One of the most annoying parts of backend development isn't building APIs. It's keeping the...

X Money, a core part of Elon Musk's mission to turn X into an "everything app," is rolling out starting today, 9to5Mac reports. The payment platform offers a digital wallet and peer-to-peer payments similar to Venmo, along with a metal Visa card users can emblazon with their X username. Other perks include Apple Wallet support […]

Only Premium and Premium X subscribers get the privilege of banking with Elon for now.

GitHub热门项目 | A lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs in the web browser, on the desktop, on mobile, and inside Jupyter notebooks. | Stars: 2,613 | 420 stars today | 语言: TypeScript

Microsoft says tools cost less than competing ones and outperform them, too.

Starlink has a Texas factory but also makes routers in Vietnam.

I was testing NodeDB-Lite and PageDB through a real memory-layer application built on top of them. NodeDB-Lite is the embedded form of NodeDB for local-first and in-process workloads, while PageDB is the encrypted page store underneath it. That application was part of the test strategy. I did not want to validate the storage stack only through unit tests, fixtures, and controlled benchmarks. I wanted a real workload to keep using it, stress it, restart it, grow its data, and exercise the boundaries that isolated tests usually miss. Then the store became corrupted. The visible symptom was an authenticated-page read failure around an FTS path. A page that should have passed its AEAD authentication check did not. The application restarted, opened the same damaged store, hit the failure again, and fell into a restart loop. The hard part was not proving that the store was corrupt. The hard part was reproducing how it became corrupt. I could not reproduce it inside PageDB . I could not reproduce it through NodeDB-Lite . I could not even make the application produce it on demand. I could use the application normally for a while and eventually see the failure, but I did not have a deterministic sequence that caused it. By the way, I still found bugs along the way. Some were real. Some looked close enough to the corruption path that I thought I had finally found the root cause. I fixed them, rebuilt, ran the tests, and went back to dogfooding. The corruption still came back. At that point, I stopped asking: Which storage bug looks plausible? The real question was: Where does it actually go wrong? I kept testing the wrong shape of failure My strongest theory was freed-page reuse, or something close to a use-after-free inside the store. It was a reasonable theory. If a page had been released and then reused while another structure still referenced it, a later authenticated read could land on bytes that were valid somewhere else but invalid for the page the reader expected. So

I have been building an open source tool called Agentic Ledger and it just reached the point where I need more eyes on it than my own. This post is an introduction and an ask. The problem AI agents run unattended. They call LLMs in loops, use tools, spawn sub-agents, and spend real money, and most of that happens where you cannot see it. When an overnight coding loop burns $40 getting stuck on the same failing test, or a multi-agent crew quietly retries itself into a huge bill, you usually find out from the invoice. The observability tools that exist mostly want you to instrument your code with an SDK, and each one speaks one framework. I wanted the opposite: something that watches everything, requires changing nothing, and keeps the data on my machine. What it is Agentic Ledger is a transparent proxy that sits between your agent and the LLM provider. You point your agent's base_url at it, and it records every request and response, assigns each call an action id, works out what it cost, and passes the response through untouched. Your agent never knows it is there. Your Agent -> Agentic Ledger Proxy -> OpenAI / Anthropic / any gateway | SQLite or Postgres | Live dashboard + API No SDK, no decorators, no monkey patching. It works with any framework and any provider because it operates at the only layer they all share: the HTTP call. Everything is local-first. Your prompts stay in a SQLite file on your machine (or your own Postgres). MIT licensed. Try it in two minutes pip install -U agentic-ledger AGENTICLEDGER_UPSTREAM_URL = https://api.openai.com python -m agenticledger.proxy Or with Docker (multi-arch, non-root, Sigstore-signed): docker run -p 8000:8000 \ -e AGENTICLEDGER_UPSTREAM_URL = https://api.openai.com \ -v $( pwd ) /data:/data \ ghcr.io/shekharbhardwaj/agentic-ledger:latest Then point your agent at it: client = OpenAI ( base_url = " http://localhost:8000/v1 " , default_headers = { " x-agenticledger-session-id " : " run-1 " }, ) For coding agents like Claude

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Gaming keyboards have evolved over the years to add RGB LEDs, extra knobs, and buttons with screens, but one feature has remained fairly consistent: the mechanical switch. That’s slowly changing, with brands introducing adjustable optical switches that are more customizable and have a faster response time. Razer’s Huntsman V3 Pro TKL is a compact, wired […]