Hi HN, I think it's safe to say that the majority of developers don't give a second thought to writing code with I/O tangled in business logic. It's all too common to see code like: const user = findUser(email); if (!user) await saveUser(user); Now, you may ask: what's the big deal? When we write code like this, two things happen: 1. It gets harder to debug production bugs. Unless you have the exact same database and remote API services to connect to, you may fail to reproduce the bug. 2. You ha

Cold Court is a brother-sister duo from Philly that seems to love nothing more than shoving all of their influences together in a messy soup that at least superficially resembles the hyperpop you've come to expect from acts like 100 Gecs. But, where songs like "Dumbest Girl Alive" goofily wink at pop punk and emo, […]

Article URL: https://www.ycombinator.com/companies/wildcard/jobs/SEmo4di-founding-applied-ml-engineer Comments URL: https://news.ycombinator.com/item?id=48620504 Points: 0 # Comments: 0

My shortened story: due to factors in Soviet controlled Poland, my uncle played my dad role. He took me to a quarry to fire Estes-style rocket cars, and all that.. he also managed to steal a Milicja (military police) siren from work, and put it on the back of my banana style bike. As an EE, he made it actually function. Thanks to all the hackers who made us who we are.

Anthropic recently reported that Claude now handles around 95% of its internal analytics requests, letting employees query business data independently instead of relying on data teams. The company attributes this result less to advances in models and more to data governance, semantic definitions, and operational discipline. By Renato Losio

Many of those videos were reportedly filmed on “near-perfect copies” of the Polymarket website, while featuring trades and winnings that were not real.

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GitHub热门项目 | OpenAI-compatible proxy that stacks the free tiers of 16 LLM providers (~1.7B tokens/month) behind one /v1 endpoint — plus any custom OpenAI-compatible endpoint. Smart routing, automatic failover, encrypted keys. Personal experimentation only. | Stars: 11,267 | 226 stars today | 语言: TypeScript

It did so in just five years.

Debugging authentication in web apps is painful. You need to test the same flow as five different user types — new visitor, returning user, admin, expired session, logged-out — and the easiest way is to constantly create new accounts or clear all your cookies and start over. There's a faster way. These five techniques use direct cookie manipulation to simulate any auth state without touching your database or creating dummy accounts. I use CookieJar for most of this — a free Chrome extension built natively on MV3 that gives you a proper UI for cookie editing. But I'll show you the underlying Chrome DevTools method too, so you understand what's actually happening. 1. Simulate a Logged-Out State Without Clearing Everything The naive approach: clear all cookies and reload. The problem: you just nuked your dev server session token, your local storage flags, your Stripe test mode cookie, and everything else you carefully set up. The targeted approach : identify and delete only the session/auth cookie. Most session cookies are named session , sid , auth_token , _session_id , or something close. In DevTools: Application → Cookies → [your domain] → find the session cookie → right-click → Delete With CookieJar: open the extension, search session , click the trash icon next to just that cookie. Your dev environment stays intact. The user state resets to logged-out. 2. Test the "Returning User" vs "New User" Path Without a Second Account Session cookies tell the server you're authenticated. But many apps use separate cookies to track whether a user has seen the onboarding flow, completed setup, or visited before. Look for cookies like onboarding_complete , setup_done , first_visit , or custom flags in your app code. To test the new user experience: Export your current cookies (CookieJar → Export → JSON format, or copy from DevTools) Delete the specific onboarding/first-visit flag cookie Reload and test the new user path Re-import or re-set the cookie to restore your state This

Originally published on Mohamad Alsabbagh's Blog . AI coding assistants are excellent at compressing known work into fast drafts. That speed is the preface boost : routine implementation arrives almost immediately. The hidden risk is that teams begin treating the model's first plausible answer as architecture. Because LLMs are trained and aligned around historically common patterns, they can pull engineering teams toward Generative Monoculture : less diverse solutions, narrower exploration, and fewer designs shaped by the exceptional constraints of the system in front of them. Give the same prompt to three engineers using the same assistant and you often get the same shape back: a tidy service layer, a familiar API boundary, a conventional retry wrapper, and code that looks clean enough to merge. That answer is useful. It may even be the right answer for ordinary work. The danger is what happens when ordinary work becomes the default posture for extraordinary constraints. Large Language Models are not neutral architecture engines. They are probabilistic systems trained over historical work and tuned toward answers people tend to reward. Used well, that makes them extraordinary accelerators. Used passively, it creates an optimization paradox: teams gain immediate implementation velocity while becoming anchored to a consensus baseline that may be too average for the actual system. 1. The Default Is a Local Optimum Wu, Black, and Chandrasekaran define Generative Monoculture as a narrowing of model output diversity relative to the diversity available in the training data. That matters because software architecture is rarely a search for the most common answer. It is a search for the answer that fits the exact failure modes, latency envelope, team topology, regulatory constraints, and operational reality of a system. The model's default is often a local optimum: a solution that is statistically likely, syntactically polished, and broadly acceptable. That can be excellent