Most HVAC contractors run 4-6 vans and zero receptionists. When the AC dies on an 85°F day, whoever answers the phone first wins the job. If you're on a ladder, you just lost it. So I sketched a hypothetical client — Ironwood Heating & Air, Denver — and built SlotLock for them. The idea: turn a missed call into a confirmed appointment in under 60 seconds, no phone tag, no back-and-forth texts trying to figure out the ZIP code or the equipment model. Here's what it actually does: - Checks the service area instantly, before anything else (currently supports only Denver) - Runs a quick diagnostic through a chat assistant — equipment age, error codes, even safety flags like a gas smell - Locks an arrival window the moment the homeowner picks one - Syncs the booking straight to the dispatch board with technician assignment and route grouping already done No manual entry. No evening spent texting reminders. What surprised me building this: the real win wasn't the booking form, it was killing the 2-3 hours of evening admin that never shows up in a demo. Built this with Lovable as part of lovablechallenge.

Most of the "best practices" floating around right now are already outdated. Opus 5.5 changed enough that your old prompts are quietly costing you more than they should. Here are the shifts worth knowing: On effort levels ➤Start at medium, not high. It's now the sweet spot between speed and quality for most tasks. ➤Don't guess, test. Run the same task at low, medium, and high once and see what your workflow actually needs. ➤You can now switch effort mid-conversation without the model re-reading the whole chat from scratch. That used to be expensive, now it's just a setting change. On instructions and memory ➤Claude Code now reads a single agents.md file. No more juggling between claude.md and agents.md to keep in sync ➤In long chats, tell the model to treat earlier answers as settled. Otherwise it keeps re-checking old decisions instead of moving forward. ➤Have it keep a running checklist for multi-step tasks. Example: think of it like a to-do list taped to the model's desk, it stops it from marking a task "done" when it's actually 80% done. On pacing and cost ➤Give it a time budget. Saying "finish this in 3 minutes" actually changes how it paces the work. ➤No specific budget in mind? Just add "time matters." It speeds things up on its own. ➤Check your settings for free usage resets that came with the 5.5 launch, easy to forget, easy money left on the table. On design and images ➤Give it an actual design system or brand guideline. Without one, it defaults to generic, forgettable output. ➤For image-heavy tasks, let it crop and zoom using tools like PIL or OpenCV instead of processing the whole image. Smaller, sharper crops mean fewer tokens burned. ➤One more thing to know: asking it to "show its reasoning" may get flagged or refused more often now, so build that into how you test outputs. Which one of these are you not doing yet, the checklist, the time budget, or the effort testing?

A new AI model launched this week that can't write a single sentence That's the whole point It's called Jev, from a startup called TypeSafe AI, built by one of the people who helped train ChatGPT Every model we talk about lately, GPT-6 Astra, Fable 5.1, is built to think out loud and answer you like a person Jev does the opposite. You feed it information and a bunch of quick questions. It just replies with instant decisions and how confident it is. No paragraphs, no explanations For example: Ask an AI Agent "is this email urgent?" → A normal AI writes you three lines explaining why. → Jev just says "yes, 92% sure" in under half a second. Why people are paying attention: - Speed: replies in 70-500 milliseconds - Cost: almost free per response, since there's barely any output to pay for - Claim: up to 190x faster and 400x cheaper than regular AI models on tasks like sorting, tagging, or picking what to do next - Adoption: became the fastest-adopted model on Vercel's AI gateway within three days It's not here to replace GPT or Claude for writing or reasoning. It's meant to sit next to them, handle the boring quick decisions, and let the expensive smart model only step in for the hard stuff. If you're building AI agents, would you trust a model that never explains itself, only gives you a number?

Last week I watched my usage bar hit its limit halfway through a single feature build Not because the model was bad I was running Opus mostly, for the good stuff But somewhere between the third file read and the fifth tool call, the context window had ballooned so much that half the tokens were just... the agent re-reading itself That bugged me enough to go digging Why does an agent that's supposedly smart still burn so much just finding the right file to edit? Turns out most tools solve this with vector search Code gets turned into numbers, and the agent matches similar-looking chunks Sounds reasonable until you realize "send an invoice" and "refund an invoice" look nearly identical to a vector search One collects money, the other gives it back The math doesn't know the difference So I started testing something called Graph instead. No vector search. One command, graft build, and it maps the entire project: how every file, function, and dependency connects to everything else The agent reads that map instead of guessing its way through grep. I ran it on one of my own client projects to see if the difference was real: - First build barely moved the needle, maybe a few minutes faster - The next change, a full landing page revamp, took under 2 minutes - The map updates itself too, only the changed parts get remapped, not the whole project That last part is what actually convinced me to use it more If you're running Claude Code or Codex on daily basis, has your agent ever felt like it was reading the same file for the fifth time? If YES, then checkout - CLICK HERE

This week I noticed a repeating pattern that happens with most of the users Claude buries simple answers in jargon it never explains and it turns a one-line question into a five-paragraph essay you have to mine for the actual answer I hit both walls myself. Asked a quick question, got a term I'd never seen, no definition, then three paragraphs of context I didn't ask for Burned tokens, burned time So I built a skill around one rule: explain it like you're talking to a 15 year old. No assumed expertise, no dense wording, no padding Under the hood it leans on ASD-STE100 — the simplified English standard manuals use One idea per sentence. Common words only. No hidden acronyms It's built for people who can't afford to misread a sentence, which is exactly the bar an AI answer should clear to Since I started using it, the difference isn't subtle. Same questions, half the words, zero re-reading Turns out most "complexity" in an answer isn't the topic being hard. It's the explanation being lazy Anyone else keeping a running list of terms Opus 5 uses without ever defining them?