OpenAI has introduced GPT-5.3 Instant, an update to its most-used ChatGPT model that aims to deliver smoother conversations, fewer unnecessary refusals, improved factual accuracy and better responses when using web information.
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OpenAI has launched GPT-5.3 Instant for ChatGPT, promising fewer unnecessary refusals, improved contextual understanding and reduced hallucinations, as the company faces scrutiny over its Pentagon deal while rival Anthropic remains locked in a dispute with the US defence department.
OpenAI dropped a significant update to ChatGPT amidst the backlash they have been receiving for the DoW Deal. GPT-5.3 Instant – the new version of its most widely used everyday model – went live on March 3, and it's one of the more user-focused releases the company has made in a while. Forget benchmark scores:
OpenAI’s GPT-5.3 Instant improves ChatGPT’s most-used model with fewer hallucinations, better web synthesis, and smoother conversations.
OpenAI upgrades Prism with Codex CLI and GPT-5.3, letting researchers run code, compile LaTeX, and refine papers inside one ...
OpenAI has released an update to ChatGPT that it says should make its most commonly used model less “cringe” and more natural. Users should see fewer overly dramatic, jarring responses as a result.
This week's second new model from OpenAI is built for more complex tasks than GPT-5.3 Instant.
The post OpenAI’s New GPT-5.4 Thinking Lets You Take the Wheel Mid-Response appeared first on Android Headlines.
Every time I used an LLM to generate structured knowledge files, the output would drift — wrong enum values, missing fields, dates in the wrong format, tags as strings instead of arrays. The files looked fine until something downstream broke: a Dataview query returning nothing, a CI check failing, a search index corrupting.The standard fix is post-hoc validation — check after writing, fix manually. That doesn't scale past a few dozen files.So I built a pipeline where the commit gate is the
Hey HN, I built Meshcraft – a web-based tool that generates 3D models (GLB) from text prompts or images.What's new since the first Show HN (Feb): Back then it was a basic TripoSR wrapper. A commenter here (thanks vunderba) pointed me to Trellis 2, which was vastly better. Since then I've rebuilt the whole thing:- Two 3D engines: Standard (Trellis 2 via HuggingFace ZeroGPU) and Premium (Hunyuan v3.1 Pro via fal.ai). Standard is free, Premium costs 50 credits and produces ~1.4M face mode
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Codex App now on Windows
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Testing AI agents is painful. Every test run calls the LLM API, costs real money, takes minutes, and gives different results each time. CI? Forget about it.Evalcraft fixes this with cassette-based capture and replay — think VCR for HTTP, but for LLM calls and tool use.How it works:1. Run your agent once with real API calls. Evalcraft records every LLM request, tool call, and response into a JSON cassette file.2. In tests, replay from the cassette. Zero API calls, zero cost, deterministic output.
