Three Predictions: 1. Some form of AI, probably neurosymbolic in nature, will come that is far more economical and data- and energy-efficient than LLMs, and it will make an absolute fortune. 2. LLMs, on the other hand, will never be all that profitable (aside from the chip
RESEARCH
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AI Answers Beat Law Professors in Blind Stanford Study
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A blind Stanford-led study of nearly 3,000 anonymized matchups found law professors across 16 schools preferred AI-generated answers to student contract-law questions over those written by fellow professors 75% of the time, and judged the AI responses far less likely to be
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Gary Marcus’ technical claims 90% correct, neurosymbolic AI in vogue
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ps someone did a study and found that my technical claims were 90% correct; neurosymbolic AI which I long advocated for is now finally in vogue, etc.
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Researchers propose CODA to keep data on chip longer for AI training
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Can AI training be fixed by keeping data on the chip longer? Researchers from MIT, Princeton, Together AI, and Meta introduce CODA — a new way to rewrite Transformer building blocks as GEMM-plus-epilogue programs. Instead of moving large intermediate tensors back and forth to
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YOLOE-26: Instance Segmentation via Text Prompts Without Training
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Instead of training a new model, what if you could just tell your AI what to look for?
— Satya Mallick (@LearnOpenCV) 4 juin 2026
That's YOLOE-26 with text prompts. Hand it a list — Person, Helmet, Safety vest — get instance segmentation masks, not just boxes.
Full tutorial: https://t.co/AZyMQYN0Tw pic.twitter.com/hty15tPBH6Instead of training a new model, what if you could just tell your AI what to look for?
That's YOLOE-26 with text prompts. Hand it a list — Person, Helmet, Safety vest — get instance segmentation masks, not just boxes.
Full tutorial: https://
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Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
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Bootstrap Your Generator Unpaired Visual Editing with Flow Matching
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GenAI ROI positive but Bain report opaque on metrics
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There are lots of issues with GenAI implementations but surveys seem to suggest broad positive ROI. Data & stories about AI failures are real and useful but need to be clearer about what they are measuring. The Bain report is super odd & opaque about that. https://
bain.com/insights/your-
ai-budget-is-growing-your-returns-arent-heres-why/
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Prior ML data issues could hinder AI investment, citing fake MIT study
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I actually read this & it is super weird, it appears to be an argument that prior machine learning systems (not generative AI) did not generate savings due to data issues so that will lead to a lack of investment into current AI systems Also it cites the mostly fake “MIT study”
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Ideogram 4.0 released: open image model with 2K resolution and layout control
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🆕The best open image model just dropped and you can run it on your own hardware.
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 4 juin 2026
Ideogram 4.0 is here!
Here's what changed:
▪️ Native 2K resolution
▪️ Best-in-class text rendering (finally, readable text in AI images)
▪️ Bounding-box layout control – place elements exactly… pic.twitter.com/STSqDQ6i9PThe best open image model just dropped and you can run it on your own hardware. Ideogram 4.0 is here! Here's what changed: Native 2K resolution Best-in-class text rendering (finally, readable text in AI images) Bounding-box layout control – place elements exactly
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Step-by-step LLM engineering projects roadmap
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Step-By-Step LLM Engineering Projects Roadmap – Build a tokenizer
– Learn embeddings
– Implement RoPE / ALiBi
– Hand-wire attention
– Build MHA
– Build a Transformer block
– Train a mini-former
– Compare objectives
– Build sampling
– Speculative decoding
– KV cache
– MQA / GQA /