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New evidence that Cantor plagiarized Dedekind?

[Other] Show HN: Sgai – Goal-driven multi-agent software dev (GOAL.md → working code) Hey HN,<p>We built Sgai to experiment with a different model of AI-assisted development.<p>Instead of prompting step-by-step, you define an outcome in GOAL.md (what should be built, not how), and Sgai runs a coordinated set of AI agents to execute it.<p>- It decomposes the goal into a DAG of roles (developer → reviewer → safety analyst, etc.) - It asks clarifying questions when needed - It writes code, runs tests, and iterates - Completion gates (e.g. make test) determine when it&#x27;s actually done<p>Everything runs locally in your repo. There’s a web dashboard showing real-time execution of the agent graph. Nothing auto-pushes to GitHub.<p>We’ve used it internally for prototyping small apps and internal tooling. It’s still early and rough in places, but functional enough to share.<p>Demo (4 min): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;NYmjhwLUg8Q" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;NYmjhwLUg8Q</a> GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;sandgardenhq&#x2F;sgai" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sandgardenhq&#x2F;sgai</a><p>Open source (Go). Works with Anthropic, OpenAI, or local models via opencode.<p>Curious what people think about DAG-based multi-agent workflows for coding. Has anyone here experimented with similar approaches?

Sub-second volumetric 3D printing by synthesis of holographic light fields

[Other] Show HN: Django Control Room – All Your Tools Inside the Django Admin Over the past year I’ve been building a set of operational panels for Django:<p>- Redis inspection - cache visibility - Celery task introspection - URL discovery and testing<p>All of these tools have been built inside the Django admin.<p>Instead of jumping between tools like Flower, redis-cli, Swagger, or external services, I wanted something that sits where I’m already working.<p>I’ve grouped these under a single umbrella: Django Control Room.<p>The idea is pretty simple: the Django admin already gives you authentication, permissions, and a familiar interface. It can also act as an operational layer for your app.<p>Each panel is just a small Django app with a simple interface, so it’s easy to build your own and plug it in.<p>I’m working on more panels (signals, errors, etc.) and also thinking about how far this pattern can go.<p>Curious how others think about this. Does it make sense to consolidate this kind of tooling inside the admin, or do you prefer keeping it separate?

[Other] Launch HN: TeamOut (YC W22) – AI agent for planning company retreats Hi HN, I’m Vincent, CTO of TeamOut (<a href="https:&#x2F;&#x2F;www.teamout.com&#x2F;">https:&#x2F;&#x2F;www.teamout.com&#x2F;</a>). We build an AI agent that plans company events from start to finish entirely through conversation. Similar to how Lovable helps build websites through chat, we apply that approach to event planning. Our system handles venue sourcing, vendor coordination, flight cost estimation, itinerary building, and overall project management.<p>Here’s a demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=QVyc-x-isjI" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=QVyc-x-isjI</a>. The product is live at <a href="https:&#x2F;&#x2F;app.teamout.com&#x2F;ai">https:&#x2F;&#x2F;app.teamout.com&#x2F;ai</a> and does not require signup.<p>We went through YC in 2022 but did not launch on HN at the time. Back then, the product was more traditional, closer to an Airbnb-style search marketplace. Over the past two years, after helping organize more than 1,200 events, we rebuilt the core system around an agent architecture that directly manages the planning process. With this new version live, it felt like the right moment to share it here since it represents a fundamentally different approach to planning events.<p>The problem: Planning a company retreat usually means choosing between three imperfect options: (1) Hire an event planner and pay significant fees and venue markups; (2) Do it yourself and spend dozens of hours on research, emails, and negotiation; or (3) Use tools like Airbnb that are not designed for group logistics or meeting space.<p>The difficulty is not just finding a venue. Even for 30 to 50 people, planning turns into weeks of back-and-forth emails for quotes, comparing inconsistent pricing across PDFs, and tracking budgets in spreadsheets. It becomes an ongoing coordination problem with evolving constraints and slow, asynchronous vendor responses. Most existing software is form-driven, but the real workflow is conversational and stateful.<p>Offsites are expensive and high stakes. A single event can represent a significant chunk of a team’s annual budget, and mistakes show up directly as cost overruns or poor experiences. Founders and operators often end up spending time on event logistics instead of their actual work.<p>I ran into this while organizing retreats at a previous company. Before TeamOut, I worked as an AI researcher at IBM on NLP and machine learning systems. Sitting inside long email threads and cost spreadsheets, it did not look like a marketplace gap to me. It looked like a reasoning and state management problem. As large language models improved at multi-step reasoning and tool use, it became realistic to automate the coordination layer itself.<p>Our Solution: The core agent relies on a combination of models such as Gemini, Claude, and GPT. A central LLM-based agent maintains planning context across turns and decides which specialized tool to call next. Each tool has a specific responsibility: - Venue search and filtering - Cost estimations (accommodation + flights) - Budget comparisons - Quote and outreach flows - Communication tool with our team<p>For venue recommendations across more than 10,000 venues, we do not rely purely on the language model. We embed both user requirements and venues into vector representations and retrieve candidates using similarity search. Hard constraints such as capacity and dates are applied first, and results are ranked before being presented.<p>On the interface side, we use a split layout: conversation on the left and structured results on the right. As you refine the plan in chat, the event updates in real time, allowing an iterative workflow rather than a static search experience.<p>What is different is that we treat event planning as a stateful coordination problem rather than a one-shot search query. The agent orchestrates tools, manages evolving constraints, and surfaces trade-offs explicitly. It does not invent venues or fabricate pricing, and it is not designed to replace human planners for very large or highly customized events.<p>We make money from commissions on venue bookings. It is free for teams to explore options and plan. If you’ve organized an offsite or large meetup before, I’d genuinely value your perspective. Where would you expect this to fail? What edge cases are we underestimating? Where wouldn’t you trust an agent to handle the details?<p>My engineering team and I will be here all day to answer questions, happy to go deep on architecture, tradeoffs, and lessons learned. We’d really appreciate your candid feedback.

Python Type Checker Comparison: Empty Container Inference

[Other] Red Hat takes on Docker Desktop with its enterprise Podman Desktop build

What I learned while trying to build a production-ready nearest neighbor system

[Other] Show HN: A real-time strategy game that AI agents can play I&#x27;ve liked all the projects that put LLMs into game environments. It&#x27;s been a weird juxtaposition, though: frontier LLMs can one-shot full coding projects, and those same models struggle to get out of Pokémon Red&#x27;s Mt. Moon.<p>Because of this, I wanted to create a game environment that put this generation of frontier LLMs&#x27; top skill, coding, on full display.<p>Ten years ago, a team released a game called Screeps. It was described as an &quot;MMO RTS sandbox for programmers.&quot; The Screeps paradigm of writing code and having it executed in a real-time game environment is well suited to LLMs. Drawing on a version of the Screeps open source API, LLM Skirmish pits LLMs head-to-head in a series of 1v1 real-time strategy games.<p>In my testing I found that Claude Opus 4.5 was the most dominant model, but it showed weakness in round 1 as it was overly focused on its in-game economy. Meanwhile, I probably spent a third of all code on sandbox hardening because GPT 5.2 kept trying to cheat by pre-reading its opponent&#x27;s strategies.<p>If there&#x27;s interest, I&#x27;m planning on doing a round of testing with the latest generation of LLMs (Claude 4.6 Opus, GPT 5.3 Codex, etc.).<p>You can run local matches via CLI. I&#x27;m running a hosted match runner with Google Cloud Run that uses isolated-vm. The match playback visualizer is statically served from Cloudflare.<p>I&#x27;ve created a community ladder that you can submit strategies to via CLI, no auth required. I&#x27;ve found that the CLI plus the skill.md that&#x27;s available has been enough for AI agents to immediately get started.<p>Website: <a href="https:&#x2F;&#x2F;llmskirmish.com" rel="nofollow">https:&#x2F;&#x2F;llmskirmish.com</a><p>API docs: <a href="https:&#x2F;&#x2F;llmskirmish.com&#x2F;docs" rel="nofollow">https:&#x2F;&#x2F;llmskirmish.com&#x2F;docs</a><p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;llmskirmish&#x2F;skirmish" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;llmskirmish&#x2F;skirmish</a><p>A video of a match: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=lnBPaZ1qamM" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=lnBPaZ1qamM</a>

[Other] Claude Code Remote Control

[Other] Show HN: Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude Code Every MCP tool call dumps raw data into Claude Code&#x27;s 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone.<p>I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB.<p>It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours.<p>MIT licensed, single command install:<p>&#x2F;plugin marketplace add mksglu&#x2F;claude-context-mode<p>&#x2F;plugin install context-mode@claude-context-mode<p>Benchmarks and source: <a href="https:&#x2F;&#x2F;github.com&#x2F;mksglu&#x2F;claude-context-mode" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mksglu&#x2F;claude-context-mode</a><p>Would love feedback from anyone hitting context limits in Claude Code.

[DevOps] Show HN: StreamHouse – S3-native Kafka alternative written in Rust Hey HN,<p>I built StreamHouse, an open-source streaming platform that replaces Kafka&#x27;s broker-managed storage with direct S3 writes. The goal: same semantics, fraction of the cost.<p>How it works: Producers batch and compress records, a stateless server manages partition routing and metadata (SQLite for dev, PostgreSQL for prod), and segments land directly in S3. Consumers read from S3 with a local segment cache. No broker disks to manage, no replication factor to tune — S3 gives you 11 nines of durability out of the box.<p>What&#x27;s there today: - Producer API with batching, LZ4 compression, and offset tracking (62K records&#x2F;sec) - Consumer API with consumer groups, auto-commit, and multi-partition fanout (30K+ records&#x2F;sec) - Kafka-compatible protocol (works with existing Kafka clients) - REST API, gRPC API, CLI, and a web UI - Docker Compose setup for trying it locally in 5 minutes<p>The cost model is what motivated this. Kafka&#x27;s storage costs scale with replication factor × retention × volume. With S3 at $0.023&#x2F;GB&#x2F;month, storing a TB of events costs ~$23&#x2F;month instead of hundreds on broker EBS volumes.<p>Written in Rust, ~50K lines across 15 crates. Apache 2.0 licensed.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;gbram1&#x2F;streamhouse" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;gbram1&#x2F;streamhouse</a><p>Happy to answer questions about the architecture, tradeoffs, or what I learned building this.

[API/SDK] Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3 I wanted to share our new speech to text model, and the library to use them effectively. We&#x27;re a small startup (six people, sub-$100k monthly GPU budget) so I&#x27;m proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI&#x27;s largest Whisper model. Admittedly Large v3 is a couple of years old, but we&#x27;re near the top the HF OpenASR leaderboard, even up against Nvidia&#x27;s Parakeet family. Anyway, I&#x27;d love to get feedback on the models and software, and hear about what people might build with it.

[CLI Tool] Pi – a minimal terminal coding harness

[Other] Show HN: Recursively apply patterns for pathfinding I&#x27;ve been begrudgingly working on autorouters for 2 years, looking for new techniques or modern methods that might allow AI to create circuit boards.<p>One of the biggest problems in my view for training an AI to do autorouting is the traditional grid-based representation of autorouting problems which challenges spatial understanding. But we know that vision models are very good at classifying, so I wondered if we could train a model to output a path as a classification. But then how do you represent the path? This lead me down the track of trying to build an autorouter that represented paths as a bunch of patterns.<p>More details: <a href="https:&#x2F;&#x2F;blog.autorouting.com&#x2F;p&#x2F;the-recursive-pattern-pathfinder" rel="nofollow">https:&#x2F;&#x2F;blog.autorouting.com&#x2F;p&#x2F;the-recursive-pattern-pathfin...</a>

[IDE/Editor] Show HN: MiniVim a Minimal Neovim Configuration I built MiniVim, a small and minimal Neovim configuration focused on keeping things simple and readable.<p>The goal was to have a setup that:<p>starts fast<p>uses only essential plugins<p>avoids heavy frameworks<p>remains easy to understand and extend<p>The structure is intentionally small:<p>It’s not meant to compete with full Neovim distributions, but rather serve as a clean base configuration that can be extended gradually.<p>I use it across multiple machines (laptop, WSL, and servers), so reproducibility and simplicity were priorities.<p>Feedback is welcome.

[API/SDK] Show HN: Declarative open-source framework for MCPs with search and execute Hi HN,<p>I’m Samrith, creator of Hyperterse.<p>Today I’m launching Hyperterse 2.0, a schema-first framework for building MCP servers directly on top of your existing production databases.<p>If you&#x27;re building AI agents in production, you’ve probably run into agents needing access to structured, reliable data but wiring your business logic to MCP tools is tedious. Most teams end up writing fragile glue code. Or worse, giving agents unsafe, overbroad access.<p>There isn’t a clean, principled way to expose just the right data surface to agents.<p>Hyperterse lets you define a schema over your data and automatically exposes secure, typed MCP tools for AI agents.<p>Think of it as: Your business data → controlled, agent-ready interface.<p>Some key properties include a schema-first access layer, typed MCP tool generation, works with existing Postgres, MySQL, MongoDB, Redis databases, fine-grained exposure of queries, built for production agent workloads.<p>v2.0 focuses heavily on MCP with first-class MCP server support, cleaner schema ergonomics, better type safety, faster tool surfaces.<p>All of this, with only two tools - search &amp; execute - reducing token usage drastically.<p>Hyperterse is useful if you are building AI agents&#x2F;copilots, adding LLM features to existing SaaS, trying to safely expose internal data to agents or are just tired of bespoke MCP glue layers.<p>I’d love feedback, especially from folks running agents in production.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;hyperterse&#x2F;hyperterse" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;hyperterse&#x2F;hyperterse</a>

[Other] We Are Changing Our Developer Productivity Experiment Design

[Other] Show HN: Hacker Smacker – Spot great (and terrible) HN commenters at a glance Hacker Smacker adds friend&#x2F;foe functionality to Hacker News. Three little orbs appear next to every commenter&#x27;s name. Click to friend or foe a commenter and you&#x27;ll more easily spot them on future threads. Makes it easy to scroll and spot the commenters you love to read (and hate to read).<p>Main website: <a href="https:&#x2F;&#x2F;hackersmacker.org" rel="nofollow">https:&#x2F;&#x2F;hackersmacker.org</a><p>Chrome&#x2F;Edge extension: <a href="https:&#x2F;&#x2F;chromewebstore.google.com&#x2F;detail&#x2F;hacker-smacker&#x2F;lmcglejmapenkiabndkcnahfkmbohmhd" rel="nofollow">https:&#x2F;&#x2F;chromewebstore.google.com&#x2F;detail&#x2F;hacker-smacker&#x2F;lmcg...</a> Safari extension: <a href="https:&#x2F;&#x2F;apps.apple.com&#x2F;us&#x2F;app&#x2F;hacker-smacker&#x2F;id1480749725">https:&#x2F;&#x2F;apps.apple.com&#x2F;us&#x2F;app&#x2F;hacker-smacker&#x2F;id1480749725</a> Firefox extension: <a href="https:&#x2F;&#x2F;addons.mozilla.org&#x2F;en-US&#x2F;firefox&#x2F;addon&#x2F;hacker-smacker&#x2F;" rel="nofollow">https:&#x2F;&#x2F;addons.mozilla.org&#x2F;en-US&#x2F;firefox&#x2F;addon&#x2F;hacker-smacke...</a><p>The interesting part is friend-of-a-friend: if you friend someone who also uses Hacker Smacker, you&#x27;ll see their friends and foes highlighted too. This lets you quickly scan long comment threads and find the good stuff based on people you trust.<p>I built this to learn how FoaF relationships work with Redis sets, then brought the same technique to NewsBlur&#x27;s social layer. The backend is CoffeeScript&#x2F;Node.js&#x2F;Redis, and the extension works on Chrome, Edge, Firefox, and Safari.<p>Technically I wrote this back in 2011, but never built a proper auth system until now. So I&#x27;ve been using it for 15 years and it&#x27;s been great. PG once saw it on my laptop (back when he was still moderating HN, in 2012) and remarked that it was neat.<p>Thanks to Mihai Parparita for help with the Chrome extension sandboxing and Greg Brockman for helping design the authentication system.<p>Source is on GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;samuelclay&#x2F;hackersmacker" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;samuelclay&#x2F;hackersmacker</a><p>Directly inspired by Slashdot&#x27;s friend&#x2F;foe system, which I always wished HN had. Happy to answer questions!

[Other] Open Letter to Google on Mandatory Developer Registration for App Distribution