A new survey from arXiv (2608.14797v1) identifies three emerging paradigms in decoding methods for large language models (LLMs) and large vision-language models (LVLMs), offering a systematic review o…
Chandra Sripada, a professor of philosophy and psychiatry at the University of Michigan, argues that large language models (LLMs) exhibit cognition more similar to human thought than commonly assumed,…
The introduction of LLMs into the software engineering lifecycle has shifted the DevOps threat model from scaling to security, as AI-generated code bypasses traditional review processes and introduces…
A 2.8M-parameter transformer achieves 0.01-bit entropy agreement with the Bayesian optimum for model selection in Bayesian wind tunnels using fixed-point-free involutions, but fails completely with op…
An exposed WebDAV server containing over 1,000 artifacts was discovered by an MDR team, revealing a fully operational malware delivery lab where attackers used generative AI to systematically test del…
Resolve argues that link building must shift from chasing volume to earning authoritative backlinks, brand mentions, and media coverage that signal trust and credibility to search engines and AI syste…
A developer launched Kettio, a tool that uses machine learning to predict audience purchase intent from ads, landing pages, and product shots, inspired by research showing LLMs can reproduce human buy…
A writer shares six story prompts, three of which explore AI themes, inspired by Jorge Luis Borges' ideas about provenance and authorship in the context of large language models. The prompts aim to in…
A developer revisiting *The Pragmatic Programmer* in the age of AI finds that core principles like the Knowledge Portfolio, orthogonality, and DRY remain essential, but their application has shifted. …
AI agents perform better when using the web's accessibility tree instead of screenshots, reducing latency from 2-5 seconds to under 500ms and cutting token costs by an order of magnitude, according to…
Researchers introduced the Human-Centric Reflective Architecture (HCRA), a framework for human-AI collaborative decision-making that models the task as a stochastic game and integrates human-calibrate…
AI startups should shift from selling productivity-enhancing software to selling completed work products, opening new verticals and pricing models. EvenUp exemplifies this by selling AI-generated dema…
AI agents suffer from limited memory due to the stateless nature of large language models, but retrieval-augmented generation (RAG) offers a solution by offloading long-term memory to external storage…
Researchers have identified Mandate Salience Decay (MSD) in large language models used for financial AI, causing them to lose sight of initial behavioral mandates like 'preserve capital' over time. Ev…
Researchers introduced Explanation Quality Markers (EQMs), a set of sixty reasoning patterns scored by large language models, to measure judgment quality in natural-language explanations. In a pre-reg…
Researchers introduced multi-agent deliberation frameworks inspired by courtroom procedures for legal reasoning tasks using large language models. The multi-agent approaches achieved comparable overal…
Vidilearn, developed by Alfo-Tech-Lab, is evolving from a YouTube transcript extractor into an AI knowledge ingestion and retrieval gateway. It processes content from YouTube, PDFs, and web pages thro…
At the VibeKode Munich conference, two parallel tracks revealed a stark divide: one focused on traditional machine learning, the other on AI-driven development with agents and autonomous pipelines. A …
A developer reflects on the pitfalls of trying to learn everything at once in software engineering, realizing that constant context-switching and overplanning led to mental exhaustion and unfinished p…
JD.com launched Oxygen AIIC, an industrial-scale platform using LLMs/VLMs to manage item knowledge for tens of billions of SKUs. The platform achieves 94.2% precision and 82.8% recall in knowledge pro…