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- GitHub shake-up | Reddit blocks Wayback | GPT-5 rollout tweaks | Datumo funding | LLM flaws
GitHub shake-up | Reddit blocks Wayback | GPT-5 rollout tweaks | Datumo funding | LLM flaws
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Thomas Dohmke, CEO of GitHub, announced on August 11, 2025, that he will step down and remain through the end of the year to lead a new venture.
Microsoft will not appoint a new CEO; GitHub’s leadership will now report directly to Microsoft's CoreAI team as part of deeper integration.
The shift reflects a strategic pivot to embed GitHub within Microsoft’s broader AI and developer tools ecosystem.
Reddit will block the Internet Archive from indexing most of its site—excluding only the homepage—to prevent AI firms from scraping its data for unauthorized use.
The restriction aims to protect user privacy and regulate compliance with platform policies after detecting misuse via the Wayback Machine.
This action is part of Reddit’s broader effort to control data access, which includes modified API policies and licensing discussions.
OpenAI’s GPT-5 deployment has encountered challenges, prompting Sam Altman to revert to legacy models (like GPT-4o) and adjust rollout parameters for Plus users.
The company is working on stabilizing infrastructure, refining personalization, and deciding how to moderate immersive AI experiences amid competition.
This reflects an iterative, feedback-driven launch strategy to correct course and deliver a reliable GPT-5 experience.
Datumo secured $15.5 million in funding to expand its no-code LLM evaluation platform, Datumo Eval, aimed at non-developers like trust, safety, and compliance teams.
The platform automates test data creation and evaluations for unsafe, biased, or incorrect model outputs—no manual scripting needed.
Datumo is positioning itself as a competitor to Scale AI, with plans to support its R&D and expand globally across South Korea, Japan, and the U.S.
LLMs’ “simulated reasoning” abilities are a “brittle mirage,” researchers find
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A study finds that chain-of-thought LLMs struggle with genuine logical inference and instead generate fluent but nonsensical responses.
Researchers describe LLM reasoning as a “brittle mirage”—good at sounding logical but faltering when asked to generalize beyond training examples.
The findings highlight that LLMs simulate coherent text rather than perform true deductive reasoning.
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