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Hoi hoi! I’m @nyaomaru, a frontend engineer who recently discovered the deliciousness of a cheese...

Thoughts? Do you guys use models like Kimi or DeepSeek? Are you worried about data privacy, or not so much concern? submitted by /u/RutabagaTechnical822 [link] [留言]

2× Claude Code Pro's usage at $15/mo Discussion | Link

Most developer tools in 2026 want your data. They want you to create an account, sync to the cloud, share analytics, and join a team plan. Every new tool is another service that knows what you are working on. I wanted something different. CodeFootprint CodeFootprint is a Mac app that tracks file changes in your project folders. It records every edit with full diff, every deletion with recoverable content, and precise timelines for everything. And it does all of this without ever connecting to the internet. How It Works Select a folder to monitor Code as normal — CodeFootprint records in the background Open it anytime to see what changed, when, and how Export change traces to share with AI tools for debugging The Design Decision I made a deliberate choice: no accounts, no cloud, no telemetry, no data leaving your machine. Not because cloud is bad, but because your project files are some of the most sensitive data you own. Your code, your configs, your unpublished work — a file change tracker sees all of it. A tool that watches everything you change should be trustworthy by design, not by promise. For Developers Who Use AI Tools If you work with multiple AI coding tools, CodeFootprint gives you something valuable: a shared context you can export. Instead of manually explaining to each new AI tool what happened in your project, you hand it a trace file and say "here is the history." Available Now CodeFootprint is on the Mac App Store . No account needed. No internet required. Your files stay on your machine. More convenience. More protection. More peace of mind.

With shiftbloom studio. I build tools and projects about a variety of experimental approaches to real-world problems. The issue for such use-case often was how most small media systems start out: one big always-on recorder that keeps costing money even when nothing is happening. For live capture you obviously need to stay ready at all times — sometimes you can’t risk losing the first minutes. But for everything else it’s complete overkill. The Core Problem Backfills, VOD downloads, clip imports, repairs and re-encodes are queue work. They can wait a few seconds, run on burst capacity, or even on a regular VPS or laptop. They don’t need the same always-hot infrastructure as the live recorder. That’s why I split the system. Instead of one large monolith, I deployed: Observer cells — only for live streams (time-critical) Harvest cells — for all queue processing (can be delayed) The Three Roles 1. Mothership A small control-plane cron job. It checks queue sizes, currently live channels and running observer tasks, then decides: how many harvest cells should exist right now which channels need an observer cell It’s intentionally simple. The database remains the single source of truth. 2. Observer Cells Each observer cell records exactly one live channel. It receives its assignment through environment variables: +++env OBSERVER_VOD_ID OBSERVER_CHANNEL_ID OBSERVER_CHANNEL_LOGIN OBSERVER_CHANNEL_NAME +++ It starts recording immediately, writes HLS segments to object storage, sends heartbeats, and waits a short standby window after the stream goes offline. This window is important because streams sometimes drop and reconnect quickly. Without it you end up with many small broken VOD fragments. 3. Harvest Cells These handle all background work: downloading VODs, re-encoding, recovering broken files, etc. They can run anywhere Docker is available — AWS tasks, a small VPS, or even a spare laptop. They only need outbound access to Postgres and object storage. What Changed Previous

pluckmd exists so an agent can pull blog posts into markdown, index them into a wiki, and generate interactive HTML to learn from. This post is about the first step, the part with no per-site code, because the design is the interesting bit. If you want the practical side, how I actually use it day to day, I wrote that up separately: https://dev.to/taisei_ide/how-i-use-pluckmd-to-read-blogs-with-an-ai-agent-1jpe It downloads articles from a blog without any per-site code. No handler for Medium, no handler for Substack, nothing keyed on a domain. Here's how that works. The core idea: treat extraction as data, not code. AdapterSpec Instead of branching on which site you're on, pluckmd resolves an AdapterSpec . It's a plain object that says which selector finds article links, what the URL pattern looks like, and how pagination behaves. interface AdapterSpec { listing : ListingExtractionSpec ; // how to find article links article : ArticleExtractionSpec ; // how to pull the body pagination : PaginationSpec ; // none | scroll | button-click | next-url | auto evidence : string ; } Because it's data, the same shape can come from a heuristic, an LLM, an agent, or a person typing it by hand. They all produce the same thing, and they all go through the same checks. Resolving it, cheapest path first cache -> heuristics (local, free) -> LLM (only if needed) Cache first, rechecked against today's DOM so a stale entry can't sneak through. Then local heuristics. The LLM only gets called when the heuristics aren't sure. Every result that works gets written back, so the second run on a site is basically instant. How the heuristics find an article list This part has no idea what site it's looking at. It takes every link, normalizes the path, and collapses the parts that vary into wildcards. / blog / my - first - post -> / blog /* / blog / another - article -> / blog /* / about -> / about Group by that shape. Any group with the same pattern repeated three or more times is a candidate f

The Setup I've been building AI agents that use tools — reading files, running commands, calling APIs. There are two main ways to give agents these tools: MCP (Model Context Protocol) — the new standard everyone's adopting Direct CLI calls — good old command-line execution Everyone says MCP is the future. But nobody talks about the token cost . So I measured it. The Test I built a simple file-reading tool and measured the exact token consumption for each approach: Method Tokens per Call Latency (avg) MCP (structured) ~3,400 tokens 280ms CLI + raw output ~200 tokens 45ms Ratio 17x 6x Why MCP Uses So Many Tokens The overhead comes from three places: 1. Tool Schema in Every Request MCP sends the full JSON Schema of every available tool with each request to the LLM. My simple file-reader schema alone is ~800 tokens. With 10+ tools, that's 8,000+ tokens of schema on every single call. { "name" : "read_file" , "description" : "Read contents of a file at given path" , "parameters" : { "type" : "object" , "properties" : { "path" : { "type" : "string" , "description" : "File path to read" } }, "required" : [ "path" ] } } 2. Structured Response Wrapping MCP wraps every response in a structured envelope with metadata, status codes, and typed content blocks. A simple "file not found" error becomes a 200-token JSON object. 3. Round-Trip Protocol Overhead Each MCP call involves: request → server parse → execute → format response → return → client parse → extract. Each step adds tokens for protocol framing. The CLI Alternative With direct CLI execution: $ cat /path/to/file.txt [ raw file content] That's it. Raw input, raw output. No schemas, no envelopes, no metadata. When MCP Is Worth It Despite the token cost, MCP shines when: You need standardized discovery — agents dynamically finding available tools You're building reusable tool servers — one MCP server serves many agents Security sandboxing matters — MCP's permission model is more granular Team collaboration — shared tool de

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,