TLDR AI 2025-06-04
NotebookLM Updates π, GitHub Copilot Spaces π», longer AI timelines β
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Deep Dives & Analysis
Why I have slightly longer timelines than some of my guests (17 minute read)
AI progress over the last decade has been driven by scaling training compute of frontier systems. This can't continue beyond this decade. After 2030, AI progress has to mostly come from algorithmic progress. However, since the low-hanging fruit will have already been plucked, the yearly probability of AGI craters.
Efficient Online Learning with TRL and vLLM (36 minute read)
Hugging Face integrated vLLM directly into TRL to reduce inefficiencies in training with GRPO, an online learning algorithm.
My AI Skeptic Friends Are All Nuts (16 minute read)
A veteran developer rails against the many highly-skilled programmers still rejecting LLMs based on experiences with early chatbots, missing how modern coding agents autonomously navigate codebases, run tests, and iterate on failures. He dismisses common objections: developers already review all code before merging regardless of source, and hallucinations become irrelevant when agents can automatically compile, catch errors, and retry until tests pass. LLMs might displace developers but software engineers similarly automated away administrative roles like travel agents, record store clerks, and countless other professions.
Vibe-Coding Ideas to Give Startup GTM Teams an Edge (10 minute read)
A startup advisor demonstrates building a polished ROI calculator for a manufacturing SaaS company in under two hours using Bolt.new, transforming a spreadsheet into an interactive tool that frames value for executives. Other examples include conference scraping tools, meeting prep dashboards, and feature prototyping that previously required engineering teams or expensive agencies but now cost around $70 in monthly subscriptions, arguing this enables non-technical teams to prove value and move faster than competitors.
Predicting and explaining AI model performance: A new approach to evaluation (7 minute read)
Microsoft researchers developed ADeLe, a framework that predicts and explains AI model performance on new tasks by assessing them across 18 cognitive and knowledge-based scales. ADeLe uncovered limitations in current benchmarks and created detailed ability profiles for various LLMs, highlighting discrepancies in strengths, weaknesses, and specific abilities. The framework showed an 88% accuracy in predicting AI success, promising improvements in AI evaluation, policymaking, and deployments.
When Will We Pay a Premium for AI Labor? (2 minute read)
AI agents often outperform humans at a fraction of the cost, not yet commanding premium prices due to ongoing technical evolution and perceived risk. Waymo, achieving significant safety improvements, remains cheaper than other options, reflecting a common pricing strategy across startups. However, in scenarios where AI's constant vigilance and processing power are crucial, premium pricing might emerge.
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