The overall idea is to chart out the thousands of Mini PCs by benchmark and reveal the Pareto Front so you can get the most Compute per Dollar. Definitely a labor of love as I have a number of Mini PCs for my "homelab" (TrueNAS, piHole, Plex, basic stuff). It uses Gemini to extract specs from listings (since they're not often strongly categorized). Quick blog post here: https://luke.zip/posts/pareto-pcs/

So ... what's your In the Weights score?

Hey everyone. My cofounder and I are open sourcing Persona.js ( https://www.persona-chat.dev/ ). It's a VanillaJS library that helps anyone build agentic experiences on the web, without a framework dependency, and full WebMCP support So, why'd we do this? 1) We're super fans of the web and the browser can do a ton today 2) We've seen AI builds be way overly complex because the FE requires a large project within an existing app OR the site wasn't using a framework to begin with If you've been a p

I posted my portfolio here about a year ago ( https://news.ycombinator.com/item?id=45154609 ) and while there was a big response, it was very mixed! It'll probably be similar this time, but regardless of your thoughts about the concept, I think I've done a pretty good job creating one of the most nostalgic corners of the internet, especially with the latest additions. It'll always be up for debate whether this is an effective way to get noticed as opposed to a standard, quick and easy portfolio,

Give every customer their own Hermes or OpenClaw agent Discussion | Link

I spent a week building a counter program in Anchor — the Rust framework for writing Solana programs. By the end I had two instructions, one authorization constraint, and a test suite I could actually trust. Here is what I built, how I tested it, and the moment I proved the tests were real. Start Here: The Accounts Struct If you come from Web2, this is the part that looks the strangest: #[derive(Accounts)] pub struct Initialize { #[account( init, payer = authority, space = 8 + Counter::INIT_SPACE, )] pub counter : Account , #[account(mut)] pub authority : Signer , pub system_program : Program , } In a Web2 backend, your handler receives a request object and talks to a database. On Solana, there is no database; there are accounts. Every account your instruction needs to read or write must be declared upfront, before the handler runs. Anchor validates them before your code ever executes. Here is what each field does: counter — the account being created. The init constraint tells Anchor to make a CPI to the System Program, allocate 8 + Counter::INIT_SPACE bytes, and fund it from authority . The 8 is for the discriminator Anchor stamps on every account so the program can later verify "this is mine." authority — the wallet signing and paying for the transaction. mut because its SOL balance is decreasing to fund the new account. system_program — required any time you create accounts. Anchor checks that the address matches the real System Program. The accounts struct is the schema. The handler is the logic. The Handlers pub fn initialize ( ctx : Context ) -> Result { let counter = & mut ctx .accounts.counter ; counter .authority = ctx .accounts.authority .key (); counter .count = 0 ; Ok (()) } ctx.accounts gives you typed access to every account declared in the struct. The handler is short because Anchor already did the hard work: allocating the account, checking the signer, paying the rent. Your code just sets the initial values. pub fn increment ( ctx : Context ) -> Resu

The space agency shared footage of tests with its Ernest prototype rover.

Atlantic reporter Alex Reisner recently uncovered four datasets of music being used to train AI models and made them fully searchable for the public. Two of the sets are absolutely enormous at 12 million and 9 million tracks. The other two are much smaller, but still represent a significant amount of training data at over […]

If you’ve spent any time working with autonomous AI coding agents recently, you know the drill. You give the agent a straightforward task: "Add a user profile page and link it to the navbar." The agent says, "I've got this." It writes some code. You run it, and it throws an import error. You paste the error back. The agent apologizes, rewrites the file, and now your routing is broken. You paste that error back. Ten iterations later, your config is mysteriously deleted, the navbar is entirely missing, and the agent is trying to install a deprecated version of React. This is the AI Agent Doom Loop. It happens because current agent frameworks mistake intelligence for discipline. We dump a 10,000-token SYSTEM_PROMPT.txt telling the agent everything about our project, hoping it remembers the architecture constraints on step 45 of its execution loop. It rarely does. I built Agent Rigor because I got tired of babysitting agents that code themselves into corners. The Root Cause: Context Rot When an agent starts a task, its context is pristine. But as it reads files, executes commands, and hits errors, its context window fills up with junk stack traces and previous failed attempts. By the time it's 20 steps deep, the original system prompt you carefully crafted is buried. The agent forgets the architecture guidelines. It starts prioritizing the immediate error in front of it over the overall goal. This is when it starts guessing, hallucinating, and making things worse. The Solution: Progressive Disclosure and Empirical Discipline Agent Rigor isn't a new LLM or a magic prompt wrapper. It's an operating system for agents that enforces strict empirical discipline . Instead of one massive prompt, Agent Rigor uses a 3-tier hierarchy: L1 (Apex Kernel): The absolute, non-negotiable laws. (e.g., "Never guess an API signature. Always grep or read the file first.") L2 (Phase Directors): Orchestration that only loads when the agent enters a specific phase (Planning, Execution, Verifica

Most carbon footprint apps do the same thing: Quiz → "Your footprint is 120 kg CO₂/week" → Generic tips → User never returns. That's not a coaching experience. That's a guilt trip with no follow-through. For PromptWars Virtual — Challenge 3 (Carbon Footprint Awareness & Reduction), I built CarbonCompass with a different premise: Not just measure. Guide. Live demo: https://prompt-wars-virtual-hackathon-8u1kxxwh1-mithunvisveshs-projects.vercel.app/ The Problem with Existing Carbon Tools I started by looking at what already exists — Capture, Klima, JouleBug. Each of them calculates a footprint accurately. But they all fail at the same step: the recommendation layer. "Install solar panels." "Buy an EV." "Go vegan." These are structurally correct but useless for a hostel student in Chennai who travels by bus and eats at the mess. They're recommendations designed for a demographic that already has money and flexibility. CarbonCompass is built around two real Indian users: Aditi — a college student in Chennai. Bus commute, hostel mess food, shared room electricity. Her biggest carbon lever is food waste, not transport. Rohan — a tech professional in Bengaluru. Petrol car + scooter commute, air-conditioned 2BHK, frequent food delivery. His biggest lever is home energy, not diet. The same app, two users with different lifestyles receive coaching tailored to their highest-impact opportunities. That's the core product promise. The Architectural Decision That Made Everything Work Before writing a single line of code, I ran this prompt in Google Antigravity's Plan Mode: You are a senior product architect. Before coding: Generate user personas Design a SINGLE shared calculation module that the Dashboard, Impact Simulator, and AI Coach all call with the same inputs Create the data schema Propose page architecture Flag risks for a one-week build Do not write code yet. Create an Implementation Plan. The agent produced a full Implementation Plan artifact — a structured document I cou

Shinkei makes a refrigerator-sized robot called Poseidon to kill fish quickly and humanely.

AI design partner for presentations, social posts, and more Discussion | Link