标签：#programming

submitted by /u/DataBaeBee [link] [留言]

submitted by /u/fagnerbrack [link] [留言]

I got tired of watching coding agents spin up from scratch every single time I sent them a prompt. Cold starts, re-cloning massive monorepos, pasting the previous context into a synthetic prompt block — it worked, but it felt fundamentally wrong for agents that are supposed to think in conversations. So we shipped persistent sessions for the Critique Coding Agent API . Here's what changed, why the harness matters, and why you should never run a coding agent without a review skill. The Problem: Agents That Forget When we first released the Coding Agent API, follow-ups were honest but clunky: every follow-up was a brand-new job. The previous output was replayed as plain text into a fresh sandbox. It was the right MVP. It billed predictably. It never pretended a dead sandbox was alive. But it was the wrong long-term shape. If your internal bot fixes a migration, then wants a follow-up test, then wants a small doc tweak — you don't want three cold starts. You want: One repository checkout One OpenCode session A control plane that understands turns What Changed: Persistent Sessions After the first turn completes, the run now enters idle status. The E2B sandbox and OpenCode server stay up until sessionExpiresAt or until you explicitly POST endSession: true . The next prompt you send is delivered as a real message in that same session — not a synthetic "prior run output" block in a brand-new sandbox. Before (Chained MVP): Turn 1 completes → Sandbox killed → Turn 2 = new job + pasted prior summary Now (Persistent): Turn 1 completes → idle → Sandbox warm → Turn 2 = message into same OpenCode session Same run.id . Same checkout. Same context. Just the next turn. How It Works Under the Hood On the first turn, Critique: Creates an E2B sandbox from the OpenCode template Clones your repository at the requested ref Bootstraps tooling and starts opencode serve on localhost inside the VM Opens an OpenCode session Instead of killing that sandbox after completion, we now store session

submitted by /u/Happycodeine [link] [留言]

Hey devs! 👋 I'm the creator of this project, and I'm looking for help with an open issue: https://github.com/PedroVitor-Dev/Ped-Os/issues/1 If you're interested in contributing, reviewing possible solutions, or simply sharing ideas, I'd love to hear your thoughts. Contributions of all sizes are welcome, especially from developers looking to get involved in open source. Thanks in advance! 🚀💚 submitted by /u/Opposite-Stranger- [link] [留言]

submitted by /u/joladev [link] [留言]

The hidden cost of AI isn't generating code. It's understanding your codebase. For a long time, I assumed AI coding tools became expensive because they generated a lot of code. These tools can produce components, tests, SQL queries, documentation, and sometimes entire features on demand. If costs were climbing, the output volume must be the reason. The more I used these tools, the more I realized I was measuring the wrong thing. The expensive part isn't writing code. The expensive part is understanding what code should be written — and that work is mostly invisible. That realization changed how I think about AI-assisted development entirely. Two Prompts, Two Very Different Problems Consider these two requests: "Create a utility function that formats dates" and "Review this feature and suggest improvements." At first glance, both look ordinary. Both might even produce short answers. But they require completely different levels of understanding. The first is narrow and well-defined. The AI needs very little information before it can produce a useful answer. The second is open-ended. Before suggesting a single improvement, the AI may need to read multiple files, understand dependencies, follow existing patterns, compare implementations, and build a mental model of why the feature exists at all. The output might still be small. The work required to reach it is not. Why Agent Workflows Feel Different From Autocomplete This became much clearer when I started using AI agents. Traditional autocomplete is predictive — you type, the AI guesses what comes next. It's fast, cheap, and deliberately context-light. Agents behave differently. When you ask one to improve a feature or review a workflow, it doesn't immediately start generating code. It starts reading. It follows imports, finds related files, and tries to understand the system before touching it. That is exactly what makes agent workflows feel slower and more resource-intensive than autocomplete: they are spending effor

submitted by /u/lelanthran [link] [留言]

submitted by /u/Pink401k [link] [留言]

submitted by /u/TryallAllombria [link] [留言]

Metrics and analytical models are a double-edged sword. They make complex systems easier to compare, automate, and reason about. But once a metric becomes important, people start optimizing for it. Not for the reality behind it. For the number itself. This is Goodhart’s law in practice: when a measure becomes a target, it stops being a good measure. submitted by /u/Sad-Interaction2478 [link] [留言]

Join me in this deep dive where I'll explain all the code changes and tricks that took me from the reference implementation which processes the billion records in 4+ minutes, to processing everything in under 2 seconds. Who knew Java could be this fast? submitted by /u/goto-con [link] [留言]

submitted by /u/f311a [link] [留言]

submitted by /u/ReasonableLoss6814 [link] [留言]

submitted by /u/jrdi_ [link] [留言]

submitted by /u/r_retrohacking_mod2 [link] [留言]

The AI world is full of old infrastructure with stochastic organs. That sentence probably explains...

"This is the sixth post in my series on Emacs completion.... This one coins a term for a special case, Incremental Suggesting Read (ISR), where the candidate set produced by incrementally typed input is a suggestion, rather than a literal completion of that input. The ability to generate inferred matches in addition to literal matches vastly expands the scope of what a 'completion' system can do. Two conceptual sources supply the suggestions: 1) semantic retrieval and 2) generative synthesis. This post is more speculative than useful, so carry that pinch of salt with you as you watch the video or read this post." submitted by /u/misterchiply [link] [留言]

submitted by /u/_a4z [link] [留言]

I run multiple Claude Code sessions all day — one per feature, one per service, sometimes five at once. Every session was asking me for permission in its own terminal. I'd miss requests buried in a background tab. I'd switch windows mid-thought just to approve a git status . I'd lose context constantly. And there was no single place to see what Claude was doing across all of them. So I built Gatekeeper — a TUI daemon that intercepts every Claude Code tool call and routes it to one unified approval dashboard. The dashboard Three panes, one terminal: Left — all active Claude sessions, with status badges: [auto] means auto-approve is on, [linked] means it's wired to a terminal window Middle — pending permission requests with an age timer so you know what's been waiting longest Right — full request detail, danger warnings, and the numbered approval menu Every Claude Code tool call — Bash , Edit , Write , Agent — passes through a PreToolUse hook before executing. The hook connects to Gatekeeper's Unix socket, sends the request, and blocks. Gatekeeper shows it in the UI. When you decide, the answer travels back and Claude proceeds or stops. Approving requests The menu in the right pane mirrors Claude Code's own style: 1 Allow once 2 Always allow 3 Deny ↑ / ↓ moves the cursor, Enter confirms. Or just press 1 , 2 , 3 directly. A and D are quick shortcuts for allow/deny. Option 2 — always allow — is where it gets useful. Choosing it saves a persistent rule so the same request never surfaces again: Bash → saves the command pattern (e.g. npm run * ) to config Edit / Write → saves the directory to an allowlist Agent → enables auto-approve for that session The rule is written both to Gatekeeper's own config and to Claude Code's settings.json allowlist — so Claude Code itself won't prompt for it either. Auto-approve sessions Press A in the Sessions pane to mark a session as trusted. It shows [auto] — routine tool calls pass silently without appearing in the queue. But some things