标签：#p

Your MCP server works. But does anyone know it exists? We scored 39,762 MCP servers. 54% scored Grade C — solid code quality, zero community adoption. They're invisible to the AI agents that need them. Here's how to go from invisible to discovered. What Your Grade Actually Means Our scoring uses an additive model: Composite Grade = Quality Score (0-100) + Community Bonus (0-60) + Trust Bonus (0-30) Grade Score What it means B+ 86+ Very good — close to elite B 76-85 Good — your target C+ 66-75 OK — getting there C 46-65 Average — this is 54% of all tools D 21-45 Needs work F 0-20 Critical If you're at C, you're not failing. You just haven't been discovered yet. Step 1: Fix Your Quality Score (Quick Wins) Quality Score is 5 dimensions. Here are the fastest fixes: Token Efficiency (25%) Every token in your tool definition counts against the agent's context window. Bad: 500+ tokens OK: 200-350 tokens Good: 100-200 tokens Elite: ≤50 tokens Fix: Cut redundant parameters. Shorten descriptions. Use concise naming. Most tools can save 40-80 tokens in 15 minutes. Schema Correctness (25%) Agents need machine-readable schemas. Fix: Add a type field. Define properties . Include required fields. A well-structured schema can add 30+ points to your quality score instantly. Description Quality (20%) Write for AI agents AND humans. AI agents need clarity. Humans need to understand what your tool does at a glance. A good description serves both. ❌ Bad (confuses everyone): "PDF tool" ✅ Good (clear to both agents and humans): "Extracts text and tables from PDF files. Supports multi-page documents. Returns structured JSON with page numbers." ✅ Better (humans can instantly understand, agents can parse): "Extracts text and tables from PDF files. Example: extract_tables('report.pdf') → [{page: 1, rows: [[...]]}]. Supports multi-page documents." A human scanning GitHub repos decides in 3 seconds whether to try your tool. An AI agent scanning tool definitions decides in 3 milliseconds. Serve

Snap one photo, get listing-ready shots from every angle Discussion | Link

AI coding tools are powerful. But they’re also wasteful. A tiny helper-function question does not need Claude Sonnet. A huge architecture review probably does. That gap costs money. So I built Badgr Auto. It’s a local OpenAI-compatible proxy that routes each AI coding request to the cheapest model that can handle it. Point your coding tool at: http://localhost:8787/v1 Badgr Auto can route between: local models cheaper OSS cloud models premium models So instead of paying premium prices for every request, you can use: local for small tasks OSS cloud for normal coding work premium only when it actually matters It also tracks: actual cloud spend which route was used fallback events tokens safely removed estimated savings vs premium models The goal is simple: stop wasting premium tokens on cheap tasks. First launch is small: small task → local normal task → cheaper cloud hard task → premium provider fails → fallback duplicate code → safely removed receipts → clear spend trail AI coding is only going to get more expensive if every agent step goes to the top model. Badgr Auto is my attempt to make AI coding cheaper without making it worse.

Local-first agent trust console with a safe local workspace Discussion | Link

I independently shipped my first open-source repo this week. The tool I built was a cli which accesses quickbooks online data. While Claude Code did speed up the build, it still took considerable effort shaping the entire user experience for the cli around the pre-existing public APIs! Major learnings during the entire process. Would also love additional feedback from open-source developers here.I'm currently looking for feedback from experienced open-source developers: Are there any improvements you'd suggest around project structure, documentation, testing, or contributor onboarding or the tool functionality? https://github.com/intuit/intuit-cli-for-quickbooks #

Something changed in software engineering, and I do not think we have fully named it yet. For years, the job was mostly about writing code directly. Then autocomplete got better. Then chat-based coding assistants arrived. Now the workflow is shifting again: we describe goals, hand off chunks of work to agents, inspect their output, tighten the tests, and decide what gets merged. That is not the same job with a faster keyboard. It is a different shape of work. I would call it agentic engineering. The engineer is becoming a director Agentic engineering does not mean the engineer disappears. If anything, it makes the engineer's judgment more visible. A coding agent can read files, make changes, run commands, open pull requests, and iterate through errors. GitHub describes Copilot agent mode as a workflow where the agent can plan, edit, run terminal commands, and keep working until a task is complete. Google describes Jules as an asynchronous coding agent that can take a task, work in a virtual machine, and produce a pull request. Anthropic's Claude Code guidance talks openly about using multiple Claude sessions in parallel, giving agents clear context, and treating them like workers that need direction. That is the shift. The engineer is no longer only the person typing every line. The engineer is also the person deciding what should be built, what constraints matter, how to verify the result, and when the agent is wrong. Prompting is too small a word for this People often describe this work as prompting, but that undersells it. A prompt can be a single instruction. Agentic engineering is more like delegation. You define the task, provide the relevant context, set the boundaries, create checks, review the work, and decide the next move. If the agent goes in the wrong direction, the failure is not always the model's fault. Sometimes the task was too vague. Sometimes the repository had no tests. Sometimes the acceptance criteria lived only in someone's head. This is why

Google just made it significantly cheaper to enjoy its budget AI subscription tier.

Target: pnpm/pnpm Issue: pnpm/pnpm#12240 PR: pnpm/pnpm#12301 Public branch: https://github.com/scarab-systems/pnpm/tree/fix/deps-status-no-manifest Latest pushed commit: cb68ac1af0dcffbe4fb607a10b0df2046d2490ba This field test targeted a pnpm command-routing failure where pnpm self-upgrade could fail outside a project directory with: ERR_PNPM_NO_PKG_MANIFEST The issue looked simple at the surface: a global/self command should not require a project manifest just because the current working directory is not inside a package. But the repair boundary was more specific than “ignore missing manifest.” The problem was in the dependency-status verification path. When dependency status was unavailable because there was no project manifest, the command could fall through into the auto-install path. That made a self-upgrade/global-style command behave as if it needed a local project manifest. Failure shape The failing behavior was: pnpm self-upgrade run outside a project directory dependency status cannot be established from a project manifest the command path falls into install/manifest expectations result: ERR_PNPM_NO_PKG_MANIFEST That is the wrong ownership boundary. A self-upgrade command should not inherit project-manifest preconditions when there is no local project context. Boundary The boundary here is: global/self command execution versus project dependency-status verification Dependency-status verification can be useful when a command is operating inside a project. But when there is no project manifest and the command is not recursive/all-projects, “dependency status unavailable” should not automatically mean “try to auto-install project dependencies.” There are two different cases: Dependency status is unavailable because there is no project manifest. Dependency status is unexpectedly unavailable even though a root project manifest exists. Those cases should not behave the same. The repair preserves that distinction. What changed The patch updates: exec/commands/src

Have you ever stared at two different medicine boxes, squinting at the tiny font of the active ingredients, wondering: "Can I actually take these together?" Modern healthcare is complex, and drug-drug interactions (DDI) are a leading cause of avoidable ER visits. In this tutorial, we’re going to leverage GPT-4o Vision , React Native , and the FDA OpenData API to build a "Drug Conflict Scanner." We will utilize multimodal AI to transform messy pill-box photos into structured data and cross-reference them against official medical databases for safety. By the end of this guide, you'll master GPT-4o OCR structuring and automated knowledge graph verification for real-world health tech applications. 🚀 The Architecture 🏗️ The logic flow involves capturing images of multiple medicine labels, using GPT-4o's multimodal capabilities to extract chemical compounds, and then querying the FDA's database for potential interactions. graph TD A[React Native App] -->|Capture Multi-Photo| B[Node.js Backend] B -->|Image Buffer| C[GPT-4o Vision API] C -->|Structured JSON: Ingredients| B B -->|Search Interactions| D[FDA OpenData API] D -->|Drug Labels & Warnings| B B -->|Safety Report| A A -->|UI Alert| E{Safe or Warning?} Prerequisites 🛠️ To follow along, you'll need: GPT-4o API Key (via OpenAI) Node.js (for our backend relay) React Native (Expo is recommended for camera access) An account at open.fda.gov (though the public API works for limited requests) Step 1: Extracting Ingredients with GPT-4o Vision Traditional OCR struggles with curved medicine bottles and shiny packaging. GPT-4o excels here because it understands context. We don't just want text; we want the Generic Name of the drug. The Backend Logic (Node.js) // backend/scanner.js import OpenAI from " openai " ; const openai = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); async function analyzeMedicineLabels ( imageUrls ) { const response = await openai . chat . completions . create ({ model : " gpt-4o " , messages :

Migrated 4 of 7 Notion automations to an MCP server in one weekend Two workflows stayed in Notion because the database UI beat any tool call MCP scope rule: one tool does one verb, never a Swiss Army function Result: 12 manual steps collapsed into 3 Claude prompts per publish I spent a weekend pulling four automations out of Notion and rebuilding them as MCP tools. Three of them got faster and one got worse before it got better. The biggest lesson was not about code. It was about deciding which jobs should never leave Notion in the first place. Why I Moved Off Notion In The First Place My Notion setup was not broken. It was just slow in a specific way. I had seven automations stitched together with Notion buttons, formula properties, and two third-party connectors. Every blog publish meant clicking through four pages, copying a title here, pasting a tag list there, and triggering a sync that took 90 seconds to confirm. Multiply that by the 18 articles I push in a normal month and the clicking adds up. The breaking point was a Tuesday where I lost 40 minutes to a connector that silently stopped firing. No error, no log, just a row that never updated. I checked the connector dashboard and it told me everything was healthy. It was not healthy. That kind of invisible failure is the worst kind because you trust it until you do not. MCP changed the math for me. An MCP server lets Claude call my own functions directly. Instead of Claude writing text and me ferrying that text into Notion by hand, Claude can call a tool that does the writing into my systems. The model becomes the operator, not just the writer. If you want the deeper context on what MCP actually is and why it matters at scale, MCP: The 97 Million Agentic Foundation goes through the bigger picture. So I made a list. Seven automations, sorted by how much human judgment each one needed. The ones at the top were pure mechanical steps: format this, push that, fetch a status. The ones at the bottom needed me to loo

If you've ever spun up an EC2 instance for a side project, accessed a remote work desktop from your personal laptop, or stored files on Google Drive without thinking about the physical hard drive it lives on, you've used virtualization. As the foundational technology behind all modern cloud computing, virtualization transformed how we build, deploy, and manage IT infrastructure—cutting hardware costs significantly for enterprises and making on-demand scalability a reality for teams of all sizes. In this guide, we'll break down exactly what virtualization is, how it powers the cloud, the 6 core types of virtualization, and best practices to implement it safely and efficiently. Table of Contents What is Virtualization in Cloud Computing? Core Virtualization Concepts You Need to Know Role of Virtualization in Cloud Computing 6 Key Types of Virtualization (With Use Cases) Top Benefits of Virtualization for Teams of All Sizes Virtualization vs. Related Technologies Virtualization vs. Cloud Computing Virtualization vs. Containerization Common Virtualization Challenges and Mitigations Real-World Virtualization Use Cases Virtualization Best Practices Conclusion References What is Virtualization in Cloud Computing? Virtualization is a technology that creates virtual, software-based representations of physical hardware (servers, storage, networks, etc.) and abstracts these resources from the underlying physical machine. A software layer called a hypervisor separates operating systems and applications from physical hardware, allowing multiple isolated, self-contained systems called Virtual Machines (VMs) to run simultaneously on a single physical host. Each VM has its own virtual CPU, memory, storage, and network interface, and operates independently of other VMs on the same host. For cloud providers, this technology is the backbone of all on-demand infrastructure services, allowing them to share physical hardware across thousands of customers securely and efficiently. Core Vi

PostgreSQL Error 2201X: invalid row count in result offset clause PostgreSQL error code 2201X ( invalid_row_count_in_result_offset_clause ) is thrown when the value provided to an OFFSET clause is not a valid non-negative integer. This commonly surfaces in applications that implement pagination using dynamic queries or user-supplied parameters, where the offset value may be negative, NULL, or a non-integer type. Top 3 Causes 1. Negative OFFSET Value The most frequent cause is a miscalculated page offset. When computing (page - 1) * page_size , a bad page number can produce a negative result. -- Triggers error 2201X SELECT * FROM orders ORDER BY created_at DESC OFFSET - 5 LIMIT 20 ; -- ERROR: invalid row count in result offset clause -- Fix: Use GREATEST to clamp the value to 0 SELECT * FROM orders ORDER BY created_at DESC OFFSET GREATEST ( 0 , - 5 ) LIMIT 20 ; 2. NULL Passed as OFFSET When an application fails to initialize or bind the offset parameter, NULL gets passed to the query. This often happens with ORMs or query builders where optional parameters are not explicitly set. -- Triggers error 2201X SELECT * FROM products ORDER BY id OFFSET NULL LIMIT 10 ; -- ERROR: invalid row count in result offset clause -- Fix: Use COALESCE to provide a default value SELECT * FROM products ORDER BY id OFFSET COALESCE ( NULL , 0 ) LIMIT 10 ; -- Parameterized query with safe fallback SELECT * FROM products ORDER BY id OFFSET COALESCE ( $ 1 :: BIGINT , 0 ) LIMIT COALESCE ( $ 2 :: BIGINT , 10 ); 3. Non-Integer Type (Float or String) Passing a float or a string that cannot be cleanly cast to a whole number causes this error. This typically happens when Python float values or unvalidated user input strings are interpolated directly into a query. -- Triggers error 2201X SELECT * FROM employees ORDER BY last_name OFFSET 10 . 7 LIMIT 5 ; -- ERROR: invalid row count in result offset clause -- Fix: Use FLOOR and explicit cast to BIGINT SELECT * FROM employees ORDER BY last_name OFFSET F

Parents want one thing, and one thing only, out of AI: to add a list of soccer games or "spirit week" theme days from an email or a poorly formatted flyer onto their calendar in one shot. And I have good news for parents with iPhones - the new Siri can finally do this. After […]

Instead of spending a year raising a formal venture fund, the Sabertooth VC founder used a captive network of LPs to invest in startups like Anthropic, Anduril, and SpaceX.

A few months ago I was doing a final pre-release review on a client project before handing it...

Microsoft today released software updates to plug nearly 200 security holes across its Windows operating systems and supported software, a record number of fixes for the company's monthly Patch Tuesday cycle. Nearly three dozen of those bugs earned Microsoft's most dire "critical" rating, and exploit code for at least three of the weaknesses is now publicly available.

On June 9, 2026, Anthropic shipped the most capable model it has ever released to the public. The most interesting thing about it is the part that sometimes refuses to talk to you. Claude Fable 5 is the first model from what Anthropic calls its Mythos class, a tier that now sits above Opus. It launched as a pair. Fable 5 is the public version. Claude Mythos 5 is the same underlying model with its guardrails loosened, and it is not for sale to most of us. It goes only to vetted cyberdefenders and infrastructure providers through a program called Project Glasswing, in collaboration with the US government. Two names, one brain. The thing that separates them is a set of classifiers. That detail is the whole story, and almost every launch-day write-up buried it under the benchmark chart. So let me start there instead. One Model, Two Names, One Classifier in Between Fable 5 ships with three classifiers running alongside it. They watch for requests about offensive cybersecurity, about biology and chemistry that edge toward weapons, and about distillation, which is using the model to train a competitor. When a classifier fires, Fable 5 does not answer. The request gets handed to Claude Opus 4.8, the model that was the top of the public stack until that morning, and Opus answers in Fable's place. For anyone building on the API, this is not an abstract safety story. It is a response shape you have to handle. A refused request comes back as stop_reason: "refusal" with a normal HTTP 200, not an error, and it tells you which classifier tripped. You can have the API retry on another model with a fallbacks parameter, or do it client side with the SDK middleware. You are not billed for a request that is refused before it generates output. { "stop_reason" : "refusal" , "stop_sequence" : null , "content" : [] } Anthropic says this is rare. Its early numbers put at least 95 percent of Fable sessions running entirely on Fable's own answers. I believe that for general work. But "rare on

Vortex 3.0 RISC-V GPGPU, Pragtical SDL GPU Backend, NVIDIA RTX Spark Launch Today's Highlights Today's top stories highlight significant advancements in open-source GPU hardware with Vortex 3.0 adding a 3D pipeline and a lightweight code editor, Pragtical, leveraging an SDL GPU backend for UI rendering. NVIDIA also unveiled RTX Spark, a new 'superchip' aimed at bringing personal AI agents to Windows PCs with accelerated on-device processing. Vortex 3.0 Released As Full-Stack, Open-Source RISC-V GPU Now With 3D Pipeline (Phoronix) Source: https://www.phoronix.com/news/Vortex-3.0-RISC-V-GPGPU Vortex, an open-source, OpenCL-compatible RISC-V GPGPU implementation developed by Georgia Tech, has released its next major version, 3.0. This significant update introduces a full 3D rendering pipeline, marking a crucial evolution from its previous focus solely on general-purpose GPU (GPGPU) compute. The expansion into 3D graphics capabilities makes Vortex a more comprehensive open-source GPU solution, enabling it to handle a wider range of visual and computational tasks. As an open-source hardware design, Vortex 3.0 provides developers, researchers, and hardware enthusiasts with unparalleled access to study, modify, and implement its architecture. Its OpenCL compatibility ensures that it can leverage existing GPGPU codebases, fostering experimentation with RISC-V-based GPU development, custom hardware accelerators, and exploring alternative GPU instruction sets and architectures. This release allows for deeper exploration into the integration of compute and graphics within an open framework. This development is pivotal for the open-source hardware and RISC-V ecosystems. It underscores the growing maturity of RISC-V for demanding compute and graphics workloads, offering a royalty-free alternative to proprietary GPU designs. The inclusion of a 3D pipeline extends its utility beyond just general-purpose compute to full graphics rendering, potentially impacting future embedded syst

Congress narrowly voted to fund President Donald Trump's mass deportation agenda, giving the Department of Homeland Security $70 billion over the next three years. The house voted 214 to 212 in favor of the reconciliation bill Tuesday, following the Senate's 52-47 vote last Friday morning. The vote fell largely along party lines. Sen. Lisa Murkowski […]

TL;DR — AI assistants are producing C# that looks correct and passes review, but reintroduces production regressions we spent years training out of teams. I'm trying to find out whether other .NET teams see the same patterns — and what's actually catching them before merge. More AI-generated C# is landing in pull requests. Most of it is fine. But a specific category keeps slipping through — and it's the dangerous one, because it compiles, tests pass, and a human skim says "looks good." The pattern The code compiles. Tests pass. Review approves. Production finds out. These aren't syntax errors. They're architectural intent violations — the kind of thing a senior dev would have caught in review before PR volume tripled. Five regressions I keep seeing 1. EF Core read paths without AsNoTracking() Fine in dev. Expensive on a hot read path in prod. // ❌ Looks reasonable. Tracks entities you never mutate. var orders = await _db . Orders . Where ( o => o . CustomerId == id ) . ToListAsync ( cancellationToken ); Fix direction: AsNoTracking() on read-only queries, or a team convention documented in CLAUDE.md / Copilot instructions. 2. Captive dependency (scoped service in a singleton) Compiles. Runs. Wrong state across requests. // ❌ Singleton lives forever; scoped dependency does not. services . AddScoped < IOrderRepository , OrderRepository >(); services . AddSingleton < ReportCache >(); // ctor takes IOrderRepository Fix direction: align lifetimes, or inject IServiceScopeFactory instead of capturing scoped services. 3. Dropped CancellationToken The method accepts cancellation. The downstream call ignores it. // ❌ Signature honours cancellation; body doesn't. public async Task RunAsync ( CancellationToken cancellationToken ) { await Task . Delay ( 500 ); // overload with token exists } Fix direction: forward cancellationToken to every downstream async call that accepts one. 4. Swallowed exception Failure disappears. Monitoring stays green. // ❌ "Handle errors gracefully" —