I have no confidence in any company's ability to be sensible about this sort of thing, and current LLMs are not old enough to be put into hackable contact with multi-ton anti-human missiles.
LLMS
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OpenAI’s Projected Training Costs and Scaling Forecasts
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OpenAI's own projections for burn shocked many :
– $35 billion in 2027
– $45 billion in 2028
(most will go into model training) Yet even numbers were already forecasted one year ago by @leopoldasch in Situational Awareness (table below) Keep calm and scale on. -
Why Language Models Hallucinate: Research Analysis
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Why Language Models Hallucinate Kalai et al.: https://
cdn.openai.com/pdf/d04913be-3
f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf
… #ArtificialIntelligence #DeepLearning #MachineLearning -
Gemini and OpenRouter Models with Extended Context Windows
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Gemini models are good for this! There’s also a new model on OpenRouter that you can try w/ a 2M token context window… should be more than enough
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Wrap-up Post on LLMs, AI Agents, and Machine Learning Insights
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That's a wrap! If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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8 Essential Skills for Production-Ready LLM Applications
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These 8 skills separate hobby projects from production-ready AI systems.
— Akshay 🚀 (@akshay_pachaar) 7 septembre 2025
Master them, and you'll build LLM applications that actually work in the real world!
Over to you: What other LLM development skills would you add? pic.twitter.com/dGiDUdPdRPThese 8 skills separate hobby projects from production-ready AI systems. Master them, and you'll build LLM applications that actually work in the real world! Over to you: What other LLM development skills would you add?
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Context Engineering: A Crucial Skill for Modern AI Engineers
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8️⃣ Context Engineering
— Akshay 🚀 (@akshay_pachaar) 7 septembre 2025
Context engineering is rapidly becoming a crucial skill for AI engineers. It's no longer just about clever prompting; it's about the systematic orchestration of context.
This post tells you more about what it actually means: https://t.co/Sf5iDWBnROContext Engineering Context engineering is rapidly becoming a crucial skill for AI engineers. It's no longer just about clever prompting; it's about the systematic orchestration of context. This post tells you more about what it actually means:
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LLM Observability: Implementing Tracing, Logging, and Dashboards
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LLM Observability No matter how simple or complex your LLM app is, you must learn how to implement tracing, logging, and dashboards to monitor prompts, responses, and failure cases. @Cometml
's Opik is 100% open-source solution for this. Check this -
LLM Optimization: Quantization, Pruning, and Distillation Techniques
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LLM Optimization An AI engineer must know how to cut costs by using quantization, pruning, and distillation to minimize memory use and inference costs. This helps you balance speed, accuracy, and hardware use. Here's a really goof article:
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LLM Deployment: Production-Grade APIs with vLLM
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LLM Deployment This skill lets you package models into production-grade APIs. Managing latency, concurrency, and failure isolation (think: autoscaling + container orchestration). You should check @vllm_project
, an open-source LLM inference engine.