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  • I Can’t Stop Thinking About Claude Mythos

    By

    @danshipper

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    I CANT STOP THINKING ABOUT CLAUDE MYTHOS

    → View original post on X — @danshipper

  • OpenAI’s $100 ChatGPT Pro Tier: Pricing and Value Analysis

    By

    @kimmonismus

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    $100 ChatGPT pro tier official. -5x codex rates -access to ChatGPT pro -unlimited thinking Yeah, that’s really worth the $100. great deal, good pricing. Well done OpenAI OpenAI (@OpenAI) We’re updating our ChatGPT Pro and Plus subscriptions to better support the growing use of Codex. We’re introducing a new $100/month Pro tier. This new tier offers 5x more Codex usage than Plus and is best for longer, high-effort Codex sessions. In ChatGPT, this new Pro tier still offers access to all Pro features, including the exclusive Pro model and unlimited access to Instant and Thinking models. To celebrate the launch, we’re increasing Codex usage for a limited time through May 31st so that Pro $100 subscribers get up to 10x usage of ChatGPT Plus on Codex to build your most ambitious ideas. — https://nitter.net/OpenAI/status/2042295688323875316#m

    → View original post on X — @kimmonismus, 2026-04-09 17:38 UTC

  • ChatGPT Pro: New $100/month tier with improved Codex

    By

    @scobleizer

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    We're updating our ChatGPT Pro and Plus subscriptions to better support the growing use of Codex. We're introducing a new $100/month Pro tier. This new tier offers 5x more Codex usage than Plus and is best for longer, high-effort Codex sessions. In ChatGPT, this new Pro tier still offers access to all Pro features, including the exclusive Pro model and unlimited access to Instant and Thinking models. To celebrate the launch, we're increasing Codex usage for a limited time through May 31st so that Pro $100 subscribers get up to 10x usage of ChatGPT Plus on Codex to build your most ambitious ideas. [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-09 17:36 UTC

  • Model Fine-Tuning: Does It Work Without Training?

    By

    @saboo_shubham_

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    What is your actual question? Sorry, I don't get it. Are you asking if the model works without fine-tuning? The answer is yes. Or if you're asking, you really need to get your hands dirty with the code fine tuning to work? That's exactly the problem Unsloth Studio is solving.

    → View original post on X — @saboo_shubham_

  • RAG is an Ecosystem: Building Modular Production-Grade Systems
    RAG is an Ecosystem: Building Modular Production-Grade Systems

    By

    @ingliguori

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    This is one of the cleanest visual summaries of a production-grade RAG (Retrieval-Augmented Generation) stack I’ve seen. What it highlights clearly is an often-ignored reality: RAG is not a single tool — it’s an ecosystem. A solid RAG system spans multiple, interchangeable layers: LLMs (open & closed): Llama, Mistral, Qwen, DeepSeek, OpenAI, Claude, Gemini Frameworks: LangChain, LlamaIndex, Haystack — orchestration is the real differentiator Vector databases: Chroma, Pinecone, Qdrant, Weaviate, Milvus Data extraction: Web crawling, document parsing, structured ingestion Embeddings: Open (BGE, SBERT, Nomic) vs proprietary (OpenAI, Cohere, Google) Evaluation: RAGAS, TruLens, Giskard — because “it sounds right” is not a metric Key takeaway for leaders and builders: RAG success is less about which model you choose and more about: data quality retrieval strategy chunking & indexing evaluation loops cost / latency trade-offs This is why mature AI teams design modular stacks, not one-vendor pipelines. RAG is no longer experimental. It’s becoming foundational infrastructure for enterprise AI. #RAG #AgenticAI #EnterpriseAI #LLMs #AIArchitecture #GenAI #DataEngineering X (Twitter) RAG isn’t a tool. It’s a stack. LLMs Frameworks Vector DBs Embeddings Extraction Evaluation Winning teams design modular RAG systems — not single-vendor pipelines. This is how enterprise AI actually scales.

    → View original post on X — @ingliguori, 2026-04-09 17:25 UTC

  • Nemotron as Primary LLM with API Model Integration

    By

    @mreflow

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    No issues here. Nemotron is still my main LLM for most simple tasks and it taps Claude and GPT API models for anything more complex. Speed isn’t really an issue. Usually get a response within 20-seconds or so. Not the fastest in the world but not slow enough to be an issue.

    → View original post on X — @mreflow

  • PDF Conversion Challenges for Large Language Models

    By

    @karpathy

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    I just tried it this morning on the 245-page Mythos pdf and it failed badly and the outputs were all mangled. Converting pdfs is really hard, I think it has to probably be a Skill not a program, for a SOTA LLM for it to work properly.

    → View original post on X — @karpathy

  • New SGLang Course: Efficient LLM and Image Generation Inference

    By

    @andrewyng

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    New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys @lmsysorg and RadixArk @radixark, and taught by Richard Chen @richardczl, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: – Implement a KV cache from scratch to eliminate redundant computation within a single request – Scale caching across users and requests with RadixAttention, so shared context is only processed once – Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale! deeplearning.ai/short-course…

    → View original post on X — @andrewyng, 2026-04-09 17:11 UTC

  • Small model makers increase but performance gap with large models persists

    By

    @emollick

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    There are more competitive small model makers, but there is still a very big gap between what small models can do and what large models can accomplish (even if the small model benchmarks say otherwise)

    → View original post on X — @emollick

  • Free Gemma 4 Fine-tuning in Google Colab No Coding Required
    Free Gemma 4 Fine-tuning in Google Colab No Coding Required

    By

    @saboo_shubham_

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    Fine-tune and run Gemma 4 and 500+ open source AI models in a free Google Colab. Just choose an AI model and hit start training. No need to write a single line of code. 100% free and Open Source.

    → View original post on X — @saboo_shubham_