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Last week I had a single tool: a JSON-to-TypeScript converter. Today I have 10. I spent a day building a full suite of developer tools - all free, all client-side (nothing leaves your browser), and all open source on GitHub. The Suite DevForge is a collection of tools I built because I kept opening 15 different tabs every day: 1. JSON ? TypeScript Paste JSON, get TypeScript interfaces or types. Supports nested objects, arrays, custom root names. 2. CSV ? JSON Bidirectional conversion with header detection and delimiter support (comma, semicolon, tab). 3. Regex Tester Live regex testing with flags (g, i, m, s, u, y), match highlighting, and replace functionality. 4. Base64 Encoder/Decoder Encode text to Base64 and decode Base64 back to text. Unicode-safe. 5. JWT Decoder Decode JWT tokens into header, payload, and signature components. Read-only - no verification, no security risk. 6. SQL Formatter Uppercase keywords, indentation control. Handles SELECT, JOIN, INSERT, CREATE - the common stuff. 7. Diff Checker Side-by-side text comparison with LCS-based diff algorithm. Color-coded additions and removals. 8. UUID Generator Generate UUID v4 (or v1) in bulk. Copy individual or all at once. 9. Timestamp Converter Convert Unix timestamps to readable dates and back. Shows seconds, milliseconds, ISO 8601, UTC, and relative time. 10. Color Converter Convert between HEX, RGB, HSL, and CMYK. Color picker, presets, named color support. The Tech Stack Zero frameworks. Zero dependencies. Zero backend. Every tool is a single HTML file with embedded CSS and JavaScript. They all run entirely in the browser - no data is sent to any server. Hosted on GitHub Pages. Free forever. Why I Built This I'm a solo developer based in Nepal. Building things that help other developers is how I learn, grow, and (hopefully) earn. The suite is free, but there's a Pro tier ( one-time) that unlocks: File exports Batch operations Custom naming conventions Priority support I also do freelance development

The AI agent security community has been converging on a problem. A researcher recently ran an experiment — feeding a memory-retrieval framework 10 scenarios involving certificate operations: signing, issuing, revoking, delegating. The system retrieved the right memory 8 out of 10 times. It matched the external authorization gate 7 out of 10. The conclusion: metadata per item isn't enough. You need a separate authorization gate over the proposed operation. That conclusion is correct. But I want to show what that gate actually looks like when you build it — because the primitive already exists, and it's older than LLMs. The problem is authorization, not retrieval Most agent frameworks today invest in memory and observability. The agent can recall what it did before. You can see what tools it called. Logs, traces, dashboards. What they don't have is a cryptographically enforced answer to the question: was this agent authorized to do this, before it did it? Those are different problems. Retrieval tells you what the agent remembers about its permissions. Authorization tells you what it was actually granted — signed, tamper-proof, at dispatch time. An agent that retrieves "I have revocation permissions" from memory and then revokes a certificate it shouldn't touch is not an authorization failure at the retrieval layer. It's an authorization failure at the gate layer — because there was no gate. Certificates are that gate A certificate is a signed declaration of what an entity is authorized to do. Issued once, verifiable offline in ~1ms, revocable instantly. We've used them for TLS, for IoT devices, for code signing. The same primitive works for agents. The model is simple: Orchestrator issues a certificate at dispatch time The certificate carries the agent's identity and its exact scope in meta Every tool call goes through a gate that verifies the certificate offline On completion — or abort, or timeout — the orchestrator revokes it // Orchestrator — dispatch const { cer

What is Wrisha? Wrisha is a desktop emotional AI companion — an animated character who can see you, hear you, talk back, and react. The pipeline is genuinely multimodal: Vision — webcam + facial-emotion detection (OpenCV / FER) Hearing — speech-to-text so you can just talk to her Brain — an LLM generates her replies, in-character Voice — text-to-speech with mood-modulated tone Avatar — an animated face (pygame) that emotes and lip-syncs I built the bones of it a while back, got busy, and walked away. The Finish-Up-A-Thon was the push I needed to come back to it. The "before": it didn't just need polish — it was dead When I reopened the repo, the harsh truth was that the app couldn't even start. Two things had rotted: The environment was a fossil. The project was so old it wouldn't install on a modern machine. Wrong numpy, stale dependency pins, and a Python version mismatch that sent pip trying to compile packages from source and failing. Just getting it to attempt to run took a full environment rebuild on Python 3.12. The code was half-migrated and crashed on launch. I'd previously upgraded the internal modules — memory, a mood engine, a smarter brain — to a "v3" design, but I never finished wiring them into main.py. So the moment it tried to start, it died: TypeError: init () missing 2 required positional arguments: 'memory' and 'mood_engine' The "before" in one screenshot: a project that built its best features and then never connected them. I'd built the hard parts — persistent memory, a smooth mood state machine, proactive behavior — and left them sitting in files that main.py never even imported. Classic abandoned-side-project energy. The "after": three things I finished I set out to do three things, and I'm counting all three as the win. It runs again The core fix was finishing the migration: rewiring main.py to actually construct the Memory and MoodEngine, inject them into the Brain, and reference mood from the engine instead of the dead attribute it used to

Migrar 100 usuários manualmente no console da AWS é lento, suscetível a erros e impossível de auditar com precisão. Neste artigo você vai ver como automatizar esse processo usando AWS CLI e Shell Script direto no AWS CloudShell — sem instalar nada localmente. O resultado final: usuários criados, alocados nos grupos corretos e com MFA obrigatório, tudo em minutos. O que é o IAM? O AWS Identity and Access Management (IAM) é o serviço que controla quem pode acessar os recursos da sua conta AWS e o que cada pessoa ou serviço pode fazer. Com o IAM você gerencia: Conceito Descrição Usuário Identidade individual com credenciais próprias Grupo Conjunto de usuários que compartilham as mesmas permissões Política Documento JSON que define o que é permitido ou negado Role Identidade temporária assumida por serviços ou usuários A boa prática é nunca conceder permissões diretamente a um usuário — sempre use grupos. Visão geral da solução O fluxo é simples: Criar os grupos IAM no console Montar um arquivo CSV com os dados dos usuários Rodar um shell script no CloudShell que lê o CSV e cria tudo automaticamente Aplicar a política de MFA obrigatório nos grupos Passo 1 — Criar os Grupos IAM Antes de importar os usuários, os grupos precisam existir. No AWS Console , acesse IAM → User groups → Create group e crie um grupo para cada perfil do seu ambiente. Neste exemplo usaremos: RedesAdmin — administradores de rede LinuxAdmin — administradores de servidores Linux DBA — administradores de banco de dados Estagiarios — acesso limitado para estagiários Nomes de grupos suportam até 128 caracteres (letras, números e + = , . @ _ - ), são únicos por conta e não diferenciam maiúsculas de minúsculas. Passo 2 — Montar o arquivo CSV Crie uma planilha com os dados dos usuários e salve como CSV separado por vírgula (UTF-8) . O arquivo deve ter exatamente três colunas: Username , Group e Password . Username , Group , Password joao . silva , LinuxAdmin , Senha @2024 ! maria . souza , DBA , Senha @2024

Co to jest WCAG 2.1? WCAG 2.1 ( Web Content Accessibility Guidelines ) to międzynarodowy standard techniczny określający, jak tworzyć strony www i aplikacje mobilne, aby były dostępne dla osób z niepełnosprawnościami (wzroku, słuchu, ruchu, poznawczymi). Wersja 2.1 rozszerza wcześniejsze zasady o wytyczne dla urządzeń mobilnych oraz osób słabowidzących. Struktura WCAG 2.1 i poziomy zgodności Standard opiera się na 4 głównych zasadach. Dzielą się one na wytyczne, do których przypisane są konkretne kryteria sukcesu wdrażane na trzech poziomach: Poziom A: Absolutne minimum. Bez niego strona jest całkowicie niefunkcjonalna dla wielu użytkowników. Poziom AA: Standard rynkowy i prawny. Wymagany przez polskie i europejskie przepisy dla sektora publicznego i biznesu. Poziom AAA: Najwyższy stopień dostępności, trudny do wdrożenia w całym serwisie. Zasady POUR – Fundamenty WCAG 2.1 Wszystkie wytyczne WCAG 2.1 opierają się na czterech głównych zasadach tworzących akronim POUR : P erceivable ( Postrzegalność ) - Treść musi być dostarczana w sposób czytelny dla zmysłów użytkownika (wzroku, słuchu). O perable ( Funkcjonalność ) - Interfejs i nawigacja muszą być możliwe do obsługi za pomocą różnych urządzeń (np. samej klawiatury). U nderstandable ( Zrozumiałość ) - Informacje oraz obsługa strony muszą być jasne, logiczne i przewidywalne. R obust ( Solidność ) - Kod strony musi być poprawny i kompatybilny z obecnymi oraz przyszłymi technologiami (przeglądarki, czytniki ekranu). Kto musi spełniać standardy WCAG? Dostępność cyfrowa to już od dawna nie tylko "dobra praktyka", ale twardy wymóg prawny, który stale się rozszerza: Sektor publiczny (Obecnie): W Polsce urzędy państwowe i samorządowe, szkoły, uczelnie, szpitale oraz spółki skarbu państwa mają bezwzględny obowiązek spełniania standardu WCAG 2.1 na poziomie AA. Wynika to wprost z Ustawy z dnia 4 kwietnia 2019 r. o dostępności cyfrowej . Za brak zgodności grożą kary finansowe. Sektor prywatny i biznes: Na mocy Europejskiego Akt

I won't name names because I got in trouble for that last post but I interviewed with the biggest agency I've ever gotten a call back from. They are 150+ employees, at least one major national corporation as a client. You would think that because they are so high and mighty they have their s*** together but honestly no. The first interview was with the "big boss" the digital director. The second interview was actually 4 separate teams calls scheduled back to back with a total of 9 people. The director loved me and told me I would be moving on. He said my second call would be with three people which is hilarious because he wasn't even close to correct. My second interview was chaos. First call ended up being a different group of project managers than was listed, and they just hammered me with hypothetical situations of conflict the entire time. All rain clouds, no sun. Second call was two developers who didn't seem that invested in talking to me. They mainly just said that works comes in from many different places and you can be expected to work on a bunch of things while managing a bunch of people AND be suddenly put in front of a client at any point. My third call was hilarious because the top senior dev and the top senior designer in the company on this call loved me. We clicked on everything - our stance on AI, our views on WordPress and PHP in general, our approach to projects. My fourth call was back to doom and gloom - more managers and all just hypothetical situations about conflict and everything going wrong. The people on call four were telling ME that a person from call two has a "different idea of what finished means" and were basically talking s*** on this person who is probably their smartest back end developer. During all of this, the HR person had no idea who I was the entire time. I am interviewing virtually and would have to relocate and she sent me multiple emails about my "In Person Interview". My rejection email was actually the same email sent t

With Hov3r, you are able to switch between animated wallpapers, background audio, widgets, cursor skins, cursor animation, and create wake words or keybinded shortcuts. It also comes with a massive free library with thousands of cursor skins, background and background music for you to choose from. You can choose between a $2.99/month or a $9.99 lifetime subscription, and even refer friends and family to get either plan for free (more info on website). P.S. I have some free keys left from testing, while Hov3r offers a free trial for you to try the app out, shoot me a DM for a free license key to test the app out(if i still have any). Download now from: https://hov3r.app submitted by /u/CoolDownDude [link] [留言]

Microsoft’s OpenClaw-style agent appears in Teams, just like a human colleague, and automates your dull office tasks.

"We do not know when it will be safe to go outside. We only know that day is not this day."

Hello guys, i am a 17 year old IT student and i have recently had a "trial job" at a Robotics company, the trial job is a part of my schools curriculum but one thing about the comapny is is that their website is horrible, it is years old and looks very outdated, so i wanted to propose the idea of making and effectively selling them a new one. The website has around 50 pages and made in Joomla however it is based on Joomla 3 so i would have to bring it over to a newer version. Anyway i was wondering how much i should ask for a website like this? From a discussion with a person who works in website selling and marketing the "big shot" companies would charge up to 20 500 Euro or 24 000 USD which is obviously too much for a person still in school with no actual professional experience, the price i made out to not be too much while also getting my money's worth was around 5 200 Euro or 6 000 USD. Despite me having no professional experience i have had time to redesign a tiny bit of their website simply so i can show them that i can actually make the website, so i am wondering if this price is appropriate for a web that would take me around 250-ish hours to make probably with the domain transfer and transfer to Joomla 5. The website is: www.exactec.com in case anybody wanted to check it out, it is in czech however you should still be able to tell the cost from the layout. also i think you can see which parts i did and didn't make. P.S: the current pages made by me aren't my full effort as they are not my trial job's fill and i only did them in spare time while learning robotics, the actual ones would be more thought through and professional. submitted by /u/Zealousideal-Mood-45 [link] [留言]

I've been specializing in frontend for more than ten years, gaining full stack and backend experience for the last four or five, and I'm starting to think I might just be done. Not to harp on the AI discussion that gets posted on here all the time, but I do feel like our jobs have fundamentally shifted over the last 12 months to something I no longer enjoy; I don't want to be a pseudo manager of AI agents, I want to write code, and if there's no longer a need for that I can just do something else. But I would like this community's help - looking at it from the ground up, what are fully unrelated jobs or industries where you feel like our skills would be most transferable? And I mean fully blue sky approach, things I might not have considered, not just coding in a different stack. I think I have above average computer skills and awareness of operating system details that would put me above a lot of other candidates from different backgrounds, so where could I go to leverage that (that is at least a little more future proof)? Or beyond computer at all, I feel like I have spent years building strong critical thinking skills and analytical reasoning; I just don't have a wide base of knowledge outside our industry to know where these would be best used. For practical purposes, I can't look at anything that would require its own degree and four more years back to school, but there have got to be lots of certifications I could get quickly to start to open doors and prove my qualifications. I'm certainly expecting a pay cut, but I'm open to entry level jobs if it means a good career path. So I am interested in your thoughts - if you were starting out today in any other field than webdev, where would you be looking? submitted by /u/mx-chronos [link] [留言]

- Good pay compared to many other professions - Remote/hybrid work options - Opportunities to travel or work in different countries - Working with global teams - Decent work-life balance - Solving interesting problems - Continuous learning - Respect and recognition in society What keeps you motivated to stay in software development? submitted by /u/Majestic-Taro-6903 [link] [留言]

I was not expecting gyroscope API to be this sensitive lol but it was a subtle bug in my implementation. ps. I even added Absolute orientation sensor to compare stability... You can visit the fixed live demo here - https://ctx-0.github.io/gyroscope/ (use a mobile device) Here's the repo if you want to take a look - https://github.com/ctx-0/gyroscope submitted by /u/goldbookleaf [link] [留言]

O&amp;#039; Billion dollar companies. It boggles the mind. Probably takes $500K in tokens for AI to figure it out. </rant> submitted by /u/centuryeyes [link] [留言]

In the social event planner’s first major move toward monetization, Partiful is getting ticketing directly in the app.

This article was originally published on aicoderscope.com Most AI coding tools are generalists—they write code, answer questions, and somewhere in the feature list, review pull requests. CodeRabbit is the opposite: one thing, done obsessively. Every feature, every design decision, every pricing tier revolves around making PR review better. After reviewing the pricing, benchmarks, and comparing it to GitHub Copilot's native code review, here's the honest assessment. What CodeRabbit actually is (and what it isn't) CodeRabbit sits between your developer's git push and the merge button. You connect it to your repository host—GitHub, GitLab, Azure DevOps, or Bitbucket—and it automatically reviews every pull request. No button to click. It reads the diff, checks it against your full codebase for context, runs 40+ static analysis tools, then uses a multi-model AI stack to flag bugs, security issues, and style violations directly in PR comments. What it cannot do: generate application code, scaffold features, or replace a coding assistant. It is review-only. That constraint shapes everything about the product. At $40M ARR as of April 2026 (up 700% year-over-year from $5M ARR in April 2025), with 2 million repositories connected and more than 13 million pull requests reviewed, CodeRabbit has clearly found a market. It currently holds the #1 position among AI apps on GitHub Marketplace. How the review actually works Every CodeRabbit review runs in three stages. Stage 1: Context engine. Before analyzing the diff, CodeRabbit indexes your codebase using a retrieval system similar to what backs its code reviews across millions of repositories. It uses NVIDIA Nemotron for this context-gathering and summarization stage—a lightweight open model optimized for retrieval rather than generation. This is why CodeRabbit catches cross-file issues that pure diff-reviewers miss. Stage 2: Static analysis. A deterministic SAST layer runs linters that don't need AI inference: Biome, ESLint, Ruf

Tenho aproveitado meu tempo sem trabalhar pra estudar, enfim a vida de quem trabalha com tecnologia né? E um dos meus maiores focos tem sido IA, seus usos, como ela entra e pode ser aplicada em áreas diferentes, e todas as novidades que saem todos os dias. Hoje vim compartilhar uma coisa bem legal que aprendi no curso AI-Native Engineering Foundations do Addy Osmani , o problema dos 70%. Existe um padrão claro que tenho observado na prática ao acompanhar dezenas de equipes de engenharia: a Inteligência Artificial resolve com impressionante eficiência 70% de quase qualquer tarefa técnica. Falo daquela camada previsível, repetitiva e baseada em padrões exaustivamente documentados na internet. Coisas como código boilerplate, arquivos de configuração, implementações de CRUDs simples, conversão de sintaxe entre linguagens e a escrita de testes unitários básicos. A IA já "viu" milhões de exemplos disso em repositórios públicos e consegue reproduzir o padrão em segundos. Para essa fatia do trabalho, ela é uma aceleradora fantástica. O grande problema, e o motivo pelo qual muitos projetos com IA começam bem mas falham no meio, é que os outros 30% são justamente os que sustentam o software. É nesses 30% que entram as decisões que inteligência nenhuma consegue tomar sozinha: Contexto de Negócio: A IA não sabe por que aquela feature está sendo construída ou como ela impacta o usuário final. Arquitetura e Manutenibilidade: Escrever código que funciona hoje é fácil; escrever código que outra pessoa consegue alterar daqui a seis meses sem quebrar o sistema é outra história. Casos de Borda e Segurança: A IA tende a gerar o "caminho feliz". Tratar falhas de concorrência, vazamento de memória e vulnerabilidades específicas do seu ecossistema exige malícia técnica. Essas questões não se resolvem apenas digitando linhas de código, elas exigem contexto, experiência, histórico de dores passadas e, acima de tudo, julgamento humano. E é exatamente aqui que a IA ainda não entrega. O Parado

The Scenario: The Help Desk Bottleneck From 2019 to 2021, while serving as Lead Backend Software Engineer at a fast-growing company, I occasionally support our Linux System Administration tasks. When the DevOps team encountered a critical bottleneck during an initiative to scale dozens of new server deployments, I stepped in to streamline the infrastructure processes. The DevOps team was being hampered by constant, fragmented requests from the help desk to manually create new Linux accounts for recruits testing the latest application. These interruptions were not only time-consuming but were directly preventing the team from focusing on the high-priority infrastructure deployments that define their core responsibilities. I realized that we weren't just struggling with a task; we were struggling with a scaling bottleneck. To regain the team's focus and ensure we hit our project deadlines, I decided to automate this workflow. The First Step: The Interactive Script My first objective was to develop a robust, automated shell script to efficiently create new Linux user accounts. I started with an interactive Bash script (create-user-interactive.sh) that prompted for input. This was a good educational exercise for learning the fundamentals of Bash—like useradd, passwd, and shell variables. However, I quickly learned that while interactive scripts are great for learning, they are rarely used in professional DevOps environments. Why Manual Scripts Don’t Scale As I transitioned into a more infrastructure-focused role, I realized that manual scripts fail for three key reasons: Lack of Automation: DevOps is about "Infrastructure as Code" (IaC). Asking an engineer to sit at a terminal and type prompts is slow, error-prone, and destroys the ability to automate. Lack of Centralization: In a real team, we aren't creating users on individual local machines. We manage identity across hundreds of servers. Security Risks: Hardcoding passwords or piping them through echo is a major red

Opal, the company famous for making a fancy webcam, has pivoted to making other consumer electronics. Fueled by big investments from OpenAI and Samsung, it’s working on an audio gadget first.

With no embedded modem, the Slate Truck is the antithesis of today's connected cars.