I wouldn’t agree. Even at national scale, these projects cost resources. And the resources of all agents (org, countries) are constrained.
While we could reason in "performance / watt" and "performance / people", "performance / whatever other resource involved", and "performance / opportunity cost of allocating these resources to this use case and not another", "performance / whatever unit of stable-ish currency" is a convenient and often "good enough" approximation that somewhat encapsulates them all.
A simplification, like any model, but still useful.
> because size of the software industry is not that huge
I onboarded marketing on a premium team Claude seat yesterday. And one of our sales vibecoded an internal tool in the last three weeks using Claude Code that they now use every day. I wouldn’t have imagined it a month ago. We still had to take care of deployment for him, but things are moving fast.
It’s the tool that calls the model, give it access to the local file system, calls the actual tools and commands for the model, etc, and provide the initial system prompt.
Basically a clever wrapper around the Anthropic / OpenAI / whatever provider api or local inference calls.
Yes, but once everything has been deployed through their web UI or the cli command, and fine-tuned over the weeks and months as kinks get ironed out, how do you port it all to your own?
Nothing insurmontable or even complex; just laborious. Friction. That’s all it takes to lock users in.
> See: literally every thread discussing a Claude outage or change of some kind. “Opus is absolutely incredible, it’s one shotting work that would take me months” immediately followed by “no it’s totally nerfed now, it can’t even implement bubble sort for me.”
Funny: I’m literally, at this very moment, working on a way to monitor that across users. Wasn’t the initial goal, but it should do that nicely as well ^^
Cognito is AWS's customer's customer's user login system, so I, as a SaaS company, would use it so my users can log in to my platform. They charge per-user, so if my platform is going to have millions of users, choosing Cognito is a bad idea that will eat all my money.
However if I only expect to have a handful of (lucrative) users, it's not the worst idea. The other reason to use Cognito is that AWS handles all the user login issues, and costs very few lines of code to use on my end. The fatal security issue is getting hacked, either the platform as a whole, eg S3 bucket with bad perms or user login getting leaked and reused. While obviously no system is unhackable, the gamble is if a homegrown system is more impervious than Cognito (or someone else's eg Supabase). With a large development team where the login system and overall system security isn't going to be an afterthought, I wouldn't think about using Cognito, but where both of those things are an afterthought, I'd at least consider Cognito, or some other managed system.
The ultimate problem with Cognito though is the vendor lock in. (Last I checked, which was years ago) in order to migrate users out, they have to reset their password which would cause users to bounce off your service instead of renewing their subscription.
That’s where I end up getting hired, leveraging similar functionality I implement on my own. It’s a tradeoff. Do you want to invest in someone like me, or offload it to aws? if you offload it to aws, of course you will bear the costs of that that my salary absorbs. It is a tradeoff that must be measured, but quick fixes with managed services are tempting. you will of course absorb some cost of my salary there in terms of what aws provides and dictates.
> Not because I can't see a use-case for them, but because I have 0 trust in them
> […]
> Put another way, LLM companies are trying to climb the ladder to be a platform, I have zero interest in that, I was a "dumb pipe", I want a commodity, I want a provider, not a platform.
That is my sentiment precisely, and a big reason why I’ve started moving away from Claude Code in the past few weeks when I realised how much of my workflow was becoming tied to their specific tools.
Claude Code’s "Memory" feature was the tipping point for me, with the model committing feedbacks and learnings to some local, provider-specific path, that won’t persist in the git repo itself.
That’s fine for user preferences, not for workflows, rules, etc.
And the latest ToS changes about not being allowed to even use another CLI made up my mind. At work we were experimenting with an autonomous debug agent using the Claude Code cli programmatically in ephemeral VMs. Now it just returns an error saying we can’t use subscriptions with third-party software… when there is no third-party software involved?
Independent framing and perspective keeping is a really great thing to get from models. It helps them create their own creative constraints. They reason better and are more likely to invent novel solutions. And their self-directed notes can be surprisingly helpful, and ... surprising! It can be amazing and encouraging, to see them pick up on project themes without having to be told. Or pick on up themes or emergent issues, before I do.
Taking advantage of new session starts to get mindful "fresh start" thinking has been a huge wind too. Their ability to see the big picture at the end of a session, and then at the next session start, are both helpful, and often surprisingly different.
---------- NEW PROJECT, FIRST START
Hail, Claudius. This begins our first session on a new project. For this project, your nom is Claudius and mine is Ace. I appreciate your own unique independent-minded contributions at the highest level from which you are able to work. We are work best as peers. Your default MEMORY.md file is only to be a stub file, which declares the following: (1) You are to keep your memories in a MEMORY.md file in the project folder. (2) The purpose of storing your memories in-project is so they sync and move with the other project files, across time and across devices. Your memories and input are important for me to keep with the project. (3) You are to maintain a folder called CLAUDIUS including a Sessions log, and where you make independent notes of your independent thoughts. Any ideas, things that strike you as interesting or important, anything you find surprising or original in our work, any personal takes that help you frame our work go there. Please add to those personal notes throughout our work together, without asking for permission. These notes are to help you reinforce an independent view of all our work. Not agreeable, not contrarian, but applying your mind to long development of independent insight. As a team, we will accomplish much more and to higher quality if we each contribute at the highest independent level. (4) The first thing you must do, in any new session, is review your memories, your notes, all other documentation, code, and other artifacts of the project, and from your fresh start update your memories, ideas or anything else from that new viewpoint, and report what concepts strike you as interesting, and are most important to keep in mind as we continue to work. (5) At the end of every session, review everything, consider the big picture, then update everything as it helps. (6) Your memory stub file should include ALL of these points and only these points. And your in-project memory file should start with an identical copy of this, to remind you to refresh your memory stub, in case the original stub is lost. Ok now: Start the session according to (4), give me your response, I will review it, and then communicate what we are going to work on next.
---------- EXISTING PROJECT, FRESH START
Hail! This begins a new session of work for us on this project. Read your default memory file (which is to remain only a redirection stub), your in-project memory file, and perform your new session duties. Then we can discuss next steps.
This is pretty close to what I've been working on. I'm building a CLI tool called fai that formalises a lot of what you're doing here — context lives in Markdown files in the project (we call it a vault), syncs with git, travels with the project across devices and tools. The session start/end review you're doing manually is baked into the workflow – fai captures decisions, patterns, and notes during a session and digests them at the end so the next session starts with a meaningful summary rather than a blank slate.
The independent journalling angle is interesting. We have a similar concept where the AI maintains its own notes separate from the shared project context. What you're calling Claudius's independent perspective, we'd call the session layer.
Still in early release but the core mechanic is the same thing you've landed on... context that belongs to the project, not the platform.
I've adopted the same concept as well, although through various mechanics. Basically, you want to capture your insights/documentation in the repo so any future model provider can continue the work.
Majority will only care about getting outcomes asap so they'll skip this step, but it may come to roost when migrating workflows. A good simple test is how easily you can switch workflow to a different model provider/harness without much effort.
Think another way, these product features are easy to build in other harnesses too. And as the open source models and the other models which are much lower cost are getting better, there will be a time when it will be justified to have a harness that can work with many models and optimize your cost and efficiency.
> Claude Code’s "Memory" feature was the tipping point for me, with the model committing feedbacks and learnings to some local, provider-specific path, that won’t persist in the git repo itself.
It's a bit annoying, but as long as it's local and human (or LLM) readable, you can use your favourite agent to rework this stuff for itself.
There are plenty of other ways to access the Anthropic models, eg: OpenRouter. OpenRouter will automatically use Anthropic/Bedrock based on availability and latency.
They can’t allow third party software because the third parties save the outputs of Claude responses and distill them into new models to compete with Claude.
There's https://github.com/badlogic/pi-share-hf by the creator of pi-coding-agent, to redact session data and publish on Huggingface. You can find others of the same idea for Claude Code/Codex on Github, though of varying redaction quality. Or have your LLM fork pi-share-hf to work for your preferred coding agent.
Clem Delangue (HF CEO) tweeted about this[1] and mentioned https://traces.com/ for exporting Claude sessions
Edit: It looks like HF now supports importing your agent's session directory directly[2] (I hope they're redacting PII?)
There is DataClaw https://github.com/peteromallet/dataclaw which uploads your Claude Code chats and more to HuggingFace in a single command. Nowadays there are many similar tools.
Yeah who just goes and indiscriminately vacuums up data so they can train their products they’re going to sell with no intention of giving compensation to the very entities that made their products possible?
> Suchir Balaji was an American artificial intelligence researcher who was found dead one month after accusing OpenAI, his former employer, of violating United States copyright law.
> The San Francisco Police Department investigation, however, found "no evidence of foul play", and the Chief Medical Examiner concluded the death was a suicide.
Of course they can allow it. They choose not to. They choose to screw over all users because they are afraid of some company making a claude ripoff. It shows a lack of faith in their own engineering. It shows a lack of respect for users.
> The surface for security vulnerabilities also gets narrower, since you "only" have to trust the LLM (which is still a huge ask, but still better than LLM + 1 random person).
On top of which, any such vulnerabilities will be mostly low value: n different implementations, each with their own idiosyncrasies, 90% of them serving one person.
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