Developer releases tracker showing AI model training cutoff dates and staleness
A live dashboard compares 20 models' release dates against their training data cutoffs, revealing how outdated models can be on launch day.
What to know
- A new tracker displays the gap between when AI models are released and when they stop ingesting training data—some models are months outdated before they ship.
- Testing showed frontier models generally decide when to use web search, while weaker models confidently answer outdated facts and occasionally cite dead leaders.
- Commenters report the problem has diminished as models improved at tool use and deciding when to search, though some still face staleness with specialized domains.
The dispute Whether training data staleness remains a practical problem: some argue modern tool use and web search have solved it, while others report it still affects specialized domains and questioning the fundamental design of static-trained models. · positions read across 12 posts and comments
Training staleness is less of a problem now due to improved tool use and web search.
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“I haven't noticed this problem in months. Model cutoff seems to be less of a problem these days.”
gjskngnf · Hacker News ↗
Staleness still matters for specialized domains like programming and AWS.
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“Gets me with AWS stuff on claude all the time, fortunately there's a official amazon MCP for their docs which helps a lot, but I still have to occasionally tell it to check the docs/mcp.”
NegativeLatency · Hacker News ↗
Models should be able to learn continuously rather than relying on static training data.
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“To me, intelligence or an intelligent entity should be able to learn from its mistakes and learn new things on its own. Having to start from scratch to teach an AI new facts or new skills is not very intelligent IMO.”
SirMaster · Hacker News ↗
joozio Developer/tracker creator
How it unfolded 2 developments, newest first · click a bar or a number to jump articlespostscomments
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Commenters report models have improved at deciding when to search
Multiple Hacker News commenters note that the stale data problem has diminished as models have gotten better at tool use and deciding when to research. One commenter reports models now correctly handle outdated information through web search and context rather than relying on training data.
“I haven't noticed this problem in months. Model cutoff seems to be less of a problem these days.”
— gjskngnf -
It's a "problem" of compute, I think. If you query without an account on ChatGPT you will see the model look up less stuff and research less, than when you have a paid account and choose "medium" or "high" in the effort slider.Which makes sense, because of you have looked into search and crawlers you notice that search is actual quite expensive…
2 more of the top 3 · 12 posts in this stretch
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For general purpose use this is interesting, but if I'm just using an LLM for coding, does this matter at all? I would hope something like a new java version after a model's publish date can be handled and understood by the model through tool calls and context even if it's not explicitly in the training data, the same way the LLM doesn't have my…
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I remember when the US captured Venezuelan president Maduro, and when I posed a prompt related to this, the model said that’s pure fiction. I told it to double check. Still didn’t want to entertain the idea. It only acquiesced when I specifically directed it to check Reuters. I haven’t noticed this problem in months. Model cutoff seems to be less…
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Testing reveals weak models answer outdated facts, name dead leaders
The creator tested 16 models with web search tools over 2000 calls. Frontier models almost always correctly decided when to search, while weaker ones stated settled facts that had changed without checking. Five models named a dead man when asked who the king of Norway is.
“I gave 16 of these models a search button and asked who the king of Norway is. Five named a dead man.”
— joozio -
background
Developer launches stale.jock.pl tracker for AI model training dates — A Hacker News post introduces a live dashboard tracking release dates and training cutoff dates for 20 current models across 8 labs. The site displays data for models from Anthropic, Google DeepMind, Meta, OpenAI, and xAI, with 10 of 20 models carrying published cutoffs.
What people are saying 9 voices from 1 site · best of 12 · verbatim
- Sep 18
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I feel like with web search, exa and fetch built into harnesses this is no longer a problem. Haven't faced this issue in months.
- Sep 16
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Qwen-3.8 for me automatically set copyright footer on a website to 2025 and thought Astro 5.x is the latest version which first came out in December 2024
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ChatGPT once told me I was the target of a sophisticated nation state misinformation campaign when I linked it a Reuters article
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This is one of the things that bothers me about AI.To me, intelligence or an intelligent entity should be able to learn from its mistakes and learn new things on its own. Having to start from scratch to teach an AI new facts or new skills is not very intelligent IMO.
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Gets me with AWS stuff on claude all the time, fortunately there's a official amazon MCP for their docs which helps a lot, but I still have to occasionally tell it to check the docs/mcp.
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Came here to say the same thing. Models used to rely heavily on world knowledge from their training data. They are now much better at tool use and deciding when to research a topic, rather than just answering from memory.I wonder how much that extends to using LLMs for programming. I assume most knowledge of programming language syntax still comes…
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Pre-AI internet data is like pre-war steelThe slop would multiply if we keep feeding it to new models in a loop
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It still matters, but in the age of good reasoning, tool use, and web search, this is much less of a problem than it used to be.
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Do people prefer the new flat style LLMs are producing? I don’t mind it as much as the gradient theme they were pumping out previously.