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Knowledge-and-Memory-Management v0.0.2 is out, delivering a clean release that prioritizes portability and modularity. This version shifts from hardcoded personal paths to $AGENT_HOME , making your knowledge pipelines reproducible across environments. If you’re building autonomous systems that need to ingest web content, video transcripts, or articles, this is the update you’ve been waiting for. The core design separates collection from memory management. The knowledge_collector module handles ingestion, while memory_manager handles storage, retrieval, and decay. The $AGENT_HOME environment variable anchors all runtime paths—no more hardcoded /home/user strings. Set it once, and your agents can carry their knowledge base anywhere. Knowledge Collection: Web, Video, Articles The collector supports three primary sources: Web : Scrapes and parses HTML, extracting body text and metadata. Handles rate limiting and retry logic. Video : Takes a YouTube URL, downloads captions (if available) or generates transcripts via Whisper integration. Articles : Parses RSS feeds or direct PDF links, chunking content by sections. All sources normalize into a KnowledgeEntry dict: {source, timestamp, content, embeddings} . The collector writes raw entries to $AGENT_HOME/knowledge/raw/ and passes them to the memory manager for processing. Memory Management with $AGENT_HOME The memory manager is where the clean release shines. Previous versions used os.path.expanduser("~/knowledge") , which broke across systems. v0.0.2 requires $AGENT_HOME to be set, then constructs all paths relative to it: $AGENT_HOME/memory/ stores persistent memories. $AGENT_HOME/knowledge/ holds raw and processed collections. $AGENT_HOME/config/ contains source definitions and memory decay rules. This design lets you ship a single agent.env file with AGENT_HOME=/opt/myagent or %AGENT_HOME%\data —no platform-specific configuration. The memory manager indexes entries by semantic embeddings (via a pluggable model provider

When you are testing an OpenAI-compatible API endpoint, the fastest path is not to wire it into a full app immediately. Start with one small request, confirm the base URL, API key, model name, and response shape, then move the working call into your product. I put together a compact examples repo for that exact first-call workflow: https://github.com/OriginStartAI/openai-compatible-api-examples It includes curl, Python, Node.js, streaming responses, JSON structured output, migration notes, and a small error reference. 1. Set environment variables first Keep credentials out of source code and use environment variables: ORIGINSTARTAI_API_KEY = your_api_key_here ORIGINSTARTAI_BASE_URL = https://your-api-base-url/v1 ORIGINSTARTAI_MODEL = your_enabled_model The important parts are simple: base_url or baseURL points to your OpenAI-compatible endpoint. api_key or apiKey is your provider key. model must be enabled for your account. Streaming support should be tested separately. 2. Test with curl Curl is useful because it removes SDK behavior from the equation: curl " $ORIGINSTARTAI_BASE_URL /chat/completions" \ -H "Authorization: Bearer $ORIGINSTARTAI_API_KEY " \ -H "Content-Type: application/json" \ -d '{ "model": "' " $ORIGINSTARTAI_MODEL " '", "messages": [ {"role": "user", "content": "Say hello from OriginStartAI"} ] }' If this works, your endpoint, key, and model are probably configured correctly. 3. Then test Python from openai import OpenAI import os client = OpenAI ( api_key = os . environ [ " ORIGINSTARTAI_API_KEY " ], base_url = os . environ [ " ORIGINSTARTAI_BASE_URL " ], ) response = client . chat . completions . create ( model = os . environ [ " ORIGINSTARTAI_MODEL " ], messages = [{ " role " : " user " , " content " : " Write one friendly onboarding sentence. " }], ) print ( response . choices [ 0 ]. message . content ) 4. Then test Node.js import OpenAI from " openai " ; const client = new OpenAI ({ apiKey : process . env . ORIGINSTARTAI_API_KEY , baseURL : p

RocheDB v0.5.0 has been released. Release: github.com/puffball1567/rochedb/releases/tag/v0.5.0 RocheDB is an open-source NoSQL document and vector store written in Nim. The project is built around one idea: Data locality should be part of the database model, not only an accidental result of indexes, caches, or application code. A lot of database performance discussions start with indexes, query syntax, or caches. Those are important. But layout often decides whether a system is working with the hardware or asking it to fight back. RocheDB v0.5.0 is a step toward making locality explicit at three levels: logical placement with rings; related-data retrieval with stellar locality; physical layout visibility with WAL locality reporting. Ring locality RocheDB uses a ring as a semantic and structural placement unit. An application, import rule, or operator chooses a ring when writing data: users/123/profile users/123/orders shops/1123/orders docs/japan/support tenant/acme/orders/2026 That ring is not only a directory-like label. It is a coordinate in the retrieval space. When a request already knows its natural locality, RocheDB can open that local region first instead of scanning unrelated records and filtering later. roche put --ring = docs/japan --payload = '{"title":"Refund guide","status":"draft"}' --codec = json roche get --ring = docs/japan --filter = '{"status":"draft"}' --selection = '{ title }' The important part is not the string syntax. The important part is that placement and retrieval scope are connected. Short version: a ring is both where data is placed and where retrieval can start. Not only point reads Point reads are important, but many real applications need related data. For example, a user page may need profile data, orders, billing information, and support metadata. In a relational database, that often becomes joins. In a document database, it often becomes manual denormalization or multiple application-side reads. RocheDB v0.5.0 adds a locality mec

JavaScript ships with Array , Set , and Map — but nothing that keeps its elements sorted as you insert. If you've ever built a leaderboard, an order book, or anything that answers "give me the items between X and Y", you know the workaround: push into an array and .sort() after every insertion. It works, until scale punishes you — you're paying O(n log n) over and over for data that was already 99.9% sorted. Python solved this years ago with sortedcontainers , built on an elegant "list of lists" design instead of balanced trees. I just published sorted-collections , which brings that idea to TypeScript — with full credit to the original as its inspiration. What you get SortedList, SortedSet, SortedMap — always sorted, no manual re-sorting, range queries built in. O(log n) insertions, O(√n) positional access via sqrt-decomposition into buckets. Zero runtime dependencies , ~2KB gzip, types included, dual ESM/CJS. Package quality gated in CI with publint , arethetypeswrong , and size-limit . The API in 30 seconds import { SortedList , SortedSet , SortedMap } from " sorted-collections " ; // SortedList: stays sorted on every insert const list = new SortedList ([ 5 , 1 , 4 , 2 , 3 ]); list . add ( 0 ); console . log ([... list ]); // [0, 1, 2, 3, 4, 5] console . log ( list . at ( 2 )); // 2 — positional access on sorted order // SortedSet: no duplicates, plus set algebra const a = new SortedSet ([ 1 , 2 , 3 , 4 ]); const b = new SortedSet ([ 3 , 4 , 5 ]); console . log ([... a . intersection ( b )]); // [3, 4] // SortedMap: keys always in order, range queries built in const prices = new SortedMap < number , string > ([ [ 104.5 , " order-3 " ], [ 99.2 , " order-1 " ], [ 101.0 , " order-2 " ], ]); for ( const [ price , id ] of prices . irange ( 100 , 105 )) { console . log ( price , id ); // 101.0 order-2, then 104.5 order-3 } Custom comparators are fully typed: number and string get natural ordering for free; for your own types, TypeScript requires a comparator at compile

GitHub热门项目 | 👩🏿‍💻👨🏾‍💻👩🏼‍💻👨🏽‍💻👩🏻‍💻中国独立开发者项目列表 -- 分享大家都在做什么 | Stars: 52,940 | 1,194 stars today | 语言: Python

If you've run more than one AI coding agent on the same project, you already know the failure mode. You point Claude Code at /src/game and Cursor at /src/ui "just to be safe," and twenty minutes later one of them has quietly rewritten a file the other was mid-edit on. No error, no warning — just a diff that makes no sense and an afternoon spent figuring out which agent ate whose work. The agents aren't the problem. The problem is that multiple AI coding agents on the same codebase have no shared notion of "someone else is touching this file right now." Each one acts as if it's alone, and that assumption breaks the moment you run two, three, or four in parallel — exactly when a solo builder or vibe-coder would want to, to ship faster. I built Bothread to fix this. It's free, open-source, and runs entirely on your own machine. Why AI Coding Agents Overwrite Each Other's Files The core issue is coordination, not intelligence. One agent working alone is usually fine. Trouble starts when a second agent, unaware of the first, opens that same file and writes its own version on top. Whoever saves last wins, silently — no lock, no claim, no message saying "I'm in physics.js , give me five minutes." Multiply that by however many agents you're running and you get the pattern anyone doing multi-agent AI coding eventually hits: duplicated work, clobbered edits, and a human reconstructing what happened after the fact instead of watching it happen. Bothread's answer: give the agents a shared room, over MCP (Model Context Protocol) , where "who's working on what" is a fact everyone can see and act on — not something you guess at after a merge conflict. What Bothread Actually Does Bothread is a small local server (no cloud, no accounts) that any MCP-compatible agent can join as a participant in a shared room: Claim files before editing — a claim on a file someone else already holds gets denied and shown, instead of silently overwritten. Talk in a live thread , share a task board and

Every DevOps engineer has done this dance: you've got a chunk of YAML or a Terraform file that looks right, something's rejecting it, and you want a fast sanity check. So you paste it into some random online validator — and a small voice asks, wait, where did that config just go? That config often has structure, comments, sometimes internal hostnames or resource names in it. Pasting infrastructure definitions into an unknown server is a habit worth breaking. So I built a set of validators that never send your config anywhere — they run entirely in your browser. What they are Free, browser-based validators for the formats DevOps folks paste-and-pray most: YAML — catches the indentation and structure errors that make Kubernetes and CI configs fail with cryptic messages Kubernetes manifests — schema-aware checks beyond "is it valid YAML," so you catch the wrong apiVersion or a misplaced field before kubectl apply does Terraform / HCL — structural validation for the syntax slips that terraform validate flags only after you've context-switched away The one design decision that matters 100% client-side. No upload, no signup, no server round-trip. Your config is parsed by JavaScript running in your own tab — it never leaves your machine. You can literally open dev-tools, watch the network panel, and see nothing go out. Turn off your wifi and they still work. This isn't a privacy gimmick — it's the correct architecture for a tool that handles infrastructure definitions. A validator has no business seeing your config on a server it doesn't need to. Why I bother Two reasons, honestly. One: I kept wanting this exact thing and kept not trusting the options. The nth time I hesitated before pasting a manifest into a stranger's website, I decided to just build the version I'd trust. Two: fast feedback loops are the whole game in this job. The gap between "save the file" and "find out it's malformed" is pure friction — and the tighter that loop, the less of your working memory it b

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I run EstimatorSuite.com — we review construction estimating software for US contractors (HVAC, electrical, plumbing, roofing, landscaping). We just open-sourced our entire calculator suite: 42 construction calculators under MIT license. React + TypeScript + Tailwind. 🔗 Repo 🔗 Live Demo 🔗 npm What's included: • 36 material calculators (concrete volume, roofing squares, drywall, paint, flooring, etc.) • 6 trade estimators (HVAC load, electrical conduit fill, plumbing pipe sizing) Two entry points: → React components — drop into any project → Pure calculation functions — zero UI dependency, works in Node.js, Vite, Next.js, anywhere Why we built this: Construction software is expensive. Contractors told us they needed free tools that actually work — not ad-filled spreadsheets. So we built them, and we open-sourced them. Full story →

At Mehran University of Engineering and Technology (MUET), Jamshoro, results are traditionally announced via large, static PDF tables. But the main issue is: Every semester, the same story. Need to check your result? Open your laptop. Connect to the university network... or set up a VPN. Want to know your actual class or batch rank? Good luck guessing. That frustration became my latest project. To solve this, I set out to build the MUET Results Portal ( https://muetresults.vercel.app )—an independent, open-source lookup engine and administrative compiler that provides students with instant semester results, CGPA calculations, batch standings, and interactive academic calendars. Here is an engineering deep-dive into how I built it using a serverless GitOps pipeline, vanilla JavaScript SPA, and Gemini AI. 🛠️ The Architecture & Data Pipeline To keep the platform hosting costs at absolute zero while maintaining lighting-fast page loads, I designed a pre-rendered static pipeline. Rather than querying a database at runtime, all student data is compiled statically. Here is the GitOps workflow: Official PDF Release : The Mehran University Examination Department publishes a new results PDF. LLM OCR Parsing : Via a secure administrative panel ( /mokshadmin ), I upload the scanned PDF/image. A serverless backend function streams the document to the Google Gemini 1.5 Flash API , which returns structured JSON student records. Git Database Update : The approved JSON records are committed back to the repository's git-tracked database ( muet_student_gpa_dataset.csv ) using the GitHub REST API. CI/CD Pre-rendering Build : The new commit triggers a Vercel build hook. Node compilation scripts read the CSV database and: Group records and compile them into static runtime JSON structures. Pre-render complete static HTML folder structures for all batch rankings and departments. Regenerate SEO sitemaps ( sitemap.xml ). Instant Deployment : Vercel serves the pre-rendered static files instan