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Uber is teaming up with drone company Zipline to start airborne takeout deliveries later this year, with the goal of reaching one million daily drone deliveries by 2029. Uber also said it was making a strategic investment in Zipline, a California-based company that has been orchestrating drone deliveries in Texas since 2025. The news comes […]

Today I’m talking to Ben Smith, the editor-in-chief of Semafor. Everywhere you go, people say they don’t trust the media — and yet they’ve never consumed more of it. Audiences have moved on from legacy names in favor of Substacks and podcasts and TikTok news influencers that seem to be everywhere in our feeds. Why […]

Grab is using AI agents to automate analytics workflows, cutting mechanical analyst work from 44% in February to 30% in June. Its approach combines agent autonomy, certified data, context management and human oversight, with self service analytics increasingly handling metric, data and SQL requests without analyst intervention. By Leela Kumili

GitHub热门项目 | Building a community around a cheap ESP32 Display with a touch screen | Stars: 4,316 | 11 stars today | 语言: Rust

GitHub热门项目 | Rust implementation of behavior trees for deterministic AI (now with Python bindings) | Stars: 938 | 69 stars today | 语言: Rust

GitHub热门项目 | Immersive AI-driven Galgame chat with emotional expressions, desktop pet, scheduling, and interactive story modules. / 一款沉浸式 AI-Galgame 聊天软件，附带桌宠，日程，剧情功能 | Stars: 1,439 | 98 stars today | 语言: Rust

GitHub热门项目 | ⚡️A native, local-first alternative to Logitech Options+, written in Rust 🦀 — remap buttons, DPI, and SmartShift over HID++. No account, no telemetry. | Stars: 8,580 | 106 stars today | 语言: Rust

Cloudflare has released cloudflare/ci, a CI SDK that defines pipelines in TypeScript on top of Cloudflare Workflows, giving each step durable retries and replay, concurrent steps by default and Sandbox snapshot caching. It targets the Workers runtime and depends on Artifacts, still in private beta, so the transferable lesson is the durable-step model rather than a drop-in CI replacement. By Mark Silvester

Meme Monday! Today's cover image comes from the last thread . DEV is an inclusive space! Humor in poor taste will be downvoted by mods.

You heard me. Alright, that was mean lol. Though based on the title, you probably already knew the...

Every programmer wants to improve. We all dream about becoming the person who can look at a...

"our work got checked by an external reviewer." That's what Fable said after I brought Gemini...

I recently learned something about building products that I probably should have understood much earlier: People liking your product doesn't necessarily mean they need it. I built a platform called Rizzzler, an open-source profile/link-in-bio platform. The idea was pretty simple. I'd seen people using platforms where they could put a link in their social media bio and create a small personal page. I thought I could build my own version — something simple, fast, customizable, and a little more fun. So I built it. And because I wanted people to be able to trust what they were using, I made the project open source too. I spent a lot of time building the actual product. There are profiles, customization, coins, notifications, milestones, community chat, and other small systems intended to make the platform feel less like a static link page and more like something people could actually interact with. At that point, I thought: "Okay, now I just need people to find it." That turned out to be the easy part. Then I started promoting it. I submitted Rizzzler to places like Product Hunt, SaaSFrame, and other platforms where people discover new products. And for a few days, things actually looked pretty good. I started getting visitors. At one point, the traffic was above the 25th percentile for the category I was looking at in GA4. People were visiting. Some people signed up. And I started getting feedback like: "Good UI." "This is good." "Someone finally made link-in-bio profiles look cool." Those comments felt great. They also gave me a slightly dangerous impression: Maybe I've built something people actually want. Then the traffic stopped. Not gradually. It just became cold again. The initial spike from launching and posting about the product disappeared, and there wasn't enough organic interest to keep bringing people back. That was the part I didn't expect. The product wasn't necessarily bad. This is something I've been thinking about a lot. I don't think the main problem

We’ve all been there: staring at a delicious plate of pasta, trying to figure out if it's 400 or 800 calories. Manual tracking is a chore, and standard apps often fail at portion estimation. But what if we could combine Computer Vision , Multimodal LLMs , and Vector Databases to build an automated nutritionist? In this tutorial, we are building a state-of-the-art Multimodal Food Estimation Pipeline . By leveraging the Segment Anything Model (SAM) for precise boundary detection and GPT-4o Vision for contextual analysis, we can bridge the gap between "looking at a photo" and "calculating nutritional density." Whether you're interested in AI-driven wellness , FastAPI development , or Multimodal RAG , this guide covers the full stack. The Architecture 🏗️ The pipeline follows a sophisticated "Identify -> Analyze -> Match" flow. We don't just ask GPT-4o "what is this?"; we use SAM to isolate food items first to ensure the LLM focuses on the right pixels. graph TD A[User Uploads Image] --> B{SAM Model} B -->|Segmentation| C[Isolated Food Patches] C --> D[GPT-4o Vision API] D -->|Item + Volume Est.| E[Embedding Generation] E --> F[PostgreSQL + pgvector] F -->|RAG Retrieval| G[Verified Nutritional Data] G --> H[Final Response: Calories & Macros] Prerequisites 🛠️ Before we dive in, make sure you have the following ready: Python 3.10+ OpenAI API Key (for GPT-4o) PyTorch (for SAM) PostgreSQL with the pgvector extension enabled FastAPI for the backend Step 1: Precise Segmentation with SAM 🎯 The biggest challenge in food AI is overlapping items. Using Meta’s Segment Anything Model (SAM) , we can extract the exact mask of a food item, which helps in calculating the relative "area" occupied on the plate. import torch from segment_anything import sam_model_registry , SamPredictor import cv2 # Load SAM model sam_checkpoint = " sam_vit_h_4b8939.pth " model_type = " vit_h " sam = sam_model_registry [ model_type ]( checkpoint = sam_checkpoint ) predictor = SamPredictor ( sam ) def get_f

Intro AI Avatar is a free app where your VRoid (VRM) avatar cheers you with all its might .🤗 It lives in your VS Code sidebar (reacts to Claude Code / GitHub Copilot) or browser side panel (reacts to ChatGPT / Claude). Animations and speech bubbles all run without AI too. This time I have three main topics. 🤝Catbot Integration 🏢AI Office 😺Cat Mode Let's see how they are! Catbot Integration I was asked to collaborate with my DEV Community friend @annavi11arrea1 Catbot . Catbot is A galactic robot cat you can talk to from any device — and a harness that lets you switch between (or combine) all of your AI models. https://github.com/AnnaVi11arrea1/catbot I was happy about this offer because I loved Anna's creativity and cool designs. I added the features below to AI Avatar to integrate Catbot. Launch Cat button: With this button, AI Avatar can run Catbot. Catbot with button: This makes Catbot stay beside AI Avatar. Cat Boss button: This changes the AI Office boss from a VRM avatar to Catbot. Cat Mode Many people feel that animals are healing and soothing. It is close to the AI Avatar concept of cheering people up. So I decided to add Cat Mode . I added the features below to make it look like a cat. Cat-like text, "Meow/Purrr" in English and "にゃ～" in Japanese Cat emojis Cat pose animations A new avatar with cat ears and cat whiskers. To tell the truth, the hardest part of making this mode was adding whiskers to the avatar using Blender . I can do basic things in Blender, but it is too difficult for me, even with the help of AI, just to add whiskers. It would be more fun if I added other animal modes too. AI Office AI Avatar displayed only one avatar. I thought it could do more things if it displayed several avatars at once. So I added AI Office mode. Two avatars are displayed and talk and move around when idle, and they also make a communication animation when using AI or clicking. I made one avatar a boss and one a worker. The hard part of making this mode was the timin

"Breakups are… tough." It's the opening lines of an interlude towards the end of DOOMED! Called "It Happens in Every Universe." It's also basically the thesis of the entire record. It's no grand revelation, but it's a well-trodden subject that Open Mike Eagle manages to mine for artistic gold. Eagle's subject matter is usually personal, […]

This tutorial walks through building an MCP server in Rust with rmcp , the official Model Context Protocol Rust SDK. The example is a real one: a devops agent that manages AWS EC2 G5g instances — Graviton2 boxes with NVIDIA T4G GPUs — serving Gemma 4 under vLLM. It launches instances, drives them over SSM, and health-checks the model. There's an existing Python version, so at the end we can put the two side by side. Follow along and you'll have a working, registerable MCP server. 🦀 Why Rust for this? Worth answering properly, because the weak version of the argument is easy to make and easy to demolish — and the real one is better anyway. Start with what it isn't: these tools are I/O bound. Every one is an AWS API call — describe_instances , send_command , polling SSM — so 100–500 ms of network per call. The caller's language contributes nothing measurable there. Anyone selling you a Rust rewrite on raw speed for this workload is selling something. Three claims that don't hold, so nobody has to make them in the comments: Claim Why it fails "462 ms startup is slow" stdio servers spawn once per session , not per call "Rust is faster" the work is network round-trips to AWS "smaller supply chain" 241 crates vs 34 Python packages — it's worse What actually justifies it, for this codebase: 1. It's a fleet, not a server. This monorepo has 16 rigs , each with its own MCP server. That changes the units: All loaded together 🐍 Python 🦀 Rust Resident memory 16 × 83 MB ≈ 1.33 GB 16 × 12 MB ≈ 192 MB Session startup 16 × 462 ms ≈ 7.4 s 16 × 2.5 ms ≈ 40 ms A gigabyte of resident Python to expose sixteen tool lists is a real cost. 2. No shared interpreter. These rigs install system-wide — no virtualenvs, by policy — so all sixteen share one Python. Sixteen servers with independently drifting boto3 and mcp pins in one interpreter is a standing conflict risk. A static binary has no such coupling; each rig pins whatever it likes in its own Cargo.lock . 3. The schema can't drift from th

This tutorial walks through building an MCP server in Rust with rmcp , the official Model Context Protocol Rust SDK. The example is a real one: a devops agent that manages AWS EC2 G5g instances — Graviton2 boxes with NVIDIA T4G GPUs — serving Gemma 4 under vLLM. It launches instances, drives them over SSM, and health-checks the model. There's an existing Python version, so at the end we can put the two side by side. Follow along and you'll have a working, registerable MCP server. 🦀 Why Rust for this? Worth answering properly, because the weak version of the argument is easy to make and easy to demolish — and the real one is better anyway. Start with what it isn't: these tools are I/O bound. Every one is an AWS API call — describe_instances , send_command , polling SSM — so 100–500 ms of network per call. The caller's language contributes nothing measurable there. Anyone selling you a Rust rewrite on raw speed for this workload is selling something. Three claims that don't hold, so nobody has to make them in the comments: Claim Why it fails "462 ms startup is slow" stdio servers spawn once per session , not per call "Rust is faster" the work is network round-trips to AWS "smaller supply chain" 241 crates vs 34 Python packages — it's worse What actually justifies it, for this codebase: 1. It's a fleet, not a server. This monorepo has 16 rigs , each with its own MCP server. That changes the units: All loaded together 🐍 Python 🦀 Rust Resident memory 16 × 83 MB ≈ 1.33 GB 16 × 12 MB ≈ 192 MB Session startup 16 × 462 ms ≈ 7.4 s 16 × 2.5 ms ≈ 40 ms A gigabyte of resident Python to expose sixteen tool lists is a real cost. 2. No shared interpreter. These rigs install system-wide — no virtualenvs, by policy — so all sixteen share one Python. Sixteen servers with independently drifting boto3 and mcp pins in one interpreter is a standing conflict risk. A static binary has no such coupling; each rig pins whatever it likes in its own Cargo.lock . 3. The schema can't drift from th

When many people are first learning how to code, they find it difficult and when they ask, "How can I get better at coding?" they are usually told, "With time and practise it will get easier." . But instead of using so much time and effort to get better at coding using hard programming languages, what if coding could get better for you instead of you getting better at coding ? Well, this is the reason that inspired me to make a new programming language called Sofya . Sofya is designed to be so simple (even simpler than Python ) so that anyone can find programming easy and fun. But to prove my point, let us use an example. Let us say that we want to make a program that will show us all the numbers from 1 to 20 . Let us compare how this program will look like in Python and Sofya . The Python Program for number in range ( 1 , 21 ): print ( number ) The Sofya Program Variable Number is 0 Do this { Increase Variable[Number] by 1 Write Variable[Number] on the screen } Until Variable[Number] = 20 From this example, we can see that the Sofya program is easier than the Python program, for a beginner in programming, for the following reasons: Sofya uses simpler commands than Python: It is easier for a beginner in programming to remember the command Do this...Until Variable[Number] = 20 , which is used for making a loop, as compared to the command for number in range(1, 21): . Sofya's syntax is closer to English as compared to Python's syntax: When we are making a loop variable in Sofya, we simply say Variable Number is 0 rather than saying number in range(1, 21) in Python. The Sofya program can easily be understood by anyone even if it is the first time that they are seeing it as compared to Python: A beginner in programming can easily tell that in the line where we say Increase Variable[Number] by 1 , that we are increasing the value of the variable called 'Number' by 1 as compared to the line number in range(1, 21) in Python. If you would like to try out Sofya for yourself

In mid-July, my Google clicks in my home market (Israel) dropped by almost half. Buyer-intent queries that used to bring steady leads just evaporated from Search Console. While I was staring at GSC dashboards trying to figure out what broke, I finally did the thing I should have done months earlier: I stopped looking at dashboards and started reading raw server logs. What I found there was a parallel universe. Google Search was sending me less than ever — but AI systems were reading my site constantly . Not "someday this will matter" constantly. Right-now constantly: an AI assistant was fetching a page of mine roughly every 26 minutes, around the clock, because a real human had just asked it a question. So I built a small log analyzer and let it run. Here's what 34 days of complete Caddy logs from a small business site (about 70 real human visitors a day) actually look like. The numbers All counts are HTTP 200 responses only (more on why below), over 34 days: Bot Requests Per day What it is bingbot 5,444 158.2 Bing's index — which feeds ChatGPT ChatGPT-User 1,388 40.3 Live fetch while a human asks ChatGPT Googlebot 1,233 35.8 Classic Google crawl GPTBot 547 15.9 OpenAI training crawler Claude-User 519 15.1 Live fetch while a human asks Claude OAI-SearchBot 281 8.2 ChatGPT search indexing Applebot 268 7.8 Apple (Siri / Apple Intelligence) ClaudeBot 214 6.2 Anthropic training crawler Amazonbot 136 4.0 Amazon (Alexa & co.) PerplexityBot 103 3.0 Perplexity indexing Three things in that table genuinely surprised me. ChatGPT-User outreads Googlebot. 40.3 fetches a day versus 35.8. This isn't a crawler building an index for later — ChatGPT-User is the user-agent OpenAI sends when a human is mid-conversation and ChatGPT decides to pull a live page to answer them. On my site, that now happens more often than Googlebot visits. For a tiny business site in a niche market, I did not expect that. Bing crawls 4.4x harder than Google. 158 requests a day versus 36. Nobody optimizes