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The tweet points out that multimodal large language models capable of processing smells do not yet exist, highlighting a current limitation in AI technology.
Discusses how voice agents lose paralinguistic signals like tone, hesitation, and speaker identity when transcribing to text, and questions whether and how these features are captured and used downstream.
Gary Marcus highlights Terence Tao's lecture on AI and mathematics, noting risks like 'proof indigestion' and that AI may only excel at certain mathematical tasks, not theory-building.
A user jokes that ChatGPT 5.6 couldn't correctly add numbers summing to 100, getting 81 instead, highlighting AI limitations with basic arithmetic.
The article discusses why LLMs cannot learn from user interactions and lack a deterministic truth layer, proposing that a dynamic knowledge graph could reduce hallucinations and improve performance in high-stakes fields.
This article argues that current AI computer-use agents often bypass the actual user interface by directly accessing APIs or scripting, leading to inflated benchmark results and unreliable real-world performance. It suggests that ignoring the interface is a flawed approach that does not generalize to messy GUI environments.
Avichal Garg of Electric Capital identifies three competitive advantages that AI cannot replicate, with the third not being a skill.
The author shares two years of experience building a platform with AI, identifying six recurring failure modes (Band-Aid, Assumption, Drift, Hallucination, Lack of Common Sense, Path of Least Resistance) and argues that even as models improve, these failure modes persist, becoming harder to detect.
The article compares the performance of OpenAI GPT-5.6 Soul and Anthropic Claude Fable 5 in physical 3D printed part replication and autonomous magazine production. Soul slightly outperforms in speed and design precision, but both require significant human intervention in complex real-world tasks, exposing the limitations of current AI in real-world manufacturing tasks.
A developer built a full 3D open-world racing game with AI assistance, detailing where AI excelled (boilerplate, isolated systems) and where it failed (spatial reasoning, system integration, game feel). The game is live with real daily players.
An analysis discussing how reliance on algorithm-driven discovery may diminish our capacity for unanticipated exploration and insight.
An analysis questioning why progress on Deep Research AI products has stalled since their impressive launch in February 2025, noting that known weaknesses like hallucinations and unreliable source verification persist despite incremental improvements.
An opinion piece arguing that truly autonomous self-driving cars are not yet achieved, criticizing Tesla's overpromises and highlighting the limitations of current AI systems like large language models.
This article discusses the limitations of AI models in maintaining context over long conversations, highlighting recency bias and the distinction between context window size and actual comprehension. It suggests practical workarounds like restating constraints and using running context documents.
A new study tests LLMs across 28 real-world studies and finds they match human majority only 53% of the time, no better than random, challenging the trend of using LLMs to replace human feedback.
A guide identifying seven common traps when relying on LLMs blindly, such as hallucination, sycophancy, and prompt injection, with practical advice on how to avoid each.
Gergely Orosz reflects on areas where AI has not accelerated progress, such as launching financial services across regions and supporting old hardware, noting that software was never the main bottleneck.
This article explores why AI systems still produce incorrect outputs even when their underlying knowledge base appears to be accurate.
The article criticizes the business model of AI companies that replace middle managers with AI, noting that licensing fees could match employee salaries and that humans inherently need interpersonal interaction, making large-scale replacement unrealistic.
Yann LeCun observes that despite AI progress, we still lack level-5 self-driving cars and domestic robots capable of performing like a human teenager or 10-year-old.