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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

With increased adoption of AI, there is often an argument that code-reviews are now the new bottleneck. And I agree with this completely. Code-Reviews, especially the review you do yourself after AI has written your code, take time. But I would object to the notion that this is a bad thing. What is a bottleneck? A bottleneck is something that slows down the process. It becomes a point where work must get in a line, to pass through a narrow space. With the speed of AI producing code, code reviews become a bottleneck. But is having a bottleneck in the process always a bad thing? The value of slowing down I can only speak from my personal experience of developing software for roughly 7 years now. But in my experience, slowing down is not always bad. On the contrary, it can be very healthy. When you slow down, and take the time to really think about things, you often come up with insights that you would not have if you always rush through things. And these insights can be golden opportunities to change something for the better. Be that a subtle bug discovered, be that a design flaw addressed or something else - the list is long. But as British computer scientist Tony Hoare famously said: "There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies." But simplicity is hard "I would have written a shorter letter, but did not have the time." If it was Mark Twain or Blaise Pascal who said it is beside the point. The point is, there is a lot of truth in this quote. A writer of prose I know also confirmed what many senior software engineers know - to make something complex simple and easily comprehensible takes way more time and effort in the form of careful thought than it takes to leave it being complicated and hard to understand. AI is good at writing code quickly, yes. But is it also good at writing code which has high q

I built an LED Strip Tetris game — without writing a single line of code. No keyboard mashing. No debugging at 2 AM. No reading 500 pages of datasheets. Just natural language prompts, an AI agent, and a Tuya T5 AI Core board. Here's the full breakdown of how it works 👇 🧩 What Is LED Strip Tetris? LED Strip Tetris is a DIY hardware game built entirely through natural language prompts using TuyaOpen IDE and Claude Code. It runs on a Tuya T5 AI Core development board with a WS2812 LED strip (72 LEDs) and three color-matched buttons — red, green, and blue. Colored LEDs fall from the top of the strip; players press the matching button to shoot a colored LED upward and eliminate the falling one on contact. The entire game — firmware, game logic, hardware wiring, sound effects, compilation, and flashing — was generated by AI. Zero manual coding. 🔌 The Hardware (Ridiculously Simple) Component Role Tuya T5 AI Core Board Main MCU — runs game logic, drives LED strip and buttons WS2812 LED Strip (72 LEDs) Display — colored LEDs fall and get eliminated 3 Push Buttons (Red / Green / Blue) Input — shoot matching color upward to clear falling LEDs Speaker Sound effects on button press That's it. No custom PCB. No complex wiring harness. Just four components plugged into a dev board. 🤔 Why This Is a Big Deal Here's what building a hardware game normally looks like: Step Traditional Approach Vibe Coding with TuyaOpen IDE Dev environment setup Install toolchain, configure SDK, fight dependencies Copy a workflow link, paste into Claude Code, click confirm Game logic Write C code from scratch, design state machines Describe the game in one sentence, AI generates the code Hardware config Read datasheets, look up GPIO mappings, manually configure Tell AI which pins you're using, it handles the rest Sound effects Write audio decoding code, integrate codecs Give AI the file path, it decodes and compiles Debugging Serial logs, oscilloscope, hours of trial and error AI self-diagnoses compile

We are so excited to announce the winners of the June Solstice Game Jam, our celebration of the...

Provided you have a library of SNES cartridges, the SN Operator is a seamless plug-and-play system for easy ’90s nostalgia.

Nilekani remains Fundamentum's anchor investor as the firm expands its leadership team and targets AI and fintech startups in India.

A platform is a collaboration system: platform teams depend on application teams, and both need shared standards. Engineers trust a platform through its predictable behavior, not its features. Being an engineer is about problem-solving and being passionate about it. And being an engineer means sharing your passion for problem-solving. By Ben Linders

SpaceX is currently ahead of last year's record-setting pace for Starlink satellite deployments. SpaceX launched 1,589 Starlink satellites into low-Earth orbit in the first half of 2026, according to launch data compiled by Jonathan McDowell's satellite tracker, compared to 1,489 satellites deployed at the same point in 2025. 2025 was already a record year for […]

The Dell 14S represents the new normal of laptop pricing, but it has the quality to back up its cost.

The Amseatec Criss Cross Office Chair gives you room to sit cross-legged, sideways, or however your body actually wants to sit.

Because of the way they are trained, large language models capture only a slice of human language. They’re trained on the written word, from textbooks to social media posts, and our speech as captured in movies and on television. These models have minimal access to the unscripted conversations we have face to face or voice to voice. This is the vast majority of speech, and a vital component of human culture. There’s a risk to this. The increased use of large language models means we humans will encounter much more AI-generated text. We humans, in turn, will begin to adopt the linguistic patterns and behaviors of these models. This will affect not just how we communicate with one another, but also how we ...

Thousands of new fossil-fuel power sources are quietly firing up across the state to power the AI boom, thanks to a regulatory loophole, leaving residents feeling blindsided.

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With pricing in flux and new models available, here’s which Surface Laptop and Surface Pro to buy.

“EMF straws” and similar products are being sold as a way to block electromagnetic frequencies that come from common electronic devices, even without scientific evidence that they work.

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NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps. By Steef-Jan Wiggers

Un’area interna che sembra una lavagna di ragionamento: non è coscienza, ma è un indizio forte su come emergono controllo e pianificazione nei transformer. Negli ultimi anni ci siamo abituati a pensare ai modelli linguistici come a enormi “scatole nere”: un prompt entra, un testo esce, e nel mezzo c’è un mare di matrici difficili da ispezionare. Ma c’è una novità interessante: alcune analisi suggeriscono l’esistenza di una piccola regione interna, relativamente organizzata, che funziona come uno spazio di lavoro per concetti . Un posto dove il modello “tiene a mente” qualcosa prima di produrre la risposta. È un’idea che fa scattare subito l’associazione più pericolosa (e più abusata) del momento: coscienza . In realtà, il punto non è stabilire se un LLM sia cosciente; il punto è molto più concreto e utile per chi sviluppa: se esiste un’area interna che concentra il ragionamento controllabile , allora possiamo capire meglio cosa guida certe risposte e come intervenire su errori, allucinazioni e comportamenti indesiderati. J-Space: una “lavagna” interna per il ragionamento L’idea chiave è questa: dentro il modello emergerebbe un piccolo insieme di pattern neurali “coerenti” (chiamiamoli J-Space ) che si comporta come una lavagna. Su questa lavagna compaiono concetti (non necessariamente parole che verranno stampate). Questi concetti influenzano la catena di ragionamento . Molte altre abilità—fluency, grammatica, stile, completamento locale—sembrano invece scorrere “automaticamente” altrove. Se questa separazione regge, spiega un fenomeno che tutti abbiamo osservato: modelli capaci di scrivere in modo impeccabile, ma fragili nel ragionamento o incoerenti quando devono mantenere vincoli. Il test più interessante: sostituire un concetto e vedere il ragionamento obbedire Un esperimento illuminante consiste nell’individuare un concetto attivo nello spazio di lavoro e sostituirlo con un altro, senza cambiare né prompt né output manualmente. Esempio (semplificato): Domanda: