标签：#ia

The problem every self-hoster hits You built something. A local API. A Minecraft world for your friends. A self-hosted dashboard. An ERP running on the office machine. It works great — on your LAN. The moment you want someone outside to reach it, the fun begins: Port forwarding? Good luck if you're behind CGNAT, a corporate firewall, or an ISP that doesn't give you a public IP. VPN? Now every person who wants access has to install a client, join a network, and stay connected. Overkill for "let me show you this one page." Cloud deploy? Now you're maintaining two environments, paying for a VPS you didn't need, and shipping data somewhere it doesn't have to live. What most people actually want is simpler: take this one local port, give it a public address, done. That's exactly what FRP does. What FRP is FRP (Fast Reverse Proxy) is an open-source tool by fatedier that exposes a local service behind a NAT or firewall to the public internet. It's battle-tested, written in Go, and has been the go-to answer in self-hosting communities for years. The model is clean — two pieces: frps (the server) — runs on a machine with a public IP (a $5 VPS is plenty). frpc (the client) — runs on your local machine, the one with the service you want to expose. The client opens an outbound tunnel to the server. The server listens on a public port and forwards traffic back through the tunnel. NAT and firewalls don't matter because the connection is initiated from inside . [Visitor] → [frps on public VPS:7000] ⇄ tunnel ⇄ [frpc on your laptop] → [localhost:8080] That's the whole idea. It works for TCP, UDP, HTTP, HTTPS. People run Minecraft servers, remote desktops, internal dashboards, and dev previews through it every day. The catch: config files FRP works great. The friction isn't the protocol — it's the workflow . To run frpc , you write a TOML/INI config file: serverAddr = "203.0.113.10" serverPort = 7000 auth.token = "your-secret-key" [[proxies]] name = "my-web" type = "tcp" localIP = "1

Non è solo “chiedere aiuto”: è chiarire obiettivi, vincoli e tentativi. E accelera sia l’apprendimento che il lavoro in team. Nel lavoro quotidiano di un frontend developer (e non solo) capita spesso di bloccarsi: un bug che non si riproduce, un layout che “quasi” funziona, una libreria nuova che sembra richiedere di leggere mezzo internet. In quei momenti la differenza tra perdere ore e sbloccarsi rapidamente non è sempre “quanto ne sai”, ma come fai le domande . Fare domande di qualità è una skill professionale a tutti gli effetti: migliora la collaborazione, riduce il ping-pong nei thread, rende più efficaci code review e pair programming, e soprattutto ti allena a ragionare in modo strutturato. Perché fare buone domande è una competenza (non un dettaglio) Una domanda ben formulata ti obbliga a mettere ordine in quattro cose: Cosa stai cercando di ottenere (obiettivo) Cosa non sai (gap di conoscenza) Cosa hai già provato (tentativi e risultati) Che cosa non serve fare adesso (scope e priorità) Questo vale sia quando chiedi aiuto su un problema tecnico, sia quando stai decidendo cosa studiare per crescere. La domanda “cosa devo imparare?” è troppo vaga “Cosa devo imparare?” sembra utile, ma spesso non porta lontano perché manca il contesto: non definisce un obiettivo, non dà vincoli, non permette a chi risponde di proporre un percorso sensato. Una versione migliore parte da: Qual è il risultato che voglio ottenere? Cosa mi avvicina a quel risultato oggi, in modo pragmatico? E c’è un punto ancora più potente: chiedersi anche cosa NON serve imparare . Perché “cosa non devo imparare” ti fa risparmiare tempo Nel frontend c’è sempre una tentazione: allargare lo scope. Esempi tipici: “Per usare React devo prima imparare perfettamente TypeScript, poi i design pattern, poi…” “Per risolvere questo problema di CSS forse devo studiare tutta la specifica di Flexbox e Grid…” Chiederti cosa non è necessario adesso ti aiuta a: scegliere il minimo set di concetti per sbloccarti;

One of the most important performance metrics for a WordPress website is Server Response Time, commonly measured as Time to First Byte (TTFB). While caching plugins like WP Rocket significantly improve performance, many server configurations still route every request through PHP before serving the cached page. In reality, cached HTML files can be delivered directly by the web server (Apache or Nginx), completely bypassing PHP and WordPress. This approach reduces CPU usage, lowers the PHP-FPM workload, and improves overall server response time. This guide explains how to optimize both Apache (.htaccess) and Nginx so they can serve WP Rocket's static HTML cache directly. Why Is This Optimization Important? By default, a typical WordPress request follows this flow: Visitor │ ▼ Apache/Nginx │ ▼ PHP │ ▼ WordPress │ ▼ WP Rocket Cache │ ▼ HTML Response Even when a page has already been cached, the request still passes through PHP before the cached content is returned. With the following configuration, the request flow becomes: Visitor │ ▼ Apache/Nginx │ ▼ WP Rocket HTML Cache │ ▼ HTML Response PHP and WordPress are only executed when a cached file does not exist. Benefits Lower Time to First Byte (TTFB) Reduced CPU usage Less PHP-FPM processing Better performance during traffic spikes Ideal for VPS and dedicated servers Improved scalability with minimal configuration changes Apache (.htaccess) Optimization If your server runs Apache, insert the following block inside the WordPress rewrite section, immediately after: RewriteBase / and before: RewriteRule ^index\.php$ - [L] The resulting configuration should look like this: # BEGIN WordPress # Die Anweisungen (Zeilen) zwischen „BEGIN WordPress“ und „END WordPress“ sind # dynamisch generiert und sollten nur über WordPress-Filter geändert werden. # Alle Änderungen an den Anweisungen zwischen diesen Markierungen werden überschrieben. < IfModule mod_rewrite.c > RewriteEngine On RewriteRule .* - [E=HTTP_AUTHORIZATION:%{HTTP:Autho

Disclosure up front: I'm Oded, co-founder of UniPaaS, the FCA-authorised Payment Institution (No. 929994) behind paas.build - so this is a vendor writing about his own product. That said, the three Base44 mechanics below are documented Base44 surfaces, and they work with any external payments API, not just ours. The wall Tell Base44 "add payments" and it installs Stripe or Base44 Payments (powered by Wix), plus Tranzila/Max for Israel. Both main options are solid if you qualify: Stripe is excellent infrastructure with first-class docs, and the Wix-powered option is native to the platform. The fine print is where builders hit a wall: Stripe live mode needs verified business and banking information before you can take a real payment. Base44 Payments requires "a business and bank account based in one of the supported countries" (their docs). The top payments request on Base44's own feedback board is "a way to setup other payment providers other than Stripe" - precisely because not every country is supported. Base44 webhooks only fire while someone is actively using your app, so 3am subscription renewals, retries and dunning silently don't run. If you have a registered company in a supported country and mostly sell one-off purchases, use the built-in Stripe path. It's the smoothest. The rest of this post is for everyone else. Base44 gives you three documented ways to wire in an external provider. I tested all three with paas.build. Here's each, and when it fits. Insertion point 1: custom MCP connection (build-time) In Base44: Settings → Account → MCP connections → Add custom MCP . Name: paas.build Server URL: https://paas.build/sse Auth: API key (your paas.build key) That's the legacy SSE endpoint Base44's form takes; streamable HTTP lives at https://paas.build/mcp for agents that support it. Base44's AI treats MCP connections as tools it can call when your request needs external data or actions. So in the editor chat you can say "use paas.build to create a live merchan

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

The move comes after Simo took significant medical leave. She will stay on as a part-time adviser.

What Is Data Masking? Data masking is a technique that replaces sensitive information with realistic but fictitious data, preserving the format and structure of the original while removing its identifiable meaning. The goal is to keep data usable for development, testing, analytics, or sharing — without exposing real personally identifiable information (PII). Common masking techniques include: Substitution — Replace a real value with a plausible fake (e.g., Alice Smith → Jane Doe ). Masking (partial obscuring) — Show only a portion of the value (e.g., 4111-1111-1111-1234 → ****-****-****-1234 ). Redaction — Remove the value entirely. Hashing — Replace with a cryptographic hash. Irreversible, but deterministic when salted. Data masking is widely used in non-production environments, analytics pipelines, data marketplaces, and any scenario where real PII is not needed but structural fidelity is. What Is Dynamic Data Masking? Static data masking (SDM) applies transformations to data at rest — you clone a production database, mask it, and ship the masked copy to a lower environment. The masking happens once, and the result is a permanent dataset. Dynamic data masking (DDM) applies transformations on the fly , at query or API time, based on who is asking. The original data stays untouched; the masking rules are applied in the response layer. This means: Different roles see different levels of detail (e.g., support agents see the last 4 digits of a credit card; auditors see the full number). No masked copies to maintain — one source of truth, many views. Masking policies are centralized and enforceable without application changes. Veramask implements a DDM-style model over an API: you send a request with payload and settings, and receive back the transformed result in real time. No data is persisted on the server — each call is independent and stateless. Anonymizing PII with the Veramask API Veramask exposes two endpoints for dynamic PII masking: Endpoint Input Use Case PO

This is a cross-post — the original (and any updates) live at broke2builtai.com . The first time I watched Claude Code reach for a skill I hadn't told it to use — read a folder, run the script inside it, and hand back the finished thing — the difference from a slash command finally landed. A slash command waits for you to type it. A skill waits for the situation . Claude decides. That one shift is the whole feature, and building one takes about five minutes once you know where the file goes. Here's the entire thing end to end, including the one gotcha that decides whether your skill ever actually fires. What a Skill actually is A Skill is a folder with a SKILL.md file inside it. The Markdown holds instructions; the YAML frontmatter at the top holds a name and a description . That description is doing the most important job in the whole file: Claude reads it to decide, on its own, whether the current task warrants invoking the skill. Nothing else you write matters if the description doesn't get you picked. That's the mental model to hold onto: a custom slash command is a prompt you trigger by typing /name ; a skill is a procedure Claude triggers when the context matches. Same reusable-instructions idea, opposite trigger. Where the file goes Two locations register, exactly like commands and subagents : Project skill — .claude/skills/<skill-name>/SKILL.md inside the repo. Committed, so your whole team gets it. Personal skill — ~/.claude/skills/<skill-name>/SKILL.md in your home directory. Follows you across every project on your machine. Each skill is its own folder, and the folder name should match the name in the frontmatter. A loose SKILL.md sitting somewhere else won't be picked up. The minimum viable skill Create the folder and the file: .claude/skills/pytest-runner/SKILL.md Then write the two-part file — frontmatter, then body: --- name : pytest-runner description : " Run, generate, or debug pytest tests for this project. Use when the user asks to run the test su

Over the past week, two narratives have been colliding everywhere I look. On one side, there's panic. AI is expected to replace marketers, engineers, and entire categories of knowledge work almost overnight. On the other, there are quieter but far more consequential signals: enterprise teams discovering their AI infrastructure is burning through API budgets far faster than expected. This isn't because the underlying models are weak, but because the systems built around them are fundamentally inefficient by design. These aren't separate stories. They're the same failure showing up in different places. A conversation with another developer made that gap visible in real time. He argued that auditing a 150,000-line codebase requires feeding the entire repository into a model in one single, massive pass. It's still a common assumption in mainstream tech: that an LLM works like a giant biological brain that you must fully load with raw text before it can begin to think. But that assumption is already outdated. Modern AI systems don't scale through brute-force context. They scale through structure. And that shift changes everything. Key takeaways Bigger context windows did not solve AI. Treating a frontier model as a monolithic processor that re-reads an entire system on every query is wasteful, dilutes attention, and hides bugs under raw volume. ARC-AGI-3 makes the gap stark: frontier models scored under 1% on interactive reasoning tasks that untrained humans solve at nearly 100%. The gap is architecture, not memory. The teams pulling ahead treat the model as one narrow component inside a larger system: intelligent routing, task decomposition, retrieval, and only the minimum necessary context. The next advantage is not the biggest model or the longest prompt. It is the system designed around the model. Prompting was the first generation; systems architecture is the next. The Myth of the Infinite Context Window When context windows expanded into the hundreds of thousands o

Yes, you heard it right, you can now run a Dockerfile on Vercel. Vercel was the go-to place where...

The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or…

Claude subscribers must soon pay usage-based fees to access Anthropic’s best consumer AI model—a sign that the golden era of AI subscriptions is ending.

I’ve been thinking about a small image-processing problem lately: how to reduce an image to a limited palette without making it look muddy. This comes up in a lot of places: pixel art tools printable pattern generators low-color previews LED matrix displays icons and small thumbnails craft or grid-based workflows The easy version is: pick the nearest color for every pixel. The hard version is: keep the important shapes readable after the palette gets much smaller. Nearest color is only the baseline A simple nearest-color pass usually works like this: Take each pixel. Compare it with every color in the target palette. Pick the closest one. Replace the pixel. That gives you a valid output, but not always a good one. The problem is that closest is local. It does not know whether the whole image still reads well. A face can lose warm midtones. A shadow can turn into a flat dark blob. A small highlight can disappear. Skin, fur, fabric, and background colors can collapse into the same bucket. So palette reduction is not just a color problem. It is also a structure problem. RGB distance can be misleading A common first attempt is Euclidean distance in RGB: function rgbDistance(a, b) { return Math.sqrt( (a.r - b.r) ** 2 + (a.g - b.g) ** 2 + (a.b - b.b) ** 2 ); } This is easy to implement, but it does not match human perception very well. Two colors can be numerically close in RGB and still feel different. Other colors can be farther apart numerically but visually acceptable. A better approach is to compare colors in a more perceptual color space, such as Lab or OKLab. You still have to be careful, but the distance metric starts closer to what the eye notices. Dithering helps, but it changes the style Error diffusion, like Floyd-Steinberg dithering, can preserve gradients and perceived detail with fewer colors. That is useful when the output is meant to look like a low-color image. But dithering is not always desirable. In grid-based outputs, it can create scattered single-p

A fintech RAG pipeline poisoned its own vector store and the LLM-as-a-judge validator approved every hallucination. The fix: gate writes with code.

Muse Image allows users to generate AI images using photos from public Instagram accounts. As long as a person's profile is public, another user can tag that account and use their images as part of an AI-generated creation.

Having proven how valuable compute can be, the company finds itself at the center of a market everyone wants to be in — while simpler technologies and less interesting companies get rich on the sidelines.

GitHub had over 14,000 repositories. Fewer than half had clear ownership. Here's how we gave every active repository a validated owner in under 45 days, archived the rest, and made ownership the foundation for everything that followed. The post How GitHub gave every repository a durable owner appeared first on The GitHub Blog .

The soft, oddly intimate home-chore robot has been given some very tactile hands.

Filingrail now ships a first-party MCP server on PyPI: pip install filingrail-mcp . One install, one config block, and Claude Desktop — or Cursor, or Continue, or any MCP-compatible client — can query SEC filings as tools. No glue code. That's worth naming directly. Most SEC-data APIs ship a REST endpoint and stop. You write the agent integration yourself: parse the response, wire up the tool schema, handle auth headers. Filingrail ships the integration as a maintained package with the same update cadence as the underlying REST API. This post covers the setup, what you can ask once it's wired in, and the honest limits. I built both the API and the MCP server — I'll be upfront about that throughout. This post covers a data API that returns SEC-registered financial information. Nothing here is investment advice. Two ways to wire it in Option 1 — pip install filingrail-mcp (recommended) Install the package, add one block to your Claude Desktop config, restart. Filingrail's endpoints appear as tools. No separate service to run, no background daemon. Option 2 — RapidAPI MCP Playground tab (no local install) The Filingrail listing on RapidAPI has an MCP tab that generates a ready-to-paste config block. Same endpoints, same auth, zero install step. Either path gives Claude the same tools. Pick the one that fits your setup. Setup — the pip install path You'll need Python 3.10+ and a RapidAPI key. 1. Subscribe to Filingrail Go to the Filingrail RapidAPI listing and subscribe. Free tier is 50 calls/day, no credit card. Copy your X-RapidAPI-Key from the RapidAPI dashboard. 2. Install the server pip install filingrail-mcp 3. Add Filingrail to your Claude Desktop config On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%\Claude\claude_desktop_config.json { "mcpServers" : { "filingrail" : { "command" : "filingrail-mcp" , "env" : { "RAPIDAPI_KEY" : "your_rapidapi_key_here" } } } } 4. Restart Claude Desktop Filingrail's endpoints appear a