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I'm probably misremembering the details, but does anyone recall a highly satirical story about a future where:- The main character starts as a beggar, but was one of the richest people in the world last night...they will win (and lose) their fortune again tonight, assumingly this will be a common pattern for the forseeable future- Similarly, the main character will marry the most desirable woman in the world when his fortune is regained, only to divorce a short while later when it is lost a
High/Low Model Split: What worked best was using larger models (Opus 4.6 has been a game-changer) to brainstorm an implementation plan in Markdown, and then handing that off to smaller models to do the actual coding. I felt this gave me a good balance between performance and costs. I recently started exploring Claude's planning mode, which I am considering using instead of my old approach.UI Struggles: While Claude’s newer UI skills are great for quickly building a frontend, I found it
Hi HN,<p>I built Butterwell after watching too many bakers sell things at a loss because pricing felt like guesswork.<p>The pattern I kept seeing: someone makes a great product, they pick a price based on what "feels right," and they never account for ingredient cost fluctuations or their own time. Most don't want to touch a spreadsheet.<p>Butterwell lets you photograph your recipe — handwritten or typed — and uses AI to detect the ingredients. It then pulls live grocery prices and calculates what you should charge, with a breakdown showing ingredient cost, labor, and margin.<p>The live price data was the tricky part to get right. Grocery prices shift constantly and the gap between stale data and real data meaningfully changes the recommended price.<p>Currently in free beta. Curious what the HN crowd thinks about the pricing model approach.
Hey guys, quick warning about a crazy MS 365 dark pattern I ran into last night. I was testing the business basic plan for a side project. Decided to upgrade to the annual tier to get the discount ($3.45/mo). Clicked convert to paid, put my burner card in, and got a $0.00 confirmation email. Thought we were good. Woke up today to my bank blocking a charge for exactly $1,035.00. Turns out when you hit the annual upgrade, Microsoft silently defaults the quantity dropdown to 25 licenses. No warning prompt at all. (25 seats x 12 months x $3.45 = $1,035). They send the zero-dollar invoice to make you think it's an auth hold, then try to drain your card while you sleep. The best part? When I went to their support chat to ask why my billing was so high, the system conveniently gave me a "System error, try again later" message. You can't even get help. Luckily I used a virtual corporate card with a strict limit for this test, otherwise my bootstrapped project would be out a grand today. Watch out for that hidden quantity field guys.
Now, I know the progress in the field of agentic ai has just really taken a pace. With the introduction of mcp, a2a protocol & anp protocols, it certainly made us ask the right question: What really is required for taking a giant leap in the progress of ai agents? Obviously, the answer lies in the question: "quick progress"! Sounds generic right? but think for a second. Nobody can give objective answer to that question, but every answer is what the progress follows.So to pace up th
I’ve been working in ML software for 4 years, and I quickly ran into a recurring problem: I kept asking myself, "Why did I write this function this way?" or "Why is this block here?"<p>I tried to organize my thoughts with Obsidian and other note-taking apps, but let’s be honest, documenting for yourself feels like a chore. Documentation always feels like it's meant for "someone else."<p>So, I decided to build a VS Code extension to save my reasoning and contextual memory directly linked to snippets, tags, and more. I even added a prioritized task list so I’d know exactly what was pending the next day.<p>And what happened? I never used it.<p>Months later, I felt disappointed. I felt like I had wasted all that time on something even I didn't find useful.<p>Then, agents arrived.<p>Working with AI agents has been a mind-opening experience, but I hit a wall: the "Cold Start" problem. Every new session required me to explain everything from scratch. I tried MEMORIES.md, AGENTS.md, and Claude’s project rules. Ironically, the cold start didn't improve as much as promised. Some benchmarks even show that agents perform worse when forced to parse too many static skill files, while others only show a marginal 10% improvement.<p>Out of curiosity, I decided to implement a local MCP so my agents could use Neurotrace autonomously.<p>The result was startling. I didn't think the agents would actually use the tool, but they are. I don't have formal benchmarks yet, but I can say with confidence that the cold start has drastically decreased. Since I use different agents from different providers, the "next" agent now knows exactly where we left off yesterday. They decide what contextual memories to save, and they do it surprisingly well. My workflow has improved significantly.<p>I'd love to hear your thoughts on agentic memory or if you've found better ways to handle context hand-off.<p>Cheers,<p>Irwing Castro (CastleOneX)<p>You can find it on the marketplaces here:<p><pre><code> VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=BlackIronTechnologies.neurotrace Open VSX: https://open-vsx.org/extension/BlackIronTechnologies/neurotrace</code></pre>
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