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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Uber is getting ready to put robotaxis on London's streets, opening an interest list for riders who want to be among the first to hail one of Wayve's autonomous vehicles when the service goes live later this year. The rollout would be a milestone in one of Uber's biggest markets and an early test of […]

Machine learning has its limits—how is it being used?

Uber customers in the UK can now join an interest list to increase their chances of being matched with a Wayve robotaxi.

Birgitta Böckeler, Distinguished Engineer at Thoughtworks, returns to discuss the rapid evolution of AI in software delivery. She touches on the evolution from vibe coding, the changing tools landscape and the more autonomous agents that, besides higher velocity, introduce higher risk. By Birgitta Böckeler

In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness. By Marco Rizzi

Apple's biggest event of the year is nearly here. The company's Worldwide Developers Conference will spotlight updates to iOS, macOS, and all of Apple's other operating systems, and this year's event could also include a major overhaul for Siri. Here's how you can watch along live. When WWDC will happen and where you can watch […]

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ArXiv has an endorsement system for a reason. I would only offer endorsement to whom I have direct academic collaboration or mentorship with, since I'm putting my own academic reputation on the stake. This is also the standard of almost any serious academic researcher I am aware of. Now ArXiv is making effort to crack down AI slop and banning accounts uploading low-quality research papers, which is a great initiative. By definition of an "endorsement", I wish ArXiv could backtrack and at least issue warnings to their endorsers, and if this happens multiple times (let's say three), people giving out careless endorsement should also face consequences. submitted by /u/AffectionateLife5693 [link] [留言]

Senior Solution Architect Viktor Vedmich shares how engineering leaders can maximize application performance using Valkey. He discusses the open-source Redis fork's 100% API compatibility, explores advanced caching strategies like lazy loading, and explains how to implement powerful data structures for real-time analytics, rate limiting, and session stores to solve the thundering herd problem. By Viktor Vedmich

A compliance team at a mid-size bank in Abu Dhabi processes new customer applications every week....

While vibecoding, you sometimes need some background music. But music can also be a massive...

Earlier this year, the “Because I Got High” rapper went viral for winning a case against the cops. Now he’s crypto’s free-speech hero, even though he isn’t quite sure how the digital currency works.

Follow our WWDC 2026 live blog for all of the updates coming to iOS, macOS, watchOS, and more.

I ran a 4-bit medical-triage model on a laptop GPU and on a CPU. For one patient, the GPU said urgent and the CPU said emergency. Same model file, same prompt, same input. Here's the mechanism and why "validated on hardware X" doesn't mean what you'd hope. I've been building Aegis-MD , a local-first emergency-department triage console. You hand it a structured clinical picture: chief complaint, vitals, age, pain score, a few risk modifiers, and it returns an urgency category on the Australasian Triage Scale (ATS 1–5), where ATS-1 means resuscitate now and ATS-5 means this can wait two hours . The whole thing runs on-device: a quantized MedGemma 4B served through Ollama, a small RAG layer over open guidelines, and a deterministic rule-based floor underneath the model. I never set out to write about floating-point arithmetic. But while running my evaluation set across two machines, I hit a result that stopped me, and the explanation turned out to be more interesting and more current than the textbook answer most people reach for. The setup, and why a 4-bit model Two things about Aegis-MD's design matter for this story. First, it's local by design. Triage data is about as sensitive as data gets, so nothing leaves the machine. The trade-off is that I'm running a small, heavily quantized model: MedGemma 1.5 4B at Q4_K_XL , about 3.4 GB rather than a frontier API. Four-bit weights are the price of running offline on consumer hardware. Second, I tested on two configurations on purpose. The intended deployment is local GPU inference (an RTX 5070 Ti Mobile, 12 GB). But the public demo runs CPU-only on Cloud Run, because GPU instances need a paid quota I don't have. So I ran the same evaluation against both: the GPU build and the CPU build, same model, same code, same prompts. The eval is 17 hand-written cases spanning all five ATS levels, cardiac arrest down to a medical-certificate request. (Seventeen is a smoke test, not a validation; I won't quote a percentage off a sampl

Key takeaways Give an AI agent live web data by connecting it to Crawlora's hosted MCP endpoint — it calls documented tools (search, maps, commerce, social, finance) and gets normalized JSON back, with no scraping code or proxies to run. MCP (Model Context Protocol) is an open standard: agents discover and call tools through one interface instead of a bespoke integration per data source. Connect over Streamable HTTP at https://mcp.crawlora.net/mcp with your API key — about three minutes in Claude, Cursor, Cline, Windsurf, or any MCP client. One connection exposes 319 tools across 33 platforms (393 REST endpoints underneath): Google/Bing/Brave search, Google Maps, Amazon, YouTube, TikTok, Yahoo Finance, CoinGecko, and more. You pay only on a successful (2xx) response — failed calls are free — and the free tier includes 2,000 credits a month with no card. Versus writing your own scrapers: no per-source glue code, normalized JSON instead of HTML, and proxy routing, rendering, and retries handled behind the endpoint. You can give an AI agent live web data by connecting it to a hosted MCP endpoint : your agent calls documented tools — search, maps, e-commerce, app stores, social, finance, and more — and gets back normalized JSON, with no scraping code to write or proxies to run. This guide explains what MCP is, what data you can pull, how to connect in about three minutes, and what a real tool call and its response look like. Most LLMs are frozen at their training cutoff and can't see the live web. The usual fix — writing a scraper per source, then maintaining proxies, headless browsers, and parsers — is exactly the work teams don't want to own. MCP plus a hosted data server removes it: the model gets a stable set of tools, and the fetching lives behind an endpoint. What is MCP, and why does it matter for agents? The Model Context Protocol (MCP) is an open standard that lets an AI agent call external tools through one consistent interface. Instead of wiring a bespoke int

The AI landscape is evolving faster than ever. Keeping track of the right tools can feel like trying to drink from a firehose. I recently dug through my extensive bookmarks folders and compiled every single AI tool and Autonomous Agent I've saved. Whether you're looking for an autonomous coding agent, a rapid app builder, an LLM benchmark, or a creative suite, you need the right tool for the job. Bookmark this page, because you're going to want to refer back to it. Superdesign Maskara.ai Google Labs: Google's home for AI experiments - Google Labs Kilo Code - Open source AI agent VS Code extension hunyuan bolt.new Rocket.new | Build Web & Mobile Apps 10x Faster Without Code AI Web Scraping Extension | Chat4Data Sarvam AI Lovable Starc- film ShumerPrompt aipai.app Flowe MiniMax Official Website - Intelligence with everyone new.website | Build Websites with AI Higgsfield HeyBoss.ai Mitte Trickle AI - Turn your ideas into live apps and websites with AI. Dora: Start with AI, ship 3D animated websites without code Kimi AI – Think Bigger. Search Smarter. Write Better. a0.dev - Create Mobile Apps with AI sesame Vogent - Create AI Voice Agents Orchids - Make something beautiful Same PromptBase | Prompt Marketplace: Midjourney, ChatGPT, Sora, FLUX & more. LM Studio Mindstone Chat with Z.ai - Free AI for Presentations, Writing & Coding AI Model & API Providers Analysis | Artificial Analysis T3 Chat - Advanced AI Assistant & ChatGPT Alternative | $8/month Poe Freepik | All-in-One AI Creative Suite Replit – Build apps and sites with AI unwind ai Magic Patterns Soapbox - Build Your Decentralized Platform Shakespeare - AI Website Builder AI recruitment engine to hire top global talent | micro1 Ponder AI | New Way to Work with Knowledge Using AI Ask AI Questions · Question AI Search Engine · iAsk is a Free Answer Engine - Ask AI for Homework Help and Question AI for Research Assistance Firecrawl Kiro: The AI IDE for prototype to production Le Chat CodeArena – Which LLM codes best?

TL;DR: I built PackagePal — paste in any package from any language, pick your target language, and AI instantly finds the equivalent. No more Googling "what's the Node.js version of Python's requests ?" The Problem That Drove Me Crazy You know that moment when you're migrating a project — or just jumping between ecosystems — and you hit a wall trying to find the right package? I do. Every time. # You're used to this in Python import requests response = requests . get ( " https://api.example.com/data " ) And you move to Node.js and think: "Okay, what do I use here? axios? node-fetch? got? undici?" So you Google it. You find a Stack Overflow thread from 2019. Half the answers recommend packages that are now deprecated. You open 6 tabs. 20 minutes later you're still not sure which one is the current best choice. This wasn't a once-in-a-while thing for me. It happened constantly — switching between Python, JavaScript, Go, and Ruby on different projects. I was wasting real hours on a problem that felt completely solvable. So I built PackagePal . What PackagePal Does PackagePal uses AI to understand what a package actually does — its purpose, not just its name — and finds the best equivalent in whatever language you're moving to. The key insight: this isn't a lookup table. A simple mapping of requests → axios misses context. What if you're using requests for its session management? Or its retry logic? PackagePal surfaces options and explains why each one is a good match. Example searches people use it for: Python's pandas → JavaScript Ruby's devise → Node.js Go's cobra → Python JavaScript's lodash → Go Just type the package, pick the target language, and get results in seconds. 👉 Try it: packagepal.dev How I Built It Tech Stack 🤖 AI: Gemini Pro — handles the semantic understanding of what a package does and why an alternative matches ⚛️ Frontend: React + TypeScript ⚙️ Backend: Node.js + TypeScript on Google Cloud ⚡ Caching: Redis — so repeat searches (e.g., "requests → No

Every time a course batch completes, you have a list of students who need certificates. The manual way: open Canva, duplicate the template, change the name, export, repeat — for every single student. If you have 10 students, that's annoying. If you have 200, that's a full afternoon. The better way A single n8n workflow that: Reads student names from Google Sheets Calls the RenderPix batch API Gets back 200 certificate images Emails each student their certificate Total time: ~2.5 minutes. Total manual work: zero. What you'll need A RenderPix account (free tier works for testing, Starter plan for production) n8n (self-hosted or cloud) n8n-nodes-renderpix community node A Google Sheet with student data Install the n8n node: npm install n8n-nodes-renderpix Or search "RenderPix" in n8n's community node panel. Step 1 — Design your certificate template Write your certificate in plain HTML. Here's a clean starting point: <div style= "width:1200px;height:850px;background:white; display:flex;flex-direction:column;align-items:center; justify-content:center;border:20px solid #0f172a; font-family:Georgia,serif;padding:60px;box-sizing:border-box" > <div style= "font-size:16px;letter-spacing:5px;color:#64748b; text-transform:uppercase;margin-bottom:24px" > Certificate of Completion </div> <div style= "width:80px;height:2px;background:#22d3ee;margin-bottom:32px" ></div> <div style= "font-size:52px;font-weight:700;color:#0f172a;margin-bottom:16px" > {{name}} </div> <div style= "font-size:18px;color:#475569;text-align:center;max-width:600px" > has successfully completed </div> <div style= "font-size:28px;font-weight:600;color:#1e293b;margin:16px 0 40px" > {{course}} </div> <div style= "font-size:14px;color:#94a3b8" > {{date}} </div> </div> Notice the {{name}} , {{course}} , {{date}} placeholders — RenderPix replaces these at render time. Step 2 — Set up Google Sheets Create a sheet with these columns: name course date Jane Smith Advanced n8n Automation June 2026 John Doe Advanced n8n

title: Your AI Agent Should Not Be Locked to One LLM Provider published: false description: Why serious AI agents need a provider-agnostic architecture, model routing, fallback, and a unified API gateway. tags: ai, llm, agents, architecture Your AI Agent Should Not Be Locked to One LLM Provider Most AI agent prototypes start the same way. You pick one model provider. You install one SDK. You write a few prompts. You add tool calling. You build a demo. It works. Until it does not. The moment you want to try another model, reduce cost, add fallback, improve latency, or support different task types, your simple agent starts turning into a messy collection of provider-specific logic. That is when you realize something important: A real AI agent should not be locked to one LLM provider. If you are building a personal AI agent, coding assistant, research assistant, internal workflow agent, or AI-native product, the model should be replaceable infrastructure — not a hardcoded dependency. The Problem with Single-Provider Agents A simple agent architecture often looks like this: CopyUser ↓ Agent ↓ One LLM Provider ↓ Response This is fine for a proof of concept. But real-world agent systems need more flexibility. Different tasks often need different models: Task Better Model Strategy Quick summarization Fast, low-cost model Complex coding Strong coding model Long document analysis Long-context model Reasoning-heavy planning Reasoning model Multilingual writing Model strong in that language Background automation Cheap and reliable model Production fallback Backup provider If your agent is deeply coupled to one provider, every optimization becomes harder. You cannot easily answer questions like: What happens if the provider is down? What if latency spikes? What if another model is cheaper for simple tasks? What if a new model is better for coding? What if a user wants Claude for writing but GPT for structured reasoning? What if you want to route Chinese tasks to a different mod