great_psy

As ai replaces more and more jobs, there should be plenty of humans available to teach. Even if the teachers are ai assisted themselves.

At least link the one made by anthropic. Can’t say if this is better or worse since I’m on mobile.

Could this be extended to lean into teaching quantum physics ?

Include seconds/ sub seconds hand in the watch, and people will realize the watch face time + time it takes to read will never equal the watch face time.

You can know the exact time, by looking at the analog watch face, or you can measure it (convert it) but it will not be the same anymore.

How do you measure quality at scale ? Is there another model that determines if it adheres to codebase standard ?

I think you’re missing how complex international operations and optimization are.

Each country has their own laws around what uber is and isn’t allowed to do. This needs to be formalized in code. For example you actually call a taxi, though the uber app, and the amount you pay is per mile, not a fixed fare decided ahead of time. To add to this complexity, some cities will have their own laws. What happens if you take an uber from town a to b, where each one has different laws ? A lawyer probably has an answer but the app needs to adhere to that. On top of that laws change all the time.

Optimization, well you can always optimize something. speed, costs, paths etc. In a way this never ends.

I think the part we interact with as consumers is a tiny sliver of the complexity those services have to build and operate.

You’re right about performance degradation, but good luck trying to sell that as a product.

You can drive this car, but the last mile of this trip will use as much gas as the first 20 miles.

I think it’s in anthropics interest to keep this fact hidden from CEOs who push for ai adoption.

My view is that ai makes it very easy to pump out a lot of code, and that makes it too time consuming to just merge it without understanding it 100%.

The person pushing the PR is getting promoted because their are delivering so mane features, but after some time the codebase is a mess.

Sure the same person might end up dealing with some of the cleaning up, but more likely those refactoring tasks end up getting spread across the team as the need arises.

People are incentivized to push out code, which under human-written coding standard usually meant a level of pro-efficiency, so they should be rewarded.

But with the new model of pushing out code with ai, a better metric of a good engineer that should be promoted would be lines deleted, or something like that. Much harder to measure, and hard to justify to management.

Problem is that it does not produce better or more work, it actually shifts the work to a different/future engineer. Today’s slop which gets engineer 1 a promotion, is engineer’s 2 problem next month when they are oncall and the codebase makes no sense.

Your horse riding analogy, is like riding a horse into battle without your weapon because it’s slowing you down. Sure you got through the enemy first by outmanoeuvring, but you missed the point all together. Maybe you got a shiny medal but all your mates are dead.

This might be silly, but … since the assistant models are so much smaller than the full models. What if we just use those smaller models?

Any idea how much worse they will be ? Or is the issue that their error will really diverge as you accept more of their tokens?

If you have a link to the study you read, please share it.

This is the first time I heard about estimating tokens for a task.

I feel like you’re on to something. Management will pick this up, and make it part of the sprint planning.

Engineers will pull out their hair wondering how you can do that.

That’s like estimating how many CPU cycles a task will take. How many instructions will your laptop use while you work on something.

Would it be possible to do some prompt injection so this string has the same effect but from a website ?

So if you add some special string to the docs, it stops Claude ?

I just suggest you use Claude to write the script for you. And then you run the script with cron. Really it’s not any more time, just takes a different view on what the goal is.

I see people highly trained engineers spend hundreds of thousand of tokens doing what can reliably be accomplished with 150 lines of python.

I think the push from management for us to use AI has made it so we don’t have to be efficient with our consumption, so now we write md files which we feed to Claude in a loop instead of python and bash scripts to do routine tasks.

Give me a python script that takes a string representing the output of a sha256 algorithm and a plain string and compares if the sha256 of plain strig matches the sha256 provided.

I think the article is making the point that it is a cultural problem about cost cutting and short term thinking.

LLM Memeory (in general, any implementation) is good in theory.

In practice, as it grows it gets just as messy as not having it.

In the example you have on front page you say “continue working on my project”, but you’re rarely working on just one project, you might want to have 5 or 10 in memory, each one made sense to have at the time.

So now you still have to say, “continue working on the sass project”, sure there’s some context around details, but you pay for it by filling up your llm context , and doing extra mcp calls

Is there any provided reason from anthropic why they changed the tokenizer ?

Is there a quality increase from this change or is it a money grab ?

What kind of things would you use this to simulate ?

Is this to make sure services are wired up with permission and communicate properly before you deploy or what is the use case ?

Also, why not have a dev environment where you can test things on the real thing (ideally with smaller size instances to save $)

Does this post feel AI generated to anyone else ?

But to actually answer the question: I’ve been putting research paper pdfs in notebook llm , and turning them into ~40 minute podcasts which I listen to on my walks. Yes it’s shallow learning, and it might have some hallucinations in there but I wouldn’t have read some of those otherwise.

Thanks for working on this!

Is there any way to get those running on iPhone ? I would love to have the ability for it to read articles to me like a podcast.

The example on the website shows one to many as well: Wikipedia, axive article, etc along with a ratio how much it influences the chunk of the answer.

Yes and no.

Regular folks will not pay Anthropic, but NSA, NASA or research labs might.

I’m not implying this will be a good time for AI companies. I am saying AI as a technology can provide value without it being controlled by only 3 companies.

Can this method be extended to go down to the sentence level ?

In the example it shows how much of the reason for an answer is due to data from Wikipedia. Can it drill down to show paragraph or sentence level that influences the answer ?

Actually I think there’s another option.

There’s the scenario where LLMs get more efficient in size, and to get 2026 SOTA performance you will be able to get it from consumer grade laptop.

Sure with a 1000B parameter you will get better performance but the average person will have it write some python script, not derive new physics equations.

So in a sense the demand for LLM intelligence with reach a plateau (arguably we are there today for avg person) so there will not be any subsidy required, because the avg person will not need the latest and greatest.

There’s not the same demand pattern for something like uber.

Maybe I’m not creative enough to see the potential, but what value does this bring ?

Given the example I saw about CRISPR, what does this model give over a different, non explaining model in the output ? Does it really make me more confident in the output if I know the data came from Arxiv or Wikipedia ?

I find the LLM outputs are subtlety wrong not obviously wrong

Any place where we can test the llm output without loading it on n64?

Curious of what we can get out of those constraints.

The introduction to the article is not paywalled. But the actual 2027 ai story is paywalled

Can you provide some names of AI apps who’s revenue > cost ?

But leaving a light on 2x the time will equal very close to 2x the price.

Asking “what day is today” vs “create this api endpoint to adjust the inventory” will cost vastly different. And honestly I have no clue where to start to even estimate the cost unless I run the query.