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Most WooCommerce product pages still show the same thing stores have shown for 20 years: a handful of flat photos. I spent the last few months building Noorifa, a plugin that replaces that with an interactive Three.js viewer — customers rotate the model, zoom in, and switch colors/materials on specific meshes in real time, synced to the store's actual WooCommerce variations. The 3D rendering part was the easy 20%. The other 80% was a series of small, specific problems that don't show up in a Three.js tutorial. Here are four of them. 1. A directional light rig can't light a face it can't see Early on, customers rotating a table model would find the underside of the tabletop rendering near-black — no matter how far I pushed the light intensity. The rig at the time was a single key light plus a hemisphere ambient: scene . add ( new THREE . HemisphereLight ( 0xffffff , 0x444444 , 1.2 ) ); const keyLight = new THREE . DirectionalLight ( 0xffffff , 1.2 ); keyLight . position . set ( 3 , 5 , 4 ); scene . add ( keyLight ); The bug was geometric, not a brightness problem: keyLight sits above the model, so its light direction only reaches surfaces whose normal faces back toward it. A downward-facing surface — the underside of an overhanging tabletop — can't receive any direct contribution from a light positioned above it, at any intensity. Cranking the brightness slider was scaling a number that was multiplying against zero. The fix was closer to actual three-point studio lighting: key, fill, and rim from above for shape and separation, plus a dedicated light from below, and a brighter hemisphere ground color to approximate bounced light: scene.add( new THREE.HemisphereLight( 0xffffff, 0x888888, 1.1 * brightness ) ); const keyLight = new THREE.DirectionalLight( 0xffffff, 1.1 * brightness ); keyLight.position.set( 3, 5, 4 ); const fillLight = new THREE.DirectionalLight( 0xffffff, 0.5 * brightness ); fillLight.position.set( -4, 2, 3 ); const rimLight = new THREE.DirectionalLigh

This is a submission for Weekend Challenge: Passion Edition TL;DR SQL can count a commit trail. It can't always find the story it tells. Name a public GitHub repo. Snowflake fetches its commit history, decides which story is actually in there, and asks Cortex to narrate that one thread. You get a card you can drop into a README. 6 storyline detectors, 15 SQL views, and 0 AI calls in any of them—the story is chosen by plain SQL. Then 1 Cortex call, on 20–140 commit lines: 25% of the repo's, clamped. The warehouse is the editor. Cloud Run paints a PNG and computes nothing. Live at commitchronicles.anchildress1.dev , code at v1.0.0 , and I'm going for Best Use of Snowflake . What I Built Commit Chronicles reads one public GitHub repo and gives it back to you as a story. Snowflake fetches the repository, decides which story exists, gathers the evidence, asks Cortex to narrate exactly that thread, validates the result, and returns structured JSON. Cloud Run just turns it into a 1200×630 PNG—the size a README embed and a social preview both want. This is one of my repos and every dot, timestamp, and quoted commit on it is real. The color isn't just decoration—Cortex picks the accent hex as a reading of the arc, so a repo that died and one that came back and shipped don't look the same. The scope is deliberately one repository , not a whole profile. A year-in-review across a profile turns to mush. A repo has a clean arc: commits start, cluster, pause, restart, or stop. Two rules hold it together: Cortex interprets the shape. It never invents the facts. Every timestamp, count, gap, and quoted message on the card is real. It reads the arc; it does not reach past it. Motivation isn't in the data, so the model is forbidden from claiming any. A repo with no real story says so. Sparse histories get an honest grey card— "no story here" —and Cortex never runs. Not every repo is an obsession, and a tool that admits that is the one you trust when it says otherwise. Why I built it 🪤

Throughout my career I have had to work with quite a few platform teams, and I was part of two for a couple of years. Some were bad, some were good and some should not have existed at all. I want to tell you my user experience, what I have seen work and what not and what you should definitely avoid doing. Be The Multiplier This is the main goal of a platform team. As a team, it needs to be a multiplier. If the platform team supports 10 teams, then each work it commits should multiply by 10. If the team member builds a new feature, it should be helpful for all the other teams. Otherwise, the platform team is an addition, and in most cases it is then better to split the platform team and add them to all other teams, instead of being a separate team. Because the amount of communication needed is in most cases quadratic in relation to the number of teams. Reducing Cognitive Load The platform should take away cognitive load for all the teams it supports. By doing so, they will have more time to implement business requirements. Let's say a platform team provides Gitlab runners or Azure Agents where people can run their CI/CD code on. They should not need to know how the runners are scaled, or how the agents are updated. This takes away the need for that skill set in all the teams. Build a Community A platform team has a unique position. It is building something that all other developers probably can build as well. Some could do it even better than the platform team itself. For some platform teams, their ego sometimes comes in to play or just straight up refuses their help because they are not the team. But it is not the job of the platform team to build a product, but a platform where everyone can thrive and/or build on. So onboard the community on the platform! The Law of Diffusion applies to almost all companies. You will have the innovators that want to build it themselves and the early adopters that will voice their opinion, but will not build it themselves. Those two

No hacking — a passive scan only looks at what your browser already downloads when it opens a page. Here's what I found: 6 of 15 load their Supabase database directly client-side. The public API key sits in the page source. That's fine if Row-Level Security is configured right — but it's one wrong setting away from "anyone can read the whole table." 14 of 15 ship no Content-Security-Policy — a simple, high-value hardening against script injection, almost always missing. Is this theoretical? No. Two apps I audited with the owner's permission: A social app: the profiles table — user names, cities, and a password hash — readable by a logged-out stranger. Closed in an afternoon. A paid learning app: 155 paid study sheets and 4,872 answers were readable by anyone, with no account and no subscription — its entire paid catalogue, a single API call away. The paywall lived only in the front-end; the database served everything to everyone. Loading Supabase in the browser isn't the mistake. Not enforcing access in the database (RLS) is. And the tools you build with won't tell you — they'll happily ship it. If you built something on Lovable / Bolt / Replit with real users (or paying ones), it's worth 60 seconds to check what a stranger can already see. I made a free tool that runs the surface check (passive, no signup): sealdy.dev Happy to answer questions on how RLS leaks happen and how to lock them down.

The Developer's Guide to Picking the Right Coding LLM at Scale Six months ago, I was staring at our monthly AI bill — $14,000 and climbing fast. We were using the "premium" model for everything, including trivial code completions. That night, I built a small internal benchmark to figure out which models actually earn their cost. What I learned reshaped how we think about AI tooling, vendor lock-in, and what "production-ready" really means. Here's the raw truth from my testing rig, what we shipped, and how we cut costs by 70% without touching output quality. Why I Stopped Trusting Default Recommendations Every vendor says their model is the best. Every benchmark site ranks things differently. Most "best of" lists are either sponsored or built on vibes. I needed numbers that matched my actual workflow: generating Python services, debugging JavaScript race conditions, implementing TypeScript algorithms, and reviewing Go for security. So I took ten models, threw identical prompts at them, and scored them myself. No vendor PR. No cherry-picked examples. Just the same five tasks, run the same way, scored on the same rubric. Here are the ten models I tested, with their output pricing per million tokens — because at scale, that's the metric that decides whether your AI strategy is viable or a margin killer. Model Provider Output $/M DeepSeek V4 Flash DeepSeek $0.25 DeepSeek Coder DeepSeek $0.25 Qwen3-Coder-30B Qwen $0.35 DeepSeek V4 Pro DeepSeek $0.78 DeepSeek-R1 DeepSeek $2.50 Kimi K2.5 Moonshot $3.00 GLM-5 Zhipu $1.92 Qwen3-32B Qwen $0.28 Hunyuan-Turbo Tencent $0.57 Ga-Standard GA Routing $0.20 Before you ask: yes, I tested against the originals. I also tested against Global API's unified routing layer, which lets you hit any of these through one endpoint. More on that later — it became the architectural decision that actually saved us. My Benchmark Methodology (No Marketing Fluff) I built five tasks that mirror what my engineers actually do every week. Not synthetic acad

We Taught a Snowflake Warehouse to Judge World Cup Conviction and Write the Verdict Back to Solana Soumyadeep Dey Soumyadeep Dey Soumyadeep Dey Follow Jul 12 We Taught a Snowflake Warehouse to Judge World Cup Conviction and Write the Verdict Back to Solana # devchallenge # weekendchallenge # snowflake 5 reactions 1 comment 16 min read

Every operating system has security gaps. Most never publish them. I am publishing mine. IONA OS is a sovereign operating system written from scratch in Rust. It has a kernel, a GUI, a blockchain protocol, a programming language, and a 140,000‑line AI running in Ring 0. It is designed to be secure by default. But secure is a journey, not a destination. Here is the hard list — the security issues I found in IONA OS, and what I am doing about them. 1. The filesystem is not encrypted at rest IONAFS reads and writes sectors in plain text directly to the disk. I already have a real ChaCha20‑Poly1305 engine with per‑file key derivation ( fs/encrypted_storage.rs ), but it is only used for backup/distribution — not for everyday local reading and writing ( fs/ionafs/mod.rs ). Why this matters: For a journalist or a civil servant, this is the central threat scenario: a lost device, confiscation at a border, or seizure. What I'm doing about it: Integrating encrypted_storage.rs into the normal IONAFS read/write path. Every write will be encrypted automatically. The key will be derived from a PIN or TPM. 2. Deleting a file does not destroy it delete_file() removes only the index entry. The data sectors remain on the disk, recoverable with standard forensic tools. Why this matters: For users with high security requirements — journalists, activists, government officials — this is a critical gap. What I'm doing about it: Adding a shred() function that overwrites the data sectors with random patterns before releasing them, with a configurable number of passes. 3. The keystore uses XOR, not real encryption security/keystore.rs pretends to use AES/ChaCha in its comments, but the actual implementation is a simple XOR stream — trivial to break once an attacker has access to the disk. Why this matters: This is a critical vulnerability. XOR is not encryption. If an attacker has access to the disk, they can recover the keys. What I'm doing about it: Replacing the XOR stream with real ChaCh

I wanted to build something different from the usual fan project. Instead of recreating gameplay, I asked a different question: How well do players actually remember the worlds of Super Mario Odyssey? The result is OdysseyGuessr, a browser game inspired by GeoGuessr. Players receive a screenshot from one of the game's Kingdoms and must place a marker on the correct location. Every meter counts. The project includes: Browser-based gameplay Single-player with customizable rounds Real-time multiplayer with a 5,000 HP battle system Community-submitted locations Admin moderation tools One of the biggest challenges wasn't the gameplay—it was collecting interesting locations that were difficult but still fair. Watching players confidently choose the wrong cliff or rooftop has been surprisingly entertaining. If you're interested in browser games or Nintendo fan projects, I'd love to hear your feedback. Play here: https://odyssey-guessr.lovable.app OdysseyGuessr is an unofficial fan project and is not affiliated with Nintendo.

What’s up everyone! Back again for my daily drop. We talk a lot about deployment frequency and lead time for changes, but if you're a solo dev or part of a small team building something like LaunchAlly , there’s one metric that rules them all: Time to Recovery (TTR) from a bad push. When you're marketing, coding, and handling support all at once, a broken main branch is a massive bottleneck. Here is my quick tip for today: Invest 20 minutes into setting up strict automated rollbacks . If a deployment fails health checks, let the system revert it instantly without your intervention. Spend lots of hours working today...happy to go to bed now:) What’s your go-to strategy for handling failed deployments on the fly?

This is a submission for Weekend Challenge: Passion Edition What I Built I'm in Port...

Submission: Edu-Insight Assistant What I build I built the Edu-Insight Assistant, a tool designed for educators to bridge the gap between complex school management data and actionable insights. It allows teachers to query students performance data using natural language, turning educational evaluation into a conversation rather than a manual data-processing task. Demo 🔗 Link: Passion-challenge How I Built It I utilized Next.js for a responsive, performant frontend and hooked it up to Google Gemini 3.5 API. The core logic involves a server-side API route that takes a teacher's natural language questions, prompt Gemini to generate the necessary SQL, and execute that query against a database. This architecture makes data exploration accessible to non-technical educators. Prize Categories: - Best Use of Google AI : Leveraged Gemini 3.5 Flash for natural language-to-SQL translation and result interpretation. - Best Use of Snowflake: Designed with an extensible data layer ready for production-scale analytical workloads in Snowflake.

Hi everyone! this is my first post here on dev. About 4 days ago I realized I'm stuck as a mid-level backend engineer, and honestly, not even a good one. Some background: I have a CS degree and started working as a software engineer in 2022. I spent a year and a half at a financial company, then moved to one in the tourism industry, and now I work at a client-based company doing multiple projects. For a while I've felt like my growth here is blocked, so I started looking for another job. That search made me realize something uncomfortable: I've been relying on AI way too much. So now I'm trying to cut down on AI for my tasks, and I've decided to build a couple of projects that'll actually challenge me and help me learn. I've got two projects in mind: a stock exchange engine and a collaborative music production system. I'll share how it goes, including the parts I get wrong! (English isn't my first language, so I used AI to help polish the wording. The story and the projects are all mine.)

Enhancing CI/CD and E2E Testing with Sentry Integration in tvview TL;DR: I integrated Sentry for error tracking and improved End-to-End (E2E) testing in the tvview project, enhancing the CI/CD pipeline. This resulted in a score increase from 85 to 95+. The Problem The tvview project lacked comprehensive error tracking and E2E testing, making it difficult to identify and resolve issues in production. The existing CI/CD pipeline needed improvement to ensure smoother deployments and better code quality. What I Tried First Initially, I focused on setting up E2E tests using Vitest, but encountered issues with the test configuration. I also attempted to integrate Sentry, but faced challenges with the DSN (Data Source Name) configuration. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created separate configuration files for the client, edge runtime, and server: // sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.edge.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.server.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io " , // Additional configuration options }); Step 2: Enhancing CI/CD Pipeline I updated the .github/workflows/ci-e2e.yml file to include Sentry configuration and E2E testing: name : 📺 CI + E2E — TVView on : push : branches : [ main ] workflow_dispatch : {} schedule : - cron : " 35 6 * * *" jobs : build-and-test : runs-on : ubuntu-latest steps : - name : Checkout code uses : actions/checkout@v2 - name : Install dependencies run : npm install - name : Generate Prisma client env : DATABASE_URL : " postgre

Enhancing CraveView's CI/CD Pipeline with Sentry and E2E Tests TL;DR: I upgraded CraveView's CI/CD pipeline by integrating Sentry for error tracking and implementing End-to-End (E2E) tests, boosting the score from 85 to 95+. This technical deep-dive explores the architecture decisions, code changes, and lessons learned. The Problem The initial problem wasn't a single error message but a series of inefficiencies in the CI/CD pipeline. The existing setup lacked comprehensive error tracking and test coverage, leading to potential issues in production. Specifically, the pipeline didn't have: Robust Error Tracking : No integrated system for capturing and analyzing errors. End-to-End Tests : Limited test coverage, which could lead to undetected issues in production. What I Tried First Initially, I focused on enhancing the test suite. I explored various testing frameworks but decided to implement E2E tests using Vitest, given its compatibility with the existing tech stack. The first approach involved setting up a basic E2E test framework. However, I encountered issues with the test environment configuration, particularly with database connectivity. The tests required a realistic database setup, which wasn't properly simulated. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created configuration files for client, edge, and server initialization: sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io/4511629 " , // Additional config options }); sentry.edge.config.ts and sentry.server.config.ts follow a similar structure, adjusted for their respective environments. Step 2: Implementing E2E Tests I added a new test file e2e-production.test.ts in src/__tests__ : import { test , expect } from ' @playwright/test ' ; test ( ' should render the homepage ' , async ({ page }) => { await page . goto ( ' https://craveview.vercel.app ' ); await expe

Upgrading CI Workflows: From Node 20 to Node 22 and Actions v5/v6 TL;DR: I upgraded the CI workflows for the content-automation repository from Node 20 to Node 22 and Actions v5/v6, addressing compatibility issues and improving performance. Key changes included updating upload-artifact from v5 to v7 and implementing retry with backoff. The Problem The CI workflows for the content-automation repository were using Node 20 internally, despite the configuration specifying Node 20. This discrepancy caused compatibility issues with newer versions of the GitHub Actions. Specifically, the upload-artifact action was still on version 5, which was internally targeting Node 20. What I Tried First Initially, I attempted to update the upload-artifact action to version 7, which supports Node 22. However, this change alone did not resolve the issue, as other actions like checkout and setup-python were still on older versions. The Implementation To address the compatibility issues, I updated the following actions: upload-artifact from v5 to v7 checkout to v5 setup-python to v6 Here are the specific code changes: // .github/workflows/main.yml steps: - name: Checkout code uses: actions/checkout@v5 - name: Setup Python uses: actions/setup-python@v6 - name: Upload artifact uses: actions/upload-artifact@v7 Additionally, I implemented a retry mechanism with backoff for the CI workflows: // .github/workflows/main.yml steps : - name : Retry with backoff run : | for i in {1..3}; do if ./script.sh; then break else echo "Retry $i failed, backing off..." sleep $((i * 2)) fi done Key Takeaway The key takeaway from this experience is the importance of keeping CI workflows up-to-date with the latest versions of GitHub Actions. This not only ensures compatibility but also improves performance and reliability. What's Next Next, I plan to monitor the CI workflows for any issues and continue to optimize the retry mechanism for better performance. I will also explore other ways to improve the reliabili

Disallow: GPTBot is a wall. Walls don't pay rent, and the crawlers that matter most either ignore them or route around them. If your content is worth training on, the interesting question isn't "how do I keep the bots out" — it's "what do they owe me, and how do I say so in a way a machine can read." That's what RSL (Really Simple Licensing) is for. It shipped 1.0 in December 2025 with around 1,500 publishers behind it — Reddit, Yahoo, Quora, O'Reilly, Medium, Vox. This post is a from-scratch walkthrough of what the format actually is, the six places you can put it, the one mistake that makes crawlers silently ignore your terms, and where the declaration stops and enforcement begins. No tooling required to follow along — it's all plain XML and HTTP. The format is an XML vocabulary, not a config file An RSL document says: for this content, here's what's permitted, what's prohibited, and what it costs. Minimal example: <?xml version="1.0" encoding="UTF-8"?> <rsl xmlns= "https://rslstandard.org/rsl" max-age= "7" > <content url= "/" > <license> <permits type= "usage" > search </permits> <prohibits type= "usage" > ai-train </prohibits> <payment type= "crawl" > <amount currency= "USD" > 0.015 </amount> </payment> </license> </content> </rsl> Read it out loud: search engines may index this; training on it is prohibited; if you want to crawl it anyway, the rate is $0.015. usage tokens include search , ai-train , ai-use (inference/grounding), and a few more. You can scope rules by user and geo too. One rule that trips people up: prohibition wins . If the same token shows up under both permits and prohibits , the content is prohibited. Don't try to express "allowed except for X" by listing X in both — just prohibit X. The namespace is the thing crawlers actually key on The single most common way to publish RSL that quietly does nothing: getting the namespace wrong. It must be exactly: xmlns="https://rslstandard.org/rsl" http instead of https , a trailing slash, or a plausible

hi, i am Aniruddha Adak, a full-stack developer from kolkata who spends way too much time building things with ai tools, shipping apps, and reading way too many github readmes at 2 am. i built 27 apps in 45 days using no-code and ai tools last year. that experience taught me one thing very clearly: the landscape of ai tooling for developers is moving insanely fast, and it is genuinely hard to keep up. so i sat down and did something about it. this is my deep research post on every ai tool, agent, builder, reviewer, and framework that developers, software engineers, and ai engineers should actually know about right now. i have organized it into categories so you can find what you need quickly. no fluff. just the tools, their sites, and what they do. why i wrote this i keep seeing developers waste time because they do not know the right tool exists. someone is manually reviewing pull requests for a week straight, not knowing coderabbit exists. someone else is hand-writing supabase schemas when emergent can do it in seconds. another person is spending days on a landing page when v0 can scaffold it in one prompt. this post is my attempt to fix that. i went through github repositories, dev communities, product hunt launches, and research aggregators to compile this. it is long. that is intentional. bookmark it. section 1: ai-native ides these are not just editors with a chatbot plugged in. these are environments built from the ground up around how language models think and work. tool site what it does cursor https://www.cursor.com forked vscode, codebase-aware context windows, multi-file edits with copilot-style background indexing windsurf https://windsurf.com cascade ai agent that writes files, runs terminal checks, and fixes things in real-time zed https://zed.dev built in rust with gpui, super low latency, native multiplayer coding support replit https://replit.com cloud ide with a full autonomous agent that runs inside serverless virtual workspaces google antigravit

Hey everyone, I change servers more often than I probably should. A discounted VPS or a good coupon is usually enough to convince me, but manually recreating the same web stack every time stopped being fun a long time ago. That is why I built HostShift , an Apache-2.0 licensed Go CLI for discovering, planning, migrating, and verifying Ubuntu and Debian servers. The rule I would not compromise on The source server must remain read-only. HostShift does not install packages, stop services, enable maintenance mode, create temporary archives, or change configuration on the source. It reads approved facts and streams data directly to the target. Any target mutation requires an explicit CLI apply command. What it currently covers Docker Compose projects and standalone containers MySQL/MariaDB, PostgreSQL, and Redis Nginx, Apache, Caddy, and systemd services SSH and firewall configuration PHP-FPM, Supervisor, Fail2ban, Certbot, and Logrotate Migration planning, audit journals, status, resume, rollback metadata, and verification checks The migration engine is deterministic Go code and does not need AI. I also added an optional Codex plugin and a deliberately non-apply MCP interface for discovery, planning, review, and dry runs. Actual changes stay in the human-operated CLI. Testing real migrations I did not want to call it tested just because a few unit tests passed. The repository includes Docker migration matrices and real Lima VM matrices covering Ubuntu 22.04, Ubuntu 24.04, Ubuntu 25.10, Debian 12, and Debian 13, including cross-distribution moves. The VM tests also reboot the target and verify persistence while comparing source snapshots before and after the migration. The project is still new, so I expect real-world edge cases. I am sharing it now because feedback from people who actually move and maintain servers will be more useful than polishing it alone forever. GitHub: https://github.com/oguzhankrcb/HostShift Documentation: https://hostshift.karacabay.com

This is a submission for Weekend Challenge: Passion Edition What I Built Every developer has a graveyard of side projects — started with fire, abandoned quietly on a Tuesday. They deserved better than an empty GitHub repo gathering digital dust. DevGraveyard is a gothic memorial platform where developers give their abandoned passion projects a proper burial. Connect your GitHub, pick a dead repo, carve its epitaph — and watch Gemini AI write a dramatic breakup letter from you to the project. Here's what it does: ⚰️ Bury a project — 3-step burial wizard: pick a repo → choose cause of death ( "Never Made it Past Localhost" , "Ran Out of Weekend" , "It Was Complicated" ...) → write an epitaph 🪦 Real tombstone data — pulls your actual commit history: peak obsession streak, most commits in a single day, last commit message ( your final words ) 🤖 AI Eulogy — Google Gemini writes a dramatic breakup letter from you to the project, referencing your real commit data 🕯️ Community mourning — light candles, leave RIP messages, vote to resurrect projects 🌐 3D Graveyard — a full Three.js interactive cemetery: bare trees, fireflies, flickering candles, soul wisps, resurrection pulse rings. Click any tombstone to interact My own ARweave repo had 56 commits, a 2-day peak streak, 30 commits on its best day. Cause of death: "Never Made it Past Localhost." Last words: "feat: overlay plane in 3D builder — drag/scale image on marker, position saved to DB and restored in AR viewer." It worked until it worked. Demo 🔗 Live → devgraveyard.varshithvhegde.in Code Varshithvhegde / devgraveyard Give your abandoned passion projects a proper burial. A gothic graveyard for dead side projects. ⚰️ DevGraveyard A memorial for your abandoned side projects. They deserved better than an empty GitHub repo gathering digital dust. Live → devgraveyard.varshithvhegde.in What is this? Every developer has a graveyard of passion projects — started with fire, abandoned quietly on a Tuesday. DevGraveyard gives them

Most things that ship under "browser MCP" are the same thing wearing different names: an autonomous agent with a do-anything tool, pointed at your browser, told to figure it out. The pitch is capability. The unspoken cost is that a runtime which can do anything can be steered into doing anything. We just published the opposite, and we published it in the open. github.com/e2llm/e2llm-sifr is now the canonical home for SiFR - the format spec, the taxonomy, the MCP server manifest, real page captures, per-client configs, and the model skill. MIT-licensed. The capture engine and the server stay a hosted product; the format and the interface are open. This post is about why that split is the whole point. E2LLM is not an agent This comes first because everything else follows from it. An agent decides and acts on its own. It plans, it loops, it takes steps toward a goal with you out of the path. That autonomy is the feature - and it is also the attack surface. A runtime that can do anything is a runtime that can be talked into anything. E2LLM is a perception layer, not an agent. It gives whatever model you already use senses for the browser: structured sight, and a small set of narrow, individually-gated actuators. It does not plan, does not loop, does not decide. Your model does the reasoning. E2LLM reports what a page is and carries out one explicit instruction at a time. Nothing runs while you look away. Perception substrate versus autonomous runtime. That line is the design, not a disclaimer on top of it. What SiFR is - and the three things it isn't SiFR (Salience-Indexed Flat Relations) is the capture format at the center of E2LLM. From a distance it can look like a tidy DOM dump or an accessibility tree. Mechanically it is neither, and the difference is the entire value. Not a DOM dump. A dump serializes the tree as-is: everything, in document order, noise included. SiFR selects and ranks. It scores every node by salience, drops scaffolding, and flattens the survivor