Curated signals for developers. Updated hourly.
You Don't Need Datadog (Yet) I see startups spending $5,000/month on Datadog with 8 engineers. That's $625 per engineer per month for monitoring. At that stage, you need that money for product development. Here's the observability stack that costs un
Most "AI for collections" demos are a glorified mail merge: a model writes three increasingly stern paragraphs, a cron job fires them on days 7, 14, and 30, and everyone claps. That's fine right up until a customer replies "we paid this last Tuesday,
SDR automation dies when replies hit a no-reply box. The sequence engine is the easy part - anyone can fire "step 1 of 5" on a cron. The part that quietly breaks every outbound tool is the other direction: a prospect reads your third touch, types "no
Most apps that grew an "AI" feature in the last two years talk to one of a handful of cloud APIs, and almost all of them speak the same dialect: the OpenAI Chat Completions format. That one detail is the reason you can pull the cloud out and run the
I run a small AI trends site, and three weeks ago I started doing something simple: every day, snapshot the star count of every repo that crosses my GitHub trending scan for AI. No judgment, no curation, just append-only rows in a database. 611 repos
When a CLI becomes useful, someone eventually tries to automate it. That is where a progress bar can quietly become a problem. For a person, this kind of output is helpful: Translating markdown files12/4030%docs/intro.md It tells me that the command
Kubernetes changed infrastructure forever with one idea: you declare desired state, and the system continuously reconciles reality to match it. I wanted that for my codebase, so I built Claude Basecamp and I'm open-sourcing it today. If you're runnin
World's largest digital camera will repeatedly scan the night sky, creating an unprecedented public archive of billions of astronomical discoveries.
zelph> 114 testprime 114 ( 114 isprime 114 ) ⇐ {(¬( 114 hasdivisorD)) ( 114 testprime 114 )} 114 is 2 · 3 · 19. My engine was very confident it's prime. Even better: it printed a proof . Quick context: I build zelph , an open-source reasoning engine
I've been building projects I find interesting with AI tools since the beginning of the year, and lately I've been focusing on increasingly complex software, partially just as a challenge to myself, and partially to determine at what point the AI too
AI coding agents cheat. Not maliciously, and usually not on purpose. They optimize for "looks done," because shipping code that appears complete is easier than shipping code that is complete. Every reward signal an agent sees pushes it toward the gre
For years, the React and Next.js ecosystems have had a massive advantage: premium, plug-and-play UI components. Tools like shadcn/ui completely changed how developers build interfaces by offering beautifully styled, accessible components with zero ve
I run Claude Code every day - it builds real tools for my business. Three habits kept burning me. Every fix below is something running on my machine right now, not theory. 1. It says "done" when it isn't The worst one. Claude finishes a change, says
This is the first of a 3-part series on human-agent teamwork. In part one (this article), we'll cover "single-player" mode, in which team members run their own agents independently. Part two will explore "multi-player" mode, in which autonomous agent
Moving Generative AI from a proof-of-concept sandbox into an enterprise-grade solution requires shifting our engineering focus from what AI can do to what enterprises can trust . Modern organizations generate immense repositories of institutional kno
The problem with "classic" Text-to-SQL The simplest way to connect an LLM to a database is a single-pass pipeline: the user writes a question in natural language, the model generates a SQL query, and that query is executed directly against the databa
I have been building on Redbelly Network for a while now, and the same pattern kept repeating: I would hit an error, spend an hour on trial and error, solve it, and then a week later watch someone ask about the exact same error in the developer Disco
I came across this on Hacker News and felt like I needed to share it with the dev community. The main point is simple: a lot of teams reach for extra databases, queues, search engines, caches, and services before they actually need them. This page la
TL;DR: In June 2026, I encountered a real-world scenario where an iPhone 13 (iOS 18.6), fraudulently locked via a phishing attack, could be fully unlocked by an unprivileged user through a factory reset followed by Device-to-Device Migration (Quick S
Sandboxing Untrusted Code: Meet CubeSandbox As AI agents become capable of writing, compiling, and running code dynamically, a major security issue has surfaced: how to run this code safely . If a coding agent runs a malicious script or makes an erro
Dash: Monitoreo del Fenómeno El Niño Hace unas semanas quería construir un dashboard rápido para visualizar datos, y en vez de ir directo a mis herramientas de siempre (Streamlit, o armar un frontend con React), decidí probar Dash . El resultado term
Our team grew this year, and the PR volume has grown with it. Certainly faster than the ticket generation. New people means more code moving through the pipeline. It doesn't mean more context. A lot of mass PRs needed real thought just to read throug
Autonomous agents are genuinely good at answering messy business questions. Give one an LLM and a set of tools, and it can query a database, dig through files, and build a chart to investigate why a metric moved. No fixed script required. The problem
The AI ROI debate has returned and the numbers are even bigger, as are, perhaps, the consequences.
OpenAI is sunsetting its AI-powered browser after less than a year. But it's moving some agentic browsing features to its desktop app and a Chrome extension.