标签：#ai

The iPhone maker claims OpenAI encouraged poached employees to bring over confidential presentations, secret prototypes, and key supplier details.

Let's be real, OpenAI isn't giving up on the browser market, it is just changing its strategy.

When Niantic dropped the first Pokémon Go trailer in 2015, it was hard to grasp how a bunch of players could work together to catch a pokémon like Mewtwo. But this week at the game's 10th anniversary event in New York City, Pokémon Go showed the world how it's done. Almost 2,000 players (many of […]

A year and a half ago, I wasn't trying to build an AI company. I was building a small social platform called spritex-social — nothing fancy, just a side project a handful of friends were testing with me. No grand plan, no investors, no roadmap beyond "let's see if people like this." At some point, users started asking the same basic questions over and over: how do I change my profile, where's this setting, how does that feature work. Instead of writing endless documentation, I thought — why not just let AI answer this? So I wired up Google's Gemini API through Google AI Studio, built a small Retrieval-Augmented Generation (RAG) system, and gave it context about the platform. It was supposed to be a support chatbot. Nothing more. That's not how it went. I found myself spending more time improving the chatbot than improving the actual social app. Every small upgrade made me ask another question: could it remember conversations? Could it use tools? Could it search the web? Could it do things instead of just answering questions? The more I asked, the less interested I became in the social platform I was supposed to be building. Eventually I had to admit it to myself: I wasn't building spritex-social anymore. I was building something else entirely. So I stopped. Not because the project failed — because my attention had already moved somewhere else, and I finally stopped pretending otherwise. That "somewhere else" became RexiO — a Bangla-first AI platform I've been building solo ever since: my own orchestration layer, an intent classifier, 30+ tools, model routing across providers, and eventually our own fine-tuned models trained from scratch on borrowed Colab GPUs. RexiO went public on July 10, 2026. This chatbot pivot is just one chapter of a much longer story — one that actually starts on a Nokia button phone, ২ টাকা data packs, and a ৳20 freelance job that became my first line of code in production. I wrote the whole thing down, unfiltered — the rewrites, the 12-hour

Stop Writing Prompt Strings: Meet PromptForge Core As AI becomes part of modern applications, prompts are no longer just strings—they're becoming part of your codebase . Yet most of us still write prompts like this: const prompt = " You are a helpful assistant. \n " + " Summarize the following text. \n " + " Return the output as JSON. \n " + " Keep it concise. \n " + " Use simple language. " ; This works... Until your project grows. The Problem As prompts become larger, they quickly become difficult to maintain. You start dealing with: ❌ Giant string templates ❌ Copy-pasted prompts ❌ Missing variables ❌ Inconsistent formatting ❌ Provider-specific implementations ❌ Difficult debugging Unlike your application code, your prompts have: No structure No validation No type safety What if prompts were treated like code? That's exactly why I built PromptForge Core . PromptForge is an open-source TypeScript toolkit for building production-ready prompts using a clean, structured API. Instead of writing strings... const prompt = " You are... " You write import { pf } from " @promptforgee/core " ; const summarize = pf . define ({ input : z . object ({ text : z . string (), }), output : z . object ({ summary : z . string (), }), messages : ({ text }) => [ pf . system ` You are an expert summarizer. ` , pf . user ` Summarize: ${ text } ` , ], }); Much easier to read. Much easier to maintain. Features PromptForge focuses on developer experience. ✅ Type-safe prompt definitions ✅ Structured prompt composition ✅ Prompt compilation ✅ Validation ✅ Provider-agnostic architecture ✅ Reusable prompt blocks ✅ Modern TypeScript API Compile Once, Use Anywhere Instead of maintaining different formats for every provider... PromptForge compiles your prompt into provider-specific formats. Prompt Definition ↓ Prompt Compiler ↓ OpenAI Anthropic Gemini Ollama Write once. Compile anywhere. Composable Prompts Large AI applications usually repeat the same instructions. With PromptForge you can compose p

Last month I watched Cursor confidently rename a field across an entire API, commit it, and open a PR. Clean diff, tests green, looked great. It had also just broken a mobile client and a partner integration that were still reading the old field name — and neither Cursor nor I noticed until much later. That's the thing about AI coding agents and APIs: they're fast, they're fearless, and they have zero awareness of your API contract . An agent will drop an endpoint, make a request field required, or change a response type without any sense that a real consumer out there depends on the old shape. The code compiles. Your tests pass (your tests — not the consumer's). The breakage is completely silent until someone downstream feels it, usually in production, usually from an angry message rather than a failing build. We keep giving agents more power to write API changes and nothing to tell them whether a change is safe to ship . So I built that missing piece as an MCP server. The gap: there's no "is this safe?" step in the loop Think about how you'd catch this manually. You'd diff the old and new OpenAPI spec, look for removed endpoints, removed response fields, tightened request contracts, enum narrowing — the classic breaking changes — and decide whether it's safe to merge or whether you need a version bump and a heads-up to consumers. An agent never does that. It has the code in context, not the contract implications . And "did I just break a consumer?" is exactly the kind of question it should be asking before it hands you a diff. Enter MCP If you haven't used it yet: the Model Context Protocol is a standard way to give AI agents tools — little capabilities they can call. Claude, Cursor, and others all speak it. Instead of the agent guessing, it can call a tool and get a real answer. So the fix is simple to state: give the agent a tool that answers "is this API change safe to ship to my consumers?" — and have it call that before it proposes the change. That's the hero

How I Supervise Three Engineering Workflows at Once Three Workflows, One Operator Right now, I have three engineering workflows open. One is under council review. Four AI roles are challenging an architectural proposal, and I will need to decide which objections actually change the plan. The second is already in implementation. That one does not need me at the moment. The specification is approved, the boundaries are clear, and the executor can keep moving. The third has come back from audit. The findings are valid, but corrective work is paused. A remediation plan exists, and someone other than the executor needs to review it before any more code changes. This is the part that still feels new: I can move between all three without reopening old chats and rebuilding the story in my head. A few months ago, even one workflow could take most of my attention. I carried context between every stage: rewriting role prompts, moving decisions between conversations, tracking the current document, and turning audit findings into the next round of work. The AI council itself was already useful. It produced strong reasoning and exposed assumptions I would probably have missed. But I was still the glue around it. The council improved the decisions. The system around it made those decisions easier to carry into implementation, audit, and correction without losing control. Conversations Were No Longer the Workflow The main change was simple to describe: I stopped treating the workflow as a series of conversations. Chats are good for thinking. They are not a good place to keep authority. Before this change, a decision might exist somewhere in a long discussion. The next agent had to interpret it, and I had to remember whether it was final, provisional, or already replaced. Now the state of the work lives in a small set of artifacts. Evidence becomes a source-grounded brief. Decisions become an approved specification. The specification becomes bounded implementation. The implementatio

Better to sacrifice a part to preserve the whole. — The 36 Stratagems, Sacrifice the Plum Tree to...

Solid-state batteries are safer and more capable—but harder to mass-produce. They also represent an opportunity for non-Chinese companies to get back in the game.

Fizz has expanded its lawsuit against rival Sidechat, alleging that a Maveron VC shared its confidential information obtained during a fundraising meeting with the competing startup.

The AI chip boom just produced its biggest Wall Street moment yet. Now SK Hynix and Samsung are being asked to build U.S. factories.

Spotify is giving listeners control to fine-tune what gets surfaced for them in Release Radar - one of its most popular weekly playlists. The new options allow you to narrow the playlist to a specific genre, focus on artists that are new to you, and more. Listeners can choose from up to five options like […]

The launch of free content would allow Disney+ to better compete with free services like YouTube and Tubi, which are capturing a growing share of consumers’ viewing time.

How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence. The post Better tools made Copilot code review worse. Here’s how we actually improved it. appeared first on The GitHub Blog .

If you were to look up into the night sky, what would you see? Countless points of light, scattered in every direction. Most of what you're looking at are stars. But some of those points are whole galaxies—vast collections of stars, spread across incomprehensible distances, compressed by that distance into a single pinprick of light. And what you can see with the naked eye is only a small fraction of what's actually out there. I want to use this as a way to offer you a way of thinking about how large language models work. Just an analogy, not literally what's happening inside the mathematics—that's not my forte. My hope is that it captures something true about the mechanics, and more importantly, it gives you a mental model you can actually use when you're working with these systems. About two years ago, I was wrestling with finding a way of explaining what an LLM does. My first analogy was that of a dictionary. The naive view was that a dictionary uses words to define other words, and an LLM holds a matrix of words with weights that describe their relationships to each other. So the parallel seemed natural: both systems work through relational structure. However, a dictionary gives you denotation—the surface-level meaning. It's a lookup tool for individual words, not a model of language itself. And critically, you have to already understand language before a dictionary is useful to you at all. The analogy didn't capture what was actually happening in the weight relationships—the distributional semantics, the contextual patterns that let an LLM generate coherent text. Ok, so back to galaxies, when you look up at the night sky, you're not seeing distance—you're seeing direction. That galaxy over there, the one that looks like a point of light, could be millions of light-years away, but what matters for our analogy isn't how far it is. It's which way you're looking. And when you point yourself in that direction and venture toward it, you discover it's not a point at a

Cloudflare has recently introduced temporary accounts that let AI agents deploy Cloudflare Workers immediately, without first creating or authenticating with a permanent account. If left unclaimed, the accounts and their deployments expire automatically after 60 minutes. By Renato Losio

The massive round was co-led by ARCH Venture Partners, Spark Capital, and Khosla Ventures.

Amid signs of slowing engagement, Netflix is reportedly considering launching "always-on" live channels, giving subscribers something to tune into 24/7.

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]

Though Instagram head Adam Mosseri doesn't want to filter out AI content on the platform, he argues that you "shouldn't have it in your feed" if you don't like it. "I don't think we should filter out AI content," Mosseri said during an interview on Lenny Rachitsky's podcast. "I think we should let you know […]