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Daily Edition · Page 3

THE DAILY INDEX

Technology, AI, software, security, crypto and business

HEADLINES & LAUNCHES

TLDR AI

TLDR AI

META TESTS AVOCADO 9B, AVOCADO MANGO AGENT, AND MORE

Meta's Avocado model has been pushed back to at least May as it still falls short of leading systems from competitors. The company appears to be running parallel experiments with multiple Avocado variants. The model appears to be able to solve complex math problems that earlier Llama models could not, but these problems have already been solved by other labs months earlier. Meta's AI leadership has reportedly discussed temporarily licensing Google's Gemini technology. Some requests within Meta AI are already being routed through Gemini models.

links.tldrnewsletter.com · 2 min

TLDR AI

FUNCTION CALLING HARNESS: FROM 6.75% TO 100%

AutoBe is an open-source AI agent that takes a single natural language conversation and generates a complete backend. qwen3-coder-next has a 6.75% function calling success rate when asked to generate API data types for a shopping mall backend. AutoBe boosts that success rate up to over 99.8%. It uses a harness where type schemas constrain outputs, compilers verify results, and structure feedback pinpoints compactly where and why something went wrong so the agent can correct itself. This post dissects the engineering behind AutoBe.

autobe.dev · 32 min

TLDR AI

AI'S CAPABILITY IMPROVEMENTS HAVEN'T COME FROM IT GETTING LESS AFFORDABLE

AI's capability improvements at the frontier have not led to increased inference costs relative to human labor. Despite rising per-task inference costs, current models achieve tasks at roughly 3% of human costs without any upward trend in median cost ratios. Models can continue advancing even under strict cost constraints, enabling profitable automation with AI cost ratios remaining well below human levels.

lesswrong.com · 12 min

TLDR AI

WHAT PRETEXT REINFORCED ABOUT AI LOOPS

Pretext is a fast, accurate, comprehensive text measurement algorithm that can lay out web pages without leaning on DOM measurement and reflow. It was created using AI agent workflows. The particular loop that was used in developing the tool (constrain -> measure -> isolate -> classify -> test -> reject -> keep only what survives broad pressure) made the engineering rigorous. This article analyzes the loop to see what makes it so successful.

nibzard.com · 5 min

TLDR AI

THINGS I LEARNED AT OPENAI

OpenAI alumni emphasize the significance of creating effective evaluations and benchmarks, noting that the best benchmarks drive collective optimization efforts. Post-training data design and model alignment are critical for unlocking new AI capabilities, particularly in subjective attributes like empathy or creativity. Fast iteration, choosing the right problems, and leveraging internal tooling are key competitive advantages in AI research.

semaphore.substack.com · 7 min

TLDR IndexPage 3