With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. What stood out in practice • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users. submitted by /u/qasimsoomro [link] [留言]

The row looked perfect. rating: 7 . Valid JSON, right type, no nulls, no missing keys. My schema check waved it through. The page had returned HTTP 200. The selectors hadn't moved. Everything green. A rating of 7 on a 5-star site is impossible. The model invented it, formatted it correctly, and handed it to me with total confidence. That's the failure I want to talk about. Not the scraper that breaks loudly. The one that hands you a clean-looking row that is quietly, plausibly false — and sails past every check you have, because your checks are all looking at the shape of the data, and the lie is in the value . TL;DR HTTP 200, intact selectors, and valid JSON tell you the form is fine. They say nothing about whether the value is true. When an LLM extracts from messy free-text, structured-output mode guarantees you get valid JSON. It does not guarantee the content is real. The model fills uncertain fields rather than leaving them empty — because the schema demands a complete row. A ~60-line value-level sanity gate (ranges, dates, cross-field, reference, language) catches the obvious lies before they hit your database. Real code and real output below. The honest catch: this gate catches rule violations , not plausible lies inside the allowed range . A rating: 4 where the truth is 2 slides right through. I'll be specific about where the gate stops. Two different ways a scraper lies to you I wrote about source drift last week — the case where the page changes underneath you and a 30-line schema check catches the structure shifting. That's an input problem. The source mutated; your agreement with the page broke; you detect it by watching the shape. This is the other end of the pipe. The source is fine. The page is intact, the selectors are correct, the structure is exactly what you expected. The thing that lied to you is the model , on the extraction step, when you asked it to pull structured fields out of a paragraph of human prose. Those two failures feel similar and t

I spent way too long staring at this error. If you're here, you probably are too. Your requirements could not be resolved to an installable set of packages. Problem 1 - Root composer.json requires php ^8.3 but your php version (8.2.30) does not satisfy that requirement. My Laravel 13 app needed PHP 8.3. My Hostinger server was running 8.2. composer install refused to budge. Here's exactly what happened and the one-liner that fixed it. The Setup I was deploying a Laravel 13 + Inertia + React app to Hostinger shared hosting. Laravel 13 requires PHP 8.3 minimum — and so do its locked Symfony 8.x and PHPUnit 12.x dependencies. My composer.lock had been generated on a local machine with PHP 8.3, but Hostinger's CLI was defaulting to 8.2. The hPanel showed PHP 8.3 selected under PHP Configuration . The website itself was running fine on 8.3. But SSH? Still on 8.2. $ php -v PHP 8.2.30 ( cli ) That disconnect — hPanel vs. CLI — is the trap. What I Tried First composer update My first instinct was to just let Composer resolve newer compatible versions: composer update No luck. The root composer.json itself declared "php": "^8.3" , so Composer refused before even touching the lock file. The PHP constraint wasn't just in dependencies — it was in my own project requirements. composer install --ignore-platform-reqs This flag skips platform checks and forces the install anyway. It works , but it's a lie — you end up with packages that may behave incorrectly or fail at runtime because they genuinely require PHP 8.3 features. Not a real fix. Changing PHP in hPanel Hostinger's control panel has a PHP version switcher under Hosting → Manage → PHP Configuration . I had already set this to 8.3. This controls the web server / FPM version — what runs your .php files in the browser. It does not change what php points to in your SSH terminal. That's the key distinction most tutorials miss. What Actually Fixed It Hostinger installs multiple PHP versions in parallel. They live in /opt/alt/ph

Keep your Mac awake Discussion | Link

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

I agent for long-running, multi-step work and coding Discussion | Link

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

This isn't a tutorial. It's real experience. Most articles about making money with Python are vague: "Learn Python to make money" (then what?) "Do data analysis freelancing" (how to get clients?) "Write web scrapers" (legal gray area) I'll share my actual path: building an Excel template generator with Python, listing it for sale, and earning my first dollar. Why This Direction My background: Know Python, but not expert Made some automation scripts No product design experience Want products (scalable) not services (time-for-money) The opportunity: Huge Excel template market (many 10k+ sales on Gumroad) Templates are static, hard to customize I can make a "template generator" for customization Technical feasibility: Python's openpyxl generates Excel programmatically JSON config is user-friendly ~300 lines of code The Product Not an Excel file. A Python script that generates Excel files . Users get: generator.py - generator code config.json - configuration README.md - documentation Workflow: Edit config.json → Run python generator.py → Get customized Excel Technical Implementation Core code is simple: from openpyxl import Workbook from openpyxl.styles import Font , PatternFill wb = Workbook () ws = wb . active # Header style header_fill = PatternFill ( start_color = ' 6366F1 ' , fill_type = ' solid ' ) header_font = Font ( bold = True , color = ' FFFFFF ' ) # Write header ws [ ' A1 ' ] = ' Project Name ' ws [ ' A1 ' ]. fill = header_fill ws [ ' A1 ' ]. font = header_font # Add dropdown from openpyxl.worksheet.datavalidation import DataValidation dv = DataValidation ( type = ' list ' , formula1 = '" In Progress,Completed,Paused "' ) ws . add_data_validation ( dv ) dv . add ( ' B2:B100 ' ) wb . save ( ' output.xlsx ' ) Loop to create sheets, set styles, add validation. Productization Process Step 1: MVP One module only (knowledge base) Test generation Use myself for a week Step 2: Expand Add 6 modules Add JavaScript version (using exceljs ) Improve docs Step 3: Package

I’m asking since I’m working on an all-in-one app for bulk image optimizing and would like to know more about my potential users. What is your workflow is like today? What tools and processes do you have? I appreciate any and all info you are open to share. Thanks! submitted by /u/TrapShot7 [link] [留言]

Introduction Understanding how to find prime numbers is one of the best ways to develop logical thinking in programming. It looks simple on the surface, but it teaches you how to break a problem into smaller steps, build a solution gradually, and then improve it into a clean and reusable structure. In this blog, we will not jump directly into code. Instead, we will start from basic thinking, slowly convert that thinking into logic, and finally refine it into a proper Python program using functions and loops. The goal is not just to find prime numbers, but to understand how programming logic is actually built in real development. 1. Understanding the Problem First Before writing anything in Python, we need to understand what a prime number actually means. A prime number is a number that: is greater than 1 has exactly two divisors: 1 and itself So the real question becomes: How do we check whether a number has any divisors other than 1 and itself? That is the core problem we are trying to solve. 2. Thinking Like a Human Before Coding Let’s take a number, for example 13. To check if 13 is prime, we naturally try dividing it by smaller numbers: 2 → does not divide 13 3 → does not divide 13 4 → does not divide 13 5 → does not divide 13 and so on If none of these numbers divide 13 completely, then 13 is prime. So the logic is simple: Try dividing the number by possible candidates and see if any divide it perfectly. 3. Turning Thinking into a Basic Algorithm From the above idea, we can form a basic structure: We need: a number to test a variable that moves through possible divisors a way to detect whether a divisor exists We start checking from 2 because every number is divisible by 1 anyway. We also do not need to check beyond half of the number, because a number cannot have a divisor greater than half (except itself). So the idea becomes: Start divisor from 2 Go up to number // 2 If any number divides it evenly, it is not prime 4. First Working Logic (Direct Implementati