W&B didn't need to raise the round, obviously. It's grown substantially on the back of the machine learning—including LLMs. OpenAI is a customer of W&B. So it was a situation where it might as well ask for a big number, and if it didn't stick, that's fine.
AI
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Weights & Biases $2B valuation amid AI startup funding trends
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The best example of this I've heard is Weights & Biases, which held some discussions earlier this year for a round that would value it at $2 billion. The conversations that picked up was that the price was high—even though startups like Pinecone were fetching 200x+ ARR multiples.
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Abacus AI Case Studies: Enterprise-Scale Machine Learning Platform
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Explore @AbacusAI Case Studies. See how their Cloud #AI platform handles all aspects of #MachineLearning and #DeepLearning at Enterprise Scale: https://
abacus.ai/casestudies #BigData #DataScience #MLOps #EnterpriseAI #PredictiveAnalytics #AnomalyDetection #NLProc #ComputerVision -
Scale Donovan: First LLM Deployed on Classified Networks
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Scale Donovan is the first LLM deployed on classified networks. Unlike other companies, we are actually on the ground with operators today. We’re seeing a lot of AI tourists pretending to be AI natives. They’re not selling solutions, they’re selling vaporware.
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ML Startups Pre-GPT Era: VC Shift to LLM-Powered Ventures
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Today's issue of Supervised is about startups that jumped on the machine learning hype train… before the GPT era of machine learning. 2020-22 investments with eye-popping valuations in ML startups. Now VCs have shifted eyes to LLM-powered startups.
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5 Stages in Design Thinking Process for Innovation
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The 5 Stages in the Design Thinking Process https://
interaction-design.org/literature/art
icle/5-stages-in-the-design-thinking-process
… @ASMEdotorg @3DSNorthAmerica @cyngn @MargaretSiegien @3DSdelmia @3DStherese @Cindybolt61 @fogoros @DrFerdowsi @CRudinschi @PawlowskiMario @IIoT_World @MEngineeringMag #Science #Engineering #Technology #SET -
New experimental feature added to core library for community feedback
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We think this is a really promising direction, so we're adding it to the core library But we also think there is a lot of work to do here, so we're adding it to the experimental module as we rapidly iterate In that vein, would love all thoughts/community contributions!
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LangChain Plan and Execute Agent Documentation
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Python Docs: https://
python.langchain.com/en/latest/modu
les/agents/plan_and_execute.html
… JS/TS Docs: https://
js.langchain.com/docs/modules/a
gents/agents/examples/plan_and_execute_agent
… Blog: -
LangChain Agents Enhance Focus on Complex Task
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Up until now, agents in LangChain have followed the algorithm of: – take user input
– think about action to take
– take action and observe response
– repeat until done This is great for simple tasks, but for more complex tasks we've noticed the agent losing focus -
Two-step planning improves language model task performance
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To fix this, we're separating the planning and execution into two steps: – plan what to do
– execute on each step This usually leads to more calls to the language model… but helps it stay on track for more complex tasks