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 ›
Three weeks ago I published Every AI Coding CLI in 2026: the complete map. It was a list for engineers in terminals. Most readers of AI coding tools are not engineers. They are builders, designers, and product people who want AI to help them ship without needing a terminal. This follow-up is the map for that audience. If you are not a daily-terminal user but you want AI helping you ship (apps, websites, side projects, internal tools), the relevant question is not "which CLI". It is which surf... Read more ›
ClickHouse 26.6 Deep Dive: Streaming Queries, MPP Execution, Geospatial Analytics, and Developer Productivity ClickHouse 26.6 is one of the most technically significant releases in recent months. Instead of introducing another collection of SQL functions or storage engine improvements, this release focuses on expanding the database's execution engine, improving developer productivity, and enabling new classes of analytical workloads. The release introduces continuous streaming queries, multi-... Read more ›
As a developer, my desk is constantly cluttered with documentation, API references, and whitepapers. A few months ago, I got tired of spending hours reading 50-page PDF specifications just to find a single configuration line. I decided to scratch my own itch and build a lightweight, web-based RAG (Retrieval-Augmented Generation) tool to "chat" with PDFs. In this post, I want to share the technical hurdles I ran into—specifically regarding PDF parsing layout traps and token cost optimization—a... 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 ›
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: 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 ›
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 ›
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 ›
I shipped my app in English, watched it do fine in the US and UK, and assumed the rest of the world would catch up once word got around. It didn't. The install rate in non-English markets was roughly a third of what I saw in English ones, even where I had decent traffic. So I dug into it, and the answer was embarrassingly simple: people couldn't read my store listing. Not the description. They can machine-translate that in their head, or the store does it for them. The problem was the screens... 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 ›
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 ›
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 ›
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 ›
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 ›
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 ›
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 ›
Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code durable memory — entirely offline. - raiyanyahya/recall Read more ›