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The Only Productivity Hack That Actually Worked for Me

The Only Productivity Hack That Actually Worked for Me I've tried every website blocker in existence. They all have the same flaw: one click and they're off. That settings toggle might as well not exist when the urge to procrastinate hits. I needed something I couldn't override. So I built the opposite. The Problem Every blocker follows the same pattern: it blocks, you unblock, you procrastinate. The issue isn't discipline — it's that your future self and your present self want different things. And that future self will happily undo anything you set up. What I needed was a system where both selves agreed on the rules upfront and then neither could break them. The Solution kblocker is a Linux kernel module that hooks into netfilter and drops TCP connections to whatever sites I configure. The key feature isn't the blocking — it's how you turn it off. When blocking is enabled, kblocker generates a 128-bit key. With PGP mode, it automatically encrypts that key to people I trust and then erases it from kernel memory. The raw key no longer exists anywhere on my system. To disable the blocker, one of those people has to decrypt it and send it back to me. I outsourced my willpower. When I want to focus, I run: sudo kblockerctl enable 120 Two hours of blocked distractions. If I feel the urge to procrastinate, I can't — I'd have to text a friend, explain why, wait for them to decrypt it, and paste the result. By then the urge is gone. The Result I've gone from losing entire afternoons to YouTube to actually finishing things. The blocker has caught me mid-reflex to type youtube.com more times than I can count. It's free if anyone wants it: github.com/Dan-J-D/kblocker If you keep breaking your own focus tools, this is for you.

2026-06-14 原文 →
开发者

I Built a 4-Sided Plot Area Calculator with 2D & 3D Visualization

I Built a 4-Sided Plot Area Calculator with 2D & 3D Visualization Most online plot calculators only work for simple rectangular plots. However, many real-world properties have four sides with different measurements, making area estimation much more difficult. That's why I built a 4-Sided Plot Area Calculator that allows users to enter the North, South, East, and West dimensions and instantly calculate the approximate plot area. 🔗 https://www.premiumconverters.com/plot-area-calculator Features 📐 Supports irregular 4-sided plots 🏠 Calculates area in Marla, Kanal, Acres, and more 🖼️ Interactive 2D top-down visualization 🏗️ Isometric 3D plot rendering 📏 Feet & inches input support 📱 Mobile-friendly experience Why I Built It In many countries, especially in South Asia, property dimensions are often recorded as side measurements rather than perfect geometric shapes. Existing tools rarely address this use case properly. I wanted to create a simple solution that homeowners, buyers, real estate professionals, and developers could use without needing complex surveying software. The Result The calculator transforms four side lengths into a practical estimate while providing visual feedback that helps users better understand their property's shape. Building tools that solve real-world problems is one of the most rewarding parts of software engineering. Have you ever built a niche tool that unexpectedly helped thousands of users?

2026-06-14 原文 →
AI 资讯

Going Remote, Without Going Reckless: Multi-LLM Orchestration and the New Front Door in llm-cli-gateway 2.9.0

The earlier posts in this series were about what the gateway lets you call (cache-aware spawning across five providers, the Codex review gate, the CLI-versus-API argument) and the one before this was about the parts that do not show up as a tool, the upstream-tracking and the website that became the project's front door. This one is about a different kind of front door: the gateway can now listen over HTTP, behind real authentication, and serve more than one caller without those callers being able to read each other's work. That sounds like a small toggle. It is not. Moving an MCP server off a local pipe and onto a network port changes the trust boundary completely, and 2.9.0 is the release where I sat down and remediated all seventeen findings from a multi-LLM red-team of exactly that surface before telling anyone the remote path was ready. Short version: llm-cli-gateway is one Model Context Protocol server that wraps five vendor CLIs (Claude Code 2.1.177, Codex 0.139.0, Google's Antigravity agy 1.0.8, Grok 0.2.51, and Mistral Vibe 2.14.1) behind a single, uniform tool surface, so one orchestrating agent can fan a task out to several models, collect independent opinions, run a red-team or a consensus check, and keep durable session and job state across all of it. Until recently that only made sense on localhost over stdio. As of 2.9.0 the same server runs over HTTP with a static bearer token or a built-in OAuth 2.0 authorisation server (PKCE on by default, an opt-in human-consent gate, and a trusted-principal-header seam for when you front it with your own identity-aware proxy), every session and job and stored request is stamped with an owner principal and access is enforced per principal, remote provider calls are refused unless a workspace is registered, and the whole thing fails closed rather than open when the configuration is dangerous. Long version is below, same shape as always: what it enables, what the remote options actually are, a stack of worked scenar

2026-06-14 原文 →
AI 资讯

How to Cut Microsoft Agent Framework Costs With a Gateway Layer

Microsoft Agent Framework is built for production multi-agent systems, which is exactly why its LLM bill can grow faster than expected. If you are running workflows with retries, handoffs, tools, and checkpoints, the easiest savings do not come from prompting harder — they come from adding a gateway layer under the framework. I built Lynkr, so obvious founder disclosure: this article uses Lynkr as the gateway example. I’ll keep it practical and focus on where the cost actually shows up in Microsoft Agent Framework workloads. Why this is a real Microsoft Agent Framework problem The current Microsoft Agent Framework README positions it as a production-grade framework for Python and .NET, with: multi-agent workflows sequential, concurrent, handoff, and group collaboration patterns middleware observability provider flexibility checkpointing and human-in-the-loop flows That is exactly the kind of stack where token usage grows quietly. A single prompt-response app is easy to reason about. A production workflow is not. Once you add routing, retries, multiple agents, MCP tools, and long-lived execution state, the same context starts getting resent over and over. That creates four predictable cost leaks. Where the spend comes from in Microsoft Agent Framework workloads 1. Repeated shared context across agents Multi-agent systems reuse a lot of the same context: task instructions tool definitions previous messages workflow state grounding context Even when the framework orchestrates cleanly, the model provider still sees repeated input tokens. 2. Tool-heavy steps explode prompt size Once agents start using tools, responses stop looking like simple chat. You get: search results file reads JSON blobs browser outputs execution traces Those payloads are often much larger than the user’s actual request. 3. Every task does not need the same model A workflow step that says “classify this,” “summarize these logs,” or “extract the next action” does not need the same model as “resolve

2026-06-14 原文 →
AI 资讯

GLM 5.2 Just Dropped: What Zhipu's New Open-Weights Flagship Means for Developers

Introduction Zhipu AI (THUDM) has officially released GLM 5.2 , the latest iteration of its flagship open-weights model family. Announced today by Jie Tang on Twitter, the release is already making waves on Hacker News — racking up 269 points and 146 comments within hours. For developers who have been watching the open-weight LLM race, this is a significant moment. What's New in GLM 5.2 GLM 5.2 builds on the GLM-4 series that put Zhipu on the global map. The release focuses on three areas that matter most to production teams: Stronger reasoning and coding : Improved performance on multi-step reasoning benchmarks and competitive code generation against closed-source models like GPT-5 and Claude 4.5. Better multilingual behavior : GLM has always been strong in Chinese; 5.2 pushes English-quality code reasoning and longer-context retrieval closer to frontier levels. Longer context window : Reports point to a 200K+ token context with reduced degradation on long-document tasks — useful for codebase-level analysis. Weights, inference code, and a technical report have landed on Hugging Face under the THUDM organization, with an OpenAI-compatible API endpoint exposed by Zhipu's own platform. Why It Matters The open-weights race has consolidated around a handful of serious contenders — Llama, Qwen, DeepSeek, Mistral, and now GLM. Zhipu's positioning is unique: a Chinese lab that consistently weights-and-releases frontier-class models while still maintaining a hosted commercial API. For developers, that translates to real options: You can self-host on a single H200 or a pair of RTX 5090s and skip per-token API costs entirely. You can route between self-hosted GLM 5.2 and a hosted Anthropic/OpenAI endpoint depending on cost, latency, and capability. You get an OpenAI-compatible endpoint, so dropping GLM into an existing stack is a config change, not a rewrite. The Bigger Picture GLM 5.2 lands on the same week that U.S. regulators have reportedly cracked down on Anthropic model

2026-06-14 原文 →
AI 资讯

I built a free extension to track Claude's usage limits before you hit them

If you use Claude daily, you've probably hit a wall mid-task. The reason it's so easy: Claude doesn't have one limit. It has several. A rolling 5-hour session window A weekly pool shared by all models Separate weekly caps per model (Opus, Sonnet, Claude Design) And the weekly pool is sneaky: Fable has no cap of its own and draws that shared pool down ~2× faster than Opus . So you can burn the week without realizing it. I wanted that state visible at a glance instead of digging through settings, so I built Claude Usage Monitor — a small browser extension for Chrome and Firefox. What it does It puts a color-coded % badge in your toolbar , and one click shows the full breakdown: Current 5-hour session + live countdown to reset Weekly all-models pool (with the Fable draw-down flagged on the card) Per-model weekly caps: Opus, Sonnet, Claude Design Daily Claude Code routine runs Extra-usage credits + prepaid balance for pay-as-you-go Auto-detects your plan (Pro / Max 5x / Max 20x / Team), so every number matches your subscription Six themes The privacy part (the part I cared about most) It's open source (MIT) and runs 100% locally — no backend, no analytics, no servers. A design decision I'm happy with: the host permissions are scoped to exactly four claude.ai endpoints (org list, usage stats, routine-run budget, prepaid balance). It physically can't read your chats, projects or files. Narrow permissions = less to trust, and less to exploit. One honest caveat: it reads claude.ai's own usage endpoints, so it can break if Anthropic changes them. Not affiliated with Anthropic. Try it It's free, no account needed (desktop Chrome & Firefox): 🔗 claude-monitor.com Chrome Web Store Firefox Add-ons Happy to hear what else you'd want tracked — drop a comment.

2026-06-14 原文 →
AI 资讯

Building a Bitcoin Education Platform, Contributing to Open Source, and Surviving a Hackathon

A few months ago, I didn't expect that I'd be spending my days debugging authentication flows, opening pull requests, analyzing backend architectures, and building a Bitcoin education platform during a hackathon. Yet here we are. What started as curiosity about Bitcoin turned into one of the most intense learning experiences I've had as a builder, and honestly, I wouldn't trade it for anything. This is the story of how I joined Hack4Freedom Lagos 2026, helped build BitPath, contributed to open source, discovered OpenCode, and learned that software engineering is often just solving one problem after another until things somehow start working. How I Ended Up Building in Bitcoin My interest in Bitcoin didn't start from price charts or trading. What attracted me was the builder ecosystem around it. I've contributed to open source before, so I already appreciated the value of collaborative software development. But what stood out about Bitcoin was how deeply open source is woven into the culture. In many ecosystems, open source feels like an option. In Bitcoin, it feels like a foundation. Everywhere I looked, people were building in public, contributing to projects, improving documentation, reviewing code, and helping newcomers find their footing. That environment made me want to participate more deeply. When the opportunity came to join the Hack4Freedom Lagos 2026 hackathon, I said yes. The Project: BitPath Our team worked on BitPath, an AI-powered learn-and-earn platform designed to make Bitcoin education more accessible. The idea was simple: Instead of overwhelming learners with technical concepts, BitPath uses conversational learning experiences, AI tutoring, quizzes, progress tracking, and rewards to help users learn Bitcoin and financial literacy in a more engaging way. Our stack looked something like this: Frontend Next.js TypeScript Tailwind CSS Zustand Backend NestJS PostgreSQL Redis Queue processing Additional Services Google OAuth OpenAI APIs Lightning Network

2026-06-14 原文 →
AI 资讯

Week 2: Pull Requests, Rejected Code, and the Art of Not Breaking Things

GSoC 2026 | CircuitVerse × Canvas LMS LTI 1.3 Integration If Week 1 was about getting familiar with the codebase and understanding what needed to be built, Week 2 was about learning the hard way that writing code is only half the job. The other half — the messier, more humbling half — is getting that code accepted by the people who actually maintain the project. This week was full of detours, rejected pull requests, reviewer feedback that stung a little, and a surprisingly frustrating fight with a two-letter word in Ruby. But by the end of it, I had something real to show: a clean, reviewed, and submitted change to CircuitVerse that lays the foundation for the entire LTI 1.3 integration. Let me walk you through it. A Quick Refresher: What Are We Building? CircuitVerse is an open-source platform where students can build and simulate digital circuits right in their browser. The project I'm working on aims to connect CircuitVerse with Canvas, one of the most widely used Learning Management Systems (LMS) in universities around the world. The technology that makes this connection possible is called LTI — Learning Tools Interoperability. Think of it as a universal plug that lets any educational tool (like CircuitVerse) slot into any LMS (like Canvas) so that students can log in once, get assignments, submit work, and have their grades flow back automatically — all without leaving their course page. There are two versions of this plug: LTI 1.1 , which is old and uses a simpler (but outdated) security mechanism, and LTI 1.3 , which is newer, more secure, and what Canvas actually recommends today. My job is to bring CircuitVerse fully up to LTI 1.3 standards. Monday–Tuesday: A Pull Request That Taught Me to Read Diffs I started the week with what I thought was a solid pull request (PR) — a fix for a bug in CircuitVerse's existing LTI 1.1 grade passback feature. "Grade passback" is the process where CircuitVerse sends a student's score back to Canvas after they complete an as

2026-06-13 原文 →
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