Show HN: SeasonMap – when to travel where? visualized with climate data https://ift.tt/RJ2t70d

Show HN: SeasonMap – when to travel where? visualized with climate data Author here. I'm trying to visit every country and I've been to 158 so far. Before I decide where to travel, I'd ask a local friend which season to avoid, or open up dozens of browser tabs on climate data to figure out what the place is like in a given month. Climate data still miss things. Cancun in September looks great on paper, with 31°C and 10 hours of sun, but it's hurricane season and the beaches can be covered in seaweed. Typical info that locals would know, which can also be captured as static data. So I built SeasonMap to answer "when should I go to ?" What you can do: - Pick a travel style (city walk, beach, hiking, skiing, max sun, low humidity, etc.) and see every place ranked on a map - See what's in season and what to avoid, and why: monsoon, hurricanes, extreme heat, bad air, peak crowds - Filter destinations by temperature, rainfall, sunshine, air quality and hazard seasons - Open a place to see its whole year: month by month weather, events (festivals, whale watching, cherry blossom), crowd levels, practical notes like scams, and traveller anecdotes summarized by AI with links to the sources Data Source & how I made it: - The climate data is ERA5 normals via Open-Meteo (2016–2025), corrected with NOAA station data where available. - Events, hazards and traveller notes were researched and by AI agents, and every one links to its source. Gathering it was easy. Checking it was the hard part. - Yes, I've used AI heavily on this project before anyone call it an AI slop. Making was easy, but it took billons of tokens of beating whack-a-mole ai to polish and tweak to make it usable and decent. Through that, I've created many skills and evals ranging from visual qa, evals for irregular data, automated i18n and others. It still feels much like AI as I was using Claude Design, which i want to improve on. I tried using local llm, but the throughput was so low. Pricing: the first 5 minutes are fully open, no signup. After that, the top 3 destinations and 25 place breakdowns a month are free. A 30-day pass is $7, $39 a year or $69 lifetime. iOS and Android apps are coming soon. Any feedback welcome. https://seasonmap.app September 16, 2026 at 09:13PM

Show HN: Pixel Agents – A pixel-art mission control for your Claude Code agents https://ift.tt/biClGRS

Show HN: Pixel Agents – A pixel-art mission control for your Claude Code agents https://mateovalle.github.io/pixel-agents/ September 16, 2026 at 10:42PM

Show HN: Pizza Bot – An inbox for AI agents that work in the background https://ift.tt/46WEU1t

Show HN: Pizza Bot – An inbox for AI agents that work in the background Hi HN - long-time lurker (since 2012!), first time poster. Pizza Bot is a self-hosted desktop app for Mac, Windows, and Linux that runs AI agents in the background and exposes them through an email-like UI. Finished work shows up in Unread, and anything waiting on your approval shows up in Action. It's Apache 2.0-licensed, there's no signup and no telemetry, and you bring your own model provider: Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or a local model through Ollama. There are builds on the releases page, or you can run it from source. Pizza Bot started as an internal passion project I worked on with a small team at Amazon. The whole thing came out of my frustration at having to manually log CRM activities through a browser form. I built a simple REST API called "JoeBot" that connected to my authenticated browser session over CDP and filled out the form for me using Playwright. Then I hacked up a quick Obsidian plugin so I could trigger it from my local notes (no AI and no MCP servers involved). This caught on quickly. My fellow AWS Solutions Architect Igor Fil joined up with me, and we rebranded the project as "Pizza Bot," named after Amazon's two-pizza teams. We started seeing what other automations we could build. We found a GraphQL API we could query and hacked up some "recipes" to pull data out of the CRM to help with meeting prep. That worked great, and it was right around the time MCP servers seemed to be taking off, so we decided to expose Pizza Bot as an MCP server instead, so it would be available to AI tools through natural language. This was a decent solution for technical users, but the Account Managers who live inside our CRM system wanted something too. We decided to rebuild Pizza Bot as an Electron desktop app modeled after an email inbox, so it would be familiar to non-technical users and would run on both Mac and Windows. We also bundled internal MCP servers as OCI images and hosted them in Amazon ECR as an "addon marketplace" so users could install them with one click without having to set up Amazon developer tooling. The project took off organically and expanded outside of AWS into the wider Amazon organization globally. More than 2,000 people ended up using it for meeting prep, email drafting, Slack summaries, CRM logging, prioritizing their day, and web research. Once apps like Claude Cowork and Amazon's own Quick Desktop came out, we realized the real growth opportunity was outside of Amazon. Rather than try to rip out the Amazon-specific integrations, we rebuilt Pizza Bot once more as an open source project. We leaned on coding agents heavily, which is the only reason a team our size could pull off a full rewrite. I'm pleased to say it's finally public, and we're hoping to bring in community members and see where it goes. We'd like to do for knowledge workers what Claude Code and Codex have done for programmers. A couple of things to know up front. Most of what made Pizza Bot useful on day one inside Amazon came from that internal catalog of skills and MCP servers for Amazon's own systems, and none of it could come out with the app. So it ships thinner than the version those 2,000 people used, and building that catalog back up for tools other people actually use is where we need the most help. It's also a community project and not an AWS service, so there's no support or SLA behind it. The Windows and Linux builds aren't signed yet either. On the technical side, Pizza Bot is a server and a client. The desktop app bundles both, or you can point a client at a remote backend; personally, I self-host the server on my home network and reach it from my phone over Tailscale. The server owns the thread lifecycle and checkpoints state with DeepAgents and LangGraph, and clients rehydrate from it as needed, so you can disconnect mid-run and pick the thread back up from another client. Approval pauses outlive the session that created them and collect in an Action filter, so you can answer an hour later from a different device. The agent you talk to has a sandboxed QuickJS interpreter that can reach your filesystem only if you grant it a folder, but its main job is to delegate. Each subagent is a 1:1 mapping of a Skill, and an Activity bar shows that subagent and the tool calls it's making as it works. Memory is opt-in and stored as plain markdown files on your machine. Every tool call is explicit, including looking up a memory - we err on the side of transparency to reduce surprises. Tools come from MCP servers, and skills are ordinary SKILL.md files with a per-tool approval policy, so existing skills that don't require a code interpreter should still work. What I'd most like to hear about is where the app itself gets in your way, the kind of problem you can't fix by writing a skill or an MCP server. I'm around today to answer questions! https://ift.tt/MAz40UX September 15, 2026 at 08:50PM

Show HN: Sass – Rust and WASM https://ift.tt/aGXo1Ry

Show HN: Sass – Rust and WASM https://ift.tt/JsKdHtN September 16, 2026 at 01:04AM

Show HN: SCIP MIP solver bindings for Go, ported from russcip https://ift.tt/mBOMxbR

Show HN: SCIP MIP solver bindings for Go, ported from russcip https://ift.tt/5usnQrh September 15, 2026 at 09:26PM

Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS https://ift.tt/WQxwIoz

Show HN: Macros with a Behringer FCB1010 MIDI Pedalboard in macOS https://ift.tt/je35Jch September 15, 2026 at 04:31AM

Show HN: Pull every comment out of a Google Sheet, in the browser https://ift.tt/Q9xZVia

Show HN: Pull every comment out of a Google Sheet, in the browser I spend a lot of time working in Google sheets. Often, I will collaborate with up to 50 other people in the same workbook. One specific org I worked at had a strong preference for using comment threads in Sheets extensively to ask questions, give feedback, and raise risks. Sheets would regularly get 70+ comment threads across multiple tabs with dozens of contributing teams. As the owner of the sheet, I wanted a way to document the comments to retain traceability on all of the various conversations. To my surprise, there was no function built into sheets to scrape, count, sort or manage comments at all. I also couldnt find any good third party add-ons to solve the problem. So I built one myself. I discovered that the Drive API gives you the comment text, author and state, but doesnt give the location of the comment which was crucial. Thankfully, the .xlsx export has xl/threadedComments with the cell reference, timestamp, author, resolved flag and parent ID. How it runs: JSZip in the browser, no sign-in, nothing uploaded anywhere. I am in the process of having the app verified to be hosted on Google Workspace Marketplace, but in the mean time the tool is available at: https://ift.tt/aKR1PvA https://ift.tt/QJYh7qK September 15, 2026 at 01:20AM