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I grew up on the Infocom games and when microsoft actually open-sourced Zork 1/2/3 I really wanted to figure out how to use LLMs to let you type whatever you want, I always found the amount language that the games "understood" to be so limiting - even if it was pretty state of the art at the time.

So I figured out how to wrap it with Tambo.. (and run the game engine in the browser) basically whatever you type gets "translated" into zork-speak and passed to the game - and then the LLM takes the game's output and optionally adds flavor. (the little ">_" button at the top exposes the actual game input)

What was a big surprise to me is multi-turn instructions - you can ask it to "Explore all the rooms in the house until you can't find any more" and it will plug away at the game for 10+ "turns" at a time... like Claude Code for Zork or something

Hey HN, we're the co-founders of LemonSlice (try our HN playground here: https://lemonslice.com/hn). We train interactive avatar video models. Our API lets you upload a photo and immediately jump into a FaceTime-style call with that character. Here's a demo: https://www.loom.com/share/941577113141418e80d2834c83a5a0a9

Chatbots are everywhere and voice AI has taken off, but we believe video avatars will be the most common form factor for conversational AI. Most people would rather watch something than read it. The problem is that generating video in real-time is hard, and overcoming the uncanny valley is even harder.

We haven’t broken the uncanny valley yet. Nobody has. But we’re getting close and our photorealistic avatars are currently best-in-class (judge for yourself: https://lemonslice.com/try/taylor). Plus, we're the only avatar model that can do animals and heavily stylized cartoons. Try it: https://lemonslice.com/try/alien. Warning! Talking to this little guy may improve your mood.

Today we're releasing our new model* - Lemon Slice 2, a 20B-parameter diffusion transformer that generates infinite-length video at 20fps on a single GPU - and opening up our API.

How did we get a video diffusion model to run in real-time? There was no single trick, just a lot of them stacked together. The first big change was making our model causal. Standard video diffusion models are bidirectional (they look at frames both before and after the current one), which means you can't stream.

From there it was about fitting everything on one GPU. We switched from full to sliding window attention, which killed our memory bottleneck. We distilled from 40 denoising steps down to just a few - quality degraded less than we feared, especially after using GAN-based distillation (though tuning that adversarial loss to avoid mode collapse was its own adventure).

And the rest was inference work: modifying RoPE from complex to real (this one was cool!), precision tuning, fusing kernels, a special rolling KV cache, lots of other caching, and more. We kept shaving off milliseconds wherever we could and eventually got to real-time.

We set up a guest playground for HN so you can create and talk to characters without logging in: https://lemonslice.com/hn. For those who want to build with our API (we have a new LiveKit integration that we’re pumped about!), grab a coupon code in the HN playground for your first Pro month free ($100 value). See the docs: https://lemonslice.com/docs. Pricing is usage-based at $0.12-0.20/min for video generation.

Looking forward to your feedback!

EDIT: Tell us what characters you want to see in the comments and we can make them for you to talk to (e.g. Max Headroom)

*We did a Show HN last year for our V1 model: https://news.ycombinator.com/item?id=43785044. It was technically impressive but so bad compared to what we have today.

Related: https://simonwillison.net/2026/Jan/27/one-human-one-agent-on...

The article introduces Script, a new programming language designed to be easy to learn and use, with a focus on simplicity and accessibility. It highlights Script's key features, including its concise syntax, cross-platform compatibility, and integration with popular web technologies.

42 validation errors in one run. Claude apologising instead of writing HTML. OAuth tokens expiring mid-digest.

Then I fixed the constraints. Eight days, zero failures, zero intervention.

The secret wasn't better prompts... it was treating the LLM as a constrained function: schema-validated tool calls that reject malformed output and force retries, two-pass architecture separating editorial judgment from formatting, and boring DevOps (retry logic, rate limiting, structured logging).

The Claude invocation is ~30 lines in a 2000-line system. Most of the work is everything around it.

https://seanfloyd.dev/blog/llm-reliability https://github.com/SeanLF/claude-rss-news-digest

This article discusses the growing field of robotics, exploring its applications in various industries and the advancements in artificial intelligence and machine learning that are driving this technology forward.

TraceOpt-AI's TraceML is an open-source Python library that provides tools for model tracing, optimization, and deployment. It offers features like model tracing, quantization, and export to support efficient and deployable deep learning models.

Hey HN,

I wanted to share a project I’ve been working on called Convoxa. It’s a native iOS transcriber/summarizer. I had two main goals: keep it efficient and keep it private.

THE TECH STACK

100% Swift & SwiftUI: No heavy cross-platform wrappers or bloated dependencies.

Binary Size: The final build is only 4.8 MB.

Transcription: Uses Apple's latest speech APIs for maximum privacy and efficiency.

THE CHALLENGE: BYPASSING THE 4K CONTEXT LIMIT

The biggest technical hurdle was working with Apple’s foundation models. The default context window is capped at 4096 tokens, which is practically useless for anything over a 10-minute meeting transcript.

I ended up building a recursive chunking method to "feed" the model long-form data without losing the global context of the conversation. I use a sliding window approach where each chunk's summary informs the next, ensuring the final output doesn't "hallucinate" at the seams where the chunks meet. It’s now stable enough for long-form audio while remaining entirely on-device for supported hardware.

PRIVACY & AI MODES

On-Device: (Apple Intelligence required) - Total local processing.

Cloud: With reasoning for intelligent insights (Zero Data Retention).

I’m currently in the pre-order phase (out on Feb 3rd) and would love to get some feedback from this community on the performance and the chunking logic.

App Store: https://apps.apple.com/us/app/convoxa-ai-meeting-minutes/id6...

The article presents a comprehensive analysis of online political discourse, examining how different communication patterns and narratives emerge on social media platforms. It explores the dynamics of political polarization, the spread of misinformation, and the implications for democratic discourse.

Hi, I'm Tony, founder of Cosmic (AI-powered headless CMS and application development platform). We kept running into the same problem: create a blog post with the help of an AI agent, use the output for another prompt to create social posts, then manually post to X, LinkedIn, Facebook. Every single time.

So we built AI Workflows — chain multiple agents together and let them run autonomously, with each step receiving outputs from previous steps.

Three agent types you can chain:

- Code Agents: Build features in GitHub with commits and pull requests.

- Content Agents: Generate CMS content with context injection from previous steps.

- Computer Use Agents: Automate browser workflows and record demos.

How it works:

1. Define steps with agent type, prompt, and configuration

2. Steps run sequentially or in parallel (configurable)

3. Context passes automatically between steps

4. Trigger manually, on a schedule (cron), or via CMS and API events (object.created, object.edited, etc.)

5. Add approval gates for human review before critical steps

Example: Autopilot feature development:

Step 1: Content Agent writes a feature spec based on user feedback

Step 2: Code Agent builds the feature, creates PR, and deploys to production

Step 3: Content Agent generates documentation and a changelog entry

Step 4: Computer Use Agent posts update to team Slack with the PR link and preview URL

Currently in beta. Would love feedback on the workflow model and what use cases you'd want to automate.

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.

The article describes a PostgreSQL to ClickHouse migration stack, which provides a framework for seamlessly migrating data from PostgreSQL to the ClickHouse analytical database, while maintaining data integrity and minimizing downtime.

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.

I built Lightbox because I kept running into the same problem: an agent would fail in production, and I had no way to know what actually happened.

Logs were scattered, the LLM’s “I called the tool” wasn’t trustworthy, and re-running wasn’t deterministic.

This week, tons of Clawdbot incidents have driven the point home. Agents with full system access can expose API keys and chat histories. Prompt injection is now a major security concern.

When agents can touch your filesystem, execute code, and browse the web…you probably need a tamper-proof record of exactly what actions it took, especially when a malicious prompt or compromised webpage could hijack the agent mid-session.

Lightbox is a small Python library that records every tool call an agent makes (inputs, outputs, timing) into an append-only log with cryptographic hashes. You can replay runs with mocked responses, diff executions across versions, and verify the integrity of logs after the fact.

Think airplane black box, but for your hackbox.

*What it does:*

- Records tool calls locally (no cloud, your infra)

- Tamper-evident logs (hash chain, verifiable)

- Replay failures exactly with recorded responses

- CLI to inspect, replay, diff, and verify sessions

- Framework-agnostic (works with LangChain, Claude, OpenAI, etc.)

*What it doesn’t do:* - Doesn’t replay the LLM itself (just tool calls) - Not a dashboard or analytics platform - Not trying to replace LangSmith/Langfuse (different problem)

*Use cases I care about:*

- Security forensics: agent behaved strangely, was it prompt injection? Check the trace.

- Compliance: “prove what your agent did last Tuesday”

- Debugging: reproduce a failure without re-running expensive API calls

- Regression testing: diff tool call patterns across agent versions

As agents get more capable and more autonomous (Clawdbot/Molt, Claude computer use, Manus, Devin), I think we’ll need black boxes the same way aviation does.

This is my attempt at that primitive.

It’s early (v0.1), intentionally minimal, MIT licensed.

Site: <https://uselightbox.app> install: `pip install lightbox-rec`

GitHub: <https://github.com/mainnebula/Lightbox-Project>

Would love feedback, especially from anyone thinking about agent security or running autonomous agents in production.

Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run commands, and react to errors.

Where things still break down is when ML workflows become long-running and feedback-heavy. Training jobs, evaluations, retries, metric comparisons, and partial failures are still treated as ephemeral side effects rather than durable state. Once a workflow spans hours, multiple experiments, or iterative evaluation, you either babysit the agent or restart large parts of the process. Feedback exists, but it is not something the system can reliably resume from.

NEO tries to model ML work the way it actually happens. It is an AI agent that executes end-to-end ML workflows, not just code generation. Work is broken into explicit execution steps with state, checkpoints, and intermediate results. Feedback from metrics, evaluations, or failures feeds directly into the next step instead of forcing a full restart. You can pause a run, inspect what happened, tweak assumptions, and resume from where it left off.

Here's an example as well for your reference: You might ask NEO to explore a dataset, train a few baseline models, compare their performance, and generate plots and a short report. NEO will load the data, run EDA, train models, evaluate them, notice if something underperforms or fails, adjust, and continue. If training takes an hour and one model crashes at 45 minutes, you do not start over. Neo inspects the failure, fixes it, and resumes.

Docs for the extension: https://docs.heyneo.so/#/vscode

Happy to answer questions about Neo.

Hey HN,

It’s Vineeth from Plastic Labs. We've been building Honcho, an open-source memory library for stateful AI agents.

Most memory systems are just vector search—store facts, retrieve facts, stuff into context. We took a different approach: memory as reasoning. (We talk about this a lot on our blog)

We built Neuromancer, a model trained specifically for AI-native memory. Instead of naive fact extraction, Neuromancer does formal logical reasoning over conversations to build representations that evolve over time. Its both cheap ( $2/M tokens ingestion, unlimited retrieval), token efficient and SOTA: LongMem (90.4%), LoCoMo (89.9%), and BEAM. On BEAM 10M—which exceeds every model's context window—we hit 0.409 vs prior SOTA of 0.266, using 0.5% of context per query.

Github: https://github.com/plastic-labs/honcho

Evals: https://evals.honcho.dev

Neuromancer Model Card: https://plasticlabs.ai/neuromancer)

Memory as Reasoning Approach: https://blog.plasticlabs.ai/blog/Memory-as-Reasoning

Read more about our recent updates: https://blog.plasticlabs.ai/blog/Honcho-3

Happy to answer questions about the architecture, benchmarks, or agent memory patterns in general

Hey HN,

A CSV parser using Go 1.26's experimental simd/archsimd package.

I wanted to see what the new SIMD API looks like in practice. CSV parsing is mostly "find these bytes in a buffer"—load 64 bytes, compare, get a bitmask of positions. The interesting part was handling chunk boundaries correctly (quotes and line endings can split across chunks).

- Drop-in replacement for encoding/csv - ~20% faster for unquoted data on AVX-512 - Quoted data is slower (still optimizing) - Scalar fallback for non-AVX-512

Requires GOEXPERIMENT=simd.

https://github.com/nnnkkk7/go-simdcsv

Feedback on edge cases or the SIMD implementation welcome.

https://microclimates.solofounders.com/

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.

Hi HN!

I’m Hélène. My husband and I are builders from Belgium, and we’ve spent the last few months building a side project called 13 Virtues.

The idea comes from Benjamin Franklin’s personal character system. Instead of tracking many habits at once, he focused on one virtue per week (Temperance, Silence, Order, etc.), cycling through 13 virtues over 13 weeks, then repeating the cycle. He documented this practice for many years.

We wanted an app that follows this structure strictly, rather than another flexible habit tracker. One virtue at a time. One day at a time. Repeat.

You can try the ledger directly on the homepage without creating an account. You can mark faults for today and see the current virtue with Franklin’s original quotes:

https://www.13-virtues.com

Why we built it:

We were frustrated with productivity apps that optimize for streaks and metrics rather than reflection. Franklin’s system felt refreshingly constrained and intentional, and we wanted something we’d actually use ourselves. My husband handled the engineering; I focused on product and design.

Pricing model:

We deliberately avoided subscriptions.

- Free tier: the full 13-week cycle and daily ledger

- Lifetime upgrade ($79 launch price): long-term history beyond the current cycle, guided reflections, data export, and a downloadable Modern Virtue Guide (PDF) that explains the method and its rationale in more depth.

Franklin’s system predates SaaS by a few centuries, and a monthly fee felt wrong for something meant to be practiced quietly over years.

Tech:

- Backend: Laravel

- Frontend: Vue (Inertia)

- CSS: Tailwind

- Hosting: Hostinger

Built over ~12 weekends.

We’ll be around today (CET) to answer questions — happy to discuss the implementation, the pricing decision, or Franklin’s original writings. Thoughtful UI/UX feedback is especially welcome.

Hey HN!

I built this after getting frustrated with losing great ideas while driving, cooking, or exercising. You know that moment when inspiration strikes but your hands are occupied?

The Problem: Doubao AI earphones (popular in China, similar to AirPods but with built-in AI) are great for Q&A, but all conversations disappear after listening. It's a closed ecosystem with no way to export valuable content.

My Solution: A Python tool that monitors the Doubao web interface and automatically syncs voice notes to Obsidian in real-time.

What makes it interesting:

30+ speech variation recognition - Works even if you say "note" instead of "take note" or use filler words like "um, note this down" Hands-free operation - Just say "Doubao, take a note, [your content]" and it appears in Obsidian instantly Smart deduplication - Won't create duplicate entries for similar content Cross-platform - Works on Windows, macOS, Linux Technical approach:

Uses Playwright to monitor DOM changes and network requests Regex engine handles speech variations and colloquialisms SQLite for deduplication logic Async I/O for real-time file operations Real use cases I've tested:

Capturing meeting insights while driving between offices Recording workout thoughts during runs Noting recipe improvements while cooking Quick idea capture during walks The tool essentially breaks the walled garden of AI earphones and turns them into a proper knowledge management device. It's like having a voice assistant that actually remembers and organizes everything you tell it.

Demo: Say "Doubao, note this: remember to research async patterns" → Instantly appears in Inbox/Voice Notes/2026-01-21.md

Built with Python + Playwright + SQLite. MIT licensed.

What do you think? Have you faced similar issues with voice assistants that don't persist information? Would love to hear about your workflows for capturing ideas on-the-go!

GitHub: https://github.com/GptsApp/doubao-earphone-to-obsidian

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.

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.

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.

ActionBase is an open-source Java framework that provides a set of tools and utilities for building scalable and maintainable web applications. It simplifies the development process by abstracting away common tasks and providing a well-structured architecture.

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)

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!

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!

It's an open-source project that lets you create, translate, and work with multiple agents at the same time, and more.

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