Show HN: Hopsule – Persistent memory and decision layer for AI developmentHi HN! I'm building Hopsule.If you use AI coding tools like Cursor, Copilot, or Claude, you’ve probably seen this happen: The AI writes good code - but it ignores your architecture.It doesn’t know: - why you chose a specific pattern - which conventions your team agreed on - which decisions are already locked in So it falls back to generic patterns, outdated examples, or random GitHub training data. Over time this slowly breaks the consistency of the codebase.Most teams try to fix this with: - giant Markdown files - wiki pages - long prompts - Slack threads But those aren't machine-readable rules.So we built Hopsule. Hopsule turns architecture decisions into enforceable context that AI tools must follow.Example: Your team approves a decision: “All database access must go through the repository layer.” Hopsule records this as a rule and injects it into the AI context before code generation.No giant prompts. No manual context stuffing. No architecture drift.Website: https://hopsule.com Do
Show HN: Claude Code skills that build complete Godot gamesI’ve been working on this for about a year through four major rewrites. Godogen is a pipeline that takes a text prompt, designs the architecture, generates 2D/3D assets, writes the GDScript, and tests it visually. The output is a complete, playable Godot 4 project.Getting LLMs to reliably generate functional games required solving three specific engineering bottlenecks:1. The Training Data Scarcity: LLMs barely know GDScript. It has ~850 classes and a Python-like syntax that will happily let a model hallucinate Python idioms that fail to compile. To fix this, I built a custom reference system: a hand-written language spec, full API docs converted from Godot's XML source, and a quirks database for engine behaviors you can't learn from docs alone. Because 850 classes blow up the context window, the agent lazy-loads only the specific APIs it needs at runtime.2. The Build-Time vs. Runtime State: Scenes are generated by headless scripts that build the node graph in memory and serialize it t
Ask HN: AI Agents vs. Gateways vs. HarnessesHi,I'd like to get everyone's take on the different components in the AI Agents ecoysystem. I find the current terminology quite confusing as it's not always obvious what I'll be actually getting when I examine the available options.A lot of things get called Agents, but that term seems hard to define as it often refers to overlapping functionality. To me, agents seem to be currently composed of the following components:## Harnesses- Adds UI and system instructions around an LLM and may also augment it with tools such as memory, tool calls, etc...- Examples are Claude Code, Code, Gemini CLI, pi.dev, ...## Gateways- These connect agents to your communication tool of choice, e.g. Whatsapp, Telegram, Slack, Discord, ...- Examples are OpenClaw and Nanoclaw## Sandboxes- Isolated environments where Agents can run with limited or auditable capabilities- Examples can be from physical ones like separate Mac Minis to docker-agent, agent-sandbox, localsandbox, ...This then takes us to *Agents*:##
Show HN: AgentArmor – open-source 8-layer security framework for AI agentsI've been talking to founders building AI agents across fintech, devtools, and productivity – and almost none of them have any real security layer. Their agents read emails, call APIs, execute code, and write to databases with essentially no guardrails beyond "we trust the LLM."So I built AgentArmor: an open-source framework that wraps any agentic architecture with 8 independent security layers, each targeting a distinct attack surface in the agent's data flow.The 8 layers: L1 – Ingestion: prompt injection + jailbreak detection (20+ patterns, DAN, extraction attempts, Unicode steganography) L2 – Storage: AES-256-GCM encryption at rest + BLAKE3 integrity for vector DBs L3 – Context: instruction-data separation (like parameterized SQL, but for LLM context), canary tokens, prompt hardening L4 – Planning: action risk scoring (READ=1 → DELETE=7 → EXECUTE=8 → ADMIN=10), chain depth limits, bulk operation detection L5 – Execution: network egress control, per-action rate limiting, human approv
Show HN: Nixcage – Sandbox AI coding agents per project with Nix and direnvI built nixcage to solve a problem that's been bugging me: AI coding agents like Claude Code have broad access to your filesystem, network, and system. That's powerful but uncomfortable, especially on client projects or machines with sensitive data.nixcage creates per-project sandboxes that activate automatically when you cd into a directory (via direnv). It uses bubblewrap on Linux and sandbox-exec on macOS — no VMs, no Docker, no overhead.Three isolation levels: strict (no network, empty home), standard (project writable, network on), and relaxed (home readable, project writable).It also controls Nix store access (shared, readonly, copy, or fully isolated) so sandboxed tools can't pollute your host store.Quick start: nixcage init --preset claude-code && direnv allowA debug mode captures every blocked syscall so you can see exactly what's denied and tune your config.https://github.com/hamidr/nixcage
Show HN: AgentCash – access 280 paid APIs with no API keysHey HN - I'm Mason, and I'm excited to share AgentCash - a CLI to help your agent discover and pay for premium APIs and paywalled data.The problem we kept running into was simple: if you want an agent to use premium data or services, the setup flow is awful.You have to find the right provider, sign up manually, subscribe, generate an API key, install tooling, and then teach your agent how to use it. Then do that again for the next provider.We wanted a simpler model: give the agent a universal balance, let it discover available resources, and let it pay per call.So AgentCash gives agents one balance with access to 280+ paid APIs. It’s built on x402 [0,1] (the Coinbase/Cloudflare payments protocol) and designed to work with tools like Claude Code, Codex, OpenClaw, Cursor, etc.I also am really proud of our onboarding flow - we actually use AgentCash itself to research you and the decide how much free cash to give based on a score.The vision for this is to essentially give agents JIT conte
Ask HN: How are you structuring Markdown-based context for AI coding agents?I’ve recently transitioned from using LLMs in-browser to a local agentic workflow in VS Code (Gemini Code Assist). I can approve/disapprove changes which is nice, but I’ve hit a wall regarding context management. Initially, I provided all the whole repo as context to the non-agentic version of Gemini code assist and it performed well.I read the agentic mode is "better" so to keep the agent aligned with my project's architecture, I’ve manually built 7 dense Markdown files that serve as the system instructions for the project. I require Gemini to update these files as we implement features.gemini.md (instructs gemini to read the other md files and handle updating) project_overview.md, architecture.md, features.md, database.md, api.md, security.mdEach file is between 500–1,500 words so I’m concerned if f this is the right way to go. There seems to be no consensus on context file best practices. I’m seeing strong arguments for both minimalist, lean instructions and dense, project-wide spec
Show HN: Lysium – cross-platform control plane for agentic software deliveryHi HN — I built Lysium to reduce context-switching when working through GitHub issue/PR queues.Lysium uses GitHub + the Devin API so you can route work to background agents and keep moving. You can: • Swipe issues/PRs to close, merge, or skip • Launch implementation requests from issues/PRs or from a freeform Code tab • Run multiple agent sessions in parallel across repositories • Trigger one-click issue/PR assessments and PR reviews • Track progress in an Activity view split into Sessions and Actions Try it: https://www.lysium.ai/ Code: https://github.com/dabit3/lysiumNo email signup flow; full functionality requires GitHub OAuth plus a Devin API key + org ID.I’d especially appreciate feedback on onboarding friction, general UX, and where agent automation should be explicit vs automatic.
Show HN: Tswap–Yubikey-backed secret injection for IaC and AI-assisted workflowsI built tswap after noticing that Claude Code, while genuinely useful for managing a Kubernetes cluster, was pulling plaintext secrets from every manifest it touched. I wanted the AI to be able to do its job without ever seeing the actual values.tswap keeps secrets in an AES-encrypted vault file on disk. The decryption key is derived from a YubiKey via HMAC challenge-response. At init you pair two YubiKeys — either unlocks the vault, so you have no single point of hardware failure.Config files use a comment-based placeholder that keeps them valid YAML: stringData: DB_PASSWORD: # tswap: db-password Deployment is a pipe: tswap apply values.yaml | helm upgrade myapp ./chart -f - The privilege split is the key design decision: `apply`, `run`, and `check` need no elevation (AI agent gets these). `get`, `list`, `delete`, and `export` require sudo/admin (human gets these). The AI can deploy; it can't read or enumerate secrets.Other features: burn tracking for rotation, `redact` for stripping
Show HN: Tag Promptless on any GitHub PR/Issue to get updated user-facing docsHi HN! I'm Prithvi—my co-founder Frances and I launched Promptless almost a year ago here (https://news.ycombinator.com/item?id=43092522). It's an AI teammate that watches your workflows—code changes, support tickets, Slack threads, etc.—and automatically drafts doc updates when it spots something that should be documented.Frances and I really appreciated the feedback from our first launch. Today we’re launching Promptless 1.0, which addresses our biggest learnings from the last 12 months.I also made it way easier to try it out. You can tag @promptless on any open-source Github PR or Issue with a doc update request, and Promptless will create a fork and open a PR for your docs to help. Feel free to use our own docs as a playground: https://github.com/Promptless/docs/issuesOr, you can sign up at https://promptless.ai to get free access for your own docs for the next 30 days. Here's a demo video: https://youtu.be/IWwimHCEY7YFor me, the coolest part of the last year has been seeing how us
Show HN: BlockHost OS – Autonomous VM provisioning through smart contractsRequirements for testing: - Metamask and some Sepolia testnet ETH (can provide, or use the faucet: https://sepolia-faucet.pk910.de/) - An old PC (with virtualization support) you have lying around, or a VM if your setup supports nested virtualization. - ipv6 connectivity Will install Debian on boot on the first detected hard drive without confirmation, after finishing up a setup wizard can be accessed with a browser (link + OTP code on console).On completing the wizard, the system will automatically deploy the needed smart contracts (point of sale + access credential NFT), and acquire a free ipv6 prefix at a decentralized tunnel broker. On reboot: a fully working VPS hosting provider, a signup page will be hosted on the public ipv6 address assigned to the Blockhost machine.Customer flow: - Connect wallet, sign message - Choose package, amount of days, and submit - Server picks up order, provisions VM, assigns ipv6, and sends access credential NFT to user containing encrypted connection
Show HN: VBAF – Machine Learning framework built in pure PowerShellI implemented neural networks and Q-learning in pure PowerShell - no Python, TensorFlow, or external dependencies.Why? I teach IT professionals who live in PowerShell. Python ML examples don't resonate. Building from scratch teaches fundamentals better than using black-box libraries.What's included:- Neural networks: Backpropagation, gradient descent, multiple activation functions - Q-learning agents: Experience replay, epsilon-greedy exploration, reward shaping - Environment simulation: Market competition, resource optimization - Real-time visualization dashboardsTechnical details:- Pure PowerShell (classes, no C# interop) - ~3000 lines of code - Compatible with PowerShell 5.1+ (Windows/Linux/Mac) - Available on PowerShell GalleryExample use case: Q-learning agent learns to build ASCII castles through trial-and-error. Agent receives aesthetic rewards and discovers patterns without hardcoded rules.Practical applications: - IT automation that adapts to patterns - Predictive maintenance
Ask HN: Am I holding it wrong?I've been steadfastly trying my best to incorporate the latest-and-greatest models into my workflow. I've been primarily using Codex recently. But I'm still having difficulties.For example: no matter what I do, I can't prevent Codex from introducing linter errors.I use tabs instead of spaces for indentation. It seems like the model is massively weighted on code written using spaces (duh). Despite having a very well articulated styleguide (that Codex helped me write after examining my codebase!) that clearly specifies that tabs are used for indentation, the model will happily go off and make a bunch of changes that incorporate spaces seemingly at random. It will use tabs correctly in certain places, but then devolve back to using spaces later on in the same files.I have a linter that I've taught the model to run to catch these things, but A) that feels like such a waste of tokens and B) the model often forgets to run the linter anyway.It's like having a junior developer who has never us
Show HN: Multi-attribute decision frameworks for tech purchasesWhat this is:Copy-paste LLM prompts that turn ChatGPT or Claude into a structured decision analyst for laptops, monitors, tablets, phones, and SaaS subscriptions. You define constraints, weight what matters to your workflow, and get scored recommendations with sensitivity analysis. Why I built this:With the rise of LLMs (AI), I wanted to find a way to harness the computing power and ease of use the chat interface provides. The major problem: LLMs don’t always provide repeatable, traceable results if you ask the same question twice or even against 2 competing products. That is the dilemma this product aims to solve. Is this a PDF, yes, but it harnesses my systems analysis experience to help hard-code a framework for a person off the street to turn their AI chat box into an objective decision-helper in just 15 to 20 minutes of use.I spent 10+ years applying decision science in defense and systems analysis—graduate work at Naval Postgraduate School, leading teams through decisions where t
Show HN: I vibecoded a Test Management app for JiraI'm a software tester since 2011. Become a Jira admin in 2013. Always wanted a tool which lives is inside jira and is a really cool test management app, where you can intuitively garden and execute your testing assets! When I decided to give it a go in 2016, found a tool which was almost perfect, called Kanoah Tests. That tool went through a lot, and as of today become one of the biggest app in jira (zephyr). But I found that they lost their mojo and stopped innovating, so after some very hard years I decided to quit my job and build my brand new test management app, just as i dreamed about it! For now, its only available through jira as an app (free under 10 users, just as jira), but planning to release it as a standalone saas.I started "development" with sonnet 3.7 and 4.0 which was terrible and slow. Used cursor 20$ and claude 20$ plans together with warp 20$ plan. Every time a model was free in cursor (grok, early gpt, anonymous) i used them in parallel for days or weeks, for maxim
Stellar JetNASA’s James Webb Space Telescope captured a blowtorch of seething gasses erupting from a volcanically growing monster star in this image released on Sept. 10, 2025. Stellar jets, which are powered by the gravitational energy released as a star grows in mass, encode the formation history of the protostar. This image provides evidence that protostellar jets […]
Project Kuiper becomes Amazon Leo ahead of LEO broadband service debutProject Kuiper has shed its seven-year-old code name, emerging as Amazon Leo Nov. 13 as the company nears the start of initial broadband services from the low Earth orbit constellation next year. The post Project Kuiper becomes Amazon Leo ahead of LEO broadband service debut appeared first on SpaceNews.
Show HN: Reverse perspective camera for OpenGL (Three.js)I've extended the standard OpenGL camera to allow smooth transition between normal perspective, orthographic, and reverse perspective projection. To demonstrate the effect, I built a demo using Three.js.Reverse perspective can feel a bit disorienting at first, but this demo is meant to highlight a few of its interesting advantages:1. An extended Dolly zoom effect. https://en.wikipedia.org/wiki/Dolly_zoomYou can think of it as an extrapolation of the classic Dolly zoom into negative space. It produces a cinematic sense of vertigo or sudden insight, and also lets you pull the camera back from an object without distracting the viewer with surrounding geometry.2. A wider and more complete spatial view.Near objects no longer obscure distant ones; the viewer can see both the façade of a building and what’s happening on its roof, or even behind it.Code and details: https://github.com/bntre/reverse-perspective-threejs
Show HN: HLinq: easy to use and extensible .NET resource query languageHi! For last two years I am working on side project: dynamically configurable, zero-downtime API, with low-code flows to write small pieces of logic - Hamster Wheel. For this I had to build my own resource query language in order for the user to be able to query specific data from the API via GET endpoints.HLinq is a library that adds query language capabilities to any API written in modern .net.Demo in memory collection: https://hlinq-demo.podbielski.it/demo/memoryDemo, db collection: https://hlinq-demo.podbielski.it/demo/dbGitHub: https://github.com/npodbielski/HamsterWheel.HLinq?tab=readme...Some information about Hamster Wheel: https://internetexception.com/why-hamster-wheel/HLinq Design: https://internetexception.com/2025/10/28/hlinq-design/Fluent Code Generators: https://github.com/npodbielski/HamsterWheel.FluentCodeGenera...I started working on dynamic API as a way to have one platform for my self-host lab tinkering. I.e. to have ability check for health of my self-hosted servic
Show HN: Share your coding agent sessionsKnowing it might soon be obsolete, I still went ahead and built aisessions.dev - a place to share sessions from Claude Code, Codex, and Gemini CLI.It was a fun zero-to-one project and an excuse to think through:- Safely accepting user generated data (security, PII scrubbing, etc)- Building a privacy conscious service- Designing for long-term low-maintenance operation- Developer experience — CLI tools, onboarding, and good defaults- UI, design, and simplicity- Keeping everything open sourceKudos to AMP and opencode for the inspiration.If OpenAI, Anthropic, or Google launch native session sharing tomorrow, that’s fine. The experience is well worth the effort (which wasn't that big considering I wrote very little of the code).




