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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. I boot up my machine. The desktop loads. And before I open my editor, before I check Slack, before I do a single productive thing, I right-click an empty patch of desktop and hit Refresh . Then I do it again. And again. I am a person who can explain event loops and reason about cache invalidation, and yet here I am, mashing F5 on a static wallpaper like it owes me money. If you've never done this, congratulations, you're better than me. If you have ... welcome. You're among friends. First, let's kill the myth There's a folk belief that refreshing the desktop is a tiny act of system maintenance. A little spring cleaning. A gift to your hardworking CPU. It is not. Manually refreshing your desktop does not : free up RAM reduce CPU load clear some mysterious cache make your PC faster in any way, shape, or form All it does is tell Windows Explorer to redraw the current view . That's it. That's the whole feature. What's actually happening under the hood Here's the part that's actually interesting (we're devs, we live for the "actually"). Windows doesn't repaint your entire screen on every frame, that would be wildly wasteful. Instead it leans on a composition engine that, with help from your GPU when one's available, only redraws the regions that changed since the last frame. Already drawn elements get cached and reused. Icons, the taskbar, your wallpaper they're all mostly static, so mostly left alone. When something genuinely changes (you save a file, delete a folder, plug in a drive), the OS detects it and tells the composition engine: "hey, this little rectangle changed, repaint just that." The desktop refreshes itself, automatically, all day long, without you ever touching anything. So the manual Refresh button is really just a manual overrid

🐍 Python for DevOps — Class 5 Notes Topic: String Methods, Conditional Statements ( if/elif/else ), Loops ( for , range() ) 📌 Key Concepts Overview Concept One-Line Definition String Methods Built-in functions to inspect and transform strings if / elif / else Execute code blocks based on conditions for loop Iterate over any iterable — string, list, range, dict range() Generate a sequence of numbers to loop over Nested if An if block inside another if block Nested for A for loop inside another for loop 🔤 Part 1 — String Methods (Continuation from Class 4) Checking String Case # islower() — True if ALL characters are lowercase ' devops ' . islower () # True ' Devops ' . islower () # False # isupper() — True if ALL characters are uppercase ' DEVOPS ' . isupper () # True ' Devops ' . isupper () # False # Practical: normalize before comparing env = input ( ' Enter environment: ' ) if env . lower () == ' production ' : print ( ' ⚠️ Deploying to PROD! ' ) Checking Digit / Numeric Strings Method Accepts Rejects DevOps Use isdecimal() 0-9 only decimals, superscripts Port validation isdigit() 0-9 + superscripts decimal points Version numbers isnumeric() 0-9 + superscripts + fractions decimal points Broadest check # Key difference — decimal point breaks all three ' 8080 ' . isdecimal () # True ' 8080 ' . isdigit () # True ' 8080 ' . isnumeric () # True ' 80.80 ' . isdecimal () # False — dot is not a digit ' 80.80 ' . isdigit () # False ' 80.80 ' . isnumeric () # False # DevOps use: validate user input before casting port_input = input ( ' Enter port: ' ) if port_input . isdecimal (): port = int ( port_input ) else : print ( ' ❌ Invalid port — must be a whole number ' ) replace() — Find and Replace in Strings # str.replace('old', 'new') ' Python ' . replace ( ' P ' , ' J ' ) # 'Jython' ' prod-server-01 ' . replace ( ' - ' , ' _ ' ) # 'prod_server_01' ' This is python class ' . replace ( ' i ' , ' a ' ) # 'Thas as python class' — ALL occurrences # DevOps: sanitize strings for us

"Email verification" sounds like one thing, but it's really a stack of checks of increasing depth and cost. Knowing what each layer actually proves helps you pick the right level instead of overpaying for verification you don't need. Layer 1: syntax The cheapest check: does the string look like a valid email address? A pragmatic regex catches obvious garbage ( asdf , a@@b , trailing spaces). It's instant and free, but weak on its own: nobody@asdf.asdf passes syntax and can't receive a single message. Layer 2: domain and MX records Next, does the domain actually accept mail? Every domain that receives email publishes MX (mail exchanger) records in DNS pointing to its mail servers. A quick DNS lookup tells you whether any exist. No MX (and no fallback A record) means the domain can't receive mail, so the address is undeliverable no matter how it's spelled. This single step removes a large class of fakes and dead domains. Layer 3: SMTP mailbox check The deepest level connects to the domain's mail server and begins the motions of sending a message to ask whether that specific mailbox exists, without actually delivering anything. It's the only layer that can hint a particular inbox is real, but it comes with real caveats: It's slow (a live connection per address). Many servers are "accept-all" and say yes to everything, so the answer is often meaningless. Lots of providers block or throttle these probes, and outbound port 25 is blocked on most modern hosting, so it's frequently unavailable anyway. SMTP checks matter most for cleaning old, cold lists, and far less for stopping junk at signup. The heuristics layer Alongside those, useful verification adds signal that has nothing to do with deliverability per se: Disposable detection: is it a throwaway provider? Role detection: is it info@ or admin@ rather than a person? Typo suggestions: "did you mean gmail.com?" for gmial.com . A deliverability score: one 0–100 number that rolls it all up so you can just threshold on it.

Even if peace holds up between the US and Iran, oil prices aren’t going back down to where they were any time soon.

GitLab 19.0 extends agentic AI beyond code generation into securing credentials, reviewing and merging changes, and scanning dependencies, adding a public beta Secrets Manager, a full merge request Developer Flow, usage-based GitLab Duo billing, and generally available SBOM dependency scanning. By Mark Silvester

In a new Windows Developer Blog post titled "Windows platform security for AI agents", Microsoft positions Windows as the trustworthy operating system for autonomous agents and introduces the Microsoft Execution Containers (MXC) SDK as the core of that strategy. The post argues that containment, identity and manageability must be built into the operating system. By Matt Saunders

With 416 horsepower, a 31-inch 8K screen and Dolby Atmos, Benz's electric "Grand Limousine" might just make minivans cool.

Why Taking Feedback Positively Can Change Your Career As developers, engineers, designers, and professionals, we all want to improve. We spend countless hours learning new technologies, building projects, and gaining experience. Yet many people overlook one of the most powerful tools for growth: feedback. Unfortunately, feedback often feels personal. When someone points out mistakes in our code, resume, communication, or project, our first reaction is sometimes defensive. We feel offended, frustrated, or misunderstood. I've experienced this myself. But over time, I learned that the ability to accept feedback positively is one of the most valuable skills anyone can develop. Feedback Is Not an Attack One of the biggest misconceptions is believing that criticism is an attack on our abilities. When a senior engineer reviews your code and suggests improvements, they are not saying you're a bad developer. When a recruiter rejects your resume, they are not saying you're incapable. When users report problems in your open-source project, they are not trying to discourage you. Most of the time, people are simply showing you where improvements can be made. The sooner we separate our ego from our work, the faster we grow. Every Rejection Contains Information Many professionals view rejection as failure. I view it differently now. A rejection is data. If ten companies reject the same resume, the market is telling you something. If users consistently struggle with a feature, they're revealing a usability problem. If interviewers repeatedly point out the same weakness, they're highlighting a skill gap. The goal isn't to feel bad about the feedback. The goal is to learn from the information hidden inside it. Growth Begins Where Comfort Ends Positive feedback feels good. Constructive feedback creates growth. Nobody enjoys hearing that their architecture can be improved, their communication needs work, or their project has flaws. But those uncomfortable conversations often lead to th

🌿 Introduction As climate awareness grows, individuals are looking for actionable ways to reduce their personal carbon footprints. However, most carbon calculators are either too complex or offer generic, unhelpful advice. To solve this, I built CarbonWise —a production-ready Carbon Footprint Awareness Platform. It combines deterministic scientific carbon calculations with real-time, personalized AI reduction strategies using the Groq LLM API. Here is a technical deep-dive into how I built, secured, and optimized this application for the PromptWars: Virtual challenge. 🏗️ Architecture & System Design The application is designed to be lightweight, secure, and highly performant, avoiding heavy framework overhead. System Data Flow ┌──────────────────────────────────────────────────────────┐ │ User Browser │ └─────────────┬──────────────────────────────▲─────────────┘ │ HTTPS (POST / GET) │ Rendered HTML/CSS ┌─────────────▼──────────────────────────────┴─────────────┐ │ Flask App (app.py) │ └─────────────┬──────────────┬───────────────▲─────────────┘ │ │ │ ┌─────────────▼──────────┐ ┌─▼─────────────┐ │ │ SQLite DB (carbon.db) │ │Secure Session │ │ │ - Users & Logs │ │ Cookies │ │ │ - WAL Mode Enabled │ └───────────────┘ │ └────────────────────────┘ │ Structured JSON Insights ┌────────────────────────────────────────────┴─────────────┐ │ Groq API (Llama 3.1) │ │ - Model: llama-3.1-8b-instant │ └──────────────────────────────────────────────────────────┘ Backend : Flask (Python) handles routing, user session state, and database operations. Database : SQLite manages users and logs. We activated WAL (Write-Ahead Logging) mode to enable concurrent reads and writes. AI Engine : Connects to the Groq API using the ultra-fast Meta Llama 3.1 8B model ( llama-3.1-8b-instant ). Frontend : Rendered server-side with Jinja2 templates and styled with a custom dark-mode glassmorphism design system in Vanilla CSS. ⚙️ Feature Deep-Dive 1. Deterministic Carbon Calculations ( carbon_engine.p

What was released / announced Lore is an open source version control system designed with scalability in mind, allowing developers to efficiently manage large-scale projects. According to the official website, Lore aims to provide a more efficient and scalable alternative to traditional version control systems. This release is particularly exciting for developers and engineers working on complex projects that require robust version control. Why it matters As someone who works with large-scale AI infrastructure and cloud systems, I can attest to the importance of reliable version control. With Lore, developers can expect improved performance and reduced latency when managing massive codebases. This is especially crucial in environments where multiple teams collaborate on the same project, and version control becomes a bottleneck. I believe Lore has the potential to streamline development workflows and enhance overall productivity. How to use it To get started with Lore, you can begin by installing the command-line tool using the following command: pip install lore Once installed, you can initialize a new Lore repository using: lore init Lore also provides a REST API for integrating with other tools and services. For example, you can use the following Python code snippet to interact with the Lore API: import requests response = requests . get ( ' https://your-lore-instance.com/api/repo ' ) print ( response . json ()) You can explore more API endpoints and usage examples in the official Lore documentation. My take As an AI infrastructure engineer and DevOps architect, I'm excited about the potential of Lore to improve our development workflows. In my experience, traditional version control systems often struggle with large-scale projects, leading to performance issues and frustration. Lore's focus on scalability and performance could be a game-changer for teams working on complex AI and machine learning projects. I'm looking forward to exploring Lore further and integr

If you've ever tried to build a trading dashboard, a crypto portfolio tracker, or any financial app, you probably ran into the "charting problem" pretty quickly. The standard industry approach goes something like this: Embed a heavy <iframe> from a 3rd party provider. Realize it doesn't quite match your app's UI/theme. Struggle with limited postMessage APIs to push real-time data. Watch the UI lag when you try to render multiple charts on the same page. I got tired of fighting with iframe embeds and DOM-based SVG charts that couldn't handle thousands of real-time ticks. I needed something native, fast, and entirely under my control. So, I built one. And today, I'm fully open-sourcing the core engine. Meet Exeria Charts Exeria Charts is a source-available, high-performance financial charting library designed for self-hosted web applications. Instead of embedding external widgets, Exeria renders directly inside your application using a highly optimized Canvas architecture. Here’s a quick look at what it can do: https://exeria.dev The Tech Constraints (Why build another charting lib?) Building a financial chart isn't just about drawing boxes and lines. It’s about performance under pressure. When designing the architecture, we had a few strict requirements: Zero iframes: It had to be a native JavaScript/React module that lives in the main DOM tree, styled perfectly to match the host application. High-frequency updates: Crypto and forex markets move fast. The library needed to handle sub-millisecond tick updates without dropping frames or blocking the main UI thread. Unified runtime: I didn't want a separate library for line charts, another for candlesticks, and another for volume histograms. We needed one engine that could switch views instantly. How to use it We designed the API to be as straightforward as possible. Here is what a vanilla JS implementation looks like: import { createChart } from " @efixdata/exeria-chart " ; // 1. Grab your container const container = d

A local-first, single-file SPA where multiple agents debate your decision and hand you a verdict. The problem: every AI I asked just agreed with me I almost named this project wrong. I'd picked a name that sounded powerful. I asked ChatGPT, and it loved it. I asked Claude, and it nodded along. Nobody warned me about the trademark conflict, the wrong search intent, or the SEO fight I'd pick with the BBC. That was the moment I realized the problem wasn't the name. It was the feedback loop. Most AI assistants are tuned to please, so they hide your blind spots instead of showing them. When you need to make a consequential decision, "sounds great" is the most expensive answer you can get. So I built the opposite: a council of AI agents that disagree on purpose. What NoFlattery does NoFlattery puts 2–4 agents in a room, gives them different reasoning biases, and makes them debate your decision. The output isn't another chat transcript. It's a Decision Record: a clear verdict, the reasoning behind it, the main risk, what would change the call, and a next step. Use it for product decisions, pricing, tech stack, hiring, or any call where one perspective isn't enough. Key product choices: Local-first: your chats and API keys stay in your browser. BYOK: bring your own OpenAI, Anthropic, OpenRouter, or Ollama key. One-time price: no subscription, no account, no data harvesting. The stack The whole app is a single-file SPA built with: React 19 + TypeScript Zustand for state Dexie over IndexedDB for local-first storage Vite + vite-plugin-singlefile for a single index.html deploy An OpenAI-compatible provider runtime so users can plug in their own keys Why single-file? Because the deploy becomes dead simple. One HTML file. No server for the data. No build orchestration. I can ship the app to Cloudflare Pages and forget about it. The turn engine: deterministic, not magical The heart of NoFlattery is a turn-based multi-agent engine. One user message triggers one round. Each agent sp

ClickHouse HTTP API: A Complete Beginner's Guide Introduction When most people think about interacting with a database, they usually imagine connecting through a database client or application. However, ClickHouse also provides a simple and powerful HTTP API that allows you to query and manage your database using standard HTTP requests. The ClickHouse HTTP API provides a universal interface for communicating with your ClickHouse server. Since almost every programming language and automation tool supports HTTP, it becomes an excellent choice for integrations, monitoring, scripting, and lightweight applications. In this guide, you'll learn what the ClickHouse HTTP API is, why it's useful, and how to perform common database operations using simple HTTP requests. What Is the ClickHouse HTTP API? The ClickHouse HTTP API is a built-in interface that enables clients to communicate with a ClickHouse server using the HTTP protocol. Instead of connecting through the native TCP protocol, you simply send HTTP requests and receive responses in formats such as JSON, CSV, TSV, XML, or plain text. The HTTP interface is: Language agnostic Easy to integrate with web applications Firewall friendly Simple to test using tools like cURL, Postman, or a web browser Because of its simplicity, the HTTP API is widely used for automation, dashboards, data pipelines, and monitoring systems. Why Use the HTTP API? The ClickHouse HTTP API offers several advantages: No dedicated database driver is required. Works with virtually every programming language. Easy integration with REST-based applications. Supports multiple output formats. Ideal for automation and scripting. Perfect for cloud-native applications and microservices. Common Operations Using the HTTP API, you can: Execute SQL queries Insert data Create and modify tables Retrieve query results Export data in different formats Automate database operations Authentication Options ClickHouse supports multiple authentication methods when using th

You added .env to .gitignore . You felt responsible. But three weeks later it's still in the repo, still pushed to GitHub, still in every clone — because adding a path to .gitignore does nothing to a file git already tracks. That's not a bug. It's documented behavior: .gitignore only stops untracked files from being added. Anything already committed keeps getting tracked, ignore rule or not. So the secrets, build artifacts, and 40 MB log files that were committed before someone wrote the rule just... stay. The fix is one command — git rm --cached — but only once someone notices . And nobody notices, because git status is clean and the file looks ignored. So I built gitslip : a zero-dependency CLI that finds every tracked file your own ignore rules say should be gone, and hands you the exact fix. $ npx gitslip 2 tracked files are ignored by your rules but still committed: config/secrets.env ↳ .gitignore:7 *.env logs/app.log ↳ .gitignore:2 *.log Fix — stop tracking them (keeps your local copy): git rm --cached -- config/secrets.env git rm --cached -- logs/app.log or let gitslip do it: gitslip --apply It tells you which rule caught each file ( .gitignore:7 *.env ), so there's no guessing. And --apply runs the git rm --cached for you — it only un-tracks, it never deletes your working copy. Why not just grep? You can grep your .gitignore patterns against git ls-files . But: A raw grep '\.env' can't tell a still-tracked leftover from a file that's correctly excluded, and it has no idea about !negation rules, build/ directory rules, nested .gitignore files, .git/info/exclude , or your global core.excludesFile . Reimplementing gitignore's matching semantics to get this right is exactly the kind of subtly-wrong code you don't want guarding your secrets. gitslip doesn't reimplement anything. It asks git. How it works (the fun part) Detection is a single git incantation: git ls-files -i -c --exclude-standard -c = tracked (cached), -i = ignored, --exclude-standard = use all the

Structuring TypeScript: Interfaces, Type Aliases, Enums, and Object Types You've learned TypeScript's primitive types and the basics of type inference here . Now it's time to model real-world data — users, orders, API responses, configuration objects. That's where interfaces, type aliases, and enums come in. These three features are what make TypeScript genuinely powerful for building applications. Let's dig in. Object Types: Describing the Shape of Data Before we get to interfaces, let's understand object types. When you want to describe the structure of an object, you define what properties it has and what types those properties are: // Inline object type annotation function displayUser ( user : { name : string ; age : number ; email : string }): void { console . log ( ` ${ user . name } ( ${ user . age } ) — ${ user . email } ` ); } This works, but it's messy to repeat everywhere. That's why we use type aliases and interfaces to name and reuse these shapes. Type Aliases: Naming a Type A type alias gives a name to any type — primitives, unions, objects, or combinations: // Alias for a primitive union type ID = string | number ; // Alias for an object shape type User = { id : ID ; name : string ; age : number ; email : string ; }; // Now use it anywhere const user : User = { id : 1 , name : " Ramesh " , age : 31 , email : " ramesh@example.com " , }; function getUser ( id : ID ): User { // ... fetch user logic } Type aliases are flexible — they can represent almost anything. Interfaces: Defining Object Contracts An interface is specifically designed to describe the shape of an object. Syntax is slightly different: interface User { id : number ; name : string ; age : number ; email : string ; } const user : User = { id : 1 , name : " Ramesh " , age : 31 , email : " ramesh@example.com " , }; Optional and Readonly Properties Properties can be marked as optional ( ? ) or read-only ( readonly ): interface UserProfile { readonly id : number ; // Can't be changed after cre

This is a submission for the June Solstice Game Jam What I Built I built a simple cipher game with 6 levels to decrypt the cipher text based on frequency of words and hints to obtain the plaintext version. Video Demo Code samarthsubramanya / JuneSolstice_GameJam_2026 ☀ Solstice Cipher A code-breaking puzzle game built for the June Solstice Game Jam — and a small ode to Alan Turing . Six encrypted messages stand between you and the longest day of the year. Each one is a substitution cipher hiding a message about a June celebration — International Sushi Day, the World Cup, Pride, Juneteenth, Turing himself, and the solstice. Crack each cipher using frequency analysis (the same statistical reasoning Turing's team used at Bletchley Park) and the sun climbs a little higher with every solve. Break all six and you bring the light all the way back. Theme The June solstice is a global moment of transition — the longest day in the north, the shortest in the south. The game makes that literal: decoding is the act of bringing back the light. A night sky brightens into dawn as your accuracy rises, a Pride rainbow… View on GitHub june-solstice-game-jam-2026.vercel.app How I Built It I built it using ReactJS and hosted on vercel. I implemented a simple frontend with no backend dependency session-specific experience for simplistic approach Using hardcoded values and configuration. Prize Category Generated with Assistance from Google AI through Antigravity.

Every startup idea looks perfect... until you start building. The first version of PixoraCloud looked amazing on paper. Then reality hit. We discovered: Some features weren't necessary Some APIs were too complicated Some ideas solved our problem, not the user's problem So we changed them. A lot. That's where we are today. Not chasing perfection. Chasing simplicity. Building in public means admitting your first idea isn't always your best one. What's one thing you've completely changed after starting a project?

This is a submission for the June Solstice Game Jam This installment brings a Firebase build to Midsommer Madness via Antigravity CLI. The Midsommer Madness web app is deployed to Firebase Web Hosting and the Maypole was saved! What I Built When it comes to Summar Solstace — the place to be is Sweden. It is one of the highlights of the calendar. This project aimed to recreate some of the mystique around the event- just in time for some fresh surestromming! Now you can get it with Firebase enhancements! FireBase Console The full project was imported to the Firebase Console: Just show me the Code!!! GitHub Repo is here: GitHub - xbill9/midsommer-firebase Midsommer Madness with Firebase So how can we use Firebase to enhance the project? Github and Skills! The GitHub repository has been successfully linked to your Firebase project midsommer-madness . ### What was configured: 1. GitHub Secrets: A service account ( github-action-1273534909 ) with Firebase Hosting admin permissions was created, and its credentials were uploaded to your GitHub repository secrets as FIREBASE_SERVICE_ACCOUNT_MIDSOMMER_MADNESS . 2. GitHub Actions Workflows: Two workflow files were generated and pushed to your GitHub repository: • firebase-hosting-pull-request.yml: Deploys a preview channel of the game whenever a PR is created against the master branch. • firebase-hosting-merge.yml: Automatically deploys the latest version to the live Firebase Hosting site whenever a PR is merged (or a commit is pushed) to the master branch. 3. Agent Skills: Installed Firebase Agent Skills in the repository to make it easier for agents to help you with Firebase configurations in the future. > /skills ⎿ Exited /skills command What else? > how is firebase enhancing the project ● Read(/home/xbill/midsommer-firebase/lib/main.dart) (ctrl+o to expand) Firebase enhances the Midsommer Madness project in four primary ways, turning it from a simple local game into a connected, production-ready mobile application: ### 1.

Build-in-public. This is the real architecture behind running ~50 local AI agents on 6GB of VRAM — one GPU lock, an eviction watchdog, a resource governor, and a model router. Originally posted on my blog. The question I get most often is some version of "there's no way you run that many agents on a 6GB laptop GPU." The honest answer: not the way you're picturing it. I don't run 50 models at once. I run one model at a time, very deliberately — and most of the engineering is about scheduling, not inference. Here's the actual architecture. The hard constraint: 6GB of VRAM A single consumer GPU with 6GB of VRAM holds roughly one 7B-parameter model at a usable quantization. Two at once? It thrashes — the GPU starts swapping, latency explodes, and eventually a driver out-of-memory can take the whole machine down. I've had the desktop freeze from exactly that. So the first design rule wrote itself: only one heavy model is allowed on the GPU at any moment. That sounds limiting. It isn't — because almost nothing I run is latency-sensitive. A blog post that publishes at 7am doesn't care if it was generated at 6:52 or 6:58. Once you accept that your AI workforce is a batch system, not a chat window, the whole problem changes shape. A lock, not a crowd Every agent that needs the GPU has to take a lock first. It's a simple file-based queue with: FIFO ordering PID-based ownership Stale-lock detection, so a crashed job can't wedge the line forever If an agent can't get the lock within its timeout, it skips gracefully and tries again on its next scheduled run instead of piling up. So at 50 agents, what's really happening is: dozens of cron-scheduled Python workers wake up throughout the day, and the ones that need the model form an orderly line for it. The fleet is huge; the GPU contention is always exactly one. That's the trick. It's less "50 models" and more "50 employees sharing one very busy workstation, politely." Eviction and a VRAM watchdog Even with the lock, idle models l

The Problem Most AI assistants give you one model's answer. If it's wrong, you catch it or you don't. If you use a cheap model, quality drops. If you use a frontier model, you pay frontier prices for everything — even a simple file rename. What is Agentic Fusion? When you run /fusion , a panel of models collaborates on your task: Planners (up to 5): Read-only models that research and propose routes in parallel. They figure out the best approach but can't touch your files or run commands. Main model : Your chosen model does the actual work — full tool access, fully visible. You watch every step. Not a black box. Reviewers (up to 5): Read-only models that critique the draft. They can see images the main model generated. They check for errors, suggest fixes, flag issues. Verdict loop : The draft is revised until reviewers approve. The answer you get has already passed independent review. Fusion : Everything combines into one reviewed, approved answer. The quality comes from synthesis — not from running the same job 5 times. Cheap open models combine into something that rivals a frontier model at a fraction of the cost. Safety by Construction Planners and reviewers are read-only at the schema level. They can research and critique, but they can never touch your files or execute commands. Only your main model has tool access, and you watch it work live. Beyond Fusion Clioloop is also: Self-improving : Keeps MEMORY.md and USER.md , updated automatically Autonomous : Set a standing goal with /goal and it loops until done Everywhere : Terminal, desktop app, web dashboard, Telegram, Slack, Discord, WhatsApp Multi-agent Kanban : Break big work into tasks with worker agents Tools : File editing, shell, web search, browser, image/video gen, TTS, MCP Scheduled jobs : Run on cron for automated workflows Open-source : Self-host everything, own your data The Omni Loop Portal One OAuth login gives you access to 300+ models. No API keys. An OpenAI-compatible proxy means any tool works