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TetrisBench is a website that provides benchmarking tools and resources for the classic puzzle game Tetris. It offers performance analysis, comparison of different Tetris implementations, and insights into the game's mechanics and optimization techniques.

SnapBench is a benchmarking tool for serverless functions, enabling developers to measure the performance and cost-efficiency of their cloud functions across different cloud providers and configurations.

Hey! I'm eshaan and I'm building Ourguide -an on-screen task guidance system that can show you where to click step-by-step when you need help.

I started building this because whenever I didn’t know how to do something on my computer, I found myself constantly tabbing between chatbots and the app, pasting screenshots, and asking “what do I do next?” Ourguide solves this with two modes. In Guide mode, the app overlays your screen and highlights the specific element to click next, eliminating the need to leave your current window. There is also Ask mode, which is a vision-integrated chat that captures your screen context—which you can toggle on and off anytime -so you can ask, "How do I fix this error?" without having to explain what "this" is.

It’s an Electron app that works OS-wide, is vision-based, and isn't restricted to the browser.

Figuring out how to show the user where to click was the hardest part of the process. I originally trained a computer vision model with 2300 screenshots to identify and segment all UI elements on a screen and used a VLM to find the correct icon to highlight. While this worked extremely well—better than SOTA grounding models like UI Tars—the latency was just too high. I'll be making that CV+VLM pipeline OSS soon, but for now, I’ve resorted to a simpler implementation that achieves <1s latency.

You may ask: if I can show you where to click, why can't I just click too? While trying to build computer-use agents during my job in Palo Alto, I hit the core limitation of today’s computer-use models where benchmarks hover in the mid-50% range (OSWorld). VLMs often know what to do but not what it looks like; without reliable visual grounding, agents misclick and stall. So, I built computer use—without the "use." It provides the visual grounding of an agent but keeps the human in the loop for the actual execution to prevent misclicks.

I personally use it for the AWS Console's "treasure hunt" UI, like creating a public S3 bucket with specific CORS rules. It’s also been surprisingly helpful for non-technical tasks, like navigating obscure settings in Gradescope or Spotify. Ourguide really works for any task when you’re stuck or don't know what to do.

You can download and test Ourguide here: https://ourguide.ai/downloads

The project is still very early, and I’d love your feedback on where it fails, where you think it worked well, and which specific niches you think Ourguide would be most helpful for.

Shows rendered preview for unfocused lines and raw markdown for the line or block being edited.

Hello HN, this is Sai and Kaushik from ClickHouse. Today we are launching a Postgres managed service that is natively integrated with ClickHouse. It is built together with Ubicloud (YC W24).

TL;DR: NVMe-backed Postgres + built-in CDC into ClickHouse + pg_clickhouse so you can keep your app Postgres-first while running analytics in ClickHouse.

Try it (private preview): https://clickhouse.com/cloud/postgres Blog w/ live demo: https://clickhouse.com/blog/postgres-managed-by-clickhouse

Problem

Across many fast-growing companies using Postgres, performance and scalability commonly emerge as challenges as they grow. This is for both transactional and analytical workloads. On the OLTP side, common issues include slower ingestion (especially updates, upserts), slower vacuums, long-running transactions incurring WAL spikes, among others. In most cases, these problems stem from limited disk IOPS and suboptimal disk latency. Without the need to provision or cap IOPS, Postgres could do far more than it does today.

On the analytics side, many limitations stem from the fact that Postgres was designed primarily for OLTP and lacks several features that analytical databases have developed over time, for example vectorized execution, support for a wide variety of ingest formats, etc. We’re increasingly seeing a common pattern where many companies like GitLab, Ramp, Cloudflare etc. complement Postgres with ClickHouse to offload analytics. This architecture enables teams to adopt two purpose-built open-source databases.

That said, if you’re running a Postgres based application, adopting ClickHouse isn’t straightforward. You typically end up building a CDC pipeline, handling backfills, and dealing with schema changes and updating your application code to be aware of a second database for analytics.

Solution

On the OLTP side, we believe that NVMe-based Postgres is the right fit and can drastically improve performance. NVMe storage is physically colocated with compute, enabling significantly lower disk latency and higher IOPS than network-attached storage, which requires a network round trip for disk access. This benefits disk-throttled workloads and can significantly (up to 10x) speed up operations incl. updates, upserts, vacuums, checkpointing, etc. We are working on a detailed blog examining how WAL fsyncs, buffer reads, and checkpoints dominate on slow I/O and are significantly reduced on NVMe. Stay tuned!

On the OLAP side, the Postgres service includes native CDC to ClickHouse and unified query capabilities through pg_clickhouse. Today, CDC is powered by ClickPipes/PeerDB under the hood, which is based on logical replication. We are working to make this faster and easier by supporting logical replication v2 for streaming in-progress transactions, a new logical decoding plugin to address existing limitations of logical replication, working toward sub-second replication, and more.

Every Postgres comes packaged with the pg_clickhouse extension, which reduces the effort required to add ClickHouse-powered analytics to a Postgres application. It allows you to query ClickHouse directly from Postgres, enabling Postgres for both transactions and analytics. pg_clickhouse supports comprehensive query pushdown for analytics, and we plan to continuously expand this further (https://news.ycombinator.com/item?id=46249462).

Vision

To sum it up - Our vision is to provide a unified data stack that combines Postgres for transactions with ClickHouse for analytics, giving you best-in-class performance and scalability on an open-source foundation.

Get Started

We are actively working with users to onboard them to the Postgres service. Since this is a private preview, it is currently free of cost.If you’re interested, please sign up here. https://clickhouse.com/cloud/postgres

We’d love to hear your feedback on our thesis and anything else that comes to mind, it would be super helpful to us as we build this out!

This is an interactive map of US navigational aids and lighthouses, which indicates their location, color, characteristic and any remarks the Coast Guard has attached.

I was sick at home with the flu this weekend, and went on a bit of a Wikipedia deep dive about active American lighthouses. Searching around a bit, it was very hard to find a single source or interactive map of active beacons, and a description of what the "characteristic" meant. The Coast Guard maintains a list of active lights though, that they publish annually (https://www.navcen.uscg.gov/light-list-annual-publication). With some help from Claude Code, it wasn't hard to extract the lat/long and put together a small webapp that shows a map of these light stations and illustrates their characteristic with an animated visualization..

Of course, this shouldn't be used as a navigational aid, merely for informational purposes! Though having lived in Seattle and San Francisco I thought it was quite interesting.

Hey, I’m Felix, an 18-year-old student from Austria. I’ve built a few small SaaS projects, mostly solo, and I kept running into the same small but persistent problem.

Whenever I wanted to understand how things were going, I’d end up jumping between Stripe, analytics, database queries, logs, and cron scripts. I even built custom dashboards and Telegram bots to notify me about certain numbers, but that just added more things to maintain.

What I wanted was something simpler: send a number from my backend and see it on a clean dashboard.

So I built a small tool for myself.

It’s essentially a very simple API where you push numeric metrics with a timestamp, and then view them as counters, charts, goals, or percentage changes over time.

It’s not meant to replace analytics tools. I still use those. This is more for things like user counts, MRR, failed jobs, or any metric you already know you want to track without setting up a full integration.

Some intentional constraints: - no SDKs, just a basic HTTP API - works well with backend code and cron jobs - stores only numbers and timestamps - flexible enough to track any metric you can turn into a number

It’s still early and very much an MVP. I’m mainly posting to get feedback: - does this solve a real problem for you? - what feels unnecessary or missing? - how would you approach this differently?

Website: https://anypanel.io

Happy to answer questions or hear why this doesn’t make sense. Thanks, Felix

Author here. I made this little TUI program for managing default applications on the Linux desktop.

Maybe some of you will find it useful.

Happy to answer any questions.

This is a hobby project of mine that I started a few years ago to learn about programming language implementation. It was created 95% without AI, although a few recent commits include code from Gemini CLI.

I started out following Crafting Interpreters, but gradually branched off that until I had almost nothing left in common.

Tech stack: Rust, Cranelift (JIT compilation), LALRPOP (parser).

Original title: "A small programming language where everything is a value" (edited based on comments)

To power the firewalling for our agents so that they couldn't contact arbitrary services, I build netfence. It's like Envoy but for eBPF filters.

It allows you to define different DNS-based rules that are resolved in a local daemon to IPs, then pushed to the eBPF filter to allow traffic. By doing it this way, we can still allow DNS-defined rules, but prevent contacting random IPs.

There's also no network performance penalty, since it's just DNS lookups and eBPF filters referencing memory.

It also means you don't have to tamper with the base image, which the agent could potentially manipulate to remove rules (unless you prevent root maybe).

It automatically manages the lifecycle of eBPF filters on cgroups and interfaces, so it works well for both containers and micro VMs (like Firecracker).

You implement a control plane, just like Envoy xDS, which you can manage the rules of each cgroup/interface. You can even manage DNS through the control plane to dynamically resolve records (which is helpful as a normal DNS server doesn't know which interface/cgroup a request might be coming from).

We specifically use this to allow our agents to only contact S3, pip, apt, and npm.

Hi HN!

Fence wraps any command in a sandbox that blocks network by default and restricts filesystem writes. Useful for running semi-trusted code (package installs, build scripts, unfamiliar repos) with controlled side effects, or even just blocking tools that phone home.

> fence curl https://example.com # -> blocked

> fence -t code -- npm install # -> template with registries allowed

> fence -m -- npm install # -> monitor mode: see what gets blocked

One use-case is to use it with AI coding agents to reduce the risk of running agents with fewer interactive permission prompts:

> fence -t code -- claude --dangerously-skip-permissions

You can import existing Claude Code permissions with `fence import --claude`.

Fence uses OS-native sandboxing (macOS sandbox-exec, Linux bubblewrap) + local HTTP/SOCKS proxies for domain filtering.

Why I built this: I work on Tusk Drift, a system to record and replay real traffic as API tests (https://github.com/Use-Tusk/tusk-drift-cli). I needed a way to sandbox the service under test during replays to block localhost outbound connections (Postgres, Redis) and force the app to use mocks instead of real services. I quickly realized that this could be a general purpose tool that would also be useful as a permission manager across CLI agents.

Limitations: Not strong containment against malware. Proxy-based filtering requires programs to respect `HTTP_PROXY`.

Curious if others have run into similar needs, and happy to answer any questions!

BonSplit is an open-source app that allows friends to easily split bills and track expenses. It simplifies the process of dividing costs and managing shared payments, making it a useful tool for groups and households.

I built NukeCast because I’ve always wanted a tool that answers one question fast. If it happened today, where would the fallout plumes go and where would you drive to get out of it. NukeCast uses weather forecasts to drive a Lagrangian particle dispersion model with wet and dry deposition. Scenarios are defined by selecting strike site(s) and yield(s); the setup uses preselected US sites and yields based on FEMA emergency data. Outputs are shown as estimated ground level radiation dose at the surface over a 12 hour integration. Free to use with limits, and a paid tier if you want more runs/features because AWS compute time ain’t cheap.

We noticed that professional networking is mostly profile-based: you create a static resume and hope the right people somehow discover you. But what actually drives meaningful connections is complementary intent: a founder looking for a technical co-founder, an engineer wanting to join a specific type of early-stage company, an investor actively looking for a certain thesis.

We built Alprina to match people on what they want right now, not just who they are on paper.

On Alprina, you create "intents" in natural language (what you're looking for), join networks (communities where matching happens), and our AI matches you with people whose intents complement yours. You can attach context like pitch decks or profiles so that when you match, the other side immediately understands why you're reaching out.

Would love feedback from the HN community - especially on the balance between match precision and serendipity. Too strict and you miss interesting connections; too loose and it's just noise.

LLMNet is an open-source library for building and deploying large language models (LLMs) using PyTorch. It provides a modular and extensible framework for training, fine-tuning, and evaluating LLMs, allowing developers to quickly experiment with different model architectures and techniques.

Hi HN, I built Beni (https://thebeni.ai ), a web app for real-time video calls with an AI companion.

The idea started as a pretty simple question: text chatbots are everywhere, but they rarely feel present. I wanted something closer to a call, where the character actually reacts in real time (voice, timing, expressions), not just “type, wait, reply”.

Beni is basically:

A Live2D avatar that animates during the call (expressions + motion driven by the conversation)

Real-time voice conversation (streaming response, not “wait 10 seconds then speak”)

Long-term memory so the character can keep context across sessions

The hardest part wasn’t generating text, it was making the whole loop feel synchronized: mic input, model response, TTS audio, and Live2D animation all need to line up or it feels broken immediately. I ended up spending more time on state management, latency and buffering than on prompts.

Some implementation details (happy to share more if anyone’s curious):

Browser-based real-time calling, with audio streaming and client-side playback control

Live2D rendering on the front end, with animation hooks tied to speech / state

A memory layer that stores lightweight user facts/preferences and conversation summaries to keep continuity

Current limitation: sign-in is required today (to persist memory and prevent abuse). I’m adding a guest mode soon for faster try-out and working on mobile view now.

What I’d love feedback on:

Does the “real-time call” loop feel responsive enough, or still too laggy?

Any ideas for better lip sync / expression timing on 2D/3D avatars in the browser?

Thanks, and I’ll be around in the comments.

This article discusses the development of a tool called AutoShorts, which automatically generates short video clips from long-form video content. The tool uses machine learning techniques to identify key moments and summarize the content, enabling efficient content repurposing and distribution.

Seven modules teaching C the way safety-critical systems are actually built: MATH → STRUCT → CODE → TEST.

Each module answers one question: Does it exist? (Pulse), Is it normal? (Baseline), Is it regular? (Timing), Is it trending? (Drift), Which sensor to trust? (Consensus), How to handle overflow? (Pressure), What do we do about it? (Mode).

Every module is closed (no dependencies), total (handles all inputs), deterministic, and O(1). 83 tests passing.

Built this after 30 years in UNIX systems. Wanted something that teaches the rigour behind certified systems without requiring a decade of on-the-job learning first.

MIT licensed. Feedback welcome.

I usually build web games in C++, but using Emscripten always felt like overkill for what I was doing. I don't need full POSIX emulation or a massive standard library just to render some stuff to a canvas and handle basic UI.

The main thing I wanted to solve was the JS/WASM interop bottleneck. Instead of using the standard glue code for every call, I moved everything to a Shared Memory architecture using Command and Event buffers.

The way it works is that I batch all the instructions in WASM and then just send a single "flush" signal to JS. The JS side then reads everything directly out of Shared Memory in one go. It’s way more efficient, I ran a benchmark rendering 10k rectangles on a canvas and the difference was huge: Emscripten hit around 40 FPS, while my setup hit 100 FPS.

But writing DOM logic in C++ is painful, so I built Coi. It’s a component-based language that statically analyzes changes at compile-time to enable O(1) reactivity. Unlike traditional frameworks, there is no Virtual DOM overhead; the compiler maps state changes directly to specific handles in the command buffer.

I recently benchmarked this against React and Vue on a 1,000-row table: Coi came out on top for row creation, row updating and element swapping because it avoids the "diffing" step entirely and minimizes bridge crossings. Its bundle size was also the smallest of the three.

One of the coolest things about the architecture is how the standard library works. If I want to support a new browser API (like Web Audio or a new Canvas feature), I just add the definition to my WebCC schema file. When I recompile the Coi compiler, the language automatically gains a new standard library function to access that API. There is zero manual wrapping involved.

I'm really proud of how it's coming along. It combines the performance of a custom WASM stack with a syntax that actually feels good to write (for me atleast :P). Plus, since the intermediate step is C++, I’m looking into making it work on the server side too, which would allow for sharing components across the whole stack.

Example (Coi Code):

component Counter(string label, mut int& value) {

    def add(int i) : void {
        value += i;
    }

    style {
        .counter {
            display: flex;
            gap: 12px;
            align-items: center;
        }
        button {
            padding: 8px 16px;
            cursor: pointer;
        }
    }

    view {
        <div class="counter">
            <span>{label}: {value}</span>
            <button onclick={add(1)}>+</button>
            <button onclick={add(-1)}>-</button>
        </div>
    }

component App { mut int score = 0;

    style {
        .app {
            padding: 24px;
            font-family: system-ui;
        }
        h1 {
            color: #1a73e8;
        }
        .win {
            color: #34a853;
            font-weight: bold;
        }
    }

    view {
        <div class="app">
            <h1>Score: {score}</h1>
            <Counter label="Player" &value={score} />
            <if score >= 10>
                <p class="win">You win!</p>
            </if>
        </div>
    }

app { root = App; title = "My Counter App"; description = "A simple counter built with Coi"; lang = "en"; }

Live Demo: https://io-eric.github.io/coi

Coi (The Language): https://github.com/io-eric/coi

WebCC: https://github.com/io-eric/webcc

I'd love to hear what you think. It's still far from finished, but as a side project I'm really excited about :)

Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents.

I didn't write a single line of code.

Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped!

I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity:

http://cannoneyed.com/projects/isometric-nyc

CertRadar is a platform that provides comprehensive information and analysis on global certification bodies and standards, helping businesses navigate the complex world of certifications.

Hi HN,

I built *Sightline*, a Shodan-style search engine for *physical-world infrastructure*.

Shodan makes it easy to explore exposed internet services. Sightline applies the same idea to the real world, using OpenStreetMap as the data source.

You can search things like:

* “telecom towers in karnataka” * “power plants near mumbai” * “data centers in paris france”

or use structured queries:

* `type:telecom operator:airtel region:karnataka` * `type:data_center operator:google`

Sightline:

* uses Overpass API for querying OSM features * uses Nominatim for resolving countries, regions, and cities * avoids hardcoded geography * uses deterministic, rule-based parsing (no AI inference)

Repo: https://github.com/ni5arga/sightline Try it out: https://sightline-maps.vercel.app

Hi HN, founders of VibeFlow (YC S25) here.

We mostly work on backend and workflow tooling, but we needed a way to turn Figma designs into frontend code as a kickstart for prototyping. It takes a Figma frame and converts it into React + Tailwind components (plus assets).

If you want to try it: You can run it locally or use it via the VibeFlow UI to poke at it without setup (https://app.vibeflow.ai/)

Basically was getting annoyed jumping between 5 different sites during this winter storm season, so I built "StormWatch". It's a no-fuss, mobile-friendly webpage (dashboard) that shows all the stuff I was looking for, but in one simple UI.

Features:

- Real-time NWS alerts with safety tips - Snow/ice/precip accumulation forecasts (+wind) - Dynamic preparation checklists based on your alerts - Supply calculator for your household size - Regional weather news

It's free, no login required, works on any device. Just enter your ZIP.

https://jeisey.github.io/stormwatch/

Uses NWS and GDELT APIs and open source. Feel free to fork and modify however you'd like.

For builders: - Used an API-testing agent to verify all endpoints, response patterns, types, and rate limits - Used a scope & validation agent to keep the slices simple, focused, and tested - VS-code Copilot (Sonnet 4 for dev agents + Opus 4.5 for scope and validation)

Hi HN,

I built a small tool that converts API logic written in plain .txt files into real, executable PHP API endpoints.

The motivation was personal: I can design and ship frontends quickly, but backend APIs (setup, boilerplate, deployment) always slowed down small projects and MVPs. I wanted a way to describe inputs, rules, and responses in text and get a working endpoint without worrying about infrastructure.

This is early and opinionated. I’m especially interested in feedback around:

trust and security concerns

where this breaks down

whether this is useful beyond prototypes

Happy to answer questions about how it works.

The nhx package is a lightweight, zero-dependency library that provides a simple and efficient way to interact with the NPM registry. It offers a set of functions to search, install, and manage NPM packages from within a Node.js application.

Writeup (includes good/bad sample generations): https://www.linum.ai/field-notes/launch-linum-v2

We're Sahil and Manu, two brothers who spent the last 2 years training text-to-video models from scratch. Today we're releasing them under Apache 2.0.

These are 2B param models capable of generating 2-5 seconds of footage at either 360p or 720p. In terms of model size, the closest comparison is Alibaba's Wan 2.1 1.3B. From our testing, we get significantly better motion capture and aesthetics.

We're not claiming to have reached the frontier. For us, this is a stepping stone towards SOTA - proof we can train these models end-to-end ourselves.

Why train a model from scratch?

We shipped our first model in January 2024 (pre-Sora) as a 180p, 1-second GIF bot, bootstrapped off Stable Diffusion XL. Image VAEs don't understand temporal coherence, and without the original training data, you can't smoothly transition between image and video distributions. At some point you're better off starting over.

For v2, we use T5 for text encoding, Wan 2.1 VAE for compression, and a DiT-variant backbone trained with flow matching. We built our own temporal VAE but Wan's was smaller with equivalent performance, so we used it to save on embedding costs. (We'll open-source our VAE shortly.)

The bulk of development time went into building curation pipelines that actually work (e.g., hand-labeling aesthetic properties and fine-tuning VLMs to filter at scale).

What works: Cartoon/animated styles, food and nature scenes, simple character motion. What doesn't: Complex physics, fast motion (e.g., gymnastics, dancing), consistent text.

Why build this when Veo/Sora exist? Products are extensions of the underlying model's capabilities. If users want a feature the model doesn't support (character consistency, camera controls, editing, style mapping, etc.), you're stuck. To build the product we want, we need to update the model itself. That means owning the development process. It's a bet that will take time (and a lot of GPU compute) to pay off, but we think it's the right one.

What’s next? - Post-training for physics/deformations - Distillation for speed - Audio capabilities - Model scaling

We kept a “lab notebook” of all our experiments in Notion. Happy to answer questions about building a model from 0 → 1. Comments and feedback welcome!

The problem with LLMs isn't intelligence; it's amnesia and dishonesty.

Hey HN,

I’ve spent the last few months building Remember-Me, an open-source "Sovereign Brain" stack designed to run entirely offline on consumer hardware.

The core thesis is simple: Don't rent your cognition.

Most RAG (Retrieval Augmented Generation) implementations are just "grep for embeddings." They are messy, imprecise, and prone to hallucination. I wanted to solve the "Context integrity" problem at the architectural layer.

The Tech Stack (How it works):

QDMA (Quantum Dream Memory Architecture): instead of a flat vector DB, it uses a hierarchical projection engine. It separates "Hot" (Recall) from "Cold" (Storage) memory, allowing for effectively infinite context window management via compression.

CSNP (Context Switching Neural Protocol) - The Hallucination Killer: This is the most important part. Every memory fragment is hashed into a Merkle Chain. When the LLM retrieves context, the system cryptographically verifies the retrieval against the immutable ledger.

If the hash doesn't match the chain: The retrieval is rejected.

Result: The AI visually cannot "make things up" about your past because it is mathematically constrained to the ledger. Local Inference: Built on top of llama.cpp server. It runs Llama-3 (or any GGUF) locally. No API keys. No data leaving your machine.

Features:

Zero-Dependency: Runs on Windows/Linux with just Python and a GPU (or CPU).

Visual Interface: Includes a Streamlit-based "Cognitive Interface" to visualize memory states. Open Source: MIT License. This is an attempt to give "Agency" back to the user. I believe that if we want AGI, it needs to be owned by us, not rented via an API.

Repository: https://github.com/merchantmoh-debug/Remember-Me-AI

I’d love to hear your feedback on the Merkle-verification approach. Does constraining the context window effectively solve the "trust" issue for you?

It's fully working - Fully tested. If you tried to Git Clone before without luck - As this is not my first Show HN on this - Feel free to try again.

To everyone who HATES AI slop; Greedy corporations and having their private data stuck on cloud servers.

You're welcome.

Cheers, Mohamad