标签：#m

So much for "no current plans" to sunset the app.

Undisclosed addition in jqwik instructed AI coding agents to delete app output.

There are strategies to improve healthcare, but US isn't trying them.

A decade ago, Nintendo made a big splash into the world of mobile gaming with a new Super Mario platformer directed by none other than Shigeru Miyamoto. But even though the game proved popular, it wasn't the success the company had hoped for. Over the ensuing years Nintendo has slowly retreated from smartphone gaming, with […]

Microsoft is launching a revamped version of Microsoft 365 Copilot, offering a cleaner design that the company claims loads twice as fast. As part of this update, Copilot will provide more reliable and structured responses that are easier to scan, according to Microsoft. The redesign, which is rolling out across desktop and mobile devices, comes […]

Copilot will look a little more consistent, and lose a little personality

Asana will incorporate StackAI into its growing suite of AI workflow tools.

If it scales up, it can help us diversify our sources of a key element.

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.

Meta’s upcoming Instagram Plus and Facebook Plus subscriptions are the latest example of the company seeing what works elsewhere and mimicking it.

FBI: Googler who knew outcome of bets in advance made $1.2M profit on Polymarket.

TRIESTE, Italy – For developers, system administrators, and digital hoarders alike, the daily struggle of locating a specific snippet of text buried deep inside hundreds of nested project files is a universal headache. While heavy-handed IDEs and clunky terminal commands exist, they often feel like using a sledgehammer to crack a nut. Enter FiXiY, a lightweight, blazing-fast utility designed to do exactly one thing flawlessly: scan a folder and find precisely what you’re looking for inside the files. Created by software engineer Lorenzo Battilocchi (known online as XeroHero), FiXiY has officially launched as a free, open-source project on GitHub. Simplicity Meets Speed Unlike built-in operating system searches that are notorious for missing code snippets or taking ages to index, FiXiY bypasses the bloat. It provides a localized, no-nonsense approach to file-content searching. Users simply point the tool to a folder, type in the phrase, string, or code block they need, and FiXiY maps out every instance across all supported file types within seconds. "As developers and creators, we waste an incredible amount of cumulative time just navigating our own file structures looking for a variable, a configuration line, or a specific piece of text," says creator Lorenzo Battilocchi. "FiXiY was built out of necessity. It’s a nimble, friction-free alternative for anyone who wants instant answers without waiting for a massive IDE to load or fighting with complex regex syntax in a terminal." Key Features of FiXiY: Deep Folder Scanning: Recursively searches through complex directory trees and nested folders seamlessly. Intelligent Text Matching: Pinpoints exact strings of text, code, or data buried within plain text, source code, scripts, and logs. Lightweight Footprint: Operates with zero background bloat, making it perfect for rapid-fire asset hunting on any machine. 100% Open Source: Built transparently for the community, ensuring full privacy with no data leaving your local mac

Hey DEV community! 👋 A few days ago, I built a VS Code extension called Marksmith to fix the most annoying parts of writing Markdown (like pasting Excel tables and syncing preview scrolls). But recently, I noticed a huge shift in my own workflow: Half the Markdown I write isn't for humans anymore. It’s being fed directly into Claude, ChatGPT, or Gemini as prompts and context. When you're constantly stuffing docs into context windows, two things happen: You worry about hitting context limits (or racking up API costs). You waste time dealing with AI "hallucinations" when you ask it to generate docs back for you. So, for the v0.3.0 release , I decided to pivot Marksmith into something new: An Agent AI-Ready Markdown Toolkit. 🚀 Here is what I added to survive the AI era: 📊 1. Real-time LLM Token Estimator Instead of just counting words, Marksmith’s Document X-Ray sidebar now includes a Heuristic Token Estimator for GPT, Claude, and Gemini. Before you copy-paste that massive README into your AI assistant, you can see exactly how "heavy" it is in terms of tokens right inside your editor. No more guessing if you're about to blow past your context limit! ✂️ 2. Copy Optimized for AI (1-Click Minify) Formatting is great for humans, but LLMs don't need all those extra spaces, perfectly aligned markdown tables, or empty lines. I added a CodeLens button at the top of your files. Click it, and Marksmith instantly minifies your Markdown (compresses tables, strips blanks) and copies it to your clipboard. Result: You save significant tokens and API costs without ruining your beautiful local .md file. 🕵️ 3. Hallucination Quick Fix Ever ask an AI to write documentation, and it leaves behind a bunch of [TODO: Insert link here] or makes up a fake local image path? Marksmith now automatically scans your document and puts a red squiggly line under AI placeholders and broken local links . Click the 💡 icon, and you can instantly strip them out or fix them. It acts as a safety net before you

DevSecOps Pipeline — Completion Runbook All code is written and pushed to GitHub. This runbook covers the remaining operational steps: Terraform applies, GitOps ARN updates, and ArgoCD deployment. Prerequisites Install these tools if not already present: # AWS CLI v2 winget install Amazon.AWSCLI # Terraform 1.6+ winget install HashiCorp.Terraform # Terragrunt # Download from https://github.com/gruntwork-io/terragrunt/releases # Place in C:\Windows\System32\ or add to PATH # kubectl winget install Kubernetes.kubectl # ArgoCD CLI winget install argoproj.argocd AWS Profile Setup The root terragrunt.hcl uses profiles named myapp-{env}-{region_alias} . Configure them in ~/.aws/config : [profile myapp-production-use1] region = us-east-1 role_arn = arn:aws:iam::591120834781:role/AdministratorAccess source_profile = default [profile myapp-production-usw2] region = us-west-2 role_arn = arn:aws:iam::591120834781:role/AdministratorAccess source_profile = default [profile myapp-staging-use1] region = us-east-1 role_arn = arn:aws:iam::690687753178:role/AdministratorAccess source_profile = default [profile myapp-staging-usw2] region = us-west-2 role_arn = arn:aws:iam::690687753178:role/AdministratorAccess source_profile = default [profile myapp-dev-use1] region = us-east-1 role_arn = arn:aws:iam::557702566877:role/AdministratorAccess source_profile = default [profile myapp-dev-usw2] region = us-west-2 role_arn = arn:aws:iam::557702566877:role/AdministratorAccess source_profile = default PHASE 1 — Terraform Applies Work from the myapp-infra/ directory. Run in the order shown — capture outputs for updating GitOps files in Phase 2. 1.1 WAF (production + staging) # Production us-east-1 terragrunt apply --terragrunt-working-dir live/production/us-east-1/waf # Output → webacl_arn (copy this value) # Production us-west-2 terragrunt apply --terragrunt-working-dir live/production/us-west-2/waf # Output → webacl_arn (copy this value) # Staging (no GitOps ARN needed, but good to have) terra

Hey , I've been building VeritasReason — an open-source Python framework that adds a structured reasoning and provenance layer on top of LLMs and AI agents. The problem it solves: AI agents today make decisions but record nothing. When something breaks in prod, you have zero audit trail. What it does: • Context Graphs — queryable graph of everything your agent knows + decides • Forward-chaining rule engine (YAML rules, no code required) • W3C PROV-O provenance — every answer traces back to its source fact • Policy compliance: ask "Which purchase orders violated SoD policy in Q1?" • Works with OpenAI, Anthropic, Groq, Ollama, any LLM 30-second demo: pip install veritas-reason veritasreason-policy-demo GitHub: https://github.com/bibinprathap/VeritasGraph PyPI: https://pypi.org/project/veritas-reason/ Happy to answer questions — built this for regulated-industry AI (healthcare, finance, legal) where "trust me bro" answers aren't enough. — Bibin submitted by /u/BitterHouse8234 [link] [留言]

Everyone is adding AI to their product right now. Most of them are doing it wrong. Not because they chose the wrong model. Not because they used the wrong library. But because they treated AI integration like a regular feature and skipped all the engineering discipline that production systems require. I have integrated LLMs into multiple production applications. This is what I wish I had known before I started. The Mental Model Shift You Need First A traditional API call is deterministic. You send a request, you get a predictable response. You can write tests against it. You can cache it. You can reason about it. An LLM call is not deterministic. The same input can produce different outputs on different runs. The model can refuse, hallucinate, or return output in a format you did not expect. Your system needs to be designed around this reality, not in spite of it. This means defensive parsing, fallback logic, output validation, and graceful degradation are not optional extras. They are the core of the feature. Choosing the Right Model for the Right Job The biggest LLMs are not always the right choice. I learned this building EditDeck Pro, an AI creative platform for music. Some tasks needed a large frontier model for nuanced creative output. Others needed a fast, cheap model that could run many times per session without accumulating significant latency or cost. The pattern that works: Use a lighter model for classification, extraction, and short structured outputs. Use a larger model for generation tasks where quality matters more than speed. Route dynamically between them based on the task type. This can reduce your inference costs by 60 to 80 percent on workloads that mix simple and complex tasks. Prompt Engineering Is Software Engineering Prompts are code. They should be versioned, tested, and reviewed like code. I store prompts in a dedicated module with version numbers. When I change a prompt I run it against a fixed evaluation set of inputs and compare the out

The port seems solid, and all DLC is supported—but there's no crossplay, sadly.

I was looking at my bank statement one day and realised I was paying for 4 things I completely forgot about. Combined it was around 40 euros a month just silently leaving my account. I'm a 17 year old developer from Cyprus and I spent the last few weeks building Capsule, a simple subscription tracker that shows you everything you pay for, alerts you before renewals, and tracks how much you save by cancelling things. No bank connection required. You just add your subscriptions manually. Privacy first. It's not on the Play Store yet but the waitlist is live at capsule.crickdevs.com if anyone wants early access. Would genuinely love feedback from real people before I launch.

AI workflow automation looks simple in demos. A form submission comes in. An AI model reads it. The CRM gets updated. A Slack message goes out. An email is sent. But once you move from demo to production, the workflow becomes more sensitive. What happens if the AI summary is wrong? What happens if the CRM is updated with incomplete data? What happens if the customer request needs human approval before the next step? What happens when a workflow fails halfway? That is where AI workflow automation needs better architecture. In one recent project, we designed an AI workflow automation system using: Make.com for workflow orchestration FastAPI for custom backend logic OpenAI/GPT APIs for summarization and structured output Monday.com CRM for record management Slack for internal notifications Gmail for email-based communication Human review steps for approval and control The goal was not to build a chatbot. The goal was to reduce repetitive manual review work while keeping the workflow controlled, traceable, and practical for daily business use. The workflow problem The original workflow had several manual steps: A new request came in. Someone reviewed the request manually. Important information was extracted. A CRM record was created or updated. The internal team was notified. A follow-up email was prepared or sent. The team tracked the workflow manually. This kind of workflow is common in service businesses, operations teams, sales teams, and CRM-heavy processes. The pain was not that any one step was too difficult. The pain was that the same steps repeated again and again. That makes the workflow slow, inconsistent, and dependent on manual copy-paste work. Why not fully automate everything? The obvious idea is: Let AI read the request and update everything automatically. But that can be risky. AI-generated output can be incomplete, overconfident, or slightly wrong. That may be acceptable if the output is only a draft. It is not acceptable if the output directly updates

Motorola’s latest Razr Ultra proves that its flip foldable format has evolved to become more than just a nostalgic gimmick. I’d understand if you’re not interested in shelling out $1,499.99 for the 2026 model, but the similar 2025 Motorola Razr Ultra with 512GB of storage is a much more palatable $699.99 unlocked at Best Buy […]