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Tell HN: Namecheap gave my account to an unverified third party

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Tell HN: Namecheap gave my account to an unverified third party

I’ve been a NameCheap customer for 13 years. I’ve also helped out an old college club paying for a .com they use (that is registered to me under my name, address, and phone number). During a recent leadership transition, the incoming club lead wanted to make changes to the DNS and didn’t know to contact me. They figured out the domain name was parked at NameCheap, so they initiated a password reset using the domain name. I got a password reset email and immediately filed a NameCheap support ticket saying “I did not initiate this”. They called me to verify I was the one who filed the ticket, and then followed up with a canned email with tips like check your anti-virus. The incoming club leader was persistent though, and called NameCheap support. He convinced them the domain registered in my name and address really belonged to his club, and with no verification or validation whatsoever, NameCheap changed my password, and changed the email address associated with my account. All because someone simply asked nicely on a phone call. Meanwhile in the background, someone advised the new club leader who I was and we were able to connect and get things transferred over. Ultimately I was happy to give them access or even ownership if they wanted (student club turnover being what it is, it’s likely a domain doesn’t get renewed and gets gobbled up by a squatter, which is why I was keeping it current for them). But NameCheap had no way of knowing any of this. As far as NameCheap was aware, this was a personal account of mine. They demonstrated they were perfectly able to pick up a phone and call me (to verify my initial support ticket) but when someone calls them and says “but I really want access to that account” they don’t bother? I’d hesitate to even call this social engineering. It’s clearly a massive vulnerability. I’ve already moved a dozen of my most critical domains out of NameCheap after seeing just how easy it is for a third party to completely take over a NameCheap account: just ask nicely.

Hacker NewsJuly 23, 20262 min read

Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

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Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

I’ve been building Echo ( https://echo.tracerml.ai/ ), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task. It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined. That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance. For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem. One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination. On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost. There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder. I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API ( https://echo.tracerml.ai/docs/api ) so the system can be tested outside the evaluation setup. Here is a short/high level video on how it works: https://www.youtube.com/watch?v=lJFJSvOdXhg I wrote up the evaluation methodology, individual model results, costs and current limitations here: https://echo.tracerml.ai/eval I would love for you to try it! Especially if you hit any weird failure cases or places where the allocation looks unintuitive.