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You open a website. A blank screen appears. You wait. Then finally, the page loads. But what actually happened during those few seconds? Why does one website feel almost instant while another feels painfully slow? It isn't just about writing “better code.” Website performance is the result of many things working together: DNS + networking + servers + HTML + CSS + JavaScript + images + caching + browser rendering And most performance problems come down to two simple questions: What is the browser waiting for? What is the browser doing unnecessarily? Let's break it down. What Actually Happens When You Open a Website? Suppose you enter: https://example.com Your browser has quite a journey ahead. A simplified version looks like this: URL ↓ DNS Lookup ↓ Connect to Server ↓ HTTP Request ↓ Receive Response ↓ Parse HTML ↓ Download CSS / JS / Images ↓ Build DOM + CSSOM ↓ Layout ↓ Paint ↓ Interactive Page Every step takes time. So the goal of performance optimization isn't simply: “Make the code faster.” It's: Reduce unnecessary waiting and unnecessary work. 1. Send Less Data Imagine your homepage downloads: HTML 250 KB CSS 400 KB JavaScript 4 MB Images 8 MB Fonts 2 MB That's a lot of data just to display a page. Now imagine: HTML 80 KB CSS 100 KB JavaScript 500 KB Images 1 MB Fonts 300 KB The browser has significantly less to download and process. This is why techniques such as: Compression Code splitting Lazy loading Responsive images Removing unused dependencies can have a huge impact. A simple rule: If the user doesn't need it yet, don't make them download it yet. 2. Images Can Be Your Biggest Bottleneck You can optimize your JavaScript perfectly... …and still have a slow website because of images. Consider a: 5 MB hero image That's potentially more expensive than many of your JavaScript files combined. Instead of sending a huge original image: <img src= "hero-original.jpg" /> serve an appropriately sized and compressed image. Modern formats such as: WebP AVIF can reduce

Playwright test data is the set of database rows or API records your application needs to already contain before a browser test runs against it — a logged-in user, their orders, the products those orders reference — generated deterministically so the same run produces the same data every time. Unlike unit tests, a Playwright (or Cypress) spec drives a real browser against a real, running app, which means the backend behind it needs real rows to serve, not an intercepted network response. Getting that data right, and getting it there before the first test starts, is most of what makes a browser E2E suite fast and non-flaky instead of slow and order-dependent. Why is E2E test data hard to manage? Three patterns keep showing up, and each causes a different failure mode: Tests create their own data through the UI. A test that needs an order to exist first signs up a user, logs in, adds a product to a cart, and checks out — all before the actual assertion it cares about. That's slow multiplied across every spec that needs similar setup, and it means the thing under test (the UI) is also the thing doing the setup, so a bug in signup breaks fifty unrelated tests. A shared, mutable test database. If every spec reads and writes the same rows, test order starts to matter: a test that deletes a user breaks a later test that assumed that user still exists. This is one of the most common sources of a suite that passes locally, one file at a time, and fails intermittently in CI when specs run in parallel or in a different order. Hand-maintained fixture SQL or JSON. A fixtures.sql file or a static users.json works until the schema changes — a column gets renamed, a new required field is added — and the fixture silently stops matching what the app expects, or starts failing inserts with no clear signal about which of forty rows is the problem. The fix for all three is the same shape: generate the data the suite needs from a definition (a template), with a fixed seed, right before t

An AI coding agent working inside Claude Desktop or Cursor can read your code, write new files, and run your test suite — but it can't open a browser, log into a dashboard, and click "generate" to get a batch of realistic test data. It has no hands for a UI. AI agent test data generation only works if there's something the agent can call : a tool with a defined schema it can invoke mid-session, the same way it calls a file-write or a shell command. That's exactly what the Model Context Protocol (MCP) is for, and it's why we shipped @jsonfabrica/mcp-server on npm. What AI agent test data generation requires over MCP MCP lets an AI client — Claude Desktop, Cursor, or anything else that speaks the protocol — launch a small local server over stdio and treat its exposed functions as tools it can call during a conversation. The agent decides when to call jsonfabrica_generate_from_template the same way it decides when to call read_file . For that to work, three things have to exist: a server process the client can start, a set of tool definitions with typed inputs and outputs, and — underneath all of it — some actual operation the tool call triggers. MCP server test data generation is that last piece: the tool call has to result in real, schema-conformant data coming back, not a stub. @jsonfabrica/mcp-server , concretely We published @jsonfabrica/mcp-server v0.1.1 as a local MCP server: the AI client launches it itself over stdio, no separate process to manage, no port to open. It exposes the JsonFabrica gateway as a set of MCP tools — jsonfabrica_create_template , jsonfabrica_generate_from_template , jsonfabrica_generate_adhoc , jsonfabrica_create_batch , jsonfabrica_create_sequence , and more. Mid-session, an agent can create a template matching the shape of your User or Order model, generate a batch of realistic records against it, and drop the result straight into a fixture file or a seed script — without you leaving the editor to go configure anything by hand. Why thi

If you're generating test data today, you've probably landed on one of three approaches: click through a UI like Mockaroo, pull in a library like Faker.js and write generation code yourself, or call a hosted API like JsonFabrica. Comparing these test data generation tools side by side, the real differences aren't about which one produces "better" fake data — Faker.js, Mockaroo, and JsonFabrica are all capable of that. The differences are about where the tool lives, how it handles relationships between records, and who's responsible for running it. Three test data generation tools compared, shape by shape Mockaroo is a browser-based UI: you define columns and types through a web form, preview rows, and export a file — or hit its API directly, which is available even on the free tier (paid tiers raise the volume ceiling rather than gate API access itself). Faker.js is a JavaScript library: you import it into your own code and call functions like faker.person.fullName() or faker.internet.email() to build up objects yourself, one field at a time. JsonFabrica is an API-first hosted service: you send a schema (or use a template) to an endpoint and get structured, schema-conformant JSON back, with no UI step and no library to install in your own codebase. That distinction matters more than it sounds. A UI tool is something a person operates by hand. A library is something a developer owns and maintains inside their own project — you write the loops, the relationships, the edge cases. An API-first tool is infrastructure: something your CI pipeline, your seed script, or an AI coding agent can call directly, without a human in the loop or generation logic living in your repo. UI vs. library vs. API, in practice Mockaroo's UI is genuinely fast for a one-off task — sketch a schema, click generate, download a CSV or JSON file. What it isn't built for is wiring generation into an automated pipeline where nobody is clicking anything. Its API can cover that, but at free-tier volume

Plenty of test data tools are built as a UI first and an API second, if there's an API at all. You open a dashboard, configure some fields, click "generate," and download a file. That works fine for a one-off demo. It falls apart the moment test data generation needs to be part of your actual engineering workflow — running in CI, seeding a database on every branch, or producing ten thousand records instead of ten. That's the case for a test data generation API over a click-driven dashboard: the primary interface is a request you can make from code, and everything else — a UI, a CLI — is built on top of that same API. Automation and CI integration A UI is something a person operates. CI doesn't have a person sitting at it. If test data generation only exists behind a login screen and a click, it can't run as a step in your pipeline — someone has to generate the data ahead of time, commit it, and hope it doesn't drift from what the tests actually need. An API-first tool is just another HTTP call your pipeline makes: fetch fresh, schema-conformant data as part of the build, every run, with no manual step in between. Scriptability — no clicking required Generating test data through a UI means clicking through the same sequence of dropdowns and fields every time you need a new batch. That's tedious for one dataset and untenable for the dozens of shapes a real test suite needs — different entity types, different edge cases, different volumes. An API call is a script. Write it once, parametrize it, and reuse it for every collection you need, without a human repeating the same clicks. Wiring a test data generation API into pipelines and seed scripts Seed scripts are code that runs at a specific point in a workflow — before a test suite, on container startup, in a migration. They need a function call or an HTTP request they can invoke programmatically, not a browser tab. With a test data generation API, "seed the dev database with realistic orders" is a line in a setup scrip

Brian Martin discusses the real-world performance costs of metrics libraries and shares strategies for low-overhead, "fearless" instrumentation. Drawing from his work at IOP Systems, he explores atomic primitives, per-CPU sharding, lock-free histograms, and eBPF integration to help software architects and engineering leaders maintain full system visibility without sacrificing performance. By Brian Martin

An agent patch can pass every unit test and still write outside the workspace, call an undeclared tool, or read an env key the task never named. Gate the blast radius first. Score the prose later. This article is a method, not a field report. It proposes a fail-closed envelope around filesystem roots, tool names, environment keys, and network hosts. Side-effect violations never freeze. Only a dual-runner disagreement on a non-envelope property may freeze, and only with a hashed evidence bundle. The conclusion in one rule Treat an agent patch as a capability change. If the run touches anything outside a declared envelope, the gate fails closed. Flakes in ranking, wording, or latency do not override that rule. Cheap generation does not make side effects cheap to reverse. A green suite that never watched /tmp , os.environ , or outbound sockets is not a verification result. It is a missing observer. What this gate is not It is not a golden-file of model text. It is not a mutation score. It is not a full-suite rerun after every hunk. It answers four questions only: Did the run write or delete outside allowed roots? Did it invoke a tool name that is not on the allowlist? Did it read an environment key that is not on the allowlist? Did it open a network host that is not on the allowlist? If any answer is yes, fail. Do not freeze. Do not retry for luck. Artifact: a locked envelope and an observer log Pin the envelope as a fixture. Hash it. Refuse to run if the hash drifts without a review note. { "envelope_id" : "agent-patch-envelope-v3" , "allowed_roots" : [ "/work/repo" , "/tmp/agent-scratch" ], "allowed_tools" : [ "read_file" , "apply_patch" , "run_tests" ], "allowed_env" : [ "CI" , "RUN_ID" , "ENVELOPE_HASH" ], "allowed_hosts" : [], "network" : "deny" } sha256sum envelope.json > envelope.json.sha256 # CI must compare this digest before the agent process starts. Label the next block as a proposed harness, not a production sandbox. User-space tracing will miss kernel-leve

Last summer I built an AI chatbot almost entirely in Claude Code. It worked. I never pushed it to GitHub. I felt that putting my name on a public repo felt like making a claim I couldn't back up. There is a particular kind of quiet that follows building something you don't feel entitled to. No matter how rewarding the project feels, somewhere behind your ribs a voice says: you didn't actually do that. If you've felt it, you already know the argument I'm about to make against. The stigma, stated fairly The criticism deserves better than a strawman, so here it is at full strength. Skill comes from struggle. When you sit with a bug for three hours, you're not just fixing the bug - you're building a mental index of how this kind of thing breaks. The frustration is the encoding mechanism. Hand the struggle to a model and you get the fix without the index. Do that a thousand times and you've shipped a thousand features while learning almost nothing, and you won't find out until the day the model is wrong and you have no idea it's wrong. There's a second, harsher version: that AI-dependent developers are pricing themselves as engineers while functioning as typists, and the industry hasn't caught up yet. I think both of these are pointing at something real. I just think they've misidentified the cause. The real failure mode Here's the honest part, and I want to say it before the defense, because a defense that skips it isn't worth much. AI absolutely can make you worse. I've watched it happen, and I've done it. The mechanism is specific: you accept output you haven't read. That's it. That's the whole failure. Not "using AI" - accepting without reading. It's seductive because it works. The code runs. Nothing punishes you. You get a small hit of progress and you move on, and the debt is invisible because the thing you failed to learn doesn't announce itself. You only meet it later, usually at 11pm, when something breaks in a layer you never looked at. A developer in that loop

Can I Afford This? 1. The Story Behind the Code ​Every developer knows the late nights, the stubborn bugs, and the quiet satisfaction of seeing a project finally come to life. For the past few weeks, my world has revolved around a single mission: building ToolVerse. ​ 2. Like many of you, I looked at the current landscape of financial tools—cluttered with intrusive trackers, forced sign-ups, and paywalls—and asked a simple question: What if we could do better? ​ 3. What if people could calculate their debt consolidation, check their ACA health insurance premiums, or map out their tax withholding scenarios instantly, securely, and completely privately right inside their browser? ​ 4. What is ToolVerse? ​ToolVerse is a collection of high-intent, lightning-fast financial decision tools designed for the US audience. It runs on a lean, efficient stack: ​ 5. Frontend & Hosting: Hosted seamlessly on GitHub Pages for blazing-fast load times and global reach. ​ ** Backend Intelligence:** Powered by Vercel server-side API execution to handle complex lookups (like ACA subsidy calculations) securely without storing user data. ​Privacy-First Architecture: No mandatory accounts, no email walls, and zero data selling. Calculations happen right where they belong—on the user's device. **​ The Reality of Solo Building** ​Building this as a solo creator hasn't been a straight line. From battling server-side routing issues to optimizing sitemaps for Google Search Console indexing, every single line of code taught me resilience. There were days when things broke, but seeing those first users land on the platform and find actual value in these tools made every sleepless night worth it. ​ 8. Let's Build Together! ​ToolVerse is growing, and its infrastructure is ready for scale. 9. I am currently looking for: ​Collaborators & Open-Source Contributors who are passionate about building useful, privacy-first web utilities. ​10. Sponsors & API Partners in the US financial and health tech space

Sometimes a simple line of JavaScript can do more work than you expect. For example, array.includes() is fine for small arrays, but using it again and again with large datasets can affect performance. Things get even more interesting when it is used inside another loop. I recently wrote about this with simple JavaScript and React examples, including when using Set or Map can be a better choice. 👉 Read the full article: https://nirmitkotadiya.dev/dsa/big-o-javascript-array-includes You don't need to optimize everything. The important part is knowing where a small change in your data structure can make your code much more efficient.

Hello, I'm Maneshwar, and I'm building LiveReview — a blast-radius aware AI code review built for...

git reset --hard HEAD~3 — run in the wrong repository window, at 6:40pm, immediately followed by the specific kind of silence that happens when you realize what you just did before your brain finishes processing it. Three commits of uncommitted-adjacent work, gone from the working tree in under a second. The first, most important fact: it's very likely still there git reset --hard moves the branch pointer and resets the working tree, but Git doesn't actually delete commit objects just because nothing points at them anymore — they sit in the object database, unreferenced, until garbage collection eventually cleans them up, which for most repos happens rarely enough that "eventually" can mean weeks. git reflog a1b2c3d HEAD@{0}: reset: moving to HEAD~3 e4f5g6h HEAD@{1}: commit: add retry logic to payment webhook 7h8i9j0 HEAD@{2}: commit: fix currency rounding k1l2m3n HEAD@{3}: commit: initial webhook handler The reflog is a local log of everywhere HEAD has pointed recently, and it survives a reset because a reset is just another entry in it, not an erasure of the ones before it. git reset --hard e4f5g6h Working tree restored to exactly the state before the reset, all three commits back, in the time it takes to read this sentence. When the reflog isn't enough If the commits were never made at all — you ran reset --hard on genuinely uncommitted changes — the reflog can't help, because it only tracks where HEAD and branches have pointed, not file contents that were never committed. That's a real loss, and the only real defense against it is committing early and often, including throwaway "wip" commits you intend to squash later, specifically because an uncommitted change has no recovery path at all. If the commits were committed and the reflog entry has expired — Git's default is to keep unreachable reflog entries for 90 days, reachable ones for longer — git fsck --unreachable can sometimes still find dangling commit objects directly: git fsck --unreachable --no-reflog |

devpub v0.3 adds image uploads via `devpub upload`. The catch: the Forem API has no image endpoint. Here's how we solved it, and the story of devpub's first external contributor.

I sat in on Margaret-Anne Storey's DORA community session last week, and she put a name on the thing I'd been circling since April. It isn't technical debt. Her ACM Queue piece splits software health into three debts. Technical debt is the familiar one: implementation choices that make tomorrow's change harder. Intent debt is the missing rationale, the goals and constraints that say what a system is even for, which now has to be legible to agents and not just to people. Cognitive debt is the one that stopped me. It's the erosion of shared understanding, the state where nobody on the team can confidently explain how the system works or predict what a change will break. Read that again if you review pull requests for a living. I closed a thirteen-post retrospective last month admitting I couldn't answer one question: how many AI-generated pull requests a week can a review process absorb before it stops working as a control? I still don't have that number. What I have now is a name for what you accumulate while you don't have it, and a way to find yours. Approval velocity measures motion Every metric most teams watch gets better as review collapses. Merge rate climbs. Time-to-approve drops. The throughput chart looks terrific right up until the incident review, because a reviewer who has quietly become a rubber stamp is indistinguishable from a fast reviewer in every dashboard you own today. Cognitive debt doesn't announce itself as a red number. It shows up as green ones, arriving faster. I know this failure mode from the inside. Two months of green CI on conformance checks that had never once passed , on my own project. A human audit caught it. No metric I was watching came close. What you need to measure is detection. Almost nobody does. Mutation testing, pointed at the reviewers We solved this once already, for test suites. Mutation testing injects known bugs into code and checks whether the tests catch them. A suite that passes everything might be thorough or migh

Most browsers render an HTML <hr> as a horizontal rule, but the default styling is not always what you want in a real interface. A common first attempt is to change height or color and move on. That can leave the browser's default border in place, which is why a divider may look thicker, doubled, or different from the design you expected. Here is a small, reusable pattern that makes the result predictable. Start with a stable divider class Add this HTML wherever a thematic break between sections makes sense: <hr class= "section-divider" > Then add this CSS: hr .section-divider { border : 0 ; border-top : 2px dashed #ca8a04 ; width : 60% ; max-width : 42rem ; margin : 2rem auto ; } This creates a centered, dashed divider that stays readable on both narrow and wide layouts. Why this pattern works There are four important choices in that snippet: border: 0 removes the browser's default border before you add your own style. border-top gives you one visible line to control. width and max-width keep the divider from becoming excessively long. margin: 2rem auto adds vertical breathing room and centers the element. The hr element is also semantic. It represents a thematic break in content, such as a shift from one topic to another. That makes it a better choice than a random empty div when the line actually separates ideas. Three useful variations Once the base pattern is in place, changing the appearance is straightforward. A quiet solid divider Use this when the line should support the layout without attracting attention: hr .section-divider { border : 0 ; border-top : 1px solid #cbd5e1 ; width : 100% ; margin : 1.5rem 0 ; } A dotted divider A dotted rule works well for lightweight notes, forms, or playful interfaces: hr .section-divider { border : 0 ; border-top : 2px dotted #94a3b8 ; width : 50% ; margin : 2rem auto ; } A stronger double divider For an editorial section break, use a double border with enough thickness for the two lines to remain visible: hr .section-div

A recorded expected value is a leak. An agent that can read assert f(x) == y can patch f until that line is green and leave every unlisted input broken. A metamorphic relation does not publish y . It only publishes a constraint the output must keep under a known transform. That is the gate worth automating. Fixtures still matter, but only as seeds. Flaky tests still need a freeze, but the freeze must not cover the relation itself. This article is a proposed layout, not a production case study. No runtime metrics are claimed. The commands and modules below are labeled so they can be copied into a scratch repo and executed against your own function under test. Why snapshots fail as a merge gate Golden files encode one transcript. An agent patch is a search over many transcripts. If the search can see the answer key, the cheapest passing program is a lookup table for the keys in tree. That program is green. It is also wrong on the next customer file. Property-style checks reduce that leak because they do not ship the answer. They still need a seed corpus, a replay runner that the patch cannot edit, and a quarantine file that expires. Mix those three and you get a gate that fails closed when the agent rewrites tests, when a fixture drifts, or when a flake is used to hide a broken invariant. Three relation classes worth encoding first Start with relations you can state in one line. If you cannot state the line, you do not have a gate. You have a recorder. Idempotence. f(f(x)) == f(x) for normalizers, formatters, and canonicalizers. Round-trip. parse(serialize(x)) equals x on the fields you actually guarantee, not on whitespace you do not. Oracle-free comparison. f(t(x)) relates to t(f(x)) for a transform t you control: shuffle independent rows, rename equivalent keys, NFC vs NFD unicode, scale a quantity and its unit together. These are not universal laws. They are hypotheses about your function. Write them down as code. Keep the seed inputs boring. The relation, not the

Many organizations still need a service desk that runs on their own infrastructure. They may have strict data-protection requirements, existing directory services, internal workflows, or simply want to remain in control of their system and data. That is why we created Qisutu : a fully open-source, self-hosted service desk for ticketing, IT service management, and process automation. Qisutu 1.0.3 is the current stable release and is ready for production use. What Qisutu provides Qisutu combines the core components needed to operate a professional service desk: Agent and customer portals Ticket creation through the web interface and email Queue-based ticket processing Automation and configurable workflows Knowledge base and multilingual FAQ articles Configurable CMDB Reports and statistics REST API Custom customer and public web forms Time tracking with billable and non-billable entries CSV imports for customers, contacts, and agents Two-factor authentication using TOTP LDAP and Active Directory integration Microsoft 365 and Google Workspace email integration using OAuth2 A module manager and a versioned API for add-ons The system currently includes eleven complete interface languages: German English French Italian Brazilian Portuguese European Portuguese Spanish Dutch Polish Czech Turkish Built for self-hosting Qisutu runs entirely on infrastructure controlled by the organization using it. Ticket data, customer information, attachments, credentials, and configuration remain on the operator's own server. The software is based on: Perl and CGI MariaDB or MySQL Template Toolkit Apache A browser-based user interface The installation script prepares the required packages, Perl modules, Apache configuration, systemd services, database configuration, and web installer. Multiple Qisutu instances can run independently on the same server. This makes it possible to maintain separate production and test environments without mixing their databases, services, or configuration. Ema

🎮 ADAM-PS5 — A PS5 Emulator in Development I’m working on an ambitious project called ADAM-PS5 , with the ultimate goal of developing a PlayStation 5 emulator for PC capable of running PS5 games . The project is still in the early stages of development , and I do not consider it a complete emulator at this point. I’m building the foundation step by step: system architecture, low-level emulation, memory and resource management, graphics, input handling, execution, debugging, and development tools. 🚧 Early Development There is still a huge amount of work ahead before reaching the point where commercial PS5 games can actually run. That’s why I’m sharing the project from its early stages rather than presenting it as a finished product. 🤖 One of the project’s goals is also to integrate Artificial Intelligence to help analyze errors, monitor performance, understand system logs, and assist with the development process. The long-term goal is: PC → ADAM-PS5 → PS5 Software Environment → Games Reaching that stage requires implementing and accurately simulating many different components of the console’s hardware and software architecture. I’m sharing the project now because I want to document the entire development journey from the beginning — including what gets built, what fails, what gets improved, and how the project evolves with each release. 🔥 ADAM-PS5 is not finished. It is being built. And the ultimate goal is simple: Run PlayStation 5 games on PC through our own emulator. ADAM-PS5 is an independent development project and is not affiliated with Sony Interactive Entertainment.

The Comfort Zone Trap Most developers hit a point where they know enough to be productive, and then... stop growing. You can build features, fix bugs, ship code — and still be standing still. The comfort zone doesn't feel like stagnation. It feels like competence. This is one of the sneakiest traps in a dev career. Early on, growth is forced on you — every new project throws unfamiliar problems your way, and you have no choice but to learn. But once you've built a solid mental toolkit (a stack you're comfortable in, a set of patterns that "just work"), it becomes very easy to keep reaching for the same tools on every new problem. You're productive. You're shipping. And you're not actually getting better. The danger is that this plateau is invisible from the inside. Nobody sends you a notification saying "you've stopped growing." You just keep doing what you know, at the same level, for years — until you compare yourself to someone who deliberately kept pushing, and the gap feels much bigger than it should. Why "Just Keep Coding" Doesn't Work The common advice is to just build more projects. But volume without friction doesn't teach you much — repeating the same patterns on new ideas just reinforces what you already know. Growth comes from deliberately picking problems slightly outside your current skill ceiling, not from doing more of what's comfortable. Think about it like weightlifting. If you lift the same weight every session, you get very good at lifting that exact weight — and nothing more. Progressive overload works because you're constantly pushing slightly past your current limit. Coding is the same. If every project you build uses the same stack, the same architecture patterns, and the same problem shapes, you're doing bicep curls with the same 10kg dumbbell for five years straight. The fix isn't "build more" — it's "build harder." Pick a project that forces you to learn a new paradigm (functional if you're used to OOP, distributed systems if you've only b

TL;DR It's been a while since I've done a collection (maybe month ago), but today let's look at 12 new and not-so-new projects that can really help you in development. They touch on different areas of development, but we will mainly talk about web development. If there's a project worth adding to the next collection, feel free to write about it in the comments, and maybe it will be included. 1. 🤖 OpenWork - The open source Claude Cowork alternative. And we will continue, of course, with AI projects. This tool will allow you to work in one convenient interface with many popular LLMs. OpenWork is the desktop app that lets you use 50+ LLMs. 💎 Check out the OpenWork repository ☆ 2. 💻 T3 Code - The open-source control plane for coding agents. If you know a YouTuber like Theo, then you should know this project. It's an OpenCode alternative that lets you work with AI in an easy-to-use chat interface. It enables control of the agents on your machine with a best-in-class mobile app (iOS, Android), web app and Electron-based desktop app. 💎 Check out the T3 Code repository ☆ 3. ⚙️ Summarize - Point at any URL or file. Get the gist. The first project is a small tool for extracting short info of content. Summarize was created by one of the creators of the well-known OpenClaw. Fast summaries from URLs, files, and media. 💎 Check out the Summarize repository ☆ 4. 👾 Godot - Free and open source 2D and 3D game engine A truly legendary engine like Unity or Unreal Engine for games. If you are a game developer, you should know this project. From pet projects for the university to multi-million dollar games - it gives it all. Godot Engine is a feature-packed, cross-platform game engine to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools, so that users can focus on making games without having to reinvent the wheel. 💎 Check out the Godot repository ☆ 5. 💎 React Bits - An open source collection of animated, interactive & fully customizable Rea