a head that is prepended to the chunks of loose class-level statements, so the docstring and class attributes still say which class they belong to. Too-big function: pack statements under the signature. pack walks the body's top-level statements and greedily merges neighbours until the next one would blow the budget. Every piece starts with the full signature, decorators included. A retrieved piece from the middle of a long function still opens with def _make_iterencode(...). This is the merge half of what cAST does: split big nodes, merge small siblings. Too-big single statement: fall back to windows. A 150-line if block is one statement. emit notices it does not fit and runs the line-window chunker over just that statement, with the signature still on top. cAST recurses into the statement's children instead, which keeps boundaries cleaner. The fallback is the cheap version; swap it for recursion when your codebase has a lot of these. !A syntax tree drawn as nested boxes, small sibling boxes grouped into one chunk, one oversized box split into pieces that each carry a copy of the same signature header Small siblings merge, big nodes split, and every split piece keeps the signature on top. Two costs to know about. The header eats budget, so a function with a 15-line signature leaves less room for the body in each piece. And pack re-tokenizes growing spans, which is quadratic in the worst case. On a few files that is nothing. On a monorepo, count tokens per statement once and add them up instead. Score both on a labelled query set A chunker is only better if retrieval gets better, so measure it. You need a corpus every reader already has, and the Python standard library is on your disk. The queries below describe what code does, mostly without using its identifiers, the way you search a codebase you did not write. Each label is a file plus a symbol path. Before the eval, append one helper to chunkers.py, so the gold line ranges come from the parser instead of from hand-typed line numbers: # chunkers.py (append) def symbols(src: str) -> dict[str, tuple[int, int]]: found = {} def walk(node, scope): for child in node.named_children: defn = unwrap(child) if defn.type not in DEFS: continue name = name_of(defn) sym = f"{scope}.{name}" if scope else name found[sym] = (child.start_point[0] + 1, child.end_point[0] + 1) if defn.type == "class_definition": walk(defn.child_by_field_name("body"), sym) walk(PARSER.parse(src.encode()).root_node, "") return found Second file, eval.py. The top holds the model, the files and the labels: # eval.py import sysconfig from pathlib import Path import numpy as np from sentence_transformers import SentenceTransformer from chunkers import ast_chunks, fixed_chunks, symbols MODEL = "BAAI/bge-small-en-v1.5" QUERY = "Represent this sentence for searching relevant passages: " FILES = [ "json/__init__.py", "json/decoder.py", "json/encoder.py", "shlex.py", "textwrap.py", "collections/__init__.py", ] LABELS = [ ("quote a string so a POSIX shell sees one argument", "shlex.py", "quote"), ("split a command line into tokens like a shell", "shlex.py", "split"), ("strip the indentation shared by every line", "textwrap.py", "dedent"), ("break a paragraph into lines of a maximum width", "textwrap.py", "wrap"), ("add a prefix to the start of selected lines", "textwrap.py", "indent"), ("decode JSON at the start of a string, return end", "json/decoder.py", "JSONDecoder.raw_decode"), ("scan a JSON string literal handling escapes", "json/decoder.py", "py_scanstring"), ("serialize a Python object to a JSON str", "json/__init__.py", "dumps"), ("walk nested lists and dicts yielding JSON text", "json/encoder.py", "_make_iterencode"), ("the n elements with the highest counts", "collections/__init__.py", "Counter.most_common"), ("move a key to either end of an ordered dict", "collections/__init__.py", "OrderedDict.move_to_end"), ("make a tuple subclass with fields accessed by name", "collections/__init__.py", "namedtuple"), ] recall embeds every chunk once, then checks the top five results for each query in two ways, explained after the code: # eval.py (continued) def recall(chunks, gold, model, k=5): vecs = model.encode([c.embed_text() for c in chunks], normalize_embeddings=True) touched = whole = 0 for query, path, (s, e) in gold: qv = model.encode(QUERY + query, normalize_embeddings=True) top = np.argsort(-(vecs @ qv))[:k] hits = [chunks[i] for i in top if chunks[i].path == path] touched += any(c.start <= e and c.end >= s for c in hits) whole += any(c.start <= s and c.end >= e for c in hits) return touched / len(gold), whole / len(gold) main reads the six files from the standard library of whichever Python runs it and builds both chunk lists: # eval.py (continued) def main(): root = Path(sysconfig.get_paths()["stdlib"]) model = SentenceTransformer(MODEL) def ntok(text: str) -> int: ids = model.tokenizer.encode(text, add_special_tokens=False) return len(ids) fixed, by_ast, spans = [], [], {} for rel in FILES: src = (root / rel).read_text(encoding="utf-8") fixed += fixed_chunks(rel, src, ntok) by_ast += ast_chunks(rel, src, ntok) spans[rel] = symbols(src) gold = [(q, f, spans[f][sym]) for q, f, sym in LABELS] for name, chunks in (("fixed", fixed), ("ast", by_ast)): t, w = recall(chunks, gold, model) print(f"{name:5} chunks={len(chunks):4} " f"touched@5={t:.2f} whole@5={w:.2f}") if __name__ == "__main__": main() The query prefix is the retrieval instruction from the BGE model card, which says passages need no prefix. Run python eval.py. The tokenizer may log a "sequence length is longer than" warning while counting tokens for whole classes; that is the counter measuring, not the embedder truncating. Read the two numbers separately. touched@5 asks whether any top-5 chunk overlaps the target function at all. That is lenient, and it is the metric a token-window chunker looks best on, because a chunk holding three lines of the right function counts. whole@5 asks whether one top-5 chunk contains the entire function, which is what the LLM needs in order to answer. The gap between them is the "body without its name" problem, measured. There are no results printed in this post, on purpose: your Python version changes the stdlib files, and 12 queries is a smoke test, not a benchmark. Run it and read your own numbers. Some labels are longer than the 480-token budget, so the AST chunker splits them too and neither chunker can score whole on them. namedtuple and _make_iterencode are the obvious ones; dumps (its docstring alone is long) and py_scanstring may be over as well, depending on your Python version. Expect that, and look at which chunk comes back instead. For a real decision, write 50 to 100 queries against your own repo, from real questions people asked in code review or on Slack, and keep the file in version control next to the chunker. Before you point it at your repo Four changes take this from a stdlib demo to something you would index a monorepo with. More languages. tree-sitter has grammars for most languages you will meet, but node type names differ per grammar. function_definition is the Python name; check each grammar's node-types.json and give every language its own DEFS set. If you want a maintained version for Python, Java, C# and TypeScript, astchunk already does it. Parse errors. tree-sitter is built to keep producing useful results even when the source has syntax errors, so a half-written file mid-refactor still produces a tree. py-tree-sitter exposes has_error on every node. Check it on the root, and when a file is badly broken, fall back to fixed_chunks for that file rather than indexing garbage boundaries. A code-aware embedder. bge-small-en-v1.5 is a general English model with a WordPiece tokenizer that shreds snake_case and camelCase into fragments. It works for the demo. For production, compare a code-trained embedding model on your labelled set using the same eval.py; that is what the set is for. Keyword search next to vectors. Exact identifiers, error codes and config keys are where dense retrieval is weakest. Index the symbol field in a keyword index too and fuse the results. A query that contains raw_decode should find raw_decode without asking an embedding model to guess. --- If this was useful A retriever can only return what the chunker kept together, and code makes that easy to see. The RAG Pocket Guide covers the stages on either side of this one, retrieval and reranking, plus the chunking patterns for production RAG. If you are building search over a codebase or anything else with structure, it is the short version of the decisions this post only touches. 

Found a bug in LiteParse 👀 Node.js & Python silently turn invalid ""yaml"" output into JSON instead of throwing an error. Issue #452 👇 liteparse#452 #OpenSource #BugHunting