The program, ChatGPT for Academic Researchers, will start with 10,000 participants this summer.

Open-source voice dictation for Mac, fully on-device Discussion | Link

Introduction When building a Flutter application, you'll often need to store data on the user's device. For example: Saving user preferences Storing login information Caching API responses Creating offline applications Building note-taking or to-do apps While there are several local storage solutions available, Hive is one of the fastest and easiest local storage for Flutter developers. In this tutorial, you'll learn Hive from scratch by building a simple example. No prior database knowledge is required. What is Hive? Hive is a lightweight, NoSQL database written entirely in Dart. It stores data directly on the device, making it perfect for Flutter applications. Why use Hive? Extremely fast Works offline No native platform code required Simple API Easy to learn Great for small and medium-sized applications Think of Hive as a collection of boxes where each box stores your application's data. Hive ├── User Box ├── Settings Box ├── Notes Box └── Products Box Each Box is similar to a table in traditional databases. Step 1: Create a Flutter Project Create a new Flutter project. flutter create hive_demo Open the project. cd hive_demo Step 2: Install Hive Open pubspec.yaml and add the following packages. dependencies : flutter : sdk : flutter hive : ^2.2.3 hive_flutter : ^1.1.0 Then install them. flutter pub get Step 3: Initialize Hive Before using Hive, initialize it inside main() . import 'package:flutter/material.dart' ; import 'package:hive_flutter/hive_flutter.dart' ; void main () async { WidgetsFlutterBinding . ensureInitialized (); await Hive . initFlutter (); await Hive . openBox ( 'settings' ); runApp ( const MyApp ()); } Here we open a box called settings . Step 4: Understanding Boxes A Box is where Hive stores data. Imagine this box: Settings Box theme -> dark username -> Alex loggedIn -> true Keys are on the left. Values are on the right. Step 5: Save Data Saving data is incredibly simple. var box = Hive . box ( 'settings' ); box . put ( 'username' , 'John' );

Everyone evaluating AI avatar platforms focuses on voice quality. The bigger UX killer is almost always latency — and it's a harder problem than picking a good TTS provider. Where the delay actually comes from: User speaks/types → STT (if voice input) → LLM generates response (streaming helps, but first-token latency matters) → TTS converts text to audio → Audio playback + lip-sync rendering Each hop adds latency. A naive implementation that waits for the full LLM response before starting TTS can easily hit 2-4 seconds of dead air — long enough for a user to assume the bot is broken. How production systems actually solve this: Token streaming into TTS — start synthesizing audio on partial LLM output (sentence-by-sentence chunks) instead of waiting for the full response Speculative rendering — start lip-sync animation slightly ahead of audio using predicted phoneme timing WebSocket/SSE persistent connections — avoid the overhead of repeated HTTP round-trips per turn Regional API routing — TTS/LLM provider latency varies a lot by user geography; this matters more than most benchmarks show A practical note: platforms that advertise "real-time" avatars but load all logic behind a single request/response cycle will feel noticeably worse than ones built around streaming pipelines, even if they use the identical LLM and TTS providers underneath. If you're evaluating a platform (or building one), test with realistic network conditions, not office wifi — that's where the architecture differences actually show up. Bottom line: the voice provider matters less than people think. The orchestration around it — how aggressively you stream and pipeline each stage — is what separates a "wow" demo from a production-ready conversational agent.

If this is too long, tldr : Google Conway’s Law wath yt video and think There is a popular rule in software development called Conway's Law. It says that organizations design systems that mirror the way people inside those organizations communicate. In simpler terms: Your architecture will eventually look like your team structure. Big company with separate frontend, backend, data, DevOps, and platform teams? You will probably end up with separate services, separate processes, separate ownership, and a lot of API calls between people who sit in different Slack channels. But what happens when the entire company is just two people? That is where things get interesting. At bundle.social, we are running a unified social media API that handles a lot of edge cases. And there are two of us. There is no dedicated platform team No analytics department No infrastructure group. No product manager translating customer feedback into Jira tickets. Just two people are trying to keep a fairly large system moving without turning it into a pile of slop services nobody fully understands. You would think Conway's Law does not really apply to such a small team. It absolutely does. It just shows up differently. How Conway’s Law Works in a 2-Person Team When you have 50 developers split across departments, Conway's Law creates microservices and cross-team dependency hell. When you have two developers, Conway's Law forces your system into one of two extremes: The "Two Halves of a Brain" Split: Service A belongs entirely to Person A, and Service B belongs entirely to Person B. Because human communication between two people has practically zero friction, it's extremely tempting to drift into the lazy version of Conway's Law: ignoring technical boundaries altogether because "we can just talk about it on Slack." Why write explicit API documentation when you sit next to the person who wrote the endpoint? Why enforce strict domain boundaries when you can just export a helper function across modul

Hey everyone, For the past few months I've been working on a side project called MeshDrop. The idea started because I wanted a simple way to send files and folders directly between my own devices (and with friends) without uploading everything to cloud storage or relying on third-party servers. MeshDrop is built on the Holepunch ecosystem using Pear Runtime, Bare JS, and Hyperswarm. It supports direct transfers over LAN and can also connect over the internet using DHT hole punching with end-to-end encryption. I've also been experimenting with a few extra features like short 8-character pairing codes, cross-device clipboard sharing, and a remote drive feature that's still a work in progress. I'm still learning as I build this project, and I've been using AI coding tools alongside documentation, testing, and a lot of trial and error to help me move faster. I'm trying to understand the code and improve with every feature instead of just generating code and hoping it works. This is very early beta, so please expect bugs, rough edges, missing features, and probably a few questionable UX decisions. I'm sharing it now because I'd rather get feedback early than spend months building something people don't actually enjoy using. If you decide to give it a try, I'd really love honest feedback on things like: Does the overall workflow feel simple or confusing? Is the UI easy to understand? Did you run into any bugs or connection issues? Are there features you'd expect from a P2P file sharing app that are missing? Is there anything that feels unnecessary or poorly designed? Please don't hold back. Constructive criticism is exactly what I'm looking for. If something feels wrong, confusing, or badly designed, I'd much rather hear about it now so I can improve it. GitHub: https://github.com/aamirali51/MeshDrop Latest Beta: https://github.com/aamirali51/MeshDrop/releases/tag/v1.0.0-beta.1 Thanks for taking the time to read this. Whether you try it, report a bug, suggest a feature, o

At TechCrunch Disrupt 2026, the AI Stage is back to dig into the single hottest topic in the community for the past few years, presented by Google for Startups.

On May 20, 2026, AWS ended support for DMS Fleet Advisor. Fleet Advisor answered a question every migration team asks first: what is actually in my database estate, and how hard will it be to move? It was free. It was fully managed. It was backed by the largest cloud provider on earth. It still lost. AWS's official notice says only: "After careful consideration, we decided to end support for AWS DMS Fleet Advisor." No reason given. But you don't need one — the documentation tells you. Here is what Fleet Advisor required before it would tell you a single thing about your databases: Install a standalone data collector in your local environment Create an Amazon S3 bucket Create IAM policies, roles, and users — via CloudFormation, which was the recommended path Create database users with the minimum required permissions on every source Establish network access from the collector to each database server Then you'd meet the ceilings: recommendations for up to 100 databases at a time, one-to-one target mapping only, no multitenant server support. Now picture running that gauntlet inside a bank. You are a Business Solution Architect. You have been asked to scope a migration. You do not yet have approval for the migration — that approval is what the assessment is for . And to produce the assessment, you must first request production database credentials, get an agent binary through software approval, provision an S3 bucket, and get an IAM stack past a security review. That is a six-week procurement conversation to answer a question you were hoping to answer this week. AWS's replacement recommendation is Migration Evaluator — a consulting-led engagement. Read that as the finding it is: AWS looked at self-serve migration assessment, and concluded that humans and services do it better than a product. I think they were half right. And the half they got wrong is the interesting part. The lesson: friction is a competitor, and it usually wins We talk about developer tools as if the

Why this combination keeps winning for IoT telemetry backends — not in a benchmark, but against flaky gateways, replayed data, and firmware that never quite agrees with itself. If you’ve ever built the backend for a fleet of IoT devices — sensors, gateways, industrial equipment reporting temperature, humidity, GPS, battery, signal strength — you’ve faced the same fork in the road early on: what do you build the ingestion layer with, and what do you store the data in? After building a telemetry backend from scratch for a real fleet of LoRa/BLE sensors and gateways — handling dual ingestion paths, binary and JSON payload formats, automatic recovery of lost data, and a growing set of operational dashboards — I keep coming back to the same combination: Python (FastAPI + asyncio) for the API, MQTT for device transport, and PostgreSQL for storage. Here’s why that combination holds up so well for this specific problem, not just “in general.” Full Article: https://medium.com/@jackpelorus/python-postgresql-and-mqtt-the-boring-stack-that-actually-survives-a-real-device-fleet-9297146cbe8d?sharedUserId=jackpelorus

Weng previously served as the VP of AI Safety Research at OpenAI.

xAI is suing Minnesota Attorney General Keith Ellison over a law passed back in May that broadly targets "nudification" apps, claiming that the statute's punitive provisions leave the company with "no practical choice but to restrict Grok Imagine's image-editing features in various ways." The law, the company argues, violates the First Amendment. Back in January, […]

Building an AI Operating Layer Episode 1 Why I Didn't Start Sooner Most engineering projects begin with an idea. This one began with a question. For months I found myself watching the explosion of AI tools, frameworks, models, and agent platforms. Every week there seemed to be another breakthrough, another library, and another opinion about where everything was headed. I could have started building immediately. Part of me thought I should have. But I realized something, and it kept bothering me. I wasn't afraid of writing code. I was afraid of solving the wrong problem. When a new technology appears, it's easy to jump straight into implementation. Pick a framework. Choose a model. Build something. Ship it. I didn't want to start there because I had a feeling there was a much bigger picture that I wasn't seeing yet. So I waited. I spent my time reading, experimenting, asking questions, and trying to understand how all of these pieces connected. The more I learned, the more I realized I wasn't actually interested in building another AI application. What fascinated me was the system behind the systems. What happens when you stop looking at models, memory, orchestration, tools, policies, and execution as separate ideas and start seeing them as parts of a much larger ecosystem? That question became the beginning of this project. This isn't a story about predicting the future. It's a story about trying to understand it. I'm sure some of my assumptions will be wrong. I'm sure parts of this architecture will change. If they do, you'll see that too. I don't want this journal to only show the polished results. I want it to capture the discoveries, the wrong turns, the redesigns, and the moments where a better idea replaces an old one. At the center of this journey is a project I'm calling the AI Operating Layer. Today it's mostly architecture, documentation, research, prototypes, and a growing collection of ideas. Maybe that's exactly where projects like this should begin. I'