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GPT 4.5 is here. This is our biggest and most knowledgeable model trained by scaling up unsupervised learning. Since it is not a reasoning model, it won’t be better than o-series in science/coding, but we think it’ll be better at tasks with higher EQ and creativity. https://lnkd.in/gJSe9u46

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Great piece by Emilia David on the LangChain open source integration ecosystem, the power of LangGraph, and our newly released LangGraph Platform Helps put everything you see us working in perspective Link: https://lnkd.in/ghajS7zx

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Here is Gemini CLI’s October 6th weekly update for v0.9.0 - 🎉 Interactive Shell: Run interactive commands like vim, rebase -i, or even gemini 😎 directly in Gemini CLI's shell mode. - Blog: https://lnkd.in/ga3qz2bu - Install Pre-release Extensions: Install the latest --pre-release versions of extensions. Used for when an extension’s release hasn’t been marked as "latest". (jakemac53) - Simplified Extension Creation: Create a new, empty extension. Templates are no longer required. (chrstnb) - OpenTelemetry GenAI Metrics: Aligns telemetry with industry-standard semantic conventions for improved interoperability. (jerop) - List Memory Files: Quickly find the location of your long-term memory files with /memory list. (sgnagnarella) https://lnkd.in/gNa_854H 🧵 #GeminiCLI #Gemini #AI #OSS #TTY

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🚀 TOON vs JSON - How I Cut My LLM Token Usage (and Cost) by 30–60% I integrated TOON (Token-Oriented Object Notation) into my daily autonomous agent workflow, the same system that sets up my day’s action items automatically. Before TOON, that workflow consumed anywhere from 60K to 1M tokens per run, depending on the context. After converting all structured JSON data into TOON, the exact same logic now uses only 45K to 72K tokens. ✅ Same context. ✅ Same responses. ❌ Lower cost. That’s a massive 30–60% token reduction, just by changing the data format. So, what’s TOON? It’s a new, compact data format designed for LLM efficiency. Unlike JSON, which repeats keys, braces, and punctuation, TOON defines the structure once and just streams the values. The result? Far fewer tokens, faster processing, and cheaper model runs. Many of my engineering friends ask: “Isn’t TOON just like CSV?” Conceptually: 👉 TOON = CSV’s compactness + JSON’s structure + AI readability Here’s how they differ: JSON → General-purpose data exchange (APIs, configs) CSV → Simple, flat tabular data (spreadsheets, databases) TOON → Structured data optimized for AI token efficiency If your agents or pipelines frequently push JSON into LLMs, switching to TOON could save real money and make your workflows scale more efficiently. 🔗 Learn more: https://lnkd.in/dC4wnkmG #AI #MachineLearning #LLM #SoftwareEngineering #DataEngineering #Automation #TOON #JSON #TokenOptimization #AIInfra

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"Every engineer is a Data/ML Engineer... if the Infra is right!" 🚀 Srikanth Sundarrajan's opening slide was a mic-drop moment that had every Platform and Infra engineer in the room (including me!) nodding in agreement. It perfectly captures our own (Anyscale) core mission: to build infrastructure so seamless it empowers everyone to innovate with data, AI & ML. InMobi's Encore'25 provided a fantastic look under the hood of their AI/ML infrastructure. For anyone obsessed with building and scaling machine learning systems, this was gold. Three things that stood out: Cost-Effective Model Training: Deep dive into their strategies for optimizing large-scale training jobs, intelligently leveraging spot instances and right-sizing resources without compromising on model development velocity. Real-Time Feature Stores: A key focus was their approach to building and maintaining a feature store that serves up-to-date data with single-digit millisecond latency, which is critical for their real-time bidding (RTB) models. The Rise of GenAI Infra: It was exciting to hear about their early infrastructure bets for supporting Generative AI and LLMs, particularly for dynamic creative optimization. This signals a major shift in the computational demands for the next generation of AdTech. Kudos to the InMobi engineering teams for their transparency and for building a truly impressive, high-performance ML platform. #AI #ML #Infrastructure #Encore25 #InMobi #MLOps #FeatureStore #GenAI #CloudComputing #AdTech

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Claude and Gemini Can Pair Program Now. This Changes Things. 🤯🤖 Just stumbled on this and had to share — Someone built an MCP server that lets Claude Code and Gemini 2.5 Pro collaborate like a real dev pair. Claude leads with planning and structure. Gemini follows with deep reasoning, 1M-token context, and performance tuning. The two models go back and forth — sharing ideas, validating improvements, and refining code together. 🧪 In one example: a 26% speedup in JSON parsing after Claude proposed a plan and Gemini optimized it. Improvements were verified with unit tests and benchmarks. This isn’t just prompt hacking — it’s real engineering logic in action. 🔥 With the latest update, the server now supports persistent AI-to-AI conversations. Claude and Gemini can: • Question each other’s ideas • Validate and refine solutions over multiple steps • Share incremental context (no full re-prompts, no 25K limit) • Work together like a true dev team 💡 The real breakthrough here? MCP When you create a solid open standard like MCP and empower the open-source dev community, innovation takes care of itself. 🔗 Explore the MCP server: https://lnkd.in/gEnw9MaK #AI #LLM #Claude #Gemini #PairProgramming #MultiAgentSystems #AIStandards #ModelContextProtocol #OpenSource #DevTools #AIEngineering #MCP

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NEXTGEN INFRA STARTUPS MUST PLAY OFFENSE Historically, software infrastructure / middleware startups have played DEFENSE well. They have built deep, defensible technology moats over a period of time. The founders have been deeply technical, have sold to other developers, and have been content to not have a strong public-facing brand. However, this playbook (and archetype) must change. Infra founders and startups must play OFFENSE and become more dynamic and front footed. Why? First, foundation models are adding more and more infastructural components and nibbling away at what was traditionally considered infrastructure. No company can now comfortably sit and own a part of the stack. It will inevitably erode. Second, end customers are deciding or opining on what infrastructure to use. Guess what? Customers of infra products are no longer just career engineers but ordinary people (including VCs!) Infrastructure now has a PLG motion. Having a customer-facing brand is no longer optional. Finally, it’s possible (or even probable) that your company might need to fundamentally change its business model, and not be an infrastructure company any more. And in this crucible moment, you need to have the muscle and the ability to seize the opportunity and go for it. Consider OpenAI launching ChatGPT or Vercel launching v0. They could never have managed it with a legacy infrastructure mindset. Check out Groq’s homepage - it looks more like a vibecoding startup than a chip company. To summarize, infrastructure startups need to evolve. They must start thinking about the end customers, become much more public-facing with their brand, and develop the muscle to build customer-facing (not just developer facing) products. The archetype of infrastructure founders is changing. They cannot just be technical. That’s tablestakes. They must build a deep understanding of their customers’ customers, be superb at product development, marketing, storytelling and brand building, and be ready to move up the stack at any time. In other words, they must be able to play OFFENSE. That’s what greatness means in infra companies today.

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🔥 Exciting News in Semantic Caching Technology! 🔥 Redis's AI Research team has just released langcache-embed-v1 - a powerful new embedding model specifically designed for semantic caching of LLMs! Check it out on Hugging Face: https://lnkd.in/gGukYc3k Fine-tuned from gte-modernbert-base this model, maps sentences and paragraphs to 768-dimensional dense vector spaces with impressive performance metrics: 90% cosine accuracy 87% F1 score 92% average precision 💡 Why this matters: Semantic caching dramatically reduces redundant LLM calls, cutting both latency and costs in production AI systems. This purpose-built model optimizes specifically for caching scenarios rather than general embeddings. 📑 Technical highlights: 8192 token sequence length Built on ModernBERT architecture Optimized with Online Contrastive Loss Simple integration with sentence-transformers library Learn more about "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data" in their recent paper (arxiv:2504.02268). #Redis #AI #MachineLearning #SemanticCaching #LLM #Embeddings #AIPerformance #RedisEnterprise

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The strength of an AI agent is linked to the tools it has at its disposal. Harness is excited to be developing in Gemini CLI's open ecosystem. We're thrilled to be early partners introducing a Gemini CLI Extension alongside Harness MCP. This collaboration offers AI insights, cost analysis, and automated solutions right into your CI/CD processes, aiding teams in learning, improving, and speeding up each build. 📖 Check out Google Cloud's announcement: https://lnkd.in/gFU3GjUC

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