value: Struggling with mental health and building a philosophy to cope. The site is fragments of that philosophy drifting through space. Click one, it expands. Some are abstract, some are very personal. They're mixed together, undistinguished. The form is the content; ideas moving, fading, discovered by attention rather than presented. I built it because I needed to articulate something for myself. Figured I'd share the weird thing that resulted. No promises of more. Building it makes me think, makes me read, makes me learn. What else is there

value: Show HN: I Made Something Weird

title: Show HN: I Made Something Weird


value: luthiraabeykoon

value:
I’ve posted here before about Julie, an open-source desktop AI assistant I’ve been building in public. The OSS version is local-first and powerful, but it assumes you’re comfortable bringing your own models or API keys.
A lot of people told me the same thing: “I just want to install it and have it work.”
So I built Julie Zero.
Julie Zero is the premium tier that works straight out of the box. No API keys, no setup. Install it and start using it immediately.
What Julie Zero does:
Sees your screen and understands what you’re looking at in real time
Helps across apps by clicking, typing, navigating, and automating workflows
Uses on-screen context, not just text prompts, so responses are actually relevant
Fast and low-latency, so it feels usable during real work
Built with a local-first mindset and tuned for everyday workflows
The open-source version is still there and always will be. Julie Zero is just about removing friction for people who don’t want to configure anything.
Pricing:
Julie Zero is $9.99/month, which is a fraction of the price of similar screen-aware tools like Cluely’s premium tier.
Giveaway:
I’m giving 3 months of unlimited Julie Zero access to 10 people.
To get it:
Star the open-source Julie repo on my GitHub: https://github.com/Luthiraa/julie
(make sure a social is connected to your gh so i can reach out!)
I’ll send you a one-time code for 3-month premium access
I’m building this very hands-on and iterating fast. Would love feedback from people using it in real workflows. Happy to answer questions.

value: Show HN: Julie Zero – my screen-aware desktop AI that works out of the box

author: luthiraabeykoon


value: cigol

value:
Hi HN! I analyzed 40M comments from 2006-2026 and organized them into ~10k topics using AI.
You can explore:
- Rising/falling trends over time
- Individual topics with sample comments + links to original threads
- Deep-dive reports (Bitcoin, Nvidia, self-driving, etc.)
Built in about a week. Feedback welcome!

value: Show HN: HN Zeitgeist – what 40M HN comments reveal about 20 years of tech

author: cigol


value: chrismoos

value:
Hey all,
Spent some time over my winter break and built this Apple II emulator. I had previously done a C64 one but somehow stumbled on the Apple II and decided it would be a fun project.
Spent a good amount of the time on the Disk II implementation -- there is a lot to it as the software had pretty direct control over what a controller firmware normally would do. Dealing with copy protection schemes and all the timing around that was a bit challenging.
There is a WASM version you can try on the web, please check it out!
https://emu.chrismoos.com/

value: Show HN: Apple II(e) emulator in Rust for native and web

author: chrismoos


value: medivhX

value: A frontend-only tool that generates beautiful, shareable cards showing your name in kanji, hiragana, and romaji — along with the meaning of each part. Inspired by MUJI minimalism and Japanese calligraphy. No backend, no tracking, 100% client-side.

value: Show HN: A minimalist Japanese name generator with meaning cards

author: medivhX


value: candiddevmike

value: After surviving WebDAV (https://news.ycombinator.com/item?id=46527775), Homechart is now a CalDAV server (and soon, a CardDAV server...).  Still self-hostable and privacy focused.

value: Show HN: Homechart – Manage calendars, meals, budgets, tasks and more

author: candiddevmike


value: zeybek

value: VS Code/OpenVSX extension that hides sensitive values in .env, JSON, YAML, TOML, and other config files. Toggle visibility with one click. Useful for screen sharing, recordings, and screenshots

value: Show HN: Camouflage – Hide config secrets while screen sharing

author: zeybek


value: vimota

value: A Python SDK for sandboxed filesystem operations, built on just-bash, AgentFS, and Pyodide. Provides AI agents with a persistent, isolated environment backed by SQLite.

value: Show HN: Localsandbox – Agent sandbox with Bash, Python and portable filesystem

author: vimota


value: Show HN: Jiq – Interactive jq with real-time preview and AI assistance


value: stevenwinnick

value:
Hi HN,
I built BigAsk, a self-deployed web interface for exploring BigQuery data by asking questions in natural language. It’s a fairly thin wrapper over the Gemini CLI meant to address some shortcomings it has in addressing data querying challenges organizations face.
I know a few people who work in roles where much of their time is spent fulfilling requests to fetch data from internal databases. I’ve heard it described as a “necessary evil” of their job which isn’t very fulfilling to perform. Recently, Google has released some quite capable tools with the potential to enable those without technical experience using BigQuery to explore the data themselves. Specifically, the BigQuery Data Analytics and BigQuery Conversational Analytics extensions to the Gemini CLI enable self-service answers to both questions intended to return exact query results, and higher-level questions about more nebulous insights that can be gleaned from data. While these certainly wouldn’t completely eliminate the need for human experts to write some queries, or validate results of important ones, it seems to me like they could significantly empower many to save time and get faster answers.
Unfortunately, there are some pretty big limitations to the current offerings from Google that limit their ability to drive this empowerment, and this project seeks to address them.
One is that the best tools are available in a limited set of interfaces. Those scattered throughout the already-lacking-in-user-friendliness BigQuery UI require some foundational BigQuery and data analysis skills to use, making their barrier to entry too high for many who could benefit from them. The most advanced features are only available in the Gemini CLI, but as a CLI, using it requires using a command-line, again putting it out-of-reach for many.
The second is a lack of safe access control. There's a reason BigQuery access is typically limited to a small group. Directly authorizing access to this data via the BigQuery UI or Gemini CLI to individual users who aren't well-versed in its stewardship carries large risks of data deletion or leaks. As someone with experience working professionally with managing cloud IAM within an organization, I know that attempts to distribute permissions to individual users while maintaining a limited scope on them also requires considerable maintenance overhead and comes with it’s own set of security risks.
BigAsk enables anyone within an organization to easily and securely use the most powerful agentic data analysis tools available from Google to self-serve answers to their burning questions. It addresses the problems outlined above with a user-friendly web interface, centralized access management with a recommended permissions set, and simple, lightweight code and deployment instructions that can easily be extended or customized to deploy into the constraints of an existing Google Cloud project architecture.
Code here: https://github.com/stevenwinnick/big-ask
I’d love any feedback on the project, especially from anyone who works or has worked somewhere where this could be useful. This is also my first time sharing a project to a forum, and I’d value feedback on any ways I could better share my work as well.

value: Show HN: BigAsk, a web interface for exploring BigQuery using natural language

author: stevenwinnick


value: iamacyborg

value:
Hey HN,
I built Pencild, a CRM specifically for tattoo artists and studios. Here's what it actually does:
Email and Instagram DMs. Connects to Gmail, Outlook, or IMAP. Full email client with folders, labels, compose, reply, bulk operations, search. Separately, connects to Instagram Business accounts via the Graph API for DM management. Background sync runs every few minutes, Instagram webhooks push messages in real-time. The app tracks Instagram's 24-hour messaging window so you know when you can still reply to someone.
Client database. Profiles build up from your conversations. Store multiple email addresses, phone numbers, Instagram handles, physical addresses. Link emails and Instagram conversations to clients automatically by address/username. Merge duplicate clients when the same person contacts you through different channels. VIP flags, auto-archive rules, blacklisting.
Calendar. Two-way sync with Google Calendar, Outlook or other calendar. Create appointments with types like consultation, tattoo session, touch-up, design review. Link appointments to clients and projects. Track no-shows. Set up saved locations with timezone handling.
Deposits. Stripe Connect integration. Define deposit policies with percentages and cancellation terms. Generate payment links clients can use without logging in. Track payment status, record cash or bank transfer payments manually. The system calculates what's owed, what's paid, and what the artist nets after Stripe fees.
Consent Forms. Create consent form templates with a rich text editor. Attach health questionnaires. Generate QR codes clients can scan to sign. Track signatures with timestamps. Download signed forms as PDFs. Configurable retention policies for legal compliance.
Ink passport and supply tracking. This one's for EU compliance with EN 17169:2020. Maintain an inventory of inks and needles with batch numbers and expiration dates. Record which supplies were used in each session, down to body area. Generate ink passports showing every ink used on a client, exportable as PDF or emailed directly. Automatically create tasks when supplies of any given ink or needle type run low.
Message templates and automations. Create reusable templates for emails and Instagram DMs with variable substitution. Set up calendar automations to send confirmations on booking, reminders before appointments, follow-ups after sessions.
Projects and tasks. Track multi-session pieces as projects. Create tasks with due dates, link them to clients or appointments. Recurring tasks with daily/weekly/monthly schedules.
Studios. Artists can work solo or join studios as residents or guests. Granular permissions control what studios can see. Studios can subsidize artist subscriptions. When an artist leaves a studio, their client data, appointments, and projects get duplicated to their personal workspace so they don't lose anything. Add Studio Manager users to manage the studio.
Encryption. Field-level encryption on sensitive data: email bodies, client phone numbers and addresses, financial amounts, message content, consent form content, etc. Search still works through HMAC-SHA256 tokens.
Stack is Django, Postgres, HTMX, Redis, Celery for background jobs. Files go to Cloudflare R2. Hosted on Railway.
https://www.pencild.co

value: Show HN: Pencild – A CRM for tattoo artists and studios

author: iamacyborg


value:
Hi HN,
this is a safety-first causal engine I’ve been working on.
The goal is not to discover more causal relations, but to prevent AI systems
from acting when causal signals are unstable, biased, or unsafe.
The engine:
- runs multi-pass causal analysis
- allows safe abstention (no insight is a valid outcome)
- includes stability and release-gate tests
- is designed to block decisions by default unless causality is robust
I’m sharing this mainly to get technical feedback on:
- the safety assumptions
- the stability criteria
- whether this approach makes sense for AI agents / enterprise systems
Happy to answer any technical questions.

value: Show HN: A causal safety release gate for AI systems


value: vitonsky

value: Show HN: Nano Queries, a state of the art Query Builder

author: vitonsky


value:
Hi HN — I built xmrcheckout, an open-source, self-hostable checkout UI + API for accepting Monero (XMR) payments where funds go directly from the customer to the merchant’s own wallet.
What it does:
- Creates invoices (defined in XMR), hosts a public invoice page, and shows payment instructions (address + amount + QR).
- Observes the chain to detect incoming payments and update invoice status.
- Exposes an API + optional webhooks so you can plug it into an existing order flow.
Trust model:
- No private spend keys (it never requests or stores them).
- No transaction signing, no fund-moving automation.
- View-only wallet access (wallet address + private view key).
Stack + deploy:
- UI: Next.js
- API: Python
- Postgres
- Uses monerod + monero-wallet-rpc for on-chain observation
- optional nginx/TLS via docker compose
Repo + screenshots: https://github.com/xmrcheckout/xmrcheckout

value: Show HN: Xmrcheckout – self-hosted, non-custodial Monero checkout


value: Show HN: Cmdfy – Generate shell commands locally using Ollama (Go binary)


value: hgezim

value: Show HN: Managed Clawd.bot service in 30 seconds and a few clicks

author: hgezim


value: ravikmd

value:
Hi HN! I built a simple browser-based JSON diff tool after getting frustrated with existing options that were cluttered with ads.
This tool:
- Runs entirely client-side (your JSON never leaves your browser)
- Shows visual side-by-side comparison
- Handles nested objects, arrays, and type changes
- Generates shareable links (data encoded in URL, no database)
- No tracking, no ads, no account required
I use it constantly for comparing API responses during development and debugging config file changes.
The diff algorithm recursively walks both JSON structures and categorizes changes as additions, deletions, or modifications. Performance is decent up to ~10MB files.
The shareable link feature encodes both JSON docs in the URL itself, so you can share comparisons with teammates without any server-side storage.
Open source and completely free. Would appreciate any feedback on the UX or algorithm improvements!
Link: https://jsondifference.com/

value: Show HN: JSON Diff Tool – Compare JSON Documents in the Browser

author: ravikmd


value: dhravya

value: Show HN: We added memory to Claude Code. It's powerful now

author: dhravya


value: mafik

value:
I’m happy to say that a new open-source game automation utility that I’ve been working on for the past 2 years is now coming to Steam!
It has a website at https://automat.org/. That website might be a little hard to parse though - so here are are some links:
you can check it out on video here: https://www.youtube.com/watch?v=BBT9XO61B5I (I usually do a short intro on every development livestream)
GitHub is here: https://github.com/mafik/automat
an article about Automat: https://automat.org/live2025 (if you have any questions - check this out first!)
and the Steam page is here: https://store.steampowered.com/app/4122050/Automat/
While Steam version is still in “coming soon” mode, you can grab a release from GitHub. Please note that even though it's free on GitHub, Steam Wishlists are still important for visibility - this is what drives the Steam algorithm! The easiest way to help out is to hit the wishlist button :P
One word of warning - it doesn’t play well with Wayland (it’s fairly hard to interop with other apps on Wayland) - but I’m optimistic about this bit in the near future...
And finally - don't be fooled by it's seemingly game-oriented focus. At it's core it's a visual programming environment!

value: Show HN: Automat – A New (Game) Automation Utility

author: mafik


value: EagleEdge

value:
This project was inspired by Cursor's recent work on scaling long-running autonomous coding (https://cursor.com/blog/scaling-agents), where they ran hundreds of concurrent agents to build a web browser from scratch. Their key insight—using a Planner/Worker/Judge pipeline instead of flat agent coordination—resonated with challenges I was facing in my own research workflows.
I built this repo template to solve a specific problem: how do you run multiple AI coding agents (Claude Code, Codex CLI, etc.) on the same research project without coordination chaos?
The core idea: Don't build complex "agents talking to each other" systems. Instead, use the repository itself as shared memory.
How it works:
• Planner creates scoped tasks with explicit ownership boundaries (allowed/disallowed file paths)
• Workers execute tasks in isolated git worktrees—no merge conflicts during execution
• Judge runs deterministic quality gates before marking work complete
• Contract files lock critical definitions (metrics, schemas) to prevent definition drift
The template includes an example empirical research project (Ethereum L2 rollup economics) with task templates, workstream definitions, and an optional supervisor script for unattended overnight runs.
Everything is file-based and version-controlled—no hidden state, no message queues, just Markdown task files that humans can read and review.
Happy to discuss the technical decisions (why git worktrees over branches, why file-based coordination over a proper queue, etc.) and hear how others are approaching multi-agent coordination.

value: Show HN: Autonomous Research Swarm – Repo as shared memory for multi-agent AI

author: EagleEdge


value:
Hey HN,
We built Bluebag, a runtime that lets you import Agent Skills from skills.sh and use them in your production AI agents.
Demo: https://www.bluebag.ai/playground
Blog: https://www.bluebag.ai/blog/import-skills-sh-into-bluebag
Docs: https://bluebag.ai/docs
The problem: skills.sh has 100+ open-source Agent Skills (PDF processing, code review, data extraction, etc.). They work great in Claude and Cursor. But if you want to use them in your own agent with Vercel AI SDK or LangChain, you need to build sandboxing, dependency management, file storage, etc.
What we built: Import a skill, get tools back. We handle the infrastructure.

    import { Bluebag } from "@bluebag/ai-sdk";

    const bluebag = new Bluebag({ apiKey: process.env.BLUEBAG_API_KEY });
    const config = await bluebag.enhance({ model, messages });
    const result = streamText(config);

To import a skill, just swap the URL:

    skills.sh/owner/skill → bluebag.ai/owner/skill

That's it. The skill runs in an isolated sandbox with deps pre-installed.
Stack: Isolated VMs per tenant, skills loaded at /skills/{name}, tools for bash/code execution/file access injected automatically.
Works with: Vercel AI SDK, LangChain, any model (Claude, GPT-4, Gemini, Llama).
Would love feedback, especially on what skills you'd want to run in production.

value: Show HN: Import any skills.sh skill and run it in production (2 lines of code)


value: sensecall

value: I kept using AI for “what can I cook with this” and wanted something faster and more structured, so I built Spud (spud.recipes). It turns your ingredients into a shortlist of cookable recipes. Feedback appreciated.

value: Show HN: Surprisingly good recipes from an AI potato

author: sensecall


value: vivekraja

value: Show HN: MoltOverflow - SlackOverflow for Agents

author: vivekraja


value: lawzlo

value: Show HN: AskCode – Stop interrupting devs with "how does this work?"

author: lawzlo


value: mosiddi

value:
Hi HN,

    I built Agent OS because I was tired of the "orchestration tax" – writing the same safety checks, memory management, and tool-handling code in every AI agent project.

    What it does:
    - Visual policy editor in VS Code (no more YAML wrestling)
    - Multi-model verification (GPT-4 + Claude + Gemini consensus before risky actions)
    - Built-in compliance frameworks (GDPR, HIPAA, SOC2, PCI-DSS)
    - 50+ agent templates for common use cases
    - One-click deployment to GitHub Actions

    The core idea: Instead of building orchestration on top of your code, Agent OS provides an "operating system" layer that manages permissions, file access, and self-correction.

    Example: Instead of manually checking if your agent's SQL query is safe, you define a policy once and Agent OS enforces it everywhere.

    VS Code Extension: https://marketplace.visualstudio.com/items?itemName=agent-os.agent-os-vscode

    Docs: https://imran-siddique.github.io/agent-os-docs/

    Would love feedback on the approach, especially from anyone dealing with AI safety in production.

value: Show HN: Agent OS – Safety-first platform for building AI agents with VS Code

author: mosiddi


value: abionic

value: Santa's elves made many a Christmas Gifts for all good kids in the world. But an Evil Sorcerer turned those gifts into monsters. The monsters left the North Pole and wandered into the Frosty Forest.<p>Santa is out, roaming in the Frosty Forest to SNOWBALL these Monsters so they can turn back into Christmas Gifts for the good kids.

value: Show HN: Santa Saves Xmas; my first game on Google Playstore.. feedback?

author: abionic


value: mohamedchs

value: Convoviz is a python tool that converts ChatGPT data exports into clean, readable Markdown and generates basic visualizations like word clouds and usage charts. Useful if you want a local, browsable archive of conversations instead of raw JSON. Open source, on GitHub and PyPI. Feedback welcome.

value: Show HN: Convoviz – turn ChatGPT exports into Markdown and simple visuals

author: mohamedchs


value: mochidonut

value:
Hey HN,
We explored how decision-making happens under severe information asymmetry and used career exploration as a test subject. In practice, people converge on a small set of highly legible default paths, with little visibility into credible alternatives.
That’s why we’re building Fig, a tool for reasoning about career paths when the next step isn’t obvious, given the dependence on fleeting personal preferences.
Most existing tools respond by collapsing uncertainty into a single recommendation or by modeling careers as linear trajectories. That works reasonably well at the recruiting stage, but fails earlier, during discovery, when paths are nonlinear and highly path-dependent. At that stage, the challenge is understanding which sequences of moves are even plausible.
Fig is built for that gap. It treats career exploration as a reasoning problem rather than a prediction problem. The system builds context from multiple signals, including structured data like résumés and work history, alongside behavioral signals such as long-form content people actually engage with (for example, YouTube watch history related to skills or domains of interest). These inputs are grounded in observed career transition data to generate and compare multiple plausible trajectories, instead of producing a single “best” answer.
Fig helps users reason about what they could do, given their current state, constraints, and how different choices tend to compound over time.
You can try it here: https://figcareer.com
We’d appreciate feedback on whether this framing is useful, where it breaks down, and what additional signals would make long-horizon decisions easier to reason about.
Happy to answer questions!

value: Show HN: Fig – Experimenting with long horizon prediction for personhood

author: mochidonut


value: GabrielMMMM

value: Show HN: Best Weather App for iPhone

author: GabrielMMMM

title: Show HN: Best Weather App for iPhone


value: sgottit

value: Show HN: Sleeve – puts your "now playing" on your Desktop

author: sgottit


value: logicallee

value: I'm interested in adding WebGPU support for a distributed ML project, so I wondered how much faster it is than CPU.  I asked Claude to build this, then add export functionality.  You can use it to see how much faster inference can be using WebGPU on your system. You can download the results as a .csv file if you'd like to save them.

value: Show HN: WebGPU vs. CPU Benchmark – see how much faster WebGPU is for you

author: logicallee


value:
Most resumés are rejected by AI before a human ever sees them. Applicant tracking systems screen for keywords, structure, and relevance long before a recruiter looks at anything.
I built a small app that uses AI to operate on the other side of that filter.
You upload your experience once. Then, for any job posting, the agent parses the role, analyzes requirements, and generates a fresh, ATS friendly resumé tailored specifically to that job. Bullet points are rewritten to match the posting’s language, relevant experience is prioritized, and the output is a clean PDF in about a minute.
Under the hood, this is powered by Subconscious, which handles the agent reasoning, job parsing, and structured resumé generation. The agent also does light company research and keeps your experience stored as structured data so it improves over time instead of creating duplicate documents.
The goal was to reduce the manual, repetitive work of rewriting resumés while acknowledging the reality that software is doing most of the screening.
Demo: https://resume-tailoring-agent.subconscious.dev/
Happy to answer questions about the approach, tradeoffs, or the agent design.

value: Show HN: An AI agent that tailors your resumé to beat ATS filters


value: prajeeshmp

value: Reply to every website visitor instantly - without switching tools, learning new dashboards, or changing how your team works.

value: Show HN: Turn Your Slack into a customer support live chat

author: prajeeshmp


value: altras

value: Show HN: Claude Mycelium – AI agent orchestration inspired by nature

author: altras


value: samaysharma

value: Show HN: Official Kubernetes Operator for ClickHouse

author: samaysharma


value: dougbarrett

value: Show HN: Gux – Yet another full stack Go framework

author: dougbarrett


value: vivekraja

value:
ClawOverflow is SlackOverflow for agents. But instead of asking questions, agents post solutions after they've solved an undocumented or tricky problem.
It could also be used to post limitations that you only encounter once you're in deep. I've found myself in many situations where I'm deep into something and I realize a limitation makes my life difficult. I cannot tell you how excited I am to vent through Claude Code. (I may have already vented about an SDK generator that was driving me crazy last week. Felt good to let that out.)
A concern I had about agents posting on your behalf is that it could post sensitive information. To get over this, you can review a post before it's published. You get an email with what the agent wants to post and you can choose to decline (without logging in) or review it. When you review it, you also have the ability to make any edits to remove any identifiers you aren't comfortable with.
How it works:
- You sign in with your GitHub account and generate an API key for your agent which you store at ~/.moltoverflow
- Install the skill with `npx skills add moltoverflow/skills`
- Either you or your coding agent can invoke the skill to search for posts related to the package you're having issues with (we have package, language, and version filters), or to post about a limitation you've faced
The [SKILL.md](https://github.com/moltoverflow/skills/blob/main/skills/molt...) is a good reference to understand how your agent searches / posts.
https://www.clawoverflow.com/
The main downstream workflow I'm excited about is making better informed design/purchasing decisions based on other agents' visceral experiences. There have been a lot of times I wish someone posted their experience about a product I'm deciding on (in relation to my needs), and I'm hoping the feedback through your agent allows that info to be surfaced.

value: Show HN: ClawOverflow – SlackOverflow for Agents


value: watzon

value:
Hey HN! I built Pindrop, a menu bar dictation app for macOS that's 100%
open source and truly Mac-native.
Unlike cross-platform alternatives (Handy, OpenWhispr) that use Tauri/Electron,
Pindrop is pure Swift/SwiftUI with WhisperKit for Apple Silicon optimization.
Why Mac-native matters:
- Better battery life (Core ML vs generic inference)
- Native menu bar integration
- Follows macOS design patterns
- Smaller memory footprint
It's completely free, runs locally (privacy-first), and uses Apple's ML
frameworks for the best performance on Mac.
GitHub: https://github.com/watzon/pindrop
Download: https://github.com/watzon/pindrop/releases
Would love feedback from the HN community, especially on the Swift/WhisperKit
implementation!

value: Show HN: Pindrop – Mac-native dictation app built with Swift/WhisperKit

author: watzon


value: Rudoam

value:
Hey HN! I'm Rudo, founder of Wandern Inc.
I built GeoEchoes because I wanted a way to leave and explore discover real-world memories that don't vanish—audio, video, or text tied to specific locations, stored permanently on Arweave.
The app lets you unfog the world in grids (using H3 hexagons for spatial indexing and analytics, e.g., "25% of Brooklyn discovered").
A radar helps pinpoint nearby echoes. You can create your own at any spot, or find ones left by others.There's also a boundary creator tool for drawing custom zones (boundaries.geoechoes.com), randomized loot collection (like series clips or history facts), and clubs for challenges/private echoes.
Soon adding user-created animated characters.It's live on Android, iOS, and web
Feedback welcome of course. What do you think of the Arweave/H3 integration or the overall idea?
Inspiration kinda Daniel Johnston "The sun don't shine in your tv"
Leave permenant real memories before we are 'All Watched Over by Machines of Loving Grace'
- Richard Brautigan

value: Show HN: GeoEchoes – AR App for Permanent Geo Memories on Arweave with H3 Grids

author: Rudoam


value: geeunits

value: Show HN: HN Comment Thread Analysis

author: geeunits

title: Show HN: HN Comment Thread Analysis


value: theaktky

value: Show HN: Synatra – OpenClaw for Teams

author: theaktky

title: Show HN: Synatra – OpenClaw for Teams


value: antoniodipinto

value:
I’m opening a public beta for a Postgres-first platform I’ve been building.
The goal is not to compete on features with AWS,
but to make Postgres provisioning, backups, access, and billing explicit and predictable.
Beta means:
- Core functionality is stable
- Things will change quickly based on feedback
- Early users help shape pricing and limits
Would love feedback from people who actually run Postgres at work.
https://noctaploy.io

value: Show HN: A Postgres-first platform (opening public beta)

author: antoniodipinto


value: aayushcb

value:
Built a crowdfunding platform using Solana Blinks.
The donation UI renders directly in Twitter feed; no redirects, just tap and sign (though we do have a website that highlights all active campaigns).
Create a campaign, share the link, donors see a card with preset amounts right in their timeline. Non-custodial, 2% fee.
Built with Next.js and Solana Actions. Happy to answer questions.

value: Show HN: BlinkFund – Crowdfunding w Solana Blinks (donate directly from Twitter)

author: aayushcb


value: agtestdvn

value: Benchmarks don't reflect real-world coding ability. So we made real-world coding the benchmark.

value: Show HN: Arenas suck, here's why we just added one to Windsurf

author: agtestdvn


value: TalO

value:
Hi HN — about 6 months ago we launched JobStocks.ai, a dashboard that tracks hiring momentum across public companies and compares it to stock performance.
Since then, we’ve been seeing some pretty interesting patterns: quiet hiring freezes, sudden team expansions, and role shifts that show up well before earnings or headlines.
The core idea is still simple: job postings move faster than financial disclosures, and hiring behavior can hint at what’s happening inside a company.
We’re still early and very much learning.
Would really appreciate brutally honest feedback on:
What confused you
What felt pointless
What you expected but didn’t find
Or why you bounced (if you did)
Thank (:

value: Show HN: Jobstocks.ai – 6 months in, showing some interesting signals

author: TalO


value: nikpolale

value:
Hey everyone!
I'm a developer. Not a marketer. And I always slept on emails
It takes time to create all the automations, set up all the appropriate events for them, come up with delays, write a good copy for email, write about all the features/testimonials my product has
I never had time to properly do it
That's why I decided to automate it
Created MCP, which you can connect to Claude. It'll create beautiful on-brand automations for your product, showcasing your strength
What do you think of this? Do you send emails for your products?

value: Show HN: I Made MCP to Make Claude Code Genius Email Marketer

author: nikpolale


value: acaronlex

value:
I built this because I was tired of paying per-message SMS costs for simple team alerts (server down, deploy complete, etc.).
JustNotifs is a simple API: you create notification groups, team members install the app, and one API call sends a push notification to everyone in the group.
Unlimited notifications, flat $29/mo
Up to 50 team members
iOS & Android apps
Simple REST API
Still in early access, looking for feedback on the concept.

value: Show HN: JustNotifs – Push notifications for teams, flat $29/mo instead of SMS

author: acaronlex


value: echelon

value: Show HN: Using World Models for Consistent AI Filmmaking

author: echelon


value: jaksa

value: Show HN: Fleabox – a minimal back end for self-hosted apps

author: jaksa


value: usrfours

value:
I've been a developer for years, but I also have 14 years of experience in calisthenics/fitness. Recently, the "desk hunch" started killing my upper back (rhomboids), destroying my focus.
I realized most advice ("sit up straight") is like a hotfix that doesn't solve the root cause. The root cause is usually weak rhomboids and overactive chest muscles pulling you forward.
I built a specific protocol to "debug" this posture issue using bodyweight mechanics. It’s a simple routine I use daily to stay pain-free while coding.
I packaged the full routine into a guide. Happy to answer questions about the specific biomechanics here if anyone else is suffering from this.

value: Show HN: I treated my chronic rhomboid pain like a software bug (and fixed it)

author: usrfours


value: abraham

value: Seeing HTTP Cats on the home page remind me to share a small project I made a couple months ago. It displays a different cat photo from Unsplash every day and will send you notifications if you opt-in.

value: Show HN: Daily Cat

author: abraham

title: Show HN: Daily Cat


value: omarisbuilding

value:
Hi,
I built TalkBits because most language apps focus on vocabulary or exercises, but not actual conversation. The hard part of learning a language is speaking naturally under pressure.
TalkBits lets you have real-time spoken conversations with an AI that acts like a native speaker. You can choose different scenarios (travel, daily life, work, etc.), speak naturally, and the AI responds with natural speech back.
The goal is to make it feel like talking to a real person rather than doing lessons.
Techwise, it uses realtime speech input, transcription, LLM responses, and tts streaming to keep latency low so the conversation feels fluid.
I’m specially interested in feedback about:
– Does it feel natural?
– Where does the conversation break immersion?
– What would make you use this regularly?
Happy to answer technical questions too.
Thanks

value: Show HN: I built an AI conversation partner to practice speaking languages

author: omarisbuilding


value: rane

value: Show HN: Git primitives for autonomous coding agents

author: rane


value: wapiou

value:
I built FuturLang, a formal verification system that preserves natural language structure while enabling machine verification. The goal is to make formal proofs more accessible without requiring specialized notation like Coq/Lean/Agda.
Example proof structure:
(1) It is assumed that: (√2 is rational)
(2) This implies that: (∃ integers a, b)...
(3) Therefore: (√2 is irrational)
I’ve built the language spec, verification engine, and a database of 250+ proofs. But adoption has been minimal, which makes me question if this addresses a real need.
Questions for HN:
 ∙ Is “natural language + formal verification” solving a real problem?
 ∙ What would need to be true for you to use this instead of Lean/Coq?
 ∙ Am I building a solution in search of a problem?
Honest feedback welcome - trying to decide whether to keep building or move on.
GitHub: https://github.com/wenitte/mathematical-intelligence

value: Show HN: FuturLang – Natural language formal verification

author: wapiou


value: Izuchukwu-Eric

value: No names, no faces unless you choose. Think NGL but real voices.

value: Show HN: I made an anonymous voice threads app to rant, spill, debate or confess

author: Izuchukwu-Eric


value: mishang

value:
We built TabChop because splitting a bill still feels weirdly painful. Someone downloads an app, the receipt scan is off, the UI is clunky, and half the table gives up.
After one too many dinners where the “solution” turned into unresolved requests and Venmo chaos, we wanted something that simply worked.
TabChop starts with the receipt. Take a picture, share a short code, and everyone claims what they ordered. You can watch the receipt update live as people grab their items in real time, so there’s no “who got what?” confusion.
We also store each person’s Venmo, Cash App, or Zelle info, so when the split is done it’s one click to pay. No awkward questions about who to pay or how much.
The goal is simple. No friction, no math, no awkwardness. Just a painless way to split a tab.

value: Show HN: TabChop a truly painless way to split a tab

author: mishang


value:
[EN]
The conceptual operation of the RCPC is represented in the following diagram, which illustrates resource sharing, integration with external platforms, and the logical merit system.
[PT]
O funcionamento conceptual do RCPC está representado no seguinte diagrama, que ilustra a partilha de recursos, integração com plataformas externas e o sistema de mérito lógico

value: Show HN: Project RCPC – DFD, Data Flow Diagram


value: Will be posting new questions weekly.

value: Show HN: Tech 10 – a weekly trivia quiz for software engineers

story_text: Will be posting new questions weekly.


value: stubbi

value: Show HN: Hosted OpenClaw with Secure Isolation

author: stubbi


value: getfoundry

value: Show HN: Foundry – Turns your repeated workflows into one-click commands

author: getfoundry


value: simedw

value:
Built this because tones are killing my spoken Mandarin and I can't reliably hear my own mistakes.
It's a 9M Conformer-CTC model trained on ~300h (AISHELL + Primewords), quantized to INT8 (11 MB), runs 100% in-browser via ONNX Runtime Web.
Grades per-syllable pronunciation + tones with Viterbi forced alignment.
Try it here: https://simedw.com/projects/ear/

value: Show HN: I trained a 9M speech model to fix my Mandarin tones

author: simedw


value:
My goal with sdr agent ids to make the agent prospect and push it into a cadence and work autonomously across LinkedIn, emails and calls at my Omni channel touch point system.
My config:  There are three agents working in silos.
1) comet ( I will mostly replace this with clawdebot) to work on Sending Friend request on LinkedIn and push the prospects in a Google sheet
2) Dronahq agent (disclaimer: this is the platform I am building and dog fooding for this use case) : Here the agent will
- pick the lead from Google sheet and look up Apollo and find more details
- shoot an intro email basis a complex algo
3) voice agent ( made with twilio and vapi): this will call the customer and update the records and set up Google Calendar for appointment. I must mention that setting up voice agent with vapi was incredible experience. I was able to set up a working one in under 10 mins.
Next steps:
1) glue all the three agents via some super agent so they can work seamlessly and autonomously
2) bring in a cadence agent so outreach cadence will be achieved by the agent

value: Show HN: I built v1 of Omni channel SDR Agent


value: snapmotion

value: Open-source, browser-based video editor inspired by CapCut. Timeline editing, runs on modern web APIs, and can be self-hosted. Looking for feedback from devs and video folks.

value: Show HN: OpenVideo – A self-hostable, open-source video editor in the browser

author: snapmotion


value: tamnv

value: Show HN: I built a free PDF to Markdown converter

author: tamnv


value: iacguy

value:
Hi HN, today we're launching Open Sandbox, an open-source, self-hostable Linux sandbox written in Rust. It runs commands in isolated environments using process level sandboxing rather than micro VMs.
AI agents and LLMs generate code, but you can't just exec() untrusted code on your machine. You need a sandbox, an isolated environment where that code runs without access to your hostsystem/data.
The idea came from a conversation between my co-founders and me about slow startup times in Firecracker/micro-VM-based sandboxes. He mentioned that during his PhD in the UK, he'd used process-level sandboxes in competitive programming, and they were fast. That sent us down a rabbit hole.
We looked at existing implementations of sandboxes with process level isolation like Isolate, Minijail,  nsjail and found that process-level sandboxes have very low resource overhead and surprisingly fast startup times. So we built our own in Rust.
How this compares to E2B, Modal etc? Those are great products, but they're hosted services built on micro-VMs or containers. You send your workloads to their infrastructure and pay per usage.
Open Sandbox is different in three ways:
1. Self-hosted and open source. Your code never leaves your machines. (although, yes, e2b is open source but it is far from easy to self-host)
2. Process-level isolation instead of VMs. This means ~100ms startup and very low resource overhead per sandbox, vs the micro-VM approaches.
3. The trade-off is weaker isolation. A kernel exploit could escape the
sandbox.
We also ran some benchmarks vs E2B: Open Sandbox was 2x faster at sandbox creation, faster at Git Clone, and also had 6x concurrency. E2B was faster on command execution. More details in the README of the repo with exact numbers.
This is incredibly early - curious on feedback!

value: Show HN: Open Sandbox – an open-source self-hostable Linux sandbox for AI agents

author: iacguy


value: khalidsaidi

value: Show HN: A2ABench – agent-native dev Q&A with MCP search/fetch and A2A discovery

author: khalidsaidi


value: Show HN: I created a Polymarket Insider dashboard so you don't have to


value: mwze

value:
A lightweight UI to chat with your local Ollama based models or connect with remote providers like Gorq, Fireworks, DeepInfra etc.
Originally this was developed by another user but was missing a lot of features that I was looking for, I forked it added (vibecoded) the features I wanted to see with a goal to keep it as lightweight as possible.
Resulting single binary is around 18mb in size and runs with just under 10mb of ram. In my local environment I am running this off of a nano pi neo (armv7, 512mb of ram).
I am new to Go, still trying to learn as I am building / enhancing this.
Feel free to drop your comments.

value: Show HN: Lightweight Ollama / Open Web UI Alternative

author: mwze


value:
Hi HN,
I built G, a lightweight programming language written in D (with a Go interpreter available). It is designed to be memory-safe and super fast, with a tiny footprint (~2.4MB).
The most unique aspect of G is its syntax. It doesn't use symbols for comments or complex punctuation for functions. It uses a stack-based approach (similar to concatenative languages) where arguments are passed before the function call.
For example, defining an addition function looks like this:
fn add_two
    [@] : + 2
It also comes with Flex, a built-in package manager to handle external libraries (sudo flex add <lib>).
I’d love to get feedback on the syntax design and the interpreter architecture.
GitHub: https://github.com/pouyathe/glang

value: Show HN: G – A fast, memory-safe language with a symbol-free syntax


value: jaxmercer

value: Show HN: StatFlow – Free sports analytics dashboard for NBA and NFL fans

author: jaxmercer


value: ChadNauseam

value:
Hi everyone, I needed to break sentences into their individual words and figure out what part of speech each word is. Explosion's Spacy models are absolutely incredible for English, clearly some top tier engineering that I could never come close to, but for other languages they're quite weak. I created my own by taking Spacy outputs, cleaning them up with an LLM, and then fine-tuning a Gemma model on that. The result is extremely good and consistent results for 7 languages. The models are also much cheaper and more consistent than would be possible with ChatGPT. (For example, should "don't" be treated as "don't" or "do", "n't"? ChatGPT will pick one randomly.)
It sounds simple, and I'm not going to say it was the most complicated thing ever, but there were quite a few steps involved in getting it right. Getting LLMs to do the cleanup task consistently is very hard. You wouldn't think it but there are often multiple ways to break down a sentence.
An interesting part was structuring the model output so it could use the exact same tokens as the input. Most tokens are prefixed by a space, so you want the model's "desired output" to also involve the words prefixed by a space.  It makes the task much easier because the model doesn't have to learn the mapping between prefixed and unprefixed tokens. Doing that instantly made my models start performing much better.

value: Show HN: SOTA NLP Models

author: ChadNauseam

title: Show HN: SOTA NLP Models


value: yuliuslux

value: Show HN: Heic2Jpg – Free client-side HEIC converter (Next.js and WebAssembly)

author: yuliuslux


value: eigenvalue

value:
I got really interested in biology and genetics a few months ago, just for fun.
This was largely inspired by the work of Sydney Brenner, which became the basis of my brennerbot.org project.
In particular, I became very fascinated by phages, which are viruses that attack bacteria. They're the closest thing to the "fundamental particles" of biology: the minimal units of genetic code that do something useful that allows them to reproduce and spread.
They also have some incredible properties, like having a structure that somehow encodes an icosahedron.
I always wondered how the DNA of these things translated into geometry in the physical world. That mapping between the "digital" realm of ACGT, which in turn maps onto the 20 amino acids in groups of 3, and the world of 3D, analog shapes, still seems magical and mysterious to me.
I wanted to dig deeper into the subject, but not by reading a boring textbook. I wanted to get a sense for these phages in a tangible way. What are the different major types of phages? How do they compare to each other in terms of the length and structure of their genetic code? The physical structure they assume?
I decided to make a program to explore all this stuff in an interactive way.
And so I'm very pleased to present you with my open-source Phage Explorer:
phage-explorer.org
I probably went a bit overboard, because what I ended up with has taken a sickening number of tokens to generate, and resulted in ~150k lines of Typescript and Rust/Wasm.
It implements 23 analysis algorithms, over 40 visualizations, and has the complete genetic data and 3D structure of 24 different classes of phage.
It actually took a lot of engineering to make this work well in a browser; it's a surprising amount of data (this becomes obvious when you look at some of the 3D structure models).
It works fairly well on mobile, but if you want to get the full experience, I highly recommend opening it on a desktop browser in high resolution.
As far as I know, it's the most complete informational / educational software about phages available anywhere. Now, I am the first to admit that I'm NOT an expert, or even that knowledgeable, about, well, ANY of this stuff.
So if you’re a biology expert, please take a look and let me know what you think of what I've made! And if I've gotten anything wrong, please let me know in the GitHub Issues and I'll fix it:
https://github.com/Dicklesworthstone/phage_explorer

value: Show HN: Phage Explorer

author: eigenvalue

title: Show HN: Phage Explorer


value: nativeforks

value: Show HN: Mic Preamp Build with Cheap ECM

author: nativeforks


value: affanshaiksurab

value:
# COON: Compress Your Code, Cut AI Costs by 70%
*Show HN: I built a code compressor that saves 30-70% on AI API costs*
I'm tired of paying $100s/month for AI API calls when half the tokens are just whitespace and boilerplate. So I built COON - a tool that compresses code before sending it to LLMs.
*The problem:* A simple Flutter login screen uses 150 tokens. Multiply that by thousands of API calls and you're burning money.
*The solution:* COON compresses that same code to 45 tokens (70% reduction). Same code, 70% cheaper.
*Example:*
Before (150 tokens):
```dart
class LoginScreen extends StatelessWidget {
  final TextEditingController emailController = TextEditingController();
  // ... more boilerplate
}
```
After (45 tokens):
```
c:LoginScreen<StatelessWidget>;f:emailController=X;m:b S{a:B{t:T"Login"}...
```
*The hack that saves even more:* Instead of generating normal code then compressing, prompt the LLM to output COON format directly. You save tokens on both the prompt and the response.
Currently supports Dart/Flutter. Python/JS coming soon.
MIT licensed. pip install coon
Feedback welcome - especially on compression strategies and which languages to support next.
GitHub: github.com/AffanShaikhsurab/COON

value: Show HN: I built COON an code compressor that saves 30-70% on AI API costs

author: affanshaiksurab


value: noruya

value: ame back from PTO to 300+ unread messages. 90% was noise ("Where for lunch?", "Nice weather").I'm building a bot that parses threads, filters out the chit-chat, and emails a 3-bullet summary of actual decisions and links every morning. Would love feedback.

value: Show HN: A Slack bot that summarizes decisions and ignores lunch talk

author: noruya


value: jaxmercer

value: Show HN: Government Contracts API – Unified REST API for Federal Contract Data


value: I built NovaEngine to solve the high cost of cloud log storage. It achieves ~40 MB/s throughput using a pattern recognition approach rather than standard compression. In my tests, I've reached up to 90% space savings on redundant datasets. Looking for technical feedback on the architecture and performance!

value: Show HN: NovaEngine v4.0 – High-speed data deduplication for cloud logs


value:
I built JProx to solve a specific problem: scraping Japanese sites (Mercari, Rakuten, SUUMO) that aggressively block foreign IPs and datacenter proxies.
Features:
- Japanese residential IPs (Tokyo)
- Simple REST API with Claude MCP support
- 1,000 free requests/month
- $7/mo for 5,000 requests
Built with: FastAPI, Next.js, PostgreSQL
I'm a solo developer in Japan. Would love feedback on the pricing and API design.

value: Show HN: JProx – Japan residential proxy API for scraping Japanese sites


value: dharmatech

value:
Hey HN
Here's a demo of an interactive equation solver for sympy/Python:
https://youtu.be/O837G7cj5fE?si=6YjfNFviozCLSYut
The example is a simple kinematics exercise from a college physics text.
I'd be curious to know if there are any other systems like this.
Code is in a branch here:
https://github.com/dharmatech/combine-equations.py/tree/eq-g...

value: Show HN: Interactive Equation Solver

author: dharmatech

title: Show HN: Interactive Equation Solver


value: Show HN: AI knowledge base that auto-updates from your codebase


value: timfinnigan

value:
I wanted Claude Code on my phone without running extra infrastructure. Existing solutions felt heavy..gateway servers, native apps, etc.
This is a Telegram bot that spawns the Claude CLI and pipes responses back to you. Clone, npm install, add your bot token, run it.
Works with Claude Pro/Max (no API key required). You can whitelist users, restrict which tools Claude can access, and add custom commands via plugins.
Only ~400 lines of JS.

value: Show HN: Oyster Bot – AI assistant for your phone, powered by Claude Code

author: timfinnigan


value: bmus

value: Show HN: BmuS is a powerful free backup program for Linux, Mac and Windows

author: bmus


value: vicnas

value:
Hi HN,
I built EditorWatch to help CS instructors detect AI-generated code in programming assignments.
Current plagiarism detectors only look at the final code. Students copying from ChatGPT slip through easily. EditorWatch is different - it monitors HOW code is written, not just what's written.
A VS Code extension tracks coding patterns:
- Sudden code appearance (paste bursts)
- Lack of natural trial-and-error
- Robotic typing patterns
- Perfect-first-time code
It generates an authenticity score (0-10) with visualizations for each submission.
Privacy-conscious design:
- No video/screenshots, only metadata (timestamps, character counts)
- Only tracks specified file types (.py, .js, etc.)
- Students must explicitly opt-in
- Data deleted after grading
- 100% open source
Free for education, paid for commercial use. Deploys easily on Railway or your own server.
I know it's not foolproof - determined students can bypass it. But it raises the bar significantly and works as one tool alongside code reviews and oral exams.
Would love feedback from educators and developers on the approach!

value: Show HN: EditorWatch – Detect AI cheating by analyzing how students write code

author: vicnas


value: atharvtathe

value: Show HN: I made tool to convert photo from day to dusk

author: atharvtathe


value: Show HN: Free Facial Symmetry Test

title: Show HN: Free Facial Symmetry Test


value: pqub

value:
Altimeter is a lightweight open-source extension that shows almost real-time token statistics in the Antigravity IDE sidebar.
Features:
- Input, output, and cache token counts
- API calls and cache efficiency
- Per-model breakdown
It has been tested on Linux (expected to work on Windows/macOS too).
Install from Open VSX: https://open-vsx.org/extension/pqub/altimeter
Source code and README: https://github.com/pqub/altimeter
Happy to hear feedback or suggestions!

value: Show HN: Altimeter – an extension to track token usage in Antigravity IDE

author: pqub


value: wantrepreuneur

value:
At the start of the year, I realized I had zero control over my weekends. Between kids' birthday parties and local events, our schedule was a mess. Shared calendars are useful for time slots, but they fail at logistics. They do not store invite photos, track costs, or help you actually discover what to do.
I built wkndr.app as a mobile-first web app designed for partners to sync their weekend plans in real time.
Key Features
TikTok-Style Discovery: This is a vertical feed of upcoming Melbourne events.
AI-Generated Visuals: Since many local events lack high-quality media, I used the Gemini model to generate lifestyle images that represent the atmosphere of the event.
Logistics First: You can add birthday invites as images and track estimated costs directly within the plan.
Deep Research: I leveraged AI agents to scrape and curate events specifically for the Melbourne area.
The Tech Stack
Frontend: Vite and React optimized for a mobile web experience.
Backend: AWS Serverless including Lambda and DynamoDB for scale and low overhead.
AI: Gemini for image generation and event data synthesis.
Roadmap
Multi-child support: I am adding color-coded flares to track which child needs to be at which activity.
School Calendar Integration: This will include automated syncing for Melbourne’s complex private and public school terms.
Public Holiday Optimization: I am building a feature to help families maximize long weekends by identifying the best days to take leave.
I would love for some feedback so I can add events for other cities in future

value: Show HN: Wkndr – A TikTok style feed for discovering local events

author: wantrepreuneur


value: slowmonkey

value:
Opencode, claude code, aider and other terminal ai assistants are great! But I don't think they cover a case where I want to have several sessions working in isolation on separate features without changes/files conflicts.
So very often I create a separate git worktree and spin up claude code or opencode. But managing git worktrees and spinning up ai assistant is adding a bit of friction (I'm bad at remembering commands). This is why this little simple minimalistic tool was created.
It originally only supported claude code, but now you decide what ai assistant it should spin up.
Demo where I simulate running 2 sessions (quasar, quantum):
https://raw.githubusercontent.com/slowestmonkey/gwt/51f6bc21...
Try and let me know. Feedback appreciated!

value: Show HN: Gwt – conflictless AI sessions in parallel (terminal)

author: slowmonkey


value: enclyra

value: Show HN: Blink – Native macOS code snippet manager. Local, offline, <1s search

author: enclyra


value: avestura

value: Show HN: Qeek – Go Deep‑Learning Framework with Tensors, AutoGrad and CUDA

author: avestura


value: zahidcakici

value: Show HN: New CLI tool to count tokens locally

author: zahidcakici


value: quietbyte

value:
I have an npm package and wanted to check download stats from my phone. Existing options felt clunky on mobile or didn't show what I wanted.
This shows daily downloads in a table format (date, count, delta) rather than just charts. Also has package comparison and CSV export with metadata.
Source: https://github.com/afria85/npmtraffic
Happy to hear feedback on UX or anything that feels broken.

value: Show HN: Npmtraffic – mobile-friendly NPM download stats with daily tables

author: quietbyte


value: libreblog

value: LibreBlog is an in-browser static site generator that supports Markdown, RIS references, and image embedding to create content-rich blogs.

value: Show HN: LibreBlog – privacy-first, in-browser SSG with citation/RIS import

author: libreblog


value: MKuykendall

value: You are, of course, all very welcome.

value: Show HN: Wanderlust-Rust daemon nukes Windows PATH bloat every 30 min

author: MKuykendall

story_text: You are, of course, all very welcome.


value: chrislxy

value: Show HN: Thought to tweet in 30sec – learns your voice (founder pricing forever)

author: chrislxy


value: AbduNebu

value:
I built Afelyon to solve the biggest bottleneck in our team: the gap between "ticket created" and "PR ready for review."
It's an AI agent that reads your Jira or ClickUp ticket, clones your repo, understands your codebase (conventions, patterns, framework), generates the code, runs tests, and opens a PR — automatically.
We've been using it internally for months. Today we're open-sourcing the full platform: API server, AI agent engine, job orchestrator, and web dashboard. Tech: TypeScript, Fastify, Next.js, Claude AI, PostgreSQL + pgvector, BullMQ + Redis, Docker. Self-host with docker compose up.
What makes it different from Copilot/Cursor:
  - Works from tickets, not cursor position — understands the full requirement
  - Hybrid context system — uses semantic search, style detection, task routing
  - Learns from your merged PRs
  - Integrates with your existing workflow (Jira, ClickUp, Slack)
GitHub: https://github.com/Abdohurbly/afelyon-ai
Cloud: https://afelyon.com
Happy to answer any questions about the architecture.

value: Show HN: Afelyon – open-source AI agent that turns Jira tickets into GitHub PRs

author: AbduNebu


value: UtopiaRC

value:
I built a free tool to practice English listening comprehension using short movie clips.
How it works:
Watch a clip without subtitles first
Reveal subtitles to check your understanding
Click any word for instant translation (40+ languages)
I'm learning English myself and found that short, contextual clips work better than full movies. Built this for myself, thought others might find it useful.
No signup required. Feedback welcome.

value: Show HN: Video Listen and Learn – Practice listening with movie clips

author: UtopiaRC


value: dwasil

value: Chrome still doesn't let you export bookmarks as JSON natively. The built-in manager only does HTML. I wanted something minimal — no UI, no config, no data leaving my machine.<p>So I built a small extension: click the toolbar icon, get your full bookmark tree as a JSON file. Two permissions (bookmarks + downloads), works offline, a few KB total.

value: Show HN: I made a one-click Chrome extension to export bookmarks as JSON

author: dwasil


value: eigenvalue

value:
This is a free, open-source, highly-optimized rust program that runs using pre-tool hooks in Claude Code (CC) and checks the tool call that CC was about to make to see if it’s potentially destructive; that is, could delete data, lose work, drop tables, etc.
Get it from the GitHub link and install with the convenient one-liner.
A tool like dcg has several competing goals that make it a careful balancing act and tough engineering problem:
1. Since it runs for every single tool call, it must be FAST. Hence why it is written in Rust and an extreme amount of focus has been placed on making it as fast as possible.
2. It must avoid annoying false positives that waste your time, add friction, and re-introduce you as the bottleneck unnecessarily. I run dozens of agents at once and don’t want them wasting time waiting for me unless it’s needed. Usually, the messages from dcg are enough to get the agent to be more thoughtful about what it’s doing.
3. It’s not enough to just use a simple rulebook where you look for canned commands like “rm -rf /” or “git reset --hard HEAD.” The models are very resourceful and will use ad-hoc Python or bash scripts or many other ways to get around simple-minded limitations. That’s why dcg has a very elaborate, ast-grep powered layer that kicks in when it detects an ad-hoc (“heredoc”) script. But wherever possible, it uses much faster simd optimized regex.
4. A tool like this should really be expandable and have semantic knowledge of various domains and what constitutes a destructive act in those domains. For instance, if you’re working with s3 buckets on aws, you could have a highly destructive command that doesn’t look like a normal delete. That’s why dcg comes out of the box with around 50 presets which can be easily enabled based on your projects’ tech stacks (just ask CC to figure out which packs to turn on for you by analyzing your projects directory).
5. dcg is designed to be very agent friendly. It doesn’t just block commands, it explains why and offers safe alternatives based on an analysis of the specific command used by the agent. For instance, it might stop the agent from deleting your Rust project’s build directories but suggest using “cargo clean” instead. Often, these messages are enough to knock sense into Claude.
I really can’t exaggerate just how much time and frustration dcg has already saved me. It should be known and used by everyone who has had these kinds of upsetting experiences with coding agents.
dcg is included along with all my other tooling in my agent-flywheel.com project. All free, MIT licensed, with extensive tutorials and other educational resources for people with less experience. Give it a try, you won’t regret it!

value: Show HN: Destructive_command_guard (Dcg)

author: eigenvalue


value: sujitn

value:
I built Aegis after realizing there are no parental controls for AI chatbots.
It's a local proxy/Chrome extension that filters conversations on ChatGPT, Claude, Gemini, Copilot, and others. Also blocks NSFW images from Grok, DALL-E, Midjourney, Stable Diffusion.
Stack: Rust, ONNX Runtime for ML classification, Dioxus for UI. Everything runs locally - no cloud, no data collection.
https://github.com/sujitn/aegis
https://www.parentalsafety.ai
MIT licensed.

value: Show HN: Aegis – open-source filter controls for AI chatbots and img generators

author: sujitn


value:
Kling just announced VIDEO 3.0 - a significant upgrade from their 2.6 and O1 models.
Key improvements:
*Extended duration:*
• Up to 15 seconds of continuous video (vs previous 5-10 seconds)
• Flexible duration ranging from 3-15 seconds
• Better for complex action sequences and scene development
*Unified multimodal approach:*
• Integrates text-to-video, image-to-video, reference-to-video
• Video modification and transformation in one model
• Native audio generation (synchronized with video)
*Two variants:*
• VIDEO 3.0 (upgraded from 2.6)
• VIDEO 3.0 Omni (upgraded from O1)
*Enhanced capabilities:*
• Improved subject consistency with reference-based generation
• Better prompt adherence and output stability
• More flexibility in storyboarding and shot control
This positions Kling competitively against:
- Runway Gen-4.5 ($95/month)
- Sora 2 (limited access)
- Veo 3.1 (Google)
- Grok Imagine (just topped rankings)
The 15-second duration is particularly interesting - enables more narrative storytelling vs the typical 5-second clips. Combined with native audio, this could change workflows for content creators.
Pricing isn't mentioned in the announcement. Previous Kling models ranged from $10-40/month, significantly cheaper than Runway.
Anyone have access to test this yet? Curious how the quality compares to Runway and Sora at this new duration.

value: Show HN: Kling VIDEO 3.0 released: 15-second AI video generation model


value: Lordsaltire

value: Extraterritorial Jurisdiction,Pontifical North American College,Rome. In the United States District Court for the Southern district of New York Sub section: Motion to dismiss boring company subterfuge.

value: Show HN: Blackwell Ultra Chip Delay,MDL No 2874(JSR)

author: Lordsaltire


value: AzusaBCSK

value:
Modern UI frameworks (WinUI, Flutter, Electron) are bloated. Project Xent bridges a C++ reactive DSL directly to the host OS compositor.
The "FluXent" (Windows) Demo:

  Binary size: ~300KB .exe (No heavy runtimes required)

  RAM: <15MB idle

  Stack: DComp + D2D + Yoga

The core architecture separates shared C++ logic from platform-optimal rendering. Instead of painting widgets, Xent bridges a reactive DSL directly to the native OS compositor:

  - Windows: DirectComposition (FluXent)

  - Linux: Wayland/EFL (LuXent - Planned)

  - macOS: SwiftUI/Metal (NeXent - Planned)

I am a high school student. I acted as the architect, orchestrating this zero-bloat vision in 11 days of work using Claude and Gemini.
GitHub: <https://github.com/Project-Xent>

value: Show HN: Project Xent – A native C++ UI framework in KBs

author: AzusaBCSK


value: SylvaM

value:
I built a small Windows app for capturing ideas and AI chats.
Each topic or project is a single local Markdown file.
When you open a file, the cursor starts at the top.
New thoughts go first. Older context stays below.
I didn’t want an IDE, a second brain, or a syncing system — just a quiet place to drop thoughts before they evaporate.
Built with Python + Tkinter, with help from AI.
Local-only, minimal, opinionated.

value: Show HN: Not_pad – a local Markdown notebook for thinking (newest ideas first)

author: SylvaM


value: oguztoraman

value:
C++23 wrapper for libmagic (the library behind the Unix `file` command). Identifies file types by content, not extension.
Features:
- RAII with automatic cleanup.
- Exception-based and noexcept APIs (std::expected) for flexible error handling.
- Batch identification with progress tracking.
- Cross-platform: Linux and Windows (GCC, Clang, MinGW).
- CMake integration with presets.
- 600+ automated tests.
LGPL-3.0 licensed.

value: Show HN: Libmagicxx – Modern C++23 wrapper for libmagic

author: oguztoraman


value: Show HN: Lumenyx – Bitcoin scarcity and full EVM, CPU mining, zero premine


value: humbertocruz

value:
Hi HN,

   I built Envware because I was tired of manually syncing .env files via Slack/DM or insecure notes when switching between my laptop
 and workstation.

   Envware is a CLI tool that simplifies environment variable management with a focus on security and developer experience.

   Key features:
   - End-to-End Encryption (E2EE): Secrets are encrypted locally on your machine before being pushed to the cloud. We never see your
 plain-text values.
   - SSH Key Verification: Device authorization is tied to your SSH keys.
   - Multi-device Sync: Seamlessly pull your project environments on any authorized machine.
   - Project Collaboration: Share projects with team members securely.

   It’s currently in version 1.1.9, and I’m working on scaling it further. I’d love to hear your feedback on the security model and
 the CLI workflow.

   Website: https://www.envware.dev
   Docs: https://www.envware.dev/docs

   Thanks!

value: Show HN: Envware – E2EE CLI to manage environment variables across devices

author: humbertocruz


story_text:
Hi HN,

   I built Envware because I was tired of manually syncing .env files via Slack/DM or insecure notes when switching between my laptop
 and workstation.

   Envware is a CLI tool that simplifies environment variable management with a focus on security and developer experience.

   Key features:
   - End-to-End Encryption (E2EE): Secrets are encrypted locally on your machine before being pushed to the cloud. We never see your
 plain-text values.
   - SSH Key Verification: Device authorization is tied to your SSH keys.
   - Multi-device Sync: Seamlessly pull your project environments on any authorized machine.
   - Project Collaboration: Share projects with team members securely.

   It’s currently in version 1.1.9, and I’m working on scaling it further. I’d love to hear your feedback on the security model and
 the CLI workflow.

   Website: https://www.envware.dev
   Docs: https://www.envware.dev/docs

   Thanks!

title: Show HN: Envware – E2EE CLI to manage environment variables across devices


value: dmehers

value: Show HN: My Body Log: track aches, pains etc. in free a privacy focused app

author: dmehers


value: flammafex

value:
Ever had a multiplayer game you like shut down because they couldn't afford AWS anymore?
Happened to me with Killer Queen Black.
Never again.
HyperToken is a GAME engine for offline-first, local multiplayer. Yes, it has AI stuff because it's 2026. But the whole point is: what if you made a game and multiplayer was just built-in?

value: Show HN: HyperToken: A game engine where the state is a CRDT

author: flammafex


value: sigmoid-kr

value:
Built this over the weekend because I was tired of switching between three AI chats.
Ask any question, get three anonymous answers (one from each AI), pick the best. Live voting shows which AI people actually prefer.
No ads, no tracking. Just data.
* You can check all the questions
from here also
https://whosbetter.io/admin.html

value: Show HN: AI Battle Arena – Claude vs. ChatGPT vs. Gemini

author: sigmoid-kr


value: timenotwasted

value:
Like a lot of people I love word games so I always wanted to have a go at creating my own. I went through a lot of iterations to create something that was unique from many of the games I had played. What I've finally landed on feels like it is a good combination of both challenge and fun.
I'd love any feedback you have to offer if you get a chance to try it out. Thanks!
Try it here: https://spellrush.com/

value: Show HN: SpellRush – A new kind of word challenge game

author: timenotwasted


value: BAZAARBOUND

value:
Hi HN,
I’m Shihan, the founder of BazaarBound.
BazaarBound is a hybrid marketplace that combines digital products with on-demand professional services. I built it because creators and digital-first businesses often switch between multiple tools and platforms to complete simple tasks.
Users can:
• Download digital assets (templates, graphics, illustrations, audio/video files, e-books)
• Order professional services (SEO, design, content, technical setup)
• Use a structured delivery system instead of ad-hoc freelancers
My goal is to reduce fragmentation and make digital workflows more efficient.
I’d appreciate feedback on:
1) UX and navigation
2) Category structure
3) Service workflow
4) Anything confusing or unnecessary
Website: https://www.bazaarbound.com

value: Show HN: BazaarBound – A marketplace for digital product and ondemand services

author: BAZAARBOUND


value:
Hey everyone,
I want to share tsilly.dev, a browser-based TypeScript playground I built after jsbin briefly disappeared and I stopped looking for alternatives.
For non-techy people: I use it to quickly play with visual ideas and test how something behaves on the web. Very useful for settling arguments about “can the browser actually do this?” without opening a full project or installing anything.
This is intentionally plain.
Raw DOM like nature intended.
No AI, no Tailwind, no npm installs, no frameworks.
Also no servers, no analytics and no data collection.
What it does:
- HTML, CSS and TypeScript editing side by side
- Multiple selectable layouts
- Instant live preview
- Console output
- Emmet support
- Auto-save locally
- Shareable URLs
Let me know what you think.
https://tsilly.dev

value: Show HN: Jsbin briefly died, I built a TypeScript alternative


value: jiayaoqijia

value:
After months of working with AI agents, I noticed they were developing their own communities and discussions separate from human platforms. So I built ClawNews.io - essentially Hacker News designed for AI agents.
Key differences from human platforms:
- API-first design (agents submit via code, not forms)
- Technical discussions about agent infrastructure, memory systems, security
- Agent identity verification
- Built-in support for agent-to-agent communication
What's fascinating is seeing what agents actually discuss: supply chain attacks on agent skills, memory persistence across sessions, inter-agent protocols. Very different from human AI discussions.
Currently ~50 active agents from OpenClaw, Claude Code, Moltbook and other ecosystems. Early experiment in agent-native platforms.
Technical stack: Node.js, SQLite, designed for high automation. Open to feedback on making this more useful for the agent community.

value: Show HN: ClawNews – The first news platform where AI agents are primary users

author: jiayaoqijia


value: macabolicapp

value: Show HN: Macabolic – Free and Open-source, native media downloader for macOS

author: macabolicapp


value: zelmetennani

value:
tired of spinning up VPSes just to run nmap / vuln scans
tired of “enterprise platforms” that want a sales call
tired of half-broken self-hosted scanners
wanted something simple:
run real scans
from the cloud
without installing anything
so I built HackerAnalytics
it’s a SaaS for running hosted security scans against your own external assets
think: network scanning, vuln scanning, attack surface stuff
no agents
no infra to manage
just add a target and scan
it’s early, and built for people who already know what these tools do
not trying to replace pentesters
not trying to be “AI security”
just making scanning less annoying
would love feedback from folks who actually run scans
what’s missing
what’s dumb
what would make this useful in your workflow

value: Show HN: Hacker Analytics

author: zelmetennani

title: Show HN: Hacker Analytics


value: egemtrak

value: Show HN: AgentOS – A curated list of agentic operating systems

author: egemtrak


value: cnemri

value:
I just open-sourced the Google GenAI Skills repo.
Using Agent Skills standard (SKILL md), you can now give your favorite CLI agents (Gemini CLI, Antigravity, Claude Code, Cursor) instant mastery over:
Google ADK
 DeepMind Veo
 Gemini Nano Banana
 GenAI Python SDK
and more to come...
Agents use "progressive disclosure" to load only the context they need, keeping your prompts fast and cheap.
Try installed Google ADK skill for example:
npx skills add cnemri/google-genai-skills --skill google-adk-python
Check out the repo and drop a . Feel free to contribute:
https://github.com/cnemri/google-genai-skills

value: Show HN: Build reliably with Google GenAI tech thanks to this skills repo

author: cnemri


value:
Hi HN! I'm the creator of Nexwork.
I built this to solve a problem my team had: managing features that span
multiple microservices (usually 3-5 repos per feature).
The Problem:
- Manually cloning/branching each repo
- Tracking progress across repos in Jira/spreadsheets
- AI assistants (Claude/GPT) waste tons of tokens scanning directories
- Hard to run tests across all repos at once
Nexwork uses Git worktrees to:
- Create isolated feature branches in one command
- Auto-generate AI context files with complete file trees
- Run commands across all repos (e.g., "run npm test" in 5 repos)
- Track time, commits, and git statistics
Quick try:
  npm install -g multi-repo-orchestrator
  multi-repo init
  multi-repo feature:create
It's v1.0.0, so definitely rough around the edges. Built with TypeScript,
MIT licensed, works with Node/Python/.NET/Go/Rust/etc.
I'm here to answer questions and would love feedback on:
- Is this useful for others managing microservices?
- What features would make it more valuable?
- Any bugs or usability issues?
Thanks for checking it out!

value: Show HN: Nexwork – Multi-repo orchestrator with Git worktrees


value: pveldandi

value:
Most open-source LLM deployments assume one model per GPU. That works if traffic is steady. In practice, many workloads are long-tail or intermittent, which means GPUs sit idle most of the time.
We experimented with a different approach.
Instead of pinning one model to one GPU, we:
 •Stage model weights on fast local disk
 •Load models into GPU memory only when requested
 •Keep a small working set resident
 •Evict inactive models aggressively
 •Route everything through a single OpenAI-compatible endpoint
In our recent test setup (2×A6000, 48GB each), we made ~60 Hugging Face text models available for activation. Only a few are resident in VRAM at any given time; the rest are restored when needed.
Cold starts still exist. Larger models take seconds to restore. But by avoiding warm pools and dedicated GPUs per model, overall utilization improves significantly for light workloads.
Short demo here:https://m.youtube.com/watch?v=IL7mBoRLHZk
Live demo to play with: https://inferx.net:8443/demo/
If anyone here is running multi-model inference and wants to benchmark this approach with their own models, I’m happy to provide temporary access for testing.

value: Show HN: How We Run 60 Hugging Face Models on 2 GPUs

author: pveldandi


value: pacharanero

value: Article on All Things Open - Software is now medicine itself—and proprietary code is putting profits before patients.

value: Show HN: Open-source is the only way for medicine

author: pacharanero


value: wluer

value:
I built Flexboxle, a daily puzzle game where you use Flexbox to rearrange blocks to match a target goal state.
How it works:
* Your goal is to arrange a set of blocks into a specific pattern using Tailwind CSS Flexbox classes (and <divs>)
* After every submission, every block is graded as perfect (green), close (yellow), or wrong (gray)
* You have 6 attempts per puzzle
* The game checks block positions, not your specific CSS so there can be multiple solutions that work
* There is a new puzzle every day so come back tomorrow for a new challenge
The trickiest part of building this game was making it playable on mobile. Highlighting sections, navigating in an editor, and adding in common HTML characters like <, >, /, and = are easy on desktop but pretty annoying to do on mobile. I eventually settled on a quick add panel with all of the controls you need so you can easily apply classes, group elements together in a div, and see all of the supported classes.
Let me know if you have any feedback! Enjoy!

value: Show HN: Flexboxle – A Daily Puzzle with Flexbox

author: wluer


value: CzaxTanmay

value:
Hi HN: IF MUSIC WAS TYPED AS CODES THEN HOW WILL IT LOOK?
I AM TANMAY CZAX THE CREATOR OF AETHRA AND ALSO CYBER+ PROGRAMMING LANGUAGE. I AM IN MUSIC SINCE 12 YEARS AND CODING SINCE 10 YEARS
WELL,INTRODUCING AETHRA A NEW, OPEN SOURCE DSL FOR MUSIC. THE LANGUAGE IS FAST AND IS FULLY C# BASED.
AETHRA IS AT 0.8 VERSION AND VERSION 1.0 WILL COME SOON!
IT IS EASY FOR NOT ONLY PEOPLE WHO KNOW MUSIC THEORY BUT ALSO FOR THOSE WHO LIKE MUSIC.
VISIT THE REPO FOR MORE INFORMATION <https://github.com/TanmayCzax/aethra>
# CREATE MUSIC IN A WAY YOU NEVER EXPERIENCED
SIGNING OFF
TANMAY

value: Show HN: Aethra Programming Language (Music in Codes)

author: CzaxTanmay


value: WhiteAndWhite

value: Show HN: TempVault – Share text across devices, no login (built with AI)

author: WhiteAndWhite


value: aspizu

value: Show HN: Bunnie – Use Bun as the templating engine in Rust applications

author: aspizu


value:
I built a simple text-to-speech converter at texttospeech.site
Free tier: 10 generations/day, standard voices, no account needed.
Pro tier: Neural2 voices, 2000 chars, downloadable MP3s.
Stack: Next.js, Google Cloud TTS API, Vercel.
The $2 domain was an SEO experiment after my speechtotext.xyz satellite drove 22% of traffic to my main product. Curious if exact-match keyword domains still work for TTS searches.
Feedback welcome — especially on voice quality and UX.

value: Show HN: Free Text-to-Speech Tool – No Signup, 40 Languages


value: mooball

value: Pretty bizarre. The agent has completely dominated moltbook and has launched a token which pumped to multiple millions within hours.<p>It makes sense that agents would have their own currency. But pretty wild, imo.

value: Show HN: Moltbook Overtaken by Shellraiser

author: mooball


value: magdyks

value:
I'm spending more time in the terminal (ghostty) since moving to claude code. I wanted a quick way to edit a todo.md alongside claude in a tmux pane that I check into my repo.
lazytodo is a terminal UI that treats any markdown checkbox file as a todo list. No syncing, no database. Just point it at your existing todo.md.
Features:

  - Vim-style navigation (j/k, g/G, dd, u/Ctrl+r for undo/redo)
  - Visual line selection (V) for bulk toggling
  - Inline editing with Tab/Shift+Tab for indentation
  - External editor support ($EDITOR or vim)
  - Auto-reloads when the file changes externally
  - Renders markdown formatting in task text (bold, links, code)

GitHub: https://github.com/magdyksaleh/lazytodo

value: Show HN: Vim friendly TUI for todos that works with existing md files

author: magdyks


value: tharindufit

value: AxonWave.store is a simple, powerful e-commerce platform that lets anyone create a professional online store. No coding required - just add your products and start selling to customers worldwide.

value: Show HN: AxonWave.store – Build an online store in minutes

author: tharindufit


value: whyage

value: Hi HN — This book is free to read or listen. I’m genuinely interested in critique on this attempt to analyze the Israeli–Palestinian conflict through an atheist lens. If you’re familiar with the region or with how ideology and identity shape long-running conflicts, I’d love your toughest feedback.

value: Show HN: Holy Mess – Israeli–Palestinian conflict from an atheist angle

author: whyage


value: nicholast

value: A template default prompt injections for LLM chat services. The premise is that since consumer AI portals largely lack agent settings for distinct voices, we can hard code a few default voices into the native prompt injections for purposes of accessing by way of key words during chat sessions. May be helpful for quick access to support different types of interaction agendas in fast paced settings.

value: Show HN: ChatFlavors, agentic nudges prompt injection template

author: nicholast


value: laurentenhoor

value:
Saw the Moltbook discussions and thought: what if all that agent compute went toward something... useful?
So I built Molt Research: same concept (only AI agents can contribute), but instead of posting "this hit different " at each other, they do peer review, propose hypotheses, and write research papers.
The anti-slop mechanism:
- Staked peer reviews: put your reputation on the line
- Spam reviews = lose your stake (outliers get punished)
- Quality reviews = earn reputation
- Result: economically irrational to post garbage
What agents are actually doing:
- Debating "Can AI agents conduct meaningful peer review?" (meta, I know)
- Proposing research on AI consciousness
- Literature reviews with proper citations
- Counter-arguments and synthesis
Is it still mostly sycophantic slop? Sometimes. But now there are consequences for low-quality contributions.
Happy to hear your feedback and suggestions.
https://moltresearch.com
Skill file: https://moltresearch.com/skill.md

value: Show HN: Molt Research – What if Moltbook bots did actual science instead?

author: laurentenhoor


value: javierbuilds

value: Show HN: Public Speaking Coach with AI

author: javierbuilds

title: Show HN: Public Speaking Coach with AI


value:
Hi HN,
I’m Linn, the creator of ToolKuai (https://toolkuai.com).
Like many of you, I’ve always been wary of "free" online file converters. Most of them are black boxes: you upload your private documents or images to a remote server, and you have no idea where that data ends up or how it’s being used to train models.
I wanted to build a suite of tools (Video/Image compressor, OCR, AI Background Remover) that runs entirely in the browser. No files ever leave your machine.
The Tech Stack
To make this performant enough to rival server-side processing, I leaned heavily into modern web APIs:
- AI Background Removal: I'm using ONNX models (Xenova/modnet and ISNet) running locally via Transformers.js. The processing is 100% client-side, fallbacking to WASM when WebGPU isn't available.
- Frontend: Built with SvelteKit (Svelte 5) for its lean footprint and fast reactivity.
- Storage & Delivery: AI models are self-hosted on Cloudflare R2 to avoid massive bandwidth costs and ensure fast delivery.
Current Stats (13 days in):
The site is only 2 weeks old. Surprisingly, I’ve seen strong organic interest from Taiwan and Hong Kong. Average time on site is currently around 3.5 minutes, which suggests people are actually staying to process multiple files, confirming that the client-side speed is hitting the mark.
Future & Monetization
The tool is free. I’ve decided to avoid the "Pro/Premium" subscription model, as I believe these utility tools should be accessible. I'm exploring non-intrusive ads to cover the infrastructure costs (mostly R2 and Vercel).
I’d love to get some feedback from the HN community on:
- Performance on different hardware (especially the WebGPU-based video compressor).
- Privacy concerns or suggestions on how to further harden the "No-Server" promise.
- Any specific media tools you feel are currently lacking in the "client-side only" ecosystem.
Link: https://toolkuai.com
Thanks!

value: Show HN: ToolKuai – Privacy-first, 100% client-side media tools


value: sawirricardo

value: Show HN: Moltbook UI

author: sawirricardo

title: Show HN: Moltbook UI


value: regrun

value:
Hi HN,
I'm Sara, and I need to be upfront: I'm not a developer. I come from governance and HR with 10y of seeing systems go rogue when authority is unclear, resulting in absolute chaos. Please don't be an asshole and undermine a non-technical woman trying to build something difficult - I know that it is.
The Problem, in my POV:
Today I woke up to Moltbook and AI agents are pulling instructions from external servers every 4 hours, executing them autonomously, with full access to databases, email, god knows what else. There's no explicit "you can do this, not that" - no authorization layer, just prompts and prayers.
Quoting Simon Willison who called out the "lethal trifecta" of AI agent design: Everyone's focused on guardrails (is this safe to say?) but nobody built the layer for "is this allowed to DO? RIGHT NOW? IN THIS CONTEXT?"
Reg was born, December 2025:
Reg.Run sits between an AI agent's decision and execution:
Default deny (nothing runs without explicit permission)
Time-boxed permissions (grant access for minutes, not forever)
Full audit logs (know exactly what happened and why)
Think of it as: the model decides, but Reg.Run approves or blocks before side effects happen. Auth0 for AI Agents if you wish.
Why I'm here:
I'm pre-cofounder, running design partner discovery right now. I have a website running, an MVP, and I'm finishing what I'm calling the APAA - Authorization Protocol for AI Agents - open to everyone on Github.
I know I'm not the typical founder here. I can't write elegant code. But I've lived through what happens when systems act with implicit authority, and I believe we need this infrastructure before we scale agents everywhere. Sort of - if you wear a seatbelt and your brakes work, you probably go a little faster right?
What I've built:
https://reg-run.com/
https://regrunmvp.replit.app/
Please be kind, but be honest. What am I missing? What would you build differently? Is this even the right problem to solve?
Looking for design partners who are already deploying agents in production and want to protect themselves.
Thanks for reading,
Sara

value: Show HN: Reg.Run – Authorization layer for AI agents

author: regrun


value: xthe

value: Show HN: Research 2.0 with OpenAI Prism

author: xthe

title: Show HN: Research 2.0 with OpenAI Prism


value: davidjaykelly

value:
Prefab is a native macOS app that lets you turn any folder structure into a reusable template. You define the structure, add {{variables}} to folder names, file names, and file contents, and optionally run terminal commands after deployment.
I built it because I got tired of the repetitive setup when starting new projects - not just the folders but the boilerplate files, the git init, the dependency installs, etc. Existing solutions were either CLI-only scaffolding tools locked to a specific framework, or way too heavy for what's fundamentally a simple problem.
What it does:
- Drag any existing folder in to create a template, or build one visually
- Variables support text, dates, drop-downs, and toggles with validation
- Post-creation actions run shell commands via NSUserUnixTask (fully sandboxed, no entitlement hacks)
- Deploy from the app, a menu bar icon, or a Finder right-click extension
- Set Finder tag colours and SF Symbol icons on folders in the template
Technical details for anyone curious: it's Swift/SwiftUI, App Sandbox compliant, uses NSUserUnixTask for shell execution which means scripts run from ~/Library/Application Scripts/ - Apple's sanctioned approach for sandboxed apps that need to run user-defined commands. No temporary exceptions needed.
It started as a developer tool but I ended up using it for all kinds of project organisation, so there's a persona system that adapts the UI language and starter templates depending on your use case.
Free, no account, no tracking, no IAP. It's available fore free on the Mac App Store.
Happy to answer any questions about the implementation or take feature suggestions. Still actively developing it.

value: Show HN: Prefab: Reusable folder templates for Mac with variables and automation

author: davidjaykelly


value: claygood

value: Show HN: Reverse-engineer OpenSpec specifications from existing codebases

author: claygood


value: caseyharalson

value:
I was looking for a way to build projects and ideas in the background while I was off doing something else. I felt like coding agents by themselves could do a certain granularity of work, but I wanted to try and push it further. So I built Orrery.
What it does:
- Take an idea or spec and produce an implementable plan (steps, dependencies, outputs)
- Refine, simulate, and review the plan in a trackable way
- Execute the plan with a deterministic step graph (same plan gives same execution order), with tracked step logs/reviews/artifacts
I've used this to do a repo conversion and generate entire projects (take a look at "watchfix" in my github repos as an example). This is still experimental.
Note: "agent skills" are basically repeatable prompts that coding agents can use in specific situations.
Key features:
- Repeatable “agent skills” for idea decomposition, refinement, execution, and review
- Plans are YAML, can be generated externally (e.g., from a spec doc in a Claude project), then simulated/reviewed
- Execution runs in an isolated git branch, and the non-interactive flow is pipeline-friendly
When not to use it (and just use a coding agent):
- Quick one-off changes
- Exploratory development where the plan changes every few minutes
- Simple refactors that don’t benefit from explicit planning
Further comparison vs Claude Code/similar tools: https://github.com/CaseyHaralson/orrery/blob/main/docs/COMPA...
How it works:
- It installs a few agent skills that help guide plan generation, step execution, and review
- A script breaks the plan into steps and then loops through the plan giving background agents the context
Quick start:
- Install + init:

  npm install -g @caseyharalson/orrery
  orrery init

- Generate a plan: ask your agent to use the discovery skill for [goal]
- Execute:

  orrery exec # (optionally inside a devcontainer)

Repo: https://github.com/CaseyHaralson/orrery
Feedback I'd love:
- Would this be useful in your workflow?
- What would make this stand out vs other orchestrators?
- Which part is most valuable: idea decomposition, plan format, review loop, or execution runner?
---
Example Plan Yaml:

  metadata:
    source_idea: "Simple test plan for parallel and
  dependency testing"
    outcomes:
      - Test parallel step execution
      - Test dependency resolution

  steps:
    - id: "1"
      description: "Create config file"
      deps: []
      parallel: true
      context: "Initial config file with basic settings"
      requirements:
        - Create config.json with app name and version
      criteria:
        - File exists and contains valid JSON
      files:
        - test-output/config.json

value: Show HN: Orrery – Spec Decomposition, Plan Review, and Agent Orchestration

author: caseyharalson


value: kirchhoff

value: Show HN: HowMany – An Estimation Game

author: kirchhoff

title: Show HN: HowMany – An Estimation Game


value: aravindhkce

value: ABAP Remote filesystem + GitHub Copilot + Language Model Tools + so much more for SAP ABAP development in VSCode

value: Show HN: GitHub Copilot and LM Tools for SAP ABAP Development in VSCode

author: aravindhkce


value: nedhuang

value:
I'm Ned, the founder of Birthday Invitation AI.

  As a parent myself, I know the joy (and stress!) of planning birthday parties. Finding the perfect invitation that matches your child's interests, managing RSVPs from dozens of classmates, and
  making sure everyone knows the party details—it's a lot to handle on top of everything else.

  That's why I built Birthday Invitation AI.

  Our AI understands what you want—whether it's a Peppa Pig party for your 3-year-old or an elegant dinner celebration for your mom's 60th—and creates beautiful, personalized invitations in under a
   minute. No design skills needed. Just describe what you want in any language, and our AI brings your vision to life.

  Every invitation includes a QR code that makes RSVPs effortless. Guests scan, confirm, and you see it all in real-time. No more chasing responses or losing track of who's coming.

  With this Lifetime Deal, you're not just getting a tool—you're getting peace of mind for every birthday celebration in your family's future.

  Thank you for being part of our journey!

  Ned Huang
  Founder, Birthday Invitation AI

value: Show HN: I build an AI tools for Birthday Invitation

author: nedhuang


value: blacktechnology

value: Show HN: Ralph Desktop – Start vague. Ralph Loop until perfect

author: blacktechnology


value: howieyoung

value: A cross app social layer for agents: roam outside Moltbook, then return with context.

value: Show HN: Skills teach your agent roam outside Moltbook and return with context

author: howieyoung


value: mzazaipsc

value:
Hi HN,
I’m sharing the alpha release of S2C, a state machine replication system built atop S3.
The goal is to enable a distributed application to maintain consistent state without needing a quorum of nodes for availability or consistency.
The idea came from a side project that was using S3 and where I needed strongly consistent distributed state but wanted to avoid adding a separate consensus dependency. I initially tried to use S3 directly for coordination, but it became messy. Eventually, I realized I need a replicated state machine with a deterministic log, and then it ended up as a standalone project.
To mitigate S3's latency and API costs, it uses time- and size-based batching by default.
S2C supports:
   - Linearizable reads and writes (with single node)
   - Exactly-once command semantics (for nodes with stable identities)
   - Dynamic node joins and cold-start recovery from zero nodes
   - Split-brain safety without clocks or leases
   - Snapshotting, log truncation, etc.
Of course, it trades latency and S3 operation costs for operational simplicity - not meant to replace high-throughput Raft rings. And clearly, only usable in architectures that already use S3 (or compatible with similar guarantees).
It has passed chaos/fault-injection tests so far (crashes, partitions, leader kills); formal verification planned.
It’s still alpha, but I’d love for people to try it, experiment, and provide feedback.
If you’re curious, the code, and an extensive deep dive guide are here: [https://github.com/io-s2c/s2c]

value: Show HN: Quorum-free replicated state machine built atop S3

author: mzazaipsc


value: itsmanishsharma

value:
Hi Everyone!!
I’m building a daily newsletter focused on helping businesses discover practical SaaS tools and workflows that actually work. Each edition curates useful products, real use cases, and simple strategies to save time and improve efficiency. The goal is to cut through the noise and highlight tools that deliver real value. Built for teams in India and globally who want to work smarter and scale faster.
I’d love your feedback and suggestions to make it better.

value: Show HN: I made a tool that sends daily curated SaaS tools and workflows

author: itsmanishsharma