yoaviram

I think this is exactly the point the article is making...

I run YourDigitalRights.org where we help people delete their personal data, so this is kind of my thing. Meta is in the top 0.1% of companies in terms of how difficult they make it for people to delete their personal data. They are notoriously difficult, and we keep on playing cat and mouse games with them. They just don't want to delete any kind of personal information. In most cases, I recommend going to the regulator, which is almost all cases involved with Meta. In some exceptional cases, going to a lawyer is a viable course of action, especially when any kind of harm is involved. Imagine a case where someone is extorting you or defaming you, and you can't delete that data even though it refers personally to you. So yeah, it's a viable course of action in some cases.

If you were Meta designing this chatbot, would you plant hidden instructions for it in public pages, increasing the size of the page, and leaking what is internal info for everyone to see? Is this the natural design choice? Or would you give the chatbot its own API with the same text + the relevant instructions? Does not seem to me to be a sensible design choice. We are not in the days of using hidden HTML to communicate with internal subsystems.

Not surprising. When privacy is a selling point on the enterprise plan regular users, even paying ones, are the product. OpenAI had this from day one, the writing was on the wall. We all knew it so let's not act surprised now.

[1](https://chatgpt.com/pricing/?type=team)

Most of the latest models are too big to fit in a single GPU instance.

Frontier models don't fit on a normal GPU. The datacenter architecture frontier labs use is not a commodity. What you're describing is beyond the state of the art, and if we go there then anything is possible.

Except they all depend on the OpenAI API. Cut that off and they all stop. Finding an alternative source of compute is not easy, and even once done it's easy to cut off.

Great job. Please consider turning on rotation by default (for those who missed it, it's in the parameters). The overwhelming majority of astrophysical black holes are expected to be spinning (Ker) black holes.

Their cookies consent is not clickable on Firefox. What a s*t show. Every single privacy-related promise from OpenAI is theatre.

I believe this is inaccurate. Vercel env vars are all encrypted at rest (on their side). The 'sensitive' checkbox means you can't retrieve the value once it's set, which would have saved your ass in this case. Also, annoying to read an article like this without a single link to source material.

"If we bomb them some more, those poor citizens will surely realize that we're on their side"

Apparently Anthropic downgraded cache TTL to 5 min without telling anyone. My biggest issue with the recent issues with Claude Code is the lack transparency, although it looks like even Boris doesn't know about one: https://news.ycombinator.com/item?id=47736476

I have a limited company and a nonprofit registered in Estonia and am about to register another company. I C can't recommend it enough. It's how it should be everywhere and the polar opposite of how it is in Italy, where i am based.

I've been using GSD extensively over the past 3 months. I previously used speckit, which I found lacking. GSD consistently gets me 95% of the way there on complex tasks. That's amazing. The last 5% is mostly "manual" testing. We've used GSD to build and launch a SaaS product including an agent-first CMS (whiteboar.it).

It's hard to say why GSD worked so much better for us than other similar frameworks, because the underlying models also improved considerably during the same period. What is clear is that it's a huge productivity boost over vanilla Claude Code.

Yesterday I wrote a post about exactly this. Software development, as the act of manually producing code, is dying. A new discipline is being born. It is much closer to proper engineering.

Like an engineer overseeing the construction of a bridge, the job is not to lay bricks. It is to ensure the structure does not collapse.

The marginal cost of code is collapsing. That single fact changes everything.

https://nonstructured.com/zen-of-ai-coding/

Anticipating modes of failure, creating tooling to identify and hedge against risks.

I just finished writing a post about exactly this. Software development, as the act of manually producing code, is dying. A new discipline is being born. It is much closer to proper engineering.

Like an engineer overseeing the construction of a bridge, the job is not to lay bricks. It is to ensure the structure does not collapse.

The marginal cost of code is collapsing. That single fact changes everything.

https://nonstructured.com/zen-of-ai-coding/

This thread reads like an advertisement for ChatGPT Health.

I came to share a blog post I just posted titled: "ChatGPT Health is a Marketplace, Guess Who is the Product?"

OpenAI is building ChatGPT Health as a healthcare marketplace where providers and insurers can reach users with detailed health profiles, powered by a partner whose primary clients are insurance companies. Despite the privacy reassurances, your health data sits outside HIPAA protection, in the hands of a company facing massive financial pressure to monetize everything it can.

https://consciousdigital.org/chatgpt-health-is-a-marketplace...

Sharing my experience with SpecKit in case anyone finds it useful.

I've been using Speckit for the last two weeks with Claude Code, on two different projects. Both are new code bases. It's just me coding on these projects, so I don't mind experimenting.

The first one was just speckit doing its thing. It took about 10 days to complete all the tasks and call the job done. When it finished, there was still a huge gap. Most tests were failing, and the build was not successful. I had to spend an equally long, excruciating time guiding it on how to fix the tests. This was a terrible experience, and my confidence in the code is low because Claude kept rewriting and patching it with many fixes to one thing, breaking another.

For the second project, I wanted to iterate in smaller chunks. So after SpecKit finished its planning, I added a few slash commands of my own. 1) generate a backlog.md file based on tasks.md so that I don't mess with SpecKit internals. 2) plan-sprint to generate a sprint file with a sprint goal and selected tasks with more detail. 3) implement-sprint broadly based on the implement command.

This setup failed as the implement-sprint command did not follow the process despite several revisions. After implementing some tasks, it would forget to create or run tests, or even implement a task.

I then modified the setup and created a subagent to handle task-specific coding. This is easy, as all the context is stored in SpecKit files. The implement-sprint functions as an orchestrator. This is much more manageable because I get to review each sprint rather than the whole project. There are still many cases where it declares the sprint as done even though tests still fail. But it's much easier to fix, and my level of trust in the code is significantly higher.

My hypothesis now is that Claude is bed at TDD. It almost always has to go back and fix the tests, not the implementation. My next experiment is going to be to create the tests after the implementation. This is not ideal, but at this point, I'd rather gain velocity, since it would be faster for me to code it myself.

Essentially what this article is asking for, in most cases, is a better UI/UX for one of the foundation models.

This is one of several deceptive design patterns used by data brokers. Last year, we (the nonprofit consciousdigital.org) published a guide titled "How Deceptive Design is Used to Compromise Your Privacy and How to Fight Back". It contains 10 data protection deceptive patterns and countermeasures:

https://consciousdigital.org/wp-content/uploads/2023/04/dece...

TDD seems to be a good strategy if you trust the AI not to cheat by writing tests that always pass.

It seems to me that the ongoing “vibe coding” debate on HN, about whether AI coding agents are helpful or harmful, often overlooks one key point: the better you are as a coder, the less useful these agents tend to be.

Years ago, I was an amazing C++ dev. Later, I became a solid Python dev. These days, I run a small nonprofit in the digital rights space, where our stack is mostly JavaScript. I don’t code much anymore, and honestly, I’m mediocre at it now. For us, AI coding agents have been a revelation. We are a small team lacking resources and agent let us move much faster, especially when it comes to cleaning up technical debt or handling simple, repetitive tasks.

That said, the main lesson I learned about vibe coding, or using AI for research and any other significant task, is that you must understand the domain better than the AI. If you don’t, you’re setting yourself up for failure.

>Trust and privacy are at the core of our products. We give you tools to control your data—including easy opt-outs and permanent removal of deleted ChatGPT chats (opens in a new window) and API content from OpenAI’s systems within 30 days.

No you don't. You charge extra for privacy and list it as a feature on your enterprise plan. Not event paying pro customer get "privacy". Also, you refuse to delete personal data included in your models and training data following numerous data protection requests.

Software is a liability, a product is an asset.

If what you are saying is that significant parts of the tech landscape will change, then that's exactly the point.