In my experience there are approx. one thousand different pdf converters that are all equally terrible for anything except the simplest documents. Post the converted Mythos pdf, figures, tables and all. If good, happy to retweet as this is essential and missing infrastructure.
TOOLS
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OpenAI’s $100 ChatGPT Pro Tier: Pricing and Value Analysis
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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
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Pro tier pricing update with extended Codex usage promo
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Our existing $200 Pro tier still remains our highest usage option. And as a thank you to our existing Pro users on the $200 tier, we’re extending our 2x Codex usage promo (until May 31st) and we’ve reset your Codex rate limits (yes, again).
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ChatGPT Pro: New $100/month tier with improved Codex
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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
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Model Fine-Tuning: Does It Work Without Training?
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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.
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Using AI to Monitor and Manage Comprehensive Lists Effectively
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Yup. I understand. My lists go for completeness. And now that I have AI to watch them all they are highly useful:
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Prefab MCP UI not rendering in Cursor FastMCP server
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I'm not having any luck getting a Prefab MCP app UI show up in Cursor through a FastMCP server – it just shows "[Rendered Prefab UI]" in the tool call result. Is this something your team has validated working? Any minimal working examples?
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RAG is an Ecosystem: Building Modular Production-Grade Systems
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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
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Gemini Adds Interactive Visualization in Chat
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Gemini can now help visualize complex topics through interactive experiences directly in chat. "Show me the visualization" button will appear under certain questions, which could trigger this new experience. Testing time
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Free Gemma 4 Fine-tuning in Google Colab No Coding Required
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Fine-tune and run Gemma 4 and 500+ open source AI models in a free Google Colab.
— Shubham Saboo (@Saboo_Shubham_) 9 avril 2026
Just choose an AI model and hit start training. No need to write a single line of code.
100% free and Open Source.pic.twitter.com/u37nmZB2iXFine-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.