标签：#RAM

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

submitted by /u/nicovank13 [link] [留言]

PageSpeed Insights had been staring at me for weeks. Desktop was holding at 91. Mobile was stuck at 63. I'd already fixed the obvious stuff — non-blocking fonts, preconnects, fetchpriority on the hero image. But there it was, every single run: Improve image delivery — Est savings of 985 KiB Nearly a megabyte of wasted transfer, just from six project screenshots. And that was just the images visible above the fold. The full list across all projects was worse. The culprit: every image I'd ever uploaded through the Django admin was a PNG. Some of them were over 1 MB. WebP would have cut most of them by 80%. I knew this. I just hadn't done anything about it. So I wrote a management command to fix the backlog, and then made the model auto-convert on every future upload so I'd never have to think about it again. The Problem With PNGs in a Portfolio When you're building a portfolio, you screenshot your work and drag it into the admin. That screenshot is usually a PNG — lossless, full-size, straight from your display. Nobody optimises it because the admin accepts it and it shows up fine in the browser. But "shows up fine" isn't the same as "loads fast." A 1.4 MB PNG of a law firm homepage does not need to be 1.4 MB. Served as WebP at quality 85, it's 175 KB. Same visual result. Eight times smaller. Multiply that across 28 projects and you're looking at tens of megabytes that mobile users on slow 4G are downloading just to scroll past thumbnails. The One-Time Backlog Fix: A Management Command First, I needed a way to convert everything that was already in S3. A management command was the right tool — it runs in the production container with full access to the Django ORM and the configured storage backend, so it can read and rewrite files without needing to know whether they're on S3, local disk, or anywhere else. # backend/projects/management/commands/convert_images_to_webp.py from io import BytesIO from django.core.files.base import ContentFile from django.core.management.b

Qwen3.7-Plus has appeared on Qwen's official research release page, with a release date of June 1, 2026. Chinese media covered the launch on June 2. The important part is not that Qwen 3.7 Plus can understand images. The bigger signal is that Qwen is pushing it as a multimodal agent model: vision, language, coding, tool use, and productivity workflows inside one task loop. For developers, the real question is simple: can it keep the same goal across software screens, web pages, screenshots, code, terminal output, and tool calls long enough to finish useful work? If your team is evaluating new agent models, keep the model shortlist in one place and compare quality, latency, cost, and failure modes by task: Compare AI models on WisGate . What Is Qwen3.7-Plus? Qwen3.7-Plus is a multimodal agent model from Qwen. Qwen describes it as an agent foundation that unifies vision and language. It builds on the Qwen3.7 text backbone, adds stronger vision-language capabilities, and keeps the agent-oriented strengths developers care about: coding, tool use, and productivity workflows. That makes it different from a basic image-question-answering model. The more useful use cases look like this: Read a UI screenshot and decide the next action. Combine web pages, docs, charts, screenshots, and text context. Turn a design or product screen into maintainable code. Use tools to verify results instead of only returning static answers. Move between GUI, CLI, browser, and code environments during one task. That is why Qwen3.7-Plus should be evaluated as an agent model first, not just as another chat model with vision support. Why This Release Matters More teams are moving models into longer workflows: read the request, inspect the code, run tests, check logs, fix the issue, verify again, and write the summary. The hard part is that real work is rarely text-only. Frontend bugs come with screenshots. Dashboards come with tables and charts. Debugging comes with terminal output, browser state,

The least interesting thing an AI coding agent can do now is generate code. That sounds harsher than I mean it. Generation still matters. Better models still matter. Faster edits still matter. But if you have used these tools on a real codebase, not a demo repo with three files and no history, you already know where the pain moved. The bottleneck is not "can the model write a React component?" The bottleneck is "does the agent understand why this repo is weird?" Real repos are full of weirdness. Naming conventions nobody wrote down. Migration leftovers. Feature flags with political history. Tests that exist because of one brutal production incident. API boundaries that look accidental until you remove them and break billing. A hundred tiny facts that separate a useful change from a confident mess. Coding agents are getting much better at editing files. The next stack has to get better at making the system legible before the edit starts. Bigger context windows are not the same as understanding The lazy answer is to throw more context at the model. Give it the whole repo. Add the README. Add the docs. Add the last five tickets. Add the architecture decision records. Add the transcript from the previous session. Add the test output. Add the package lock, because why not. That works until it does not. A larger context window can hold more text. It does not automatically turn that text into a map. It does not know which files are architectural boundaries and which are incidental wrappers. It does not know that one directory is deprecated unless the repo says so clearly. It does not know that a scary-looking validation branch is protecting a partner integration from 2021. More context can even make the problem worse. You get the pleasant illusion that the agent has seen everything, while the useful signal is buried under raw file dumps and old notes. Repo understanding needs structure. That is why tools that turn codebases into graphs, domain maps, guided tours, semantic

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Crypto APIs are no longer just tools for fetching Bitcoin prices. In 2026, they are becoming the...

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

Since the early days of GenAI, when ChatGPT launched in late 2022, we began using prompt engineering to direct chatbots (and later LLMs) with human language instructions to provide us answers to questions or take actions (in a high-level…) In 2025, companies such as OpenAI and Anthropic began releasing a new agentic concept called “AI Agent”, an autonomous system that uses an AI model as its "brain" to perceive an environment, make independent decisions, and execute multi-step tasks using digital tools. Unlike passive chatbots that just answer questions, an agent can plan its own workflow, run commands, and browse the web to achieve a specific goal without constant human supervision. In this blog post, I will explain the concept of Context-as-Code and share some coding examples. Introducing Context-as-Code Traditional prompting is a one-way street. You type out your instructions, send them off, and that text never changes. AI agents operate completely differently. Because they work on their own, every action they take creates a mountain of new data. Every time an agent opens a file, checks an error, or runs a tool, it adds more information to the pile, which quickly overwhelms a standard chat screen. Context-as-Code treats the agent like a stateless compute engine. Instead of a massive text prompt, we use version-controlled files ( CLAUDE.md , AGENTS.md ) to establish structural boundaries, separating the permanent project rules from the temporary, dynamic session memory. Context-as-Code transforms loose AI prompts into version-controlled engineering assets by using structured Markdown files to establish permanent, auditable boundaries directly within a project repository. The Discovery Stage (Onboarding the Agent) Before an agent writes a single line of code, it must parse the overall project layout. These files act as the "map" for an incoming AI. llms.txt Serves as a lightweight text directory mapped out in Markdown format. Placed at the root of a project or webs

submitted by /u/someone-very-cool [link] [留言]