April 21 : OpenAI said on Tuesday it is expanding partnerships with major global consulting firms to speed up enterprise adoption of its Codex artificial intelligence tools, as competition in the rapidly evolving AI market intensifies.It is also launching Codex Labs, which will place OpenAI specialists direct
OpenAI Group PBC today launched ChatGPT Images 2.0, an upgraded version of the image generator built into its popular chatbot. The company also debuted a new technical training service called Codex ...
CGI is strengthening its relationship with OpenAI, specifically around its Codex platform for building and managing artificial intelligence agents. The Canadian IT and business consulting services ...
Last week, OpenAI released an all-new version of Codex for Mac that includes the best example of AI-driven computer ...
OpenAI's Codex is growing faster than anyone anticipated, and it isn't just for coders anymore. OpenAI is now sending experts ...
OpenAI is announcing its upgraded ChatGPT image generation model with ChatGPT Images 2. The company is also scaling up ...
OpenAI's coding assistant Codex now has over 4 million weekly developers, adding 1 million users in just two weeks. The ...
OpenAI's coding assistant, Codex, has seen its user base grow to 4 million, with a remarkable addition of 1 million users in just two weeks.
OpenAI names Cognizant and CGI as the first SI partners in a Codex enterprise programme as the coding agent hits 3M weekly users.
The ChatGPT maker said it has four million weekly active users for its AI coding tool, up from three million two weeks ago.
I am Rohan, and I have grown really frustrated with CC's search and read tools. They use Haiku to summarise all the search results, so it is really slow and often ends up being very lossy.I built this MCP that you can install into your coding agents so they can actually access the web properly.Right now it can:- search the general web- search Reddit- read and scrape basically any webpageInstall it:npx openalmanac setupThe MCP is completely free to use. We have also built a central store whe
Most AI coding agents assume you have a 200k-context model. In reality, the local models most people actually use have 8k windows — barely enough for one large file, let alone a whole project.This tool works in three steps:-Map: on init, it writes plain Markdown context files: one project-level overview, one per folder, plus a line range index for any file over 150 lines.-Plan: one LLM call reads the map and turns your request into a task list, with dependencies.-Execute: it gives only one file
I have a really good salary as a remote engineer (and I live in a part of the world where one salary could last me 6 months). However, I am getting bored of my work (due to many reasons; AI could be one of them).I want to try different side projects (not all of them being tech-related); for example, opening an online niche bookshop would be ideal. But when I consider the effort I would have to put in and the money I would earn (a small percentage of my salary), I know it's not worth it. It
I built a plugin that lets Claude Code delegate work to Gemini CLI.I started this after finding myself reaching for Gemini more often on long context repo work. I have been especially liking Gemini’s codebaseinvestigator for long context.This is inspired by openai/codex-plugin-cc.Code Review, adversarial review. Under the hood its Gemini CLI over ACPWould love feedback from people using Claude Code, Gemini CLI, or ACP. I am especially curious whether this feels useful outside my own workflo
Aide is an Android app that replaces your default digital assistant. It can register as your default assistant, so corner-swipe and power-button-hold summon it instead of the Google assistant. I wanted to do something other than Google, but ChatGPT and Claude's integration couldn't do anything on device, so I built this.Bring your own key for Claude, OpenAI, or any OpenAI-compatible endpoint (Ollama, LM Studio, vLLM). Keys are encrypted on-device; conversations go straight to your prov
I built an open source version of OpenAI Chronicle.Some design decisions I made:1. Local first: OCR uses Apple Vision, summarization supports local AI providers via Vercel AI SDK. Nothing leaves your computer. 2. Multiple Provider: exposes MCP so any coding agents can use it. 3. Swift menubar app: efficient, low-footprint 4. Blacklist apps: password managers, messaging apps (Slack, WhatsApp, Messenger), mail clients are on default blocklist.Current Limitations: 1. Mac only. Mac-first is a featur
I built this small website to study chess openings using a visual tree. I personally find it helpful for comparing them, especially on desktop because it supports keyboard navigation. It works well on mobile, though.<p>It’s simple, has no ads. I decided to make it public in case others find it useful as well. I’ll be adding more openings over time.<p>I’d really appreciate any feedback.
We built an RL environment for credit card reward optimization and trained Qwen 32B with GRPO against it. The trained model scores ~0.51 on held-out tasks vs. Opus 4 at ~0.41 and GPT-4o at 0.36. Environment is open source (Apache 2.0). Blog post explains the reward design, what broke during training, how we fixed it, and what we'd do differently.
I kept hitting the same wall at work every time we needed to ship an AI feature. What looked like a week of work turned into picking a model, setting up a vector DB, managing embeddings, wiring up chat history, handling retries — none of it was the actual feature. So I built Modular. You register a function that returns your app's data, then call ai.run() for one-shot features or ai.chat() for stateful conversation. Everything else — context management, embeddings, session history, model ro
Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet.- Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token co
