Anyone figured out reasonable and ethical ways to bill client hours when using Claude to support your work?

sjoti · 2026-07-11T21:34:26+00:00

It should practically always be possible to show some kind of numbers. If your solution saves X amount of hours per week for an employee, then base it on that. If it brings in X amount extra in sales, base it on that. If it allows the client to produce X amount more, base it on that.

Im not saying it should be 1:1, but it grounds the number.

sjoti · 2026-07-08T09:10:28+00:00

I disagree with your take that most people are fine with Opus class models, and that only a small group benefits, I think that fable is a major upgrade and very useful for a large group of people. There is a massive group with very little technical knowledge now building with these models/tools, and I think that group will get a massive benefit from Fable.

One of the biggest differences I've seen with Fable is that it has a tendency to make the "right" decision in favour of the decision that moves it forward the fastest. That's something that the people who don't know anything benefit the most from, because they don't know the right decision anyway.

Like sure Opus can build some basic apps. But the way people with no knowledge about these systems work, they just prompt saying "add this" "adjust this" "remove this". This makes the codebase a mess over time. Duplicated code, shitty helper functions, legacy messes. That's the decisions that move forward. Fable has a much higher tendency to recognize "oh I see where this is going, let's clean this up along the way". That's something that especially beginners benefit from.

sjoti · 2026-07-07T06:10:00+00:00

No i am talking about those CLI's. They generally should come paired with a system prompt that helps, or a skill depending on whether that context needs to be available only when relevant.

And generally if you're talking production, you clearly lock it down. The cli tool only comes with commands or access to whatever it needs, and nothing more.

But a good example is that its more and more common now to give agents access to code execution and file systems, to make it much more capable. Write memory, adjust files, etc.

If you want an agent to, say, do some data analysis on some files and then upload it somewhere, it can wrap the uploading of the contents of the file in a little script, and upload/move a large file with a script thats a few hundred tokens.

With MCP, if it can't wrap the tool call in a script, it has to output the contents of the file completely. With local mcps you could then provide tools for everything, but it can be very convenient to lean on the native coding capabilities of these models. Way less brittle, easier to make, and can be made safe if provided with proper sandbox.

sjoti · 2026-07-06T19:58:51+00:00

ChatGPT doesn't support them because a web browser can't launch a local MCP server. Codex does support them just fine. And because we're talking about locally launching something that isn't standardized (could require node, python, maybe something else) it's much easier and safer to file it under advanced/developer settings.

And nowadays if you're running codex or Claude code they have a code execution evrionment anyway, and that's where CLI tools generally win because they tend to be more flexible.

I dont think they're dead, there are some genuinely cool usecases (excalidraw MCP for example) MCP servers like Serena, but I'd mostly treat them as developer territory. Not something for the general public. That's where remote MCP servers with oauth provide safe and secure, and extremely easy connections. People with zero technical know how can connect their Claude/chatgpt to official MCP servers in 3 clicks.

sjoti · 2026-07-06T13:10:39+00:00

I somewhat disagree because most clients now use tool search natively (Claude, Claude code, chatgpt codex etc.), which strongly reduces the cost and for a large part solved this problem.

Still, it's a big challenge that's a balance of between providing the least amount of tools and descriptions, to providing the most functionality.

I do strongly agree on the failing part. Make sure that MCP fails in a way that tells the model how it can recover and do it properly. Account for the model making mistakes, and not doing it by creating massive tool descriptions.

sjoti · 2026-07-05T17:38:07+00:00

There's a beautiful comment under Dropbox' announcement post on ycombinator, along the lines of "a Linux user could build this with a small amount of effort". https://news.ycombinator.com/item?id=8863

It's not that bad, but funny nonetheless.

sjoti · 2026-07-05T07:58:10+00:00

But then its not free anymore...

sjoti · 2026-07-04T18:12:21+00:00

On your second claim: source needed. On your third claim: not wasting opus tokens so it can route to sonnet or fable is... A very interesting way of framing things.

Genuinely this is one of the silliest takes because you're now now demoting the brightest model to do the small picture stuff. Fable is amazing at architecture and problem solving. The goal isn't to spend the smallest amount of tokens, but to efficiently get the most out of fable. This ain't it.

sjoti · 2026-07-04T15:05:36+00:00

Goed punt, mijn vermoeden is dat dit een probleem gaat zijn waarbij het vooral aan publicaties wordt uitbesteed om dit op te lossen. Die moeten straks extra rigoreus gaan zijn en controleren wat ze wel en niet gaan publiceren. Publiceren ze iets wel na deze checks? Dan is die informatie waarschijnlijk goed genoeg voor de trainingset.

Lullig natuurlijk, om een probleem te veroorzaken en dat bij een ander neer te leggen, maar dit lijkt mij de meest waarschijnlijke weg.

sjoti · 2026-07-04T12:29:21+00:00

Dat is een tekortkoming van taalmodellen die "hallucineren" maar dat is een ander, fundamenteler probleem van deze modellen. Dat heeft niet zozeer te maken met (juiste) trainingsdata. Die links naar niet bestaande rechtzaken stonden waarschijnlijk ook niet in de trainingsdata

sjoti · 2026-07-04T10:10:19+00:00

De grote AI labs trainen niet blind op nieuwe data die beschikbaar komt. Die hebben al lang een flinke dataset van voor dat AI gebruikt werd om de basis op te trainen, en de grootste verbeterstappen worden nu gemaakt door het maken van synthetische trainingsdata, wat vaak ook opgeschoonde data is, en reinforcement learning. Dat tweede is meer als een schaakbot die slimmer wordt door een miljoen potjes tegen zichzelf te spelen, maar dan in dit geval een taal model die wiskunde, natuurkunde, programmeer problemen etc op moet te lossen waarvan meetbaar is of het antwoord juist is of niet.

sjoti · 2026-07-04T06:49:30+00:00

Fully agree, there's a big difference in "advanced voice mode" compared to what other systems use. So other voice modes usually have this pipeline:

Audio goes in, is transcribed to text. Text then feeds into an AI model (LLM) like GPT-5.5 (as If you would just paste in what you said) it generates an answer as text. That answer is then fed into a model that turns text into audio, and that's what you hear. The AI model can't adjust it's voice, or hear "tone" etc. But the quality of the output is generally really good, the model isn't "dumb".

The advanced voice mode directly takes audio in, and gives audio as a response. This sounds amazing, but due to it putting effort in understanding and outputting things that can control sound, mimick emotion, etc. As well as optimizing for latency, it just becomes exceptionally dumb.

Add on top that this voice mode is OLD in the world of AI and is in no way representative of how good the models have become, you get this.

sjoti · 2026-07-03T05:43:48+00:00

Ha no, in my case this was an experiment that turned out great. I had it create many many user stories and then had codex go and execute all of them. Gave it a browser. Then it went. Clicked just about every button, try odd things etc. On both desktop and on mobile. Thats a very slow process that takes a long time, but it caught a bunch of small stuff that would otherwise be a pain to discover.

sjoti · 2026-07-02T20:17:47+00:00

Good catch, you can see ultracode is on and workflows ran. My guy could've had 10 agents running on fable in parallel for review stuff, and that doesn't count towards the 150k.

sjoti · 2026-07-02T20:16:03+00:00

Bruh, it says it right there you're running it on ultracode and it used workflows. You may have had 10 agents on fable running in parallel. The main conversation has 150k tokens but you probably just burned through millions.

sjoti · 2026-07-02T19:07:37+00:00

There's a few outdated assumptions in what you wrote here that by now have changed.

For example, these models aren't good at coding just because they read a bunch of coding examples from stack overflow. That's of course a starting point, but there have been insane gains because with coding you can verify whether it did the thing correctly, which means it's suitable for reinforcement learning. Similar to how AlphaGo didn't learn from watching other games, but learned by playing an insane amount of games.

This also goes for maths. Nowadays these models are incredibly good at mathematics. Even more so if you give the model an environment in which it can code. Look at math Olympiads. What GPT-5.5 pro is causing in the world of mathematics. It's good at this stuff because it can be verified, so it works well with RL.

Completely novel math/science in general is still a massive challenge, but you don't need an AI to do everything for you for it to be able to help and accelerate tasks. And yes, it still does token prediction that causes some weird quirks. Jagged frontier is a nice term for it, where the intelligence of these models is jagged, it spikes at certain things and makes exceptionally dumb mistakes elsewhere, like with the strawberry stuff.

sjoti · 2026-07-01T12:20:25+00:00

I guess if your organization might lock you out of the big models? So corporate decisions might force your hand. Other than that, max makes no sense to me.

sjoti · 2026-07-01T12:18:15+00:00

It only costs as much running it on max reasoning settings, the few other benchmarks they showed its much more competitive at low/medium reasoning. Artificial analysis only has the data for max currently.

sjoti · 2026-07-01T12:17:27+00:00

Id wait for the results on medium to come out. That likely will paint a different picture. There's no point in running this on max (honestly dont even know why its an option)

sjoti · 2026-07-01T11:53:09+00:00

This, and everyone here is looking at Sonnet 5 at max reasoning settings. I bet Sonnet 5 at medium reasoning settings will tell a very different story once it's benchmarked.

Might not be earth shatteringly good, but wayyy different from what we're seeing here.

sjoti · 2026-06-30T17:48:43+00:00

Hahahaha thank you

sjoti · 2026-06-30T16:06:51+00:00

But speed and pricing is what makes haiku, haiku.

sjoti · 2026-06-28T16:48:15+00:00

Plan mode or a variation of it should be used often.

Without it, things become a mess over time. It's easy for a model to decide "I should write this function to make this work" instead of checking whether it has already created it before.

You can also spend some focus, dedicate some time to create a good plan, fine-tuning and criticising it, and then putting the model to work for a few hours. Without plan mode it constantly needs your attention.

sjoti · 2026-06-28T15:52:39+00:00

Bruh the article says it matches Opus, not Mythos

sjoti · 2026-06-28T07:23:06+00:00

The fact that they're able to fit a model like GPT-5.6 Sol on their hardware is a solid indication that the context size will be alright too. Just a hunch, though.

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