If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems. That’s why we built AgentSwarms.fyi —the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows. 🚀 The Core AgentSwarms Ecosystem: Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments. Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics. Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments. 💣 The New Drop: 60+ Browser-Native TypeScript Notebooks We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction. Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem: 🟢 LangChain.js (Fundamentals & Middleware Guardrails) 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration) 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals) ⚡ Vercel AI SDK (Streaming UI Integration) 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops) Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms. 👉 Dive in for free: agentswarms.fyi/learn submitted by /u/Outside-Risk-8912 [link] [留言]

These are the best Bluetooth, Wi-Fi, GPS, and cellular gadgets to ensure you never lose anything ever again.

I was working with being tortured by Instagram API for a client project. Eventually I delivered something that barely worked with duct tapes and prayers. Never again I'll take on any instagram/meta related project. And after that horrible experience github's rate limits, API coverage and documentation, everything about it felt like a breath of fresh air. Whereas instagram was like; "I'll give you one API call per sometimes and you better cache it" I thought I'd add some pictures from the app I'm working on since it's showoff saturday. Though there's still a lot I need to work on and I'm not sure if I'll even finish it. I just found a way to build desktop apps in python using html/css and I'm going nuts with it lol Edit : If I ever finish this app, it'll be free and open source. Basically you generate an API key, put it into the app and it lets you view, filter, and update your issues. Can also click on repo name or issue title to view in your default browser. I recently downloaded github desktop and it didn't have this feature, so I thought I'd do it myself and learn more python in the meantime. I have a few more ideas for it but I'm trying not to scope creep myself submitted by /u/mekmookbro [link] [留言]

submitted by /u/f311a [link] [留言]

Plus: Hackers use Meta’s AI bots to hack Instagram accounts, Anthropic helps NSA hackers, a decades-long GPS satellite mystery may have been solved, and more.

I am a senior full stack dev with 7+ YOE and I think we can all agree the market sucks right now! Primarily I have been applying to full stack roles but I am backend leaning (PHP/Laravel) I seem to be seeing a lot of .NET/C# roles for backend-only roles. Is the market for those devs less chaotic? I'm considering learning .NET anyway, but would like to know if it's worth fully investing my time into it if things are better. submitted by /u/EthanCPP [link] [留言]

I've been working on an open-source project called Canary. It reads your code diffs, understands which UI flows are likely affected, and lets Claude Code tests those flows in a real browser. Under the hood, Canary ships with a QuickJS WASM sandbox that exposes the full Playwright API, allowing Claude to perform long-running browser workflows such as authentication, onboarding flows, form submissions, and navigation across complex applications. Each run captures: Screen recordings Playwright traces HAR files Network requests Console logs Screenshots Unlike most agent runs, every Canary session also generates a reusable Playwright script that can be cleaned up and replayed locally or in CI with zero inference cost. Canary UI submitted by /u/wixenheimer [link] [留言]

This wide-tired bike rolls comfortably over dirt, gravel, and whatever curbs you happen to bounce down.

You pick a tool on the canvas, and in real-time you get suggestions on what you can add next, how your tools pair together, or what can be missing. There are also recommended tech stacks available for different project types. Web apps are one of the primary types, but the tooling also covers data projects, automation and so on. In total there are over 150 tools present. Everything in the app is rule-based, there’s no AI there yet whatsoever. I do plan to add an AI chat later, but it will be built on the same deterministic engine. It’s a beta for now. The builder and the tools catalogue are fully functional, user accounts are also planned for later. But this core part will stay free to use without accounts. Paid plans, if introduced, would cover extras like AI features. For my own tech stack, it’s Vue.js for frontend, FastAPI for the backend and PostgreSQL for the database. https://tekyous.dev submitted by /u/sartek1 [link] [留言]

Modern browsers let you share a link that jumps straight to whatever text you wish to highlight. Here’s how the feature works.

Google has just signed a $30 billion AI computing power deal with SpaceX.

5 Principles of Survival for Software Engineers Adapted from Leon Business School's "5 Principles of Survival" Your stack won’t save you. Your principles will. In the wild, survival isn’t about having the best gear. In software, survival isn’t about having the absolute best framework. It’s about how you operate when production is on fire, the roadmap shifts overnight, and AI just turned your "moat" into a weekend hobby project. Here are 5 core principles that keep you alive in modern software engineering. 1. 🔥 Adapt or Perish Change is not optional; it is the price of survival. In the wild: The species that cannot adapt to winter dies. In software: The team that cannot adapt to change dies slowly at first, then all at once. "Localhost is for amateurs" used to be a strongly held belief. Now, Claude writes a full CRUD API in 30 seconds on localhost . "We’re a React shop" was a proud identity. Now, HTMX ships the same feature before your Webpack build even finishes. Your identity as an engineer cannot be tied to a specific tool. Your identity is solving problems . The syntax is temporary. Agreement on what to build is what actually matters. 🛠️ Survival Action Every quarter, deliberately kill one "we’ve always done it this way" rule in your workflow. 2. 🧭 Stay Calm Under Pressure Panic is the first casualty of poor preparation. In the wild: Panic burns critical calories and gets you lost. In software: Panic causes a git push --force to main on a Friday at 4:59 PM. Outages don’t kill companies. Panicked responses do. The team that has clear runbooks, relies on feature flags, and can execute a rollback in under 90 seconds stays calm. Why? Because they prepared when it was quiet. If your first step in incident response is opening X (Twitter) or complaining in a public Slack channel, you have already lost. 🛠️ Survival Action If you don't have a tested rollback plan, you don't have a deployment plan. Write it down before your next release. 3. 💡 Resourcefulness Over Resources

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Published on : 2026-06-06 Reading time : 6 min Tags : #python #async #performance #optimization The Problem: Fake Async The supabase-async library claimed to be async but actually wrapped synchronous calls with ThreadPoolExecutor: # ❌ Fake async (old code) class SupabaseAsync : def __init__ ( self ): self . _executor = ThreadPoolExecutor ( max_workers = 3 ) async def select ( self , table : str ): loop = asyncio . get_event_loop () r = await loop . run_in_executor ( self . _executor , lambda : requests . get ( url ) # Sync call wrapped as async ) return r . json () Problems : Max 3 concurrent requests (not scalable) Thread overhead per request High memory usage No connection pooling Solution: httpx AsyncClient Use true async HTTP with httpx: # ✅ Real async (new code) import httpx class SupabaseAsync : def __init__ ( self ): self . _client : Optional [ httpx . AsyncClient ] = None async def _get_client ( self ) -> httpx . AsyncClient : if self . _client is None : self . _client = httpx . AsyncClient ( headers = self . _headers , timeout = 30 , limits = httpx . Limits ( max_connections = 10 ) ) return self . _client async def select ( self , table : str ): client = await self . _get_client () r = await client . get ( f " { self . _base } / { table } " ) r . raise_for_status () return r . json () Performance Gains Metric ThreadPoolExecutor(3) httpx(10) Max concurrent 3 requests 10 requests Avg response 450ms 150ms Memory usage 250MB 180MB Throughput 6.7 req/s 20 req/s Real benchmark : 100 concurrent requests ThreadPoolExecutor: 15 seconds httpx AsyncClient: 5 seconds 3x faster ⚡ Migration Steps 1. Client Initialization with Lazy Loading async def _get_client ( self ) -> httpx . AsyncClient : if self . _client is None : self . _client = httpx . AsyncClient ( headers = self . _headers , timeout = 30 , limits = httpx . Limits ( max_connections = 10 , max_keepalive_connections = 5 ) ) return self . _client 2. HTTP Methods (GET, POST, etc.) async def _request ( self , metho

I'm making this Arduino project for my maths class and column 4 on the keypad isn't working. This is how I want the buttons to work: A -> start over (whole game) B ->skip question C -> clear D -> delete one space What mistaks did I do and how exactly can I fix them? Thank youu <3 submitted by /u/livie4lifexx [link] [留言]

If you've ever hit Unexpected token in JSON at position 42 or Unterminated string , there's a good chance an unescaped character broke your payload. JSON is strict about what's allowed inside a string, and the fix is almost always escaping . Here's the practical version. What does escaping a JSON string mean? A JSON string is wrapped in double quotes. Any character that would confuse the parser must be replaced with a backslash escape sequence. Escaping doesn't change the meaning of your text — it just makes the string valid JSON so parsers can read it. Unescaping is the reverse: turning those sequences back into readable characters (handy when you copy a value out of logs or an API response). The characters you must escape JSON defines exactly seven characters that must be escaped inside a string: Character Escaped as Double quote " \" Backslash \ \\ Newline \n Carriage return \r Tab \t Backspace \b Form feed \f The forward slash / may optionally be escaped as \/ , but it isn't required. Unicode can be written as \uXXXX (four hex digits). JSON escape examples Double quotes — He said "hello" becomes: "He said \" hello \" " Backslashes (Windows paths) — C:\temp\file.txt becomes: "C: \\ temp \\ file.txt" Newlines and tabs — a two-line, tabbed string becomes: "Line 1 \n Line 2 \t Tabbed" How to escape and unescape JSON in code In production you rarely escape by hand — every language has it built in. JavaScript const escaped = JSON . stringify ( text ); // escape const back = JSON . parse ( escaped ); // unescape Python import json escaped = json . dumps ( text ) # escape back = json . loads ( escaped ) # unescape Java (Jackson) ObjectMapper mapper = new ObjectMapper (); String escaped = mapper . writeValueAsString ( text ); String back = mapper . readValue ( escaped , String . class ); C# (.NET) using System.Text.Json ; string escaped = JsonSerializer . Serialize ( text ); string back = JsonSerializer . Deserialize < string >( escaped ); Common escaping mistakes (and f

I shared last Tuesday, but the post was removed as Showoff which can post only on Saturdays. Here again! No external CSS, JS, icon, font frameworks. Manage your composable CSS and HTML partials with a YAML data-driven approach, side-by-side using pure Markdown files. Home widgets: Hero, Bento, Showcase, Action Cards (plug and play) Responsive and adaptive layouts. Support for light and dark modes. Support for multiple documentation sets. Support for a blog. Implement a menu via Hugo configs. Customizable sidebars using Hugo data templates. Plug-and-play/ repeatable home page blocks using separate partials/ css/ data templates. Integrate a search via Pagefind. Demo: https://dumindu.github.io/E25DX/ Demo Setup: https://github.com/dumindu/E25DX/tree/gh-pages/example GitHub: https://github.com/dumindu/E25DX So, No need Hundreds of NPM Packages & Thousands of CSS Classes For A Modern And Modular Technical Documentation & Blog Setup In 2026 submitted by /u/dumindunuwan [link] [留言]

In the last update, I introduced MDL as an HTML-first language for building websites and apps with less noise. This update is about the next layer: adapters . The goal is simple: MDL source should describe intent. Adapters decide how that intent becomes deployable HTML. Why adapters? I don’t want MDL to become locked into one frontend framework. Instead, the same MDL structure should be able to target different deployment styles: static HTML MDL-native runtime attributes HTMX future template or framework adapters So this: form@api(post /api/login)@result(loginResult)@swap(replace): .input@type(email)@required .btn-primary@type(submit)(Sign in) status@id(loginResult): Waiting. Can become plain HTML: html <form method= "post" action= "/api/login" > Or HTMX: html <form hx-post= "/api/login" hx-target= "#loginResult" hx-swap= "outerHTML" > Security before convenienceThe important part is that MDL does not pass behavior attributes through blindly. Raw HTMX attributes like this are blocked: mdl form@hx-post(/api/login): Instead, MDL uses intent-based attributes: mdl form@api(post /api/login): Then the adapter validates and translates it. Current safety rules include: external api(...) URLs are rejected raw @hx-* attributes are blocked raw browser events like onclick(...) are blocked unsafe URL schemes like javascript: are blocked broad form inclusion like @params( ) is blocked @inherit( ) is blocked @disinherit(*) is allowed because disabling inherited behavior is safer That means MDL can support HTMX without making MDL source depend directly on HTMX’s full raw surface area. New HTMX adapter attributesThe HTMX v2 adapter now supports more behavior attributes: mdl @select-oob(...) @swap-oob(...) @disabled(...) @disinherit(...) @encoding(...) @history-elt(...) @inherit(...) @params(...) @preserve(...) @prompt(...) @replace(...) @request(...) @sync(...) @validate(...) Example: mdl form@api(post /api/profile)@result(profileResult)@swap(replace)@params(email csrfToken)@disable

Published on : 2026-06-06 Reading time : 6 min Tags : #python #async #performance #optimization 문제: 거짓 비동기 supabase-async 라이브러리는 이름은 async이지만, 실제로는 ThreadPoolExecutor로 동기 호출을 래핑하고 있었습니다. # ❌ 거짓 비동기 (기존 코드) class SupabaseAsync : def __init__ ( self ): self . _executor = ThreadPoolExecutor ( max_workers = 3 ) async def select ( self , table : str ): loop = asyncio . get_event_loop () r = await loop . run_in_executor ( self . _executor , lambda : requests . get ( url ) # 동기 호출을 async로 포장 ) return r . json () 문제점 : 최대 3개 동시 요청만 가능 (동시성 부족) 스레드 오버헤드 (각 요청마다 스레드 생성) 높은 메모리 사용량 해결책: httpx AsyncClient 진정한 비동기 HTTP 클라이언트인 httpx를 사용합니다. # ✅ 진정한 비동기 (수정된 코드) import httpx class SupabaseAsync : def __init__ ( self ): self . _client : Optional [ httpx . AsyncClient ] = None async def _get_client ( self ) -> httpx . AsyncClient : if self . _client is None : self . _client = httpx . AsyncClient ( headers = self . _headers , timeout = 30 , limits = httpx . Limits ( max_connections = 10 ) ) return self . _client async def select ( self , table : str ): client = await self . _get_client () r = await client . get ( f " { self . _base } / { table } " ) r . raise_for_status () return r . json () 성능 개선 동시성 비교 지표 ThreadPoolExecutor(3) httpx(10) 최대 동시 요청 3개 10개 평균 응답 시간 450ms 150ms 메모리 사용량 250MB 180MB 초당 처리량 6.7 req/s 20 req/s 벤치마크 # 100개 동시 요청 처리 시간 ThreadPoolExecutor : 15 초 httpx AsyncClient : 5 초 → 3 배 빠름 마이그레이션 단계 1. 클라이언트 초기화 async def _get_client ( self ) -> httpx . AsyncClient : if self . _client is None : self . _client = httpx . AsyncClient ( headers = self . _headers , timeout = 30 , limits = httpx . Limits ( max_connections = 10 , max_keepalive_connections = 5 ) ) return self . _client 2. 요청 메서드 async def _request ( self , method : str , url : str , ** kwargs ): client = await self . _get_client () if method == " GET " : return await client . get ( url , ** kwargs ) elif method == " POST " : return await client . post ( url , ** kwargs ) # ... 3. Context Manager 지원 async def clos

Published on : 2026-06-06 Reading time : 8 min Tags : #security #python #audit #devops 개요 3개월에 걸쳐 개발한 6개 Python 프로젝트(3개 봇 + 3개 라이브러리)를 종합 감사했습니다. 25개 보안/코드 이슈를 발견했고, 23개를 즉시 수정했습니다. 감사 대상 : FastAPI + Telegram Bot + LLM 통합 시스템 총 파일 : 91개 Python 파일 발견 이슈 : 25개 (심각 5개, 중간 18개, 경미 2개) 수정율 : 92% (23/25) 심각도 높음 - 5개 이슈 1. API 키가 Git 히스토리에 노출됨 🔴 문제 : Anthropic, Supabase, Telegram API 키가 .env 파일로 커밋됨 # ❌ 노출된 상태 (git log에서 확인 가능) ANTHROPIC_API_KEY = sk - ant - api03 - xxxxxxxxxx SUPABASE_KEY = sb_publishable_xxxxxxxxxx 위험도 : 누구든 이전 커밋으로 API 키 접근 가능 → 리소스 도용, 데이터 침해 해결책 : # 1. BFG로 히스토리 정리 bfg --delete-files ".env" --no-blob-protection . # 2. Git에서 제거 git rm --cached .env echo ".env" >> .gitignore # 3. API 키 로테이션 (필수) # - Anthropic: console.anthropic.com/account/keys # - Supabase: app.supabase.com → Settings → API # - Telegram: @BotFather → /token 2. SSL 검증 비활성화 (MITM 공격 위험) 🔴 문제 : requests 호출에 verify=False 사용 (10곳) # ❌ 위험한 코드 response = requests . get ( url , verify = False ) # ✅ 안전한 코드 response = requests . get ( url , verify = True ) # 기본값 영향 : HTTPS 중간자 공격(MITM) 가능 → 민감한 데이터 도청 수정 : contest-agent, supabase-async 전체 10곳 제거 3. 광범위한 예외 처리 🔴 문제 : except Exception 으로 모든 오류를 무시 (114곳) # ❌ 버그 추적 불가 try : result = await db_select ( " contests " ) except Exception : print ( " failed " ) # 어떤 오류인지 알 수 없음 # ✅ 구체적인 처리 try : result = await db_select ( " contests " ) except requests . HTTPError as e : logger . error ( f " DB error: { e } " , exc_info = True ) raise 영향 : 버그 원인 파악 불가 → 프로덕션 문제 대응 시간 증가 4. 라이브러리 __init__.py 부실 문제 : llm-router, supabase-async, telegram-agent의 __init__.py 비어있음 # ❌ 기존 (빈 파일) # __init__.py # (아무것도 없음) # ✅ 수정 후 from llm_router import LLMRouter __version__ = " 0.1.0 " __all__ = [ " LLMRouter " ] 영향 : PyPI 설치 후 import 실패 from llm_router import LLMRouter # ❌ ImportError 5. 문법 오류 (try-except 들여쓰기) ai-insight-curator의 processor.py에서 DB 작업이 try 블록 밖에 있었음 → 예외 처리 안 됨 심각도 중간 - 18개 이슈 의존성 버전 불일치 Anthropic: 0.25.0 / 0.34.0 혼재 → 0.34.0으로 통일 Supabase: 2.0.0 / 2.4.0 혼재 → 2