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Based on real system architecture decisions. About a $660K AI platform, three AI agents that kept the dashboard green, and a P0 incident that cost $3.15M over one weekend. Act 1 · The All-Hands Meeting Wang Lei, VP of Product, stood in front of the big screen, a smile on his face. Behind him, a dashboard rolled data from the "Axon AI Client Engineering Platform — Q1 Performance Report." Numbers cascaded across the wall: Metric Axon Platform Human Team (Last Q1) Improvement Avg daily tickets processed 847 312 +171% Avg first response time 12s 4h 17m ↓ 99.92% Customer satisfaction 4.8/5 4.1/5 +17% Monthly operating cost $52K $133K −61% Twelve department heads sat in the room. Dead silence. Wang Lei planted both hands on the table and scanned the room. His eyes landed on me. "Alex. Your team processed 312 tickets last Q1. Axon processed more than that in a single day last month." He smiled. Not a friendly smile. A sentencing smile. "And Axon costs less than a third of your team's operating expense." "We invested $660K in the whole platform. At current operating costs, it pays for itself in eighteen months." "After management review — the Client Engineering technical liaison function is being fully transitioned to the Axon platform." He clicked to the next slide. "Employees in replaced roles will complete exit interviews within the week." Someone inhaled sharply. I didn't. I opened my notebook to page 37. "Wang, what dimensions are these numbers from?" "What do you mean, 'what dimensions'?" His smile tightened. "Of those 847 daily tickets — how many are auto-tagging and routing, and how many are actual technical resolutions?" The room went quiet for about five seconds. Wang Lei looked at me. "Axon's ticket closure rate is ninety-three percent." "What's the reopen rate?" He paused. "What?" "After Axon replies — how many customers reopen the same ticket within twenty-four hours?" "We're still collecting that —" "Let me save you the trouble." I turned my notebook toward th

[Paper Notes] JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture 🔗 TL;DR: JEPA learns a a generalized semantic representation with less data pairs by predicting missing information in the embedding space , which helps it disregard unnecessary noisy from input(pixel)-level details and learns at a higher abstraction level with good semantic generalization. 1. Innovation & Significance The Bottleneck: Image-text data pair labels are hard to find Pixel level pre-training paired & data augmentation are strongly biased towards trained data distribution, hard to determine proper generalization and level of abstraction. JEA's (Joint Embedding Architecture) collapse probelm: encoder & decoder attempts to cheat by always landing on trivial constant when predicting itself (reconstruction) and gets away with an easy Error=0. The Solution: > Chain-of-thought ⭕ Mask pre-training to reduce data & generalize↓❌ Bad/lower semantic representation without semantic target, could be learning noisy local pixel correlation↓⭕ Learn at the embedding level to omit pixel input and generalize⭕ Adds context encoder & positional encoding to inject context and force model to pick up image inherent structure from reconstructing multiple masked patches with one target.↓❌ JEAs wants to cheat: if I always map all pixels to a constant for both the predictor and end target encoder then the reconstruction error is always collapsed to zero! Hehe~ ↓ ⭕ EMA (Exponential moving avg.): Update target encoder parameters from the EMA of context encoders. This 'delays' the target encoder to prevent collapsing (a trick from the BYOL paper[2020], proven essential to training JEAs with ViT). 2. Model & High-Level Intuitions 2.1 Model Architecture Input: randomly samples block masks from original image within certain aspect ratio changes, and apply mask for context image 2.1.2 Context Context Encoder: ViT encodes context image to embedding SxS_x S x ​ Mask Token : an [1,D] random

I know the most popular are Claude, chat got and Gemini but idk which one to use submitted by /u/Ok_Durian3627 [link] [留言]

Last year I needed to ask for a raise. I knew my number, I'd read the guides, I had bullet points in my notes app. Then my manager said "let's chat about your goals for next quarter" and I said "sounds great, looking forward to it" and hung up. Never brought up money. Same thing kept happening elsewhere. Coworker taking credit for my work, I said nothing. Relationship that should've ended months earlier, I kept postponing. I always knew what to say. I just couldn't say it with someone actually looking at me. So I started building a thing to practice on. That thing became cosskill . What it actually is You pick a persona, tell it the situation in a sentence, and start talking. The persona doesn't help you. It holds position and pushes back. You practice not folding. Think of it as a flight simulator for hard conversations. You rehearse until your opener comes out steady, then go do the real thing. 20 personas across five categories: Operators (Musk, Jobs): first-principles thinking, harsh product feedback Strategists (Trump, Buffett): treat everything as a deal or a bet Relationship (Ex, Coworker): breakups, workplace friction, family money Philosophy (Socrates, Aurelius, Confucius, Sun Tzu, four more): each tradition frames problems differently Psychology (Rogers, Rosenberg, Ellis, Frankl, Kahneman, Jung): therapeutic frameworks on real situations These aren't celebrity impressions. The Buffett persona won't hype your startup idea. It'll ask "what's the downside?" and keep asking until you have something concrete. Tech stack Next.js 16 on Cloudflare Workers. DeepSeek for inference. Cloudflare D1 (SQLite at edge) for the bits that need to persist. No user accounts, chat history lives in localStorage. Monthly cost stays low enough that the free tier (10 messages/day) doesn't worry me. Why I made these choices DeepSeek instead of GPT-4/Claude. Each conversation is 10-30 messages. At GPT-4 pricing a free product bleeds money. DeepSeek gives maybe 90% of the quality for

Frontend request code often involves repetitive state management. This article compares Axios and alova through a paginated list example, analyzing how request strategization reduces boilerplate and when it's a good fit. The Pattern: Paginated List in Two Ways A common requirement: fetch a user list with pagination. Approach 1: Axios const [ data , setData ] = useState ([]); const [ page , setPage ] = useState ( 1 ); const [ total , setTotal ] = useState ( 0 ); const [ loading , setLoading ] = useState ( false ); const [ error , setError ] = useState ( null ); const fetchUsers = async ( currentPage ) => { setLoading ( true ); setError ( null ); try { const res = await axios . get ( ' /api/users ' , { params : { page : currentPage , pageSize : 10 }, }); setData ( res . data . list ); setTotal ( res . data . total ); } catch ( e ) { setError ( e . message ); } finally { setLoading ( false ); } }; useEffect (() => { fetchUsers ( page ); }, [ page ]); This pattern appears in nearly every data-fetching component. The actual business logic — GET /api/users — occupies a single line. The rest is infrastructure: state declarations, loading toggles, error handling, and effect management. Approach 2: alova with usePagination const { data , total , loading , error , page , pageSize , nextPage , prevPage , } = usePagination ( ( page , pageSize ) => alovaInstance . Get ( ' /api/users ' , { params : { page , pageSize }, }), { page : 1 , pageSize : 10 } ); Both implementations are functionally identical. The key difference is where the state management logic lives: in the component (Axios) vs. inside the hook (alova). What Changed Component of Axios version Handled by alova loading state + toggling Managed internally by usePagination error state + try/catch Managed internally by usePagination data state + assignment Returned as reactive value page state + change handler Built-in nextPage / prevPage total state extraction Extracted from response automatically useEffect dependency tr

ive been scrolling on tiktok and instagram reels, found out that the subjects in these specific ai skit videos generated by chinese people tend to have a really bad negative canthal tilt and same face syndrome. after a while, i noticed some ai advertisements are getting the same negative canthal tilt issue, the ethnicity, age, gender dont matter in this case, they all have a same eyes i can only attach one image, but i have 2 other examples i came across. submitted by /u/Deanphoque [link] [留言]

Introduction to AWS certifications First things first, lets understand what the AWS Security Specialty certification is and where it fits in the AWS certification ecosystem. AWS certifications are divided into levels, each one targeting a different stage of your journey: Practitioner Associate Professional Specialty The practitioner level is where most people start. It focuses on foundational cloud concepts and basic AWS knowledge. As of today, there are two certifications at this level: AWS Cloud Practitioner AWS AI Practitioner The Cloud Practitioner covers core concepts like IAM, security, availability, pricing, and general cloud architecture. The AI Practitioner follows a similar structure, but focused on AI concepts and AWS AI services. The associate level is where things start to get more practical. At this level, you are expected to understand how to design and build solutions using AWS services. Some well-known certifications here are: Solutions Architect Associate Developer Associate SysOps Administrator The professional level goes much deeper. Here, you are expected to design complex architectures, handle trade offs, and make decisions based on real world constraints. The main certifications are: Solutions Architect Professional DevOps Engineer Professional Finally, we have the specialty certifications. These are focused on specific domains and require deep knowledge in a particular area. Examples include: Security Specialty Machine Learning Specialty (Retired) Advanced Networking Specialty And this is exactly where things start to get serious. At this level, AWS is no longer testing if you understand the services. It's testing if you can actually apply them in complex, real world scenarios. What it is and who this certification is for The AWS Security Specialty is one of the most difficult certifications in your AWS journey. This exam expects that you already know the basics and are comfortable with complex and long detailed scenarios that you often come

This is a submission for the Hermes Agent Challenge Agentic Web3: Automating Blockchain Workflows with Hermes Tags: #hermesagentchallenge , #web3 , #agents , #solana The blockchain industry has spent the last decade building decentralized, permissionless infrastructure. However, the user experience layer interacting with this infrastructure remains overwhelmingly manual. Decentralized applications (dApps) require users to constantly monitor markets, parse complex data, and manually sign every transaction. The next evolution of Web3 isn't just about faster blockchains; it is about autonomous execution. By integrating large language models and agentic frameworks with smart contracts, we can transition from a paradigm of manual execution to intent-based autonomy . In this article, we will explore how to bridge the gap between AI and decentralized networks by automating blockchain workflows using the Hermes Agent framework. We will look at the architecture of an on-chain agent, how it reads and writes to a network, and how high-performance environments like Solana are making these agentic experiences viable. The Paradigm Shift: From Passive Wallets to Active Agents Currently, most AI in Web3 is limited to read-only analytical tools—chatbots that can summarize a smart contract or pull token prices from an API. While useful, these are fundamentally passive systems. An active agent is different. Powered by a framework like Hermes Agent, an active agent can: Observe: Continuously monitor on-chain events via RPC nodes or webhooks. Reason: Use its LLM core to interpret those events against a set of user-defined goals or risk parameters. Act: Formulate a transaction, sign it via a secure wallet environment, and broadcast it to the network. This opens up massive possibilities. Imagine an agent that automatically manages your decentralized finance (DeFi) positions, rebalancing a portfolio based on yield changes across different protocols. Or consider fully on-chain gaming, where

Six months into running my SaaS, my "feedback system" was three browser tabs, a starred Gmail folder, and a sticky note on my monitor that said "check Discord." That was the whole system. It held together until the day I found a three-paragraph email from a paying user — a genuinely detailed feature request with a real use case — sitting unread for 24 days. His last line was: "Happy to pay more if you can support this." I replied the same afternoon I found it. His reply: "Switched last week, thanks anyway." That was the moment I stopped treating feedback management as a nice-to-have. Why the usual fixes didn't fix anything I tried the obvious things first. I want to document them because I see a lot of people cycling through the same failed solutions. Notion database 🪦 Built a beautiful one. Color-coded tags, priority columns, status tracking. It lasted 11 days before nobody — including me — was maintaining it. The friction of "open Notion, find the right database, fill in six fields" is invisible when you're designing the system and fatal when you're in the middle of a support conversation. Airtable form 🪦 Better entry point, still disconnected from where users actually were when they had feedback. Nobody bookmarks your Airtable form. They DM you on Discord and you think "I'll add that later" and you don't. Canny — this one actually worked, for a while I genuinely liked Canny. Clean interface, users could upvote requests, I could see what was popular. It felt like a real system. Then our user count grew and the pricing tier jumped. I was looking at $99/month for a feedback board for a product still finding its footing. That's not a moral judgment on Canny — it's a fair product — but for a bootstrapped indie dev, it started feeling like a tax on momentum. The deeper problem with all three solutions was the same: they were inboxes, not loops. User submits → enters the void → user never knows if anyone saw it → user assumes nobody did → trust erodes → churn. I had bui

submitted by /u/WishbringerAurus [link] [留言]

Canvas first AI agent harness. MCP native. Local first. Discussion | Link

Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [留言]

Hey everyone, I'm trying to get back into the loop on world models. The last time I followed SSL closely, the buzz was all about Barlow Twins and DINO, but now everything just looks like scaled-up video generation from big industry labs. What is the actual academic research community stressing over right now? submitted by /u/nat-abhishek [link] [留言]

okay so this is embarrassing to admit but here it is took a reasoning test in 2022, scored pretty well. Retook the same test last month out of curiosity, dropped significantly, like not a small difference. The only major change in my life is using AI tools daily for work and the worst part? i kind of knew something was off before the test. I noticed i couldn't sit with a problem anymore without immediately opening chatgpt, like my brain forgot how to be uncomfortable for even 5 minutes memory is worse. attention is worse, i feel slower in conversations. but my productivity at work has never been higher lol so what is actually happening here , are we trading long term cognitive health for short term output? Has anyone else noticed this or is it just me being paranoid ⊙⁠﹏⁠⊙ genuinely asking because i don't want to just accept this as normal (⁠｡⁠ŏ⁠﹏⁠ŏ⁠) submitted by /u/Difficult-You9582 [link] [留言]

Hi, I'm Brazilian and I'm a web developer. I'm eager to start a project (without involving money at the moment). I'd like to start developing something together. I've been working in this field for a while now, I have several projects underway, and I'd like to start another one with someone.If anyone is interested, I can give more details about myself and some ideas I've had in private 🙂 I think two people working together learn more and wouldn't spend anything; it would only involve money for selling the website submitted by /u/BitesBitesou [link] [留言]

The Logging Nightmare Deploying microservices across Multi-Cloud environments using tools like Terraform is an exhilarating milestone. But the moment something breaks, that excitement quickly turns into a nightmare. The SSH Grind : If you find yourself SSH-ing into disparate instances just to run tail -f and grep through scattered log files, you're doing it wrong. The Agnostic Approach : The industry standard demands Centralized Logging, but chaining your application to vendor-specific solutions like AWS CloudWatch or GCP Cloud Logging limits your architectural freedom. Implementing a true "Cloud-Agnostic" ELK stack gives you back control over your observability data. Clean Architecture & The Non-Blocking Logger Factory Building this robust observability pipeline requires adhering to Clean Architecture principles, specifically through a Non-Blocking Logger Factory. Standardized Interface : By wrapping modern logging libraries like Winston or Pino , we standardize our application's logging interface. The Secret Sauce : The winston-elasticsearch transport module buffers your logs and pushes them directly to your Elasticsearch cluster in the background. Non-Blocking : This architectural choice is crucial: it ensures that high-volume log streaming happens without blocking the Node.js event loop . Here is how the data flows through the system: Resilience Fallback (The Failsafe) A centralized system introduces a dangerous dependency. Your logging infrastructure must never be the reason your application crashes. The Threat : If the remote Elasticsearch cluster is unreachable due to network partitions or rate limits, a poorly configured logger will throw uncaught exceptions, bringing down the app. The Solution : We implement a strict Resilience Fallback (Failsafe) mechanism. The transport module safely catches the connection errors and seamlessly falls back to standard output (console), guaranteeing continuous operation. The 512MB Local-Test Challenge While this setup is a

A stack de autenticação em .NET fica sólida quando separamos duas responsabilidades: ✅ Argon2id para guardar senhas (hash irreversível, lento, memória-intensivo) ✅ JWT Bearer para provar identidade depois do login ✅ Validação de iss , aud , exp e assinatura em cada request ✅ Segredos fora do repositório (ambiente / Key Vault) Se o ecossistema .NET já oferece hosting, APIs e pacotes maduros, combinar Argon2 (referência da Password Hashing Competition , testável em argon2.online ) com JWT é o caminho natural para microsserviços e Web APIs. Neste artigo, mostro o fluxo registo → login → token → rotas protegidas com foco no que implementar no dia a dia. ⚠️ Observação importante JWT não substitui Argon2. Nunca coloque senha ou hash no payload do token. Argon2 protege a credencial na base de dados; JWT é sessão assinada com expiração. 🧠 Visão Geral Aspecto Argon2 (senha) JWT (sessão) Foco Resistir a offline cracking Autorizar requests após login Onde vive Coluna password_hash na BD Header Authorization: Bearer Algoritmo Argon2id (OWASP) HMAC-SHA256 ou RSA (config) Ferramenta de estudo argon2.online docs Microsoft JWT Bearer Runtime Biblioteca .NET (ex.: Konscious Argon2) Microsoft.AspNetCore.Authentication.JwtBearer Erro clássico MD5/SHA rápido na senha Token sem validar aud / iss 🧩 O que o Argon2 resolve (camada 1) O Argon2 é o vencedor da Password Hashing Competition — hoje a referência para novas passwords . 1️⃣ Hash irreversível com Argon2id var hash = hasher . Hash ( password ); await store . CreateAsync ( email , hash ); ✅ Salt único por utilizador ✅ Parâmetros m , t , p documentados no próprio hash ✅ Verificação com tempo constante ( FixedTimeEquals ) 2️⃣ Calibrar custo com consciência Em argon2.online podes experimentar memory cost e iterations — útil em laboratório. 📌 Em produção usa biblioteca auditada (.NET), não hashes de utilizadores reais em sites públicos. 3️⃣ O que não fazer na senha ✅ Não “criptografar” senha com AES reversível ✅ Não MD5 / SHA-1 / SHA-256

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built VKara is a browser-based karaoke room app for singing at home with friends or family. It is not trying to replace YouTube. YouTube is already great at playing videos. It already has almost every karaoke song we need. But YouTube is not really designed to manage a karaoke night where many people want to choose songs together. That is the gap VKara tries to fill. You open VKara on a TV or laptop as the main playback screen. Everyone else joins the same room from their phone using a 4-digit room code or QR code. Then anyone can search for songs, add them to the queue, pause, resume, or skip. The TV only needs to play the video. Everyone's phone becomes their own remote. That is the whole idea. Simple enough to explain in one sentence. Not simple enough to build in one weekend. I learned that part the hard way. Demo Links: Live demo: https://vkara.vercel.app/en GitHub repo: https://github.com/lehuygiang28/vkara Before branch: https://github.com/lehuygiang28/vkara/tree/before Old backend repo: https://github.com/lehuygiang28/vkara-api Small warning: the demo is running on limited resources, so if it is slow, please give it a moment. My wallet is still a student wallet. lol. The flow is: Open VKara on a TV or laptop. Join the room from a phone by code or QR. Search for a karaoke video. Add it to the shared queue. Control playback together. Before: the idea worked, but the product still felt like a video app squeezed into a karaoke use case. After: the mobile flow is now focused on joining, searching, choosing an action, and controlling playback. The Comeback Story I started VKara around early 2025. At that time, my goal was very personal. I wanted a better way to sing karaoke at home with friends. The normal setup was: open YouTube on a TV, search for karaoke videos, and pass control around. It worked, but it was awkward. One person was searching. Another person accidentally played a video immedia

He's been on the same exestencial crisis for a while so how do I end it submitted by /u/Huge_Heart3957 [link] [留言]

There is a specific failure mode in AI-assisted QA work that most tooling discussions skip entirely, and it shows up earliest when you are working solo on a real engagement. Every new chat session is stateless. You paste the ticket, describe the feature, explain your severity logic, set up the context, and by the time the AI is actually useful, you have rebuilt your methodology from scratch for the third time that week. That is not a workflow problem you fix with better prompts. It is an architecture problem, and the fix is a skill file. QAJourney has a full breakdown of this system at qajourney.net/ai-qa-workflow-for-real-projects, including the actual skill files as free downloads. The short version: a skill file is a context document you load as a system prompt. It carries your test surface tiers, your three-path testing framework, your bug report format, your severity and priority logic, your Playwright conventions, and an explicit definition of what the AI does and does not get to call. Load it once per session. The AI operates inside your methodology from the first message instead of a blank slate. The local LLM layer solves a different problem. On a freelance or retainer engagement, tickets contain real product logic and real client data. Sending that to a cloud API on every session is a data exposure question whether or not it rises to a compliance issue. Running Ollama locally with the same skill file as system context keeps the engagement data on the machine. For the output quality required on QA tasks, current 7B to 14B models are sufficient. The cost at zero marginal per token makes it infrastructure rather than a service you pay by the session. The three-role setup in the workflow: engineer as judgment layer, cloud AI loaded with the skill file for complex reasoning and active session output, local LLM for lightweight tasks and client data work. The skill file is the constant across all three. The part that took time to internalize: AI dev teams already