Can we just maybe get "line go up now but might not forever?"
Who's Who of AI
Isaiah Bishop
Why they matter
Tracked through public AI activity and peer connections inside the directory.
- AI signals
- 13 past 30d
- Sources
- 13 distinct domains
- Discussões
- 150 past 30d
- Latest signal
- 2d ago
ML Eng. and econometrics. Honorary Commodities Trader**. more left-posting than normal.
**Regrettably degen trading on here, im sorry
Views dont reflect my employer
What they're sharing
When AI builds itself anthropic.com
openai.com
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Qwen/Qwen3.8-27B · Hugging Face huggingface.co
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Claude Fable 5 and Claude Mythos 5 anthropic.com
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How LLMs Distort Our Written Language arxiv.org
Generative artificial intelligence creates delicious, sustainable, and nutritious burgers | npj Science of Food nature.com
wsj.com
openai.com
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nytimes.com
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openai.com
How Apple Stumbled Into AI Hardware Success With the Mac theinformation.com
Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns arxiv.org
Articles & links
Isaiah Bishop reposted
Astra, the next version of GPT, solves 10 unsolved problems in math Each of these problems has had little to no progress in ten years or more openai.com/index/ten-ad...
Isaiah Bishop reposted
Qwen3.8-27B is out! On the benchmarks they included, is close to or exceeds Opus 4.6 huggingface.co/Qwen/Qwen3.8...
Isaiah Bishop reposted
www.anthropic.com/news/claude-...
Isaiah Bishop reposted
Fantastic paper demonstrating how LLM editing is gradually distorting our writing and the way we think to write: arxiv.org/abs/2603.18161
AI Weekly's analysis
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- Heavy LLM users produced nearly 70% more essays that stayed neutral on the topic question, and reported the writing felt less creative and not their own.
- Even when asked only to fix grammar in 2021 human-written essays, LLMs significantly altered the semantic meaning of the text.
- About 21% of peer reviews at a recent top AI conference were AI-generated, and those reviews scored papers a full point higher on average.
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Isaiah Bishop reposted
Finally, AI finds its ultimate use case. A diffusion model trained on burger recipes "discovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition." ASI= automated slider intelligence www.natur…
AI Weekly's analysis
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Isaiah Bishop reposted
In addition to CRO, COO, and a very high up advisor to the CEO, OAI has now lost the guy in charge of building its data centers.
AI Weekly's analysis
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Isaiah Bishop reposted
Shots fired. openai.com/index/our-de... "We are making this choice because we cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk's companies violating contracts."
Isaiah Bishop reposted
It's interesting. Don't have an entirely clear picture from just this, but I would have questions about the counterfactuals here that I doubt could be answered (would these groups' tactics be substantively different without this access? etc.). www.nytimes.com/2026/07/10/u...
Man look at the change in the sales pitch openai.com/index/gpt-5-6/
Isaiah Bishop reposted
both OpenAI and Anthropic are using Mac Minis and Mac Studios for reinforcement learning Somehow Apple backed its way into being a key AI company?? www.theinformation.com/articles/app...
AI Weekly's analysis
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- OpenAI has bought tens of thousands of Mac minis and Mac Studios in recent months to run reinforcement learning and train computer-use agents.
- Apple refreshed both machines earlier than its usual autumn window and pitched linking multiple Mac Studios into one system for large frontier AI models.
- Higher-RAM Mac Studio orders now quote delivery waits stretching from about two weeks to nearly two months.
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Isaiah Bishop reposted
I'm so happy when other people write papers on nondeterministic factors in training. embrace the chaos
AI Weekly's analysis
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- Emergent capabilities arise stochastically: the same model can gain or fail to gain a capability depending on its random initialization.
- Researchers used Pythia models from 14M to 410M parameters to show attention pattern learning is the key bottleneck to capability emergence.
- More attention heads improved learning efficiency; MLP-Mixer outperformed standard transformers on tasks with complex attention patterns.
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Their own posts
Recent commentary
I think i have approached my limit on hearing about AI just generally in my everyday life when i'm not seeking it out
Honestly, i think the biggest threat from AI safety is that its now not unusual to have a thing on your system doing a bunch of shit you don't know about
Im still kind of in awe of how bad the LLM business people want consumer facing chatbots to do all purchasing. I just dont really get it. it sounds very alexa to me
I dont see a reason to think AGI is impossible, but i do think a lot of people are getting over their skis in part because the anti-AI backlash was pretty dumb
If both anthropic, and OpenAI gor from 10 bil ARR in Q1, to 100-200 bil ARR in Q4. surely we are having a hole somewhere thats being created
A guy made a homeautomation-based weather station and it looks really good, and all the reddit comments are like "actually you shouldve tagged this AI??? disgusting"
Contrary to what a lot of people think about LLM code quality, what its really enabled us to do is parallelize tech maintenance. And as a result our repos are so much cleaner
Have openAI and Anthropic cracked continual learning? Well its simple, if its what you expect they wouldn't need to keep raising money
the matt levine thing that agents might lead to better refinancing and thats bad for investors is really interesting because it seems infinitely more plausible than AI for every purchase
it does kind of feel like a lot of things have become correlated in ways that are undesirable. Idk how to say this but like it was useful when bonds and stocks werent correlated. Useful when US and ex-US werent just different flavours of the AI trade. And like same politically.
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