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You know the drill for adding an MCP server to a project: dig the exact command string out of the docs, hand-write a .mcp.json with an absolute path you'll typo once, restart the editor, and discover no tools showed up because the server expected a config file you haven't created yet. Plenty of MCP servers lose their would-be users somewhere inside that loop. Infrawise collapses the whole loop into one command. It's an open-source tool ( npm ) that statically analyzes your codebase, AWS infrastructure, and database schemas, then exposes that context to AI coding assistants over MCP — so Claude Code knows your actual partition keys, GSIs, and indexes instead of guessing from source files. This post is about the part that usually kills tools like this before they deliver any value: setup. Section 1: One command, four steps npm install -g infrawise # or skip install and use npx cd your-project infrawise start --claude start does four things, in order: 1. Probes your environment. If there's no infrawise.yaml in the project, it generates one. It reads AWS_PROFILE if set; otherwise it looks at your configured AWS profiles — one profile means zero questions, several means one prompt asking which to use. That's the entire interview. (If you want the full guided wizard instead, infrawise start --interactive runs it.) 2. Runs the analysis. It scans your AWS services, database schemas, and codebase, builds a graph of services, tables, indexes, and query patterns, and runs rule-based analyzers over it. No LLM is involved in this step — extraction and analysis are deterministic, so the same infrastructure always produces the same graph. 3. Writes .mcp.json to your project root. This is the file you'd otherwise write by hand: { "mcpServers" : { "infrawise" : { "command" : "infrawise" , "args" : [ "serve" , "--stdio" , "--config" , "/absolute/path/to/infrawise.yaml" ] } } } 4. Opens Claude Code. Claude Code reads .mcp.json automatically and starts the session with all 21 infrawise

MongoDB queries look simple. You type a field, give it a value, hit run, and you get your data back. But just because a query runs without throwing an error doesn't mean it worked right. Sometimes you get a blank screen. Sometimes you get way too many records. Other times, the data looks fine at first glance, but it doesn't actually match what you asked for. Most of these slip-ups happen for one basic reason: the query structure doesn't match the way the data actually sits in the database. To show you what we mean, we’ll use a clinic database with a collection called visits . Here is what a typical document looks like: JSON { "_id": "6871b6f9c3f1d1a4c2a10001", "status": "completed", "visitDate": "2026-07-01T09:30:00.000Z", "patient": { "name": "Anna Keller", "age": 34 }, "doctor": { "name": "Dr. James Carter", "specialty": "Cardiology" }, "symptoms": ["cough", "fever"], "prescriptions": [ { "name": "Ibuprofen", "active": false }, { "name": "Paracetamol", "active": true } ], "invoice": { "paid": true, "method": "card", "total": 250 } } You can run these examples right in the VisuaLeaf MongoDB Shell . Using visual tools makes a big difference because you can see exactly what MongoDB is returning in real time. 1. Forgetting the Curly Braces This is just a quick typo, but it breaks things right away. The Mistake: db . visits . find ( status : " completed " ) The Correct Query The find() tool always expects an object. Even if you are only looking for one specific thing, you still need to wrap that condition in curly braces {} . 2. Treating $or Like a Regular Object This one trips a lot of people up because the broken version looks like it should work. The Mistake: db.visits.find({ $or: { status: "completed", "invoice.paid": false } }) What is wrong: $or expects an array of conditions, but this query gives it one object. The error will usually be something like: MongoServerError: $or must be an array The Correct Query The first query is wrong because $or needs an array, n

✓ Human-authored analysis; AI used for formatting and proofreading. Scott Piper published a twenty-year retrospective on cloud security research in March 2026. It's the most useful structural history of the field I've seen — four eras, each with defining milestones, each with the tools and research that shaped cloud security. If you work in cloud security, read it first. What follows is a question about what the history reveals when you examine one detail it doesn't discuss. The four eras Piper divides two decades into four eras: 2006–2016, Foundational. Cloud providers built the security primitives — IAM (2011), CloudTrail (2013), Organizations and SCPs (2016). Before these existed, there was no mechanism for least privilege, no audit trail, and no organizational boundary. Security research in this era was part-time work from people with broader careers. 2016–2021, CSPM. Cloud security became a full-time job. CIS Benchmarks standardized what to check. Open-source tools proliferated — Prowler, CloudMapper, Pacu, Cloud Custodian, ScoutSuite. Cloud security during this time largely meant deploying a CSPM. 2021–2025, CNAPP. Point solutions gave way to platforms. Vendors integrated CSPM with container scanning, vulnerability management, and workload protection into a single product category. Research teams at vendors began finding cross-tenant vulnerabilities in the cloud providers themselves. 2025–present, AI. AI accelerates both attack and defense. Exploits that required deep language expertise are generated in minutes. A CTF challenge was solved by an AI within minutes of release. The industry is speed-running the cloud eras. This is a well-evidenced narrative. Every era is defined by a change in what tools could do and who was building them. The verb that didn't change Look at what each era's defining tools do. The direct action each tool performs on its direct object. In the CSPM era, the defining tools match API responses against rule databases. Prowler, ScoutSuit

I'm wondering if there exists a US equivalent of Anabin[0] -- a German portal and database for evaluating foreign educational certificates. [0]: https://anabin.kmk.org/cms/public/startseite

Private buyers are increasingly outbidding museums for fossils. That’s making it difficult—or even impossible—for researchers to improve our understanding of the past.

https://xcancel.com/i/article/2076957440109625718 https://www.economist.com/business/2026/07/14/demis-hassabis... , https://archive.ph/GOUcN

AI Spreadsheet for Business Discussion | Link

AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely. By Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman

Kid-friendly tech company Pinwheel announced the launch of a new landline phone designed to let children stay connected without the distractions of a smartphone.

In response to a public records request, HUD has withheld documents about DOGE’s use of AI—in part by citing a privilege that doesn’t exist.

Two years ago I open-sourced KeyEcho, a small desktop app that plays a mechanical-keyboard sound the...

The Hisense A10 has an E Ink main display and a magnetic LCD screen you can attach to its rear.

The FCC authorized Reflect Orbital to launch the mirror satellite Eärendil-1. “For optical astronomy, this poses an existential threat,” the European Southern Observatory said.

The Creative Agent OS — create better with desktop AI agents Discussion | Link

Drawing from the enduring adaptability of HTML and HTTP, Seph Gentle proposes embedding self-contained schemas directly into file headers, ensuring data remains readable without external definitions. His experimental format prioritises forward, backwards, and sideways compatibility, enabling data format evolution without central coordination or data loss By Olimpiu Pop