标签：#chatgpt

Joshua Achiam spent nearly nine years at OpenAI researching AI safety and made a memorable appearance in the Musk v. Altman trial.

Nephew saw a YouTube ad. Someone was selling a "secret prompt" for ₹199, claiming ChatGPT and Claude can analyze the stock market and place trades with 90% accuracy — no technical analysis, no fundamentals, just paste this prompt. He brings it straight to Uncle. The Ad 👦 Nephew: Uncle, I saw an ad on YouTube. Some guy was saying, "Use ChatGPT and Claude AI for stock market analysis, take trades with 90% accuracy. You don't even need to know technical analysis or fundamentals — just use this prompt and you'll get all the results." Is that actually possible? 👨‍🦳 Uncle: (laughs) Ah, here we go. This is exactly how a lot of scams happen — and honestly, it's rarely because of some clever new invention. It's because of a lack of understanding, and people treating these models as a magic black box. 👦 Nephew: So are they scamming us? Or genuinely fooling themselves too? 👨‍🦳 Uncle: Not exactly a straightforward scam, and not exactly genuine either. Here's the honest split: they're maybe 30% correct, and 70% wrong. 👦 Nephew: What does that even mean? 👨‍🦳 Uncle: I'll accept this much — there genuinely are AI models out there that can do a solid job predicting stock trends or running fundamental analysis, because that kind of prediction is heavily mathematical, numerical work. But — and this is the important part — ChatGPT, Claude, and Gemini are not that kind of model. 👦 Nephew: Why not? It can literally write code. It can do math inside code. Why can't it just... do math for stock prediction too? I genuinely don't get it. 👨‍🦳 Uncle: Come, sit. This needs a proper, from-scratch conversation. We're going to dig all the way down to what these models actually are , and by the end, you'll understand exactly why ChatGPT, Claude, and Gemini are the wrong tool for this specific job — not a scam exactly, but sold by people who never actually opened the box themselves. Part 1: What Is an LLM, Really — In One Honest Sentence 👨‍🦳 Uncle: Before anything else, one sentence, and hold onto i

A ChatGPT subscription starts at $20 a month and is one of the cheapest ways to run inference. OpenAI has also been fairly relaxed lately about third-party agents using them , which makes the deal even better for a lot of us. But a subscription can't be used as freely as pay-per-token access , and the providers police the difference. Anthropic recently narrowed its subscriptions to first-party apps; OpenAI has its own limits. Here's what will get you banned from an OpenAI subscription. Sharing your subscription A ChatGPT subscription is strictly personal. One subscription, one user. Sharing yours breaks OpenAI's terms of service. That also covers account pooling and account rotation, where several people share the same credentials to dodge rate limits. Running it in automation Automation (CI, runners, schedulers) should run on per-token pricing, not a subscription . Once a system calls the OpenAI API with your token while you're not in the loop, the usage stops being personal. No unattended production system should run on a ChatGPT subscription. Serving other users For now, you can point an autonomous agent like OpenClaw or Hermes at your ChatGPT subscription, as long as it only talks to you. The moment that agent starts chatting with other people, or serving them in any way, it turns into a team use case , and that inference should be paid per usage. Putting it in a commercial product Same logic here. Making an LLM call authenticated with an individual ChatGPT subscription inside a product you ship breaks OpenAI's terms. That access is subsidized, and reselling it in any form isn't what it's meant for. If you've built something just for yourself and you're the only user, you're probably fine. The bottom line A ChatGPT subscription is personal . Anything that stretches past personal use can get you restricted or banned. If you're not sure your usage counts, move it to pay-as-you-go. If you want to keep the subscription for your own work and fall back to per-token pr

You've got a folder of a few hundred screenshots and you want the text out of each one. Or you want to generate a batch of images for a side project. Or you just want to drop a single "summarize this" call into a script you're writing on a Sunday afternoon. So you open the pricing page for the official API, do the math on per-token billing plus setting up keys and a payment method, and it's hard to justify, because the exact same model will do the exact same thing for free in a browser tab. There are really two ways to get a model like ChatGPT or Gemini to do work for you. The web UI is free, or already covered by a subscription you're paying for anyway, but you drive it by hand. The API is scriptable, but you pay by the token. Most of the time that trade-off is fine. But for a whole category of work like hobby projects, throwaway scripts, research, or anything that doesn't need production-grade reliability, you're stuck picking between "free but manual" and "automated but paid." Which raises the obvious question: why not automate the free web UI? It's just a webpage. You open it, type in the box, click send. It turns out that hides a few fiddly problems, which I ran into enough times that I eventually built a small library for them. In this article we'll work through what it takes to automate these UIs, and at the end I'll show how little code it comes down to. 1. What it takes to drive a chat UI A single round trip with ChatGPT or Gemini breaks down into four jobs: Get your text into the input box Optionally attach a file Wait for the model to finish answering And read the answer back out. Every one of these is harder than it sounds, because the page is a modern single-page app that was never built to be driven by a script. We'll use Selenium with undetected-chromedriver, and for now assume the browser is already open (we'll get to launching it in the next section). To keep the code readable I'll show whichever of the two platforms makes each problem clearest, and

I have a confession. Somewhere around day nine of this experiment, I almost quit and went back to my old setup. Not because ChatGPT was bad. Because I was bad at using it. I kept typing half-questions the way I'd type into Google, hitting enter, and getting answers that were technically correct and completely useless. It took me about a week to realize the problem wasn't the tool. It was twelve years of muscle memory. This post is the long version of what happened when I tried to go a full month without my usual stack of developer crutches — Google, Stack Overflow, Regex101, JSONLint, a SQL formatter site, a commit message generator, a pile of bookmarked Docker cheat sheets, and a few other tabs I didn't even realize I kept open until they were gone — and replaced all of it with a single ChatGPT window. I work as a backend-leaning full stack engineer at a small e-commerce company. Python and Django on the server, a chunk of Node for a couple of internal services, Postgres, Docker, and an AWS setup that I inherited rather than designed. Nothing exotic. Which is actually why I think this experiment is useful — most of you reading this aren't working on some bleeding-edge ML pipeline either. You're maintaining stuff, fixing stuff, shipping features under deadlines that someone in another department picked without asking you. So here's what happened. All of it. The good parts, the embarrassing parts, and the parts where I quietly reopened Stack Overflow in an incognito tab because I didn't want my browser history to judge me. TL;DR I tried to replace 12 daily developer tools with ChatGPT for 30 days straight, tracking what worked and what didn't. Google search volume dropped by roughly 70%, but it never hit zero — and I don't think it should. Stack Overflow was the hardest habit to break, and also the one I missed least once I'd broken it. The small utility sites (Regex101, JSONLint, SQL formatters) were the easiest wins. ChatGPT replaced almost all of them outright. Do

Despite ChatGPT's commanding market lead, consumers who pay for AI have been increasingly choosing Anthropic's Claude, data shows.

The silicon race is heating up amid the struggle to keep up with demand.

Three AI stories landed this week that all poke at the same nerve: the images, video, and films we actually look at are getting an AI layer — and the line between "real" and "AI-made" keeps thinning. Quick rundown in the short, then my take below: 1. ChatGPT will start showing real, licensed photos — not AI fakes. OpenAI signed a multi-year display deal with Getty Images, so licensed photography shows up inside ChatGPT's search and discovery. It's display-only — the photos aren't used to train models. The twist I can't get over: AI image generation had nearly wiped Getty out (stock down ~55% on the year), and this one deal sent the shares up ~145%. The thing AI almost broke got rescued by AI. 2. ByteDance — yes, TikTok's parent — teased Seedance 2.5: a full 30-second video generated in a single shot, no stitching, up to 50 reference inputs, 4K. Most tools still cap out around 5–10 seconds, so "30s native, one pass" is a real jump in how usable the output is. Public launch is early July. 3. Google DeepMind is partnering with A24 on AI filmmaking — a ~$75M, non-exclusive deal to co-build Veo-powered tools. Notably Google gets no access to A24's film library or data. A prestige studio building with AI in the open makes the whole "AI in Hollywood" debate a lot less hypothetical. As someone building a daily AI-news pipeline on the side, the Getty one is the story I keep chewing on. So much of the "AI vs creators" fight has been framed as scrape-or-die. A display-licensing deal is a third option — pay to show the real thing, instead of generating a confident fake or quietly training on someone's work. I don't know if it scales, but it's the first move in a while that didn't feel zero-sum. The Seedance + A24 pair points the other way though: generation is getting longer, more controllable, and is walking straight into real production. So we get both at once — more verified real media and more convincing synthetic media, in the same week. Curious where other builders land:

Your prompts are fine. Your AI output is still garbage. You write carefully. You're specific. You ask for the format, the tone, the length. Hit enter. The AI responds with something that sounds like it was written by a committee of lawyers having a really bad day. Here's what you don't realize: You're not telling the AI to do something. You're describing the problem, and the AI is solving for the statistical average. The fix isn't more detailed instructions. It's three examples. That's it. Three. Not ten, not one, three. This post is the complete guide to few-shot prompting — the single highest-leverage move in prompt engineering. By the end, you'll have a template you can copy into any AI and watch your output quality jump 5x. Prefer watching? Here's the 3-minute version Otherwise, read on — everything's below. Why Instructions Fail (And Examples Work) When you tell an AI to "be funny," it's working off a fuzzy statistical average of everything labeled "funny" in its training data. When you show an AI what you think is funny, you're giving it a precise pattern to match. Here's the difference: ❌ Instruction: "Write a funny one-sentence movie summary" Result: A lukewarm joke that lands in the middle of the comedy bell curve. ✅ Pattern: Funny summary of The Lion King: Cub loses dad. Cub becomes king. Funny summary of Finding Nemo: Dad fish swims very far for his son. Funny summary of Titanic: [AI fills this in] Result: Boy meets girl. Boat meets iceberg. Oops. Same AI. Different universe. The only thing that changed: you showed it the pattern instead of describing it. The Science (Why This Isn't Magic) Language models predict the next token by pattern matching. They've seen millions of prompt-response pairs and learned: "When a prompt looks like this , the output usually looks like that ." One example could be a fluke. Two examples might be a coincidence. Three examples are clearly a pattern. The AI recognizes the pattern and completes it. This is exactly how humans l

46.4%. That number — ChatGPT's June 2026 market share — ends a streak that held since November 2022. For the first time since the product launched, OpenAI holds less than half the AI assistant market. Gemini is at 27.7%. Claude is at 10.3%. The monopoly phase of AI assistants is over. The data comes from a June 2026 market report tracking monthly active users across major AI assistants. ChatGPT still leads with 1.11 billion monthly users — a number that would define the entire category in any other software market. But Gemini has 662 million, up 129 million in five months. Claude sits at 245 million, nearly four times its December 2025 count of 60.2 million. The trajectory is the story, not the absolute numbers. Why the 50% Threshold Actually Matters Below 50% doesn't mean decline. ChatGPT's absolute user count keeps growing. What the threshold signals is the end of single-platform dominance — the condition where building for "AI users" meant building for ChatGPT users. That assumption no longer holds in mid-2026. For context: search engine market share stayed above 90% for Google for nearly a decade after competitors entered. Social network market share for Facebook stayed above 70% for years after Instagram and Twitter had genuine scale. The pace of AI assistant fragmentation is meaningfully faster than those precedents. Three products above 10% share in under two years of real competition is an unusually fast split. What fragmentation means practically: the community knowledge base — YouTube tutorials, Reddit threads, prompt libraries — that once pointed almost exclusively at ChatGPT now covers three platforms with genuine depth. That changes how you can expect your users to arrive at your AI-integrated product, and what they already know about AI when they get there. Gemini's 662 Million Users Are Not What They Look Like Gemini's surge from under 500 million to 662 million monthly users in five months is impressive on paper. The driver is less impressive: Google

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.

The chatbot still remains the most popular AI assistant worldwide with over 1.1 billion monthly users, followed by Gemini with 662 million and Claude with 245 million.

I was thinking about some tests for a service that uses language models; there are several, even prompt injection. A question came to mind: is it possible to make multiple requests asking for any text like Lorem Ipsum, generating many unnecessary tokens and incurring costs? But creating a test where there are multiple accounts making the same request to generate 10,000 Lorem Ipsum tokens simultaneously, could that cause a service outage? Because most of the infrastructure I see doesn't use any queuing method when the chat is free of tasks involving an agent or even heavier functionalities. I didn't actually generate anything, I just wanted to start a discussion on this topic.

Did chatbot abandon mental health guardrails when a vulnerable user pushed back?

AI coding tools are powerful. But they’re also wasteful. A tiny helper-function question does not need Claude Sonnet. A huge architecture review probably does. That gap costs money. So I built Badgr Auto. It’s a local OpenAI-compatible proxy that routes each AI coding request to the cheapest model that can handle it. Point your coding tool at: http://localhost:8787/v1 Badgr Auto can route between: local models cheaper OSS cloud models premium models So instead of paying premium prices for every request, you can use: local for small tasks OSS cloud for normal coding work premium only when it actually matters It also tracks: actual cloud spend which route was used fallback events tokens safely removed estimated savings vs premium models The goal is simple: stop wasting premium tokens on cheap tasks. First launch is small: small task → local normal task → cheaper cloud hard task → premium provider fails → fallback duplicate code → safely removed receipts → clear spend trail AI coding is only going to get more expensive if every agent step goes to the top model. Badgr Auto is my attempt to make AI coding cheaper without making it worse.

OpenAI has shipped more developer-facing infrastructure in the first half of 2026 than in the prior two years combined. GPT-5.5 is live. The Agents SDK is production-ready. Codex hit 5 million weekly active users. And yet most of the coverage is about ChatGPT's chat UX. Let's skip that and talk about what actually matters: ChatGPT's biggest upgrade ever and what developers actually need to know in June 2026. What changed at the API layer, which features are production-grade versus demo-ware, and whether it's finally time to move workloads back from Claude or Gemini. I spent the last two weeks migrating an internal agent pipeline from the Chat Completions API to the new Responses API. The difference is not subtle. This isn't a model bump with a new blog post. It's a platform rearchitecture. ChatGPT's Biggest Upgrade: The Responses API Changes Everything Forget GPT-5.5 for a second. The single most important change for developers building on OpenAI is the Responses API . If you've been building with Chat Completions, you know the drill: you manage conversation history client-side, pass the full message array on every request, and bolt on your own tool-calling orchestration. The Responses API eliminates most of that. Three things that actually matter: Server-side conversation state. OpenAI manages conversation history for you now. No more serializing and replaying message arrays on every call. For long-running agentic sessions, this alone cuts your infrastructure code in half. The reasoning_effort parameter. You can tell the model, per request, how much compute to burn on chain-of-thought reasoning before answering. Low effort for latency-sensitive paths like autocomplete and classification. High effort for accuracy-critical ones like analysis and code generation. Neither Claude nor Gemini expose anything equivalent at the API level right now. Background Mode. This is the one that changes architectures. Fire off a long-running task. Get results via webhook callback ins

The ChatGPT maker announced it has filed paperwork to go public, just a week after rival Anthropic took the same step.

OpenAI to recast hit chatbot as a route to higher-margin products before a potential IPO.

"Chat is dead" — at least, according to a senior OpenAI employee.

A few weeks ago I was on a call sharing my screen, walking a teammate through a prompt I'd been iterating on in ChatGPT. Mid-sentence I scrolled up — and there, three messages back, was a chunk of a customer's data I'd pasted in earlier to debug something. Real email, real account info, sitting right there on a shared screen. Nobody said anything. Maybe nobody noticed. But I noticed, and I spent the rest of the call only half-present, trying to remember everything else still in that thread. If you live in ChatGPT all day, you already know the problem. The thread is your scratchpad. You paste logs, keys, customer rows, half-finished internal docs — things you'd never put in a doc you planned to share. And then someone says "can you share your screen real quick" and suddenly your scratchpad is a presentation. Why the usual advice doesn't work The standard answers are all some version of "be careful": Open a clean tab before sharing. Scroll to the top. Use a separate "demo" account. These fail for the same reason all manual checklists fail under pressure: the moment you actually need them is the moment you're distracted, talking, and not thinking about hygiene. You remember after . The fix has to happen before the screen goes live, and it has to require zero discipline in the moment. What I wanted instead I wanted something that just sat there and blurred sensitive parts of a page automatically, so that even if I forgot, the leak couldn't happen. A few requirements: Local only. Whatever it does, it never sends page content anywhere. A privacy tool that phones home is a contradiction. Before, not after. It blurs while the page renders, not after I've already exposed it. Per-element, not whole-screen. A full black box is useless for a demo. I still need to show the working parts. The interesting technical bit The naive approach is to listen for some "I'm sharing now" signal and react. That's too late — there's a visible frame where the data is exposed before the blur kic