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It is time to put my proposition made in my previous blog post to the test. Is it possible to spec an application for execution by an agent without encoding it in source? Let's find out. One type of application every knowledge worker is familiar with is task management. Every task has a lifecycle status, dependencies on other tasks, and a history of progress. Let's give agents their own. TaskTrack is a simple but non-trivial task management system variant implemented as a Specify spec. It goes beyond checkbox-based to-do lists that agents sometimes use internally and mimics the key system features listed above. TaskTrack defines two procedures: a "Plan Authoring Run" to create an interconnected set of tasks from requirements and a "Plan Execution Run" to advance a previously authored plan toward completion. One execution run might not always be enough to achieve completion, because TaskTrack allows requesting human feedback and incorporating it during the next execution run. Furthermore, every execution run is divided into "Task Processing Run" sub-procedures to allow for advanced agent context management. TaskTrack implements all of this in less than 300 lines of text. If the implementation used source code, then, depending on the programming language, this would be enough space to implement only the required file I/O operations (TaskTrack uses files for simplicity, not a database). Natural language can easily become quite bloated, but a stringent, scientific writing style and extensive use of what the Specify standard offers can effectively counter that. The official test is, how could it be any other way, the implementation of yet another uninspired Breakout clone. The requirements, the completed TaskTrack plan, and the deliverable are contained in the repository. If you want to run the test yourself, the included README file contains the necessary information, including the launch prompts for both the authoring agent and the execution agent. Please note how both

Fallback rules are where an AI API gateway becomes operationally valuable. The goal is not to blindly retry every failed LLM call. The goal is to choose the right backup model, provider, or budget path based on the workflow, customer tier, latency target, and risk of a lower-quality answer. A practical fallback policy should define: which failures are retryable; which workflows may downgrade models; which customers or API keys are allowed to use premium fallback routes; how budget caps change routing behavior; what metadata gets logged so the team can debug cost and quality later. 1. Classify traffic before routing Do not write one global fallback rule for every request. Start by classifying traffic: Critical user-facing : support chat, checkout assistance, customer-facing agent answers. Non-critical user-facing : summaries, title generation, enrichment, recommendations. Internal automation : triage, labeling, data cleanup, back-office agents. Batch jobs : long-running summarization, extraction, report generation. Experiments : tests, staging, evaluation, prompt tuning. Each class should have a different fallback budget and quality floor. 2. Decide what counts as a retryable failure Good retry candidates: upstream timeout; 429 rate limit; temporary 5xx provider error; network interruption; overloaded model endpoint; streaming connection drop before useful output. Poor retry candidates: invalid API key; malformed request payload; unsupported tool-call schema; content policy rejection; user quota exhausted; deterministic validation failure. Retrying non-retryable failures usually burns tokens and hides product bugs. 3. Example fallback policy matrix Traffic class Primary route First fallback Second fallback Hard stop Critical user-facing frontier model same-class model on second provider cheaper model with explicit uncertainty after 2 provider failures Non-critical user-facing balanced model cheaper model cached/default response after budget cap Internal automation lo

I keep seeing shit all over about companies producing 95% of their code using AI. I see people saying they now write 100k lines of code a day. I am seeing this everywhere and as a CTO and solo eng, this has given me major fomo. For the past few months I have been working on everything I can to get to this level. I tried Ralph loop, goals, compound engineering, and my own mixture of things. No matter what I just keep finding that slop is coming out the other end and my time to review and fix (by prompting or by manually fixing) is 1. feeling like a pretty huge time sink and 2. sucks way worse than just using my brain to write to code. it is much less rewarding/ fun fixing retarded code than it is writing something elegant myself. I know AI is a huge productivity booster when I can use it for internal tools, scripts, prototypes, and boiler plate or highly defined work. other than that, it seems like it is kind of a time sink, especially for production and legacy apps. You guys experiencing this too? seriously, is the shit about crazy good ai output out there real and if so, how do I attain it? my tech advisor thinks it’s total BS and even said Google engineering is pulling back their AI use in coding some submitted by /u/l300TS [link] [留言]

This specific undertaking is not fundamentally burdensome in terms of labor; however, this endeavor serves as the crucial support for my unwavering commitment to see it through to its ultimate conclusion. It is precisely the motivation behind my relentless 72-hour shifts and the impetus that prevents me from ceasing my efforts. My affection amidst my grief—my aspiration is to assist others and ensure that the tragedy you experienced is never repeated. Caitlyn Walmsley, RIP. I will love you always.

My Previous Deployment Choices Since I mostly do web development using Ruby on Rails, these were my go-to options for deployment: PaaS like Heroku, Render, or Railway Serverless setups (Cloud Run + NeonDB) Honestly, I didn’t have any major complaints. PaaS costs a bit more, but in return, you get a clean UI and dead-simple workflows like GitHub integration. If the cost bothered me, I’d just go serverless. For my personal servers—where huge traffic isn't exactly a concern—going serverless meant the app would just sleep when inactive, allowing me to run services for around 50 yen a month. Compared to the headache of clicking through complex AWS or GCP dashboards to piece things together based on architecture diagrams, it was a walk in the park. I was perfectly content. Seriously. Kamal Became the Default in Rails 8 Everything changed when Rails 8 dropped. I heard they adopted Kamal as the official deployment tool. Kamal — Deploy web apps anywhere From bare metal to cloud VMs using Docker, deploy web apps anywhere with zero downtime. kamal-deploy.org Kamal? Is deploying really going to get any easier? I mean, I’m doing completely fine right now, though... That’s what I thought. But once I gave it a shot, it felt like being struck by lightning. This is an absolute game-changer. All you have to do is run rails new , throw your server's IP address into deploy.yml , and run kamal setup . That’s it—your app is deployed. For every release after that, it's just kamal deploy . I couldn't believe how simple the deployment workflow was. # deploy.yml service : my-app image : my-user/my-app servers : web : - 192.0.2.1 # Just swap in your VPS IP address here proxy : ssl : true host : app.example.com # Set up your domain here Sure, you have to bring your own server, but Kamal prides itself on being able to deploy absolutely anywhere. I rented a couple of VPS instances from Hetzner for about $10 a month each to host SuperRails and LazyCafe . From what I looked into, you can't really

Im a freshman college student, for a credit, I have to intern at an NGO and do community outreaches The one that I'm fixed to join asked me to revamp their website, understandable since I'm an engineering student, except i only have minimal knowledge. I'm interning for a month and would really like to learn this into a learning lesson but as of now, im extremely overwhelmed and have no idea where to start. All I've done is very basic HTML, CSS and JAVASCRIPT, nothing practical on an actual website. I would really really appreciate any help with how to approach this challenge. All help would be appreciated, thank you submitted by /u/Internal_Sector_1802 [link] [留言]

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Big update. The PDF hub is fully built out and a whole set of Office conversion tools just shipped. PDF hub complete (18 tools): Merge, split, compress, rotate, reorder, extract pages, remove pages, watermark, page numbers, protect, unlock, extract images, and more. Office ↔ PDF (6 tools): Word to PDF Excel to PDF PowerPoint to PDF PDF to Word PDF to Excel PDF to PowerPoint Office ↔ Office (3 tools, fully browser-side, no file upload): Word to Excel Excel to Word PowerPoint to Word Other recent updates: WordPress plugin updated to include the new PDF tools Chrome extension v1.1.0 updated with PDF support 1.6K pages now indexed on Google across 25 languages That brings the site to 61 free tools across three hubs (Image, PDF, Office), no signup required.

Building a Real-Time Chat Feature with Django Channels and React Real-time features have become table stakes for modern web applications. Whether it is a customer support widget, a collaborative tool, or a social platform, users expect instant communication without page refreshes. In this article, I will walk through how we built a production-ready real-time chat feature using Django Channels and React at UCDREAMS. Why Django Channels? Django is traditionally synchronous. It handles one request at a time per worker. This works fine for standard HTTP requests, but WebSocket connections require persistent, bidirectional communication. Django Channels extends Django to handle WebSockets, background tasks, and asynchronous protocols alongside traditional HTTP. The beauty of Channels is that it does not replace Django. It layers on top, letting you keep your existing models, ORM, authentication, and admin panel while adding real-time capabilities. For a team already invested in Django, this is a massive advantage over introducing an entirely separate real-time server. Setting Up the Backend Start by installing Django Channels and a channel layer. Redis is the recommended backend for production use: channels == 4.0 . 0 channels - redis == 4.2 . 0 daphne == 4.0 . 0 Configure your Django settings: INSTALLED_APPS = [ ... " channels " , ] ASGI_APPLICATION = " your_project.asgi.application " CHANNEL_LAYERS = { " default " : { " BACKEND " : " channels_redis.core.RedisChannelLayer " , " CONFIG " : { " hosts " : [( " 127.0.0.1 " , 6379 )], }, }, } Building the WebSocket Consumer The consumer handles WebSocket connections: import json from channels.generic.websocket import AsyncWebsocketConsumer class ChatConsumer ( AsyncWebsocketConsumer ): async def connect ( self ): self . room_name = self . scope [ " url_route " ][ " kwargs " ][ " room_name " ] self . room_group_name = f " chat_ { self . room_name } " await self . channel_layer . group_add ( self . room_group_name , self . cha

I work on a computer all day. Multiple projects, lots of switching, constant interruptions. A while back I noticed I was losing track of my own work. Not really the big things but mostly the small stuff. The decision I made on Tuesday about why I structured something a certain way. The thing I was halfway through when a Slack message pulled me away. The task that never made it onto any list because it felt too small to write down (but I ended up forgetting about after lunch until 2 days later). By Friday I'd look back at the week and genuinely struggle to piece together everything I actually did. I tried obsidian (and still actively use it). I also tried just being more disciplined. None of it fully stuck because the friction of capturing things manually meant I only ever captured the stuff I already remembered. The messy ad-hoc stuff that actually eats a lot of my time never made it anywhere. I'm curious if other people deal with this. Not the big project management stuff because that's mostly solved, but rather, the stuff in between. The context that lives in your head and disappears the moment you get interrupted. How do you handle it?

I've been working on a map poster editor as a side project. You pick a location, choose a style and color palette, and export a print-quality map. The tricky part is that each palette controls 15+ individual colors (road hierarchy from motorway down to service roads, water, terrain, buildings, text, etc.) and there are 80+ palettes organized into three categories. Current flow in screenshots: Full editor, style controls in the left sidebar Entry point with active palette preview + Browse and Fine Tune buttons Category picker (Terrain / Urban / Balanced), these are basically folders describing what the palette emphasizes Palette grid within a category, around 10 per category with swatch thumbnails Fine Tune panel with every individual hex color, grouped by section (Base, Roads, Water & Land, Buildings, Terrain) The tension is that casual users want "pick a palette, done" in one or two clicks. But power users want to tweak individual road colors or swap the water tone. Right now these are two completely separate flows and I'm not sure either one is great. Things bugging me: Two-click drill-down (category then palette) before anything changes. Is that necessary organization or just unnecessary friction? Fine Tune is hidden behind a button. People who find it love it, but is it too buried? 15+ hex inputs grouped by label. It works but feels intimidating. Are there better patterns for this? The preview problem. Right now palettes show diagonal color swatches, which are compact but pretty abstract. A mini-map preview showing each palette applied would be way more useful, but then I'd basically be replacing a clean card grid with a wall of tiny maps that are probably too small to actually read. Would a hover preview work? A single shared preview pane that updates as you browse? Or are swatches actually fine and I'm just overthinking this? If you landed on this editor cold and wanted to change the color scheme, what would you expect to see? What would you change? React + Ma

Does anyone know a cheaper way to track trending TikToks, Instagram Reels, and YouTube Shorts? I’ve already looked at Apify and Virlo, but they seem too expensive for what I need right now. I’m mainly trying to find viral formats, hashtags, sounds, creators, and short-form trends without spending a lot on scraping tools. Are there any cheaper tools, APIs, open-source projects, or manual/automated workflows people recommend? submitted by /u/InternalGrab3189 [link] [留言]

Are there any languages we don't usually associate with webdev, but where they have great libraries that deserve more attention? So I'm not asking about the language itself. I'm asking about the tooling people have built for webdev in the language. submitted by /u/returned_loom [link] [留言]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

VSCode extensions were how Github were breached earlier this year. What are people doing around VSCode security best practices around extensions. Approved Extensions Only Disable Auto update Is there anything else like minimum age or settings like that can be done? submitted by /u/ruddet [link] [留言]

This is a super dumb question (considering I've been working for like 5-6 years in this field) but I just want to get to the bottom of this so I can finally stop worrying that by searching "frontend developer" isn't reducing my future prospects LOL. submitted by /u/WitnessConfident2451 [link] [留言]

I currently have a website which heavily relies on the Google Places Photo API. Users can scroll through a bunch of POIs for planning trips, as well as various other things. This can mean that each user could 50+ unique photos. I understand that it’s against ToS to cache these photos and reuse them. The API costs are too high to maintain a scalable solution, but no real competitors come close to the quality of Google. At this current rate I’ll probably have to cap my users on the amount the amount of requests they can make. Is there some work around that I’m missing? Thanks submitted by /u/TWJ32 [link] [留言]

If you have never written a line of game code and you are trying to choose between Unity, Godot, and Unreal, the internet will give you fifty contradictory answers in your first hour of searching. This article is the answer we give to people who ask us in person. We are a Unity-specialist studio. I have spent 12 years building games, including a tenure as Mobile Team on RuneScape Mobile at Jagex. Over that time I have mentored a steady stream of new developers entering the industry. We picked Unity for our commercial work, deliberately, and we will say upfront where that lens does and does not serve you. The advice below is what I would tell a friend's teenager who asked which engine to learn first, not the version where I am trying to win you as a client. Three things drive whether a beginner finishes their first game or quietly abandons it: the engine's first-week friction, the quality of the free learning material, and whether the language and tools punish you or reward you when you make a mistake. Comparison articles obsess over feature lists. Beginners obsess over whether they can get something on screen by Saturday. We are going to talk about Saturday. The 30-Second Answer If you skim nothing else, take this: Pick Godot if you want the gentlest first week. The editor is small, the language reads like Python, and you can ship a 2D game to the web in a single afternoon. Best chance of you actually finishing a project. Pick Unity if you want a future career in the games industry. Largest tutorial library, biggest job market, most transferable skills. C# is harder than GDScript but every hour you spend on it pays back in the long run. Pick Unreal if you have always wanted to make games specifically because of the visuals. Blueprints let you avoid C++ at the start, the rendering looks beautiful from day one, and the long learning curve has the highest payoff if you commit. For the rest of the article, we will explain why each of those is true, where the engines gen

I Read Your AI Agent Logs So You Don't Have To: A $149 Service That Beats Another Dashboard What if the cheapest fix for your broken AI agent is a stranger reading 40 hours of traces for $149? I spent the last month doing exactly that — reading roughly 40 hours of production logs from teams running LangGraph, CrewAI, and AutoGen agents for paying customers. Not building observability dashboards. Not comparing LangSmith vs Langfuse. Reading the actual traces and writing up what was wrong, what to fix, and in what order. Three observations from those 40 hours: The dashboard was never the problem. Every team already had LangSmith or Helicone or a homegrown equivalent logging every LLM call. None of them were reading the logs. The "fix" was almost always one of seven patterns. I kept seeing the same shapes — stuck retry loops, idempotency gaps, tool-call argument drift, etc. — dressed up in different framework jargon. The teams that asked for "another tool" were the ones least likely to use it. They had 14 tools. The teams that paid for an hour of my time were the ones who said "I don't have time to look at this myself." That second group is who I'm now building a $149 service for. Here's why I think it works, what the deliverable looks like, and where the limits are. Why a $149 fixed-fee reading and not an hourly rate I tested three pricing models against the same deliverable: a written diagnostic of an agent's last 7 days of traces, prioritized fixes with code-level examples, and a 30-minute async follow-up. Model Conversion Avg revenue / inquiry Notes $200/hr (estimated 3hr) 2/40 inquiries $15 (lost 38 to sticker shock) Freelance default, fails on cold traffic $1,500 flat project 0/40 inquiries $0 Above the "I'll just keep it broken" threshold for most small teams $149 fixed diagnostic 11/40 inquiries $41 Below "another contractor" threshold, above "free advice" The $149 number is the inversion point — low enough that a stressed eng lead can expense it without a meet