If your LLM costs are climbing, the instinct is almost always the same: swap to a cheaper model. GPT-4 to GPT-4-mini. Claude Opus to Claude Haiku. Sometimes that helps a little. It rarely fixes the actual problem. The actual problem, in most workflows I've looked at, is that every step gets routed through the LLM, even the steps that don't need language reasoning at all. This post breaks down a simple mental model for deciding what should and shouldn't touch an LLM, with a working example you... Read more ›
🙋 I’m Luhui Dev, a developer who has been breaking down Agent engineering and exploring how AI can be applied in education. I focus on Agent Harness, LLM application engineering, AI for Math, and the productization of education SaaS. Introduction A new term has been circulating in the Silicon Valley agent world: Loopcraft. My first reaction was: isn't this just putting an agent inside while true? A few years ago people called it Agent Loop. Then it became Workflow and Harness Engineering. No... Read more ›
Conversational search lets Manticore Buddy answer questions over an existing vectorized table. Buddy retrieves the most relevant rows with KNN search, turns those rows into context, and sends the context plus the conversation history to an LLM. Read more ›
React Native 0.86 Release Notes: Stability, Android Edge-to-Edge & The Future If you scan the React Native 0.86 release notes, you’ll see something unusual: stability headlines the story, not splashy features. For years, React Native upgrades were a gamble—teams braced for breakage. With 0.86, the whole tone is different. “No user-facing breaking changes” is front and center, signaling maturity, lower upgrade risk, and the kind of predictability real product teams need. That is a bigger shift... Read more ›
Most distraction blockers treat you like a criminal. They lock you out. They shame you. They charge you monthly to feel restricted. There's a better way. Introducing Lugn Lugn (Scandinavian for "calm") is a privacy-first browser extension that helps you build real focus habits - without the guilt. No hard locks. No punishment. Just a simple, mindful nudge: "Are you sure you want to visit this site?" Yes or no - your choice. Always. Why Lugn is different Lugn Cold Turkey Freedom Soft blocking ... Read more ›
Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code durable memory — entirely offline. - raiyanyahya/recall Read more ›
Originally published at ffmpeg-micro.com You've got a Notion database full of product names, launch dates, and promo copy. Every time you add or update a row, someone has to manually open a video editor, swap in the new text, export, and upload. That process takes 15 minutes if you're fast. There's a better way. Connect Notion to Make.com, hit the FFmpeg Micro API on every row change, and get a rendered video back automatically. Why Notion + Make + FFmpeg Micro Notion works well as a lightwei... Read more ›
Series — Fine-Tuning, Smallest to Largest: LoRA (1.5B) ← you are here In I fully fine-tuned a 270M model — updating every weight. That's fine for a tiny model. It gets painful as models grow, because full fine-tuning needs gradients and optimizer state for every parameter (~4× the model size in memory). So: what do you do when the model is too big to comfortably fine-tune all of? The idea behind LoRA LoRA (Low-Rank Adaptation) rests on one observation: the change fine-tuning makes to a weight... Read more ›
My Testing Setup I used Midjourney V7 (midjourney.com, Standard plan at $30/mo for this project — volume was too high for Basic) over five weeks across six product photo projects: lifestyle context images, background replacement concepts, packaging mockups, and mood-board style reference images for briefing photographers. Some outputs went live in ads. Others were used internally. A few were scrapped entirely. Two specific examples: I generated 12 lifestyle context images showing a skincare p... Read more ›
Here's the abstract for my talk on Wednesday of the conference (July 1): Everyone is using Figma's MCP tools, Claude Code, or Codex. The demos are seamless. The narrative is compelling. What's actually happening under the hood is something else entirely. And the gap between the story and the reality is where your next six months of pain is going to come from. I'm Jonathan Gordon, founder of ReWeaver AI and a programmer-turned-UX designer who spent 30 years in developer tools at Google, Micros... Read more ›
Why adaptability beats perfection in startup software development The Startup Trap: Building for a Future That Doesn't Exist Yet Many startup founders make the same mistake. They spend months building the "perfect" product architecture. The code is clean. The design patterns are flawless. The test coverage is near 100%. The infrastructure can scale to millions of users. There's just one problem: They don't have any users. In the startup world, survival depends on learning faster than competit... Read more ›
Over the last year, I've lost count of how many conversations I've seen about prompt engineering. Every week there seems to be another article explaining how a different prompt structure, a new framework, or a carefully chosen set of words can dramatically improve the quality of AI-generated responses. It's an interesting topic, and there is certainly some truth to it. A well-written prompt usually produces a better answer than a vague one. But after spending more time using AI in real DevOps... Read more ›
I Spent $8,857 Using Claude Code to Build 6 Projects. Here's What I Learned. Not a sales pitch. I just noticed there's almost no deep-use experience with Claude Code on Dev.to or Reddit — mostly "look I built a todo app" posts. After burning through nearly nine grand, I have some real data to share. The Numbers 14 days. $8,857.62. 3.884 billion tokens. 47,235 API requests. All on Claude Opus 4.8 (1M context window). You probably think I'm insane. Honestly, when I saw the bill after week one, ... Read more ›
Malaria still killed nearly 600,000 people in 2023 with 94% of them in Africa. For 140 years, diagnosis has meant a stained slide, a trained eye, and 20 to 60 minutes per patient. In some areas in rural Africa, that expert often isn’t there. A new scoping review in npj Digital Medicine maps what happens when AI steps in. The headline numbers are striking: Convolutional Neural Networks, CNNs now hit 97–98% accuracy on curated blood-smear images AI platforms scan 200,000 red cells in 7–10 minut... Read more ›
Originally published on andrew.ooo — visit the original for any updates, code snippets that aged out, or follow-up posts. TL;DR OpenMontage is an open-source, agentic video production system that turns any AI coding assistant — Claude Code, Cursor, Copilot, Windsurf, or Codex — into a full video production studio. You describe what you want in plain language ("Make a 60-second animated explainer about how neural networks learn"), and the agent handles research, scripting, asset generation, vo... Read more ›
You call an API, get back JSON, and someone non-technical asks for it "in Excel." Here are three ways to do that — pick based on whether it's a one-off or a repeatable pipeline — plus the part everyone trips on: nested objects. The sample data [ { "id": 1, "name": "Ada", "address": { "city": "London", "zip": "EC1" }, "roles": ["admin", "editor"] }, { "id": 2, "name": "Alan", "address": { "city": "Oxford", "zip": "OX1" }, "roles": ["viewer"] } ] The catch: address is a nested object and roles ... Read more ›
Kubernetes Security Best Practices 2026: The Complete Hardening Guide Introduction A single misconfigured Kubernetes cluster can expose your entire infrastructure in minutes. In 2025 alone, over 60% of organizations reported at least one Kubernetes security incident — and the majority traced back to preventable misconfigurations, not zero-day exploits. Kubernetes ships with minimal security defaults. Everything is open. Pods can talk to each other freely. Service accounts carry cluster-admin ... Read more ›
Originally published on AIdeazz — cross-posted here with canonical link. The first time I watched two of my agents undo each others work for forty straight minutes I wanted to throw my laptop into the Pacific. I am Elena Vakeva, a solo founder in Panama building multi-agent systems with real production constraints and almost no margin for wasted cycles. On paper my setup looks intelligent: one agent reads new papers, one writes code, one reviews pull requests, one speaks to users, and one mai... Read more ›
Series — Fine-Tuning, Smallest to Largest: QLoRA (7B) ← you are here In , LoRA let me fine-tune a 1.5B model by freezing it and training tiny adapters. But the frozen base still sat in memory in 16-bit (~3GB). Now I wanted to go to Qwen2.5-7B — and hit a wall that LoRA alone doesn't solve. The problem A 7B model is ~15GB in 16-bit precision. A free-tier T4 GPU has 16GB. It would barely load, with no room left to actually train. The QLoRA insight QLoRA asks the question that naturally follows ... Read more ›
In March 2023, GPT-4 could tell you whether a number was prime with 97.6% accuracy. By June of the same year, the same model name answered those same questions correctly 2.4% of the time. Nobody pushed a bad commit. No prompt changed in your repo. The thing behind the API just... moved. That number comes from a Stanford and Berkeley study, "How is ChatGPT's behavior changing over time?", and it's the cleanest illustration I know of the core problem with LLMs in production: the model is not a ... Read more ›