value: sentientiq

value:
Building Real-Time Emotional Intelligence for E-Commerce: Why Mouse Telemetry Beats Sentiment Analysis
Long road building honest emotional intelligence for marketing. Every path led to bullshit. Social listening? Performative. People tweet what they want you to think they're feeling. Intent data? Worse. Anonymous IPs labeled "happy" or "frustrated" based on aggregated dwell time. Astrology for B2B marketers. The solution: Involuntary behavioral data captured before conscious filtering. Mouse telemetry.
The Technical Thesis
Hypothesis: Sub-second mouse behavior (rage clicks, erratic movement, rapid back-button mashing) correlates with emotional states that predict abandonment.
Why it works:
Involuntary (faster than conscious thought)
First-party (you own the data)
Attributable (session → intervention → outcome)
Falsifiable (if wrong, conversions don't improve)
Application: Real-time behavioral intervention. Detect frustration, intervene before bounce, measure lift.
The Stack
Frontend: sentientiq-unified.js captures mouse physics at 20Hz. Batches 50 events every 500ms, sends to NATS over WebSocket. Messaging: NATS pub/sub (telemetry.raw, intervention.trigger) Processing: Dual-LLM cascade
Haiku 3.5: Fast triage (200 calls/min)
Sonnet 4.5: Deep analysis (50 calls/min)
Flow: NATS → Haiku triage → Sonnet deep analysis with ML-learned patterns → Intervene or monitor Key innovation: Dynamic ML knowledge injection. Proven patterns from seer_ml_intelligence table get injected into prompts. Intervention: Seer (Sonnet 4.5 chat) receives emotional context via EITP packets (Emotional Intelligence Transfer Protocol). 7D emotional state vectors: valence, arousal, urgency, confusion, interest, intent_strength, session_phase. Persistence: Supabase (emotional_events, seer_ml_intelligence, interventions) Feedback Loop (The Moat): Thompson Sampling aggregates outcomes by (tenant, vertical, emotion, page_section, intervention). System writes proven patterns back to ML table. Gets smarter autonomously.
Intelligence Layer:
contagion-detector.py - Emotional spread across geographies
cross-vertical-analyzer.py - Transferable patterns
evi-collector.py - Emotional Volatility Index (VIX for digital purchase behavior)
Why EITP?
LLMs waste compute on uncertainty. With EITP packets, Sonnet receives:
{
  "emotional_state": {"valence": -0.6, "arousal": 0.8, "confusion": 0.7},
  "context": "financing_calculator",
  "proven_pattern": "automotive_financing_anxiety_intervention_v2"
}
Now it doesn't guess. It knows. Aspirational claim: ~80% compute waste reduction.
Current State
Stress-tested to 300 concurrent sessions
Scales to ~1500 (AWS ALB + EC2)
Session-level rate limiting (100 events/session/hour)
24 microservices via PM2
Pricing: $999/mo or $4,999 lifetime (first 100)
The Wedge: Automotive Retail
Why auto dealers:
$30k-60k transactions
Massive emotional volatility (sticker shock, financing anxiety)
One saved session = months of fees
Demo: User browses $45k F-150 → Frustration on financing calculator → Seer intervenes → Lead submitted → Sale saved.
The Protocol Play
EITP published as RFC 9999 (https://eitp.io). If standardized:
Any LLM receives emotional context
Any app subscribes to volatility feeds
Industry compute waste drops
We're calling it Layer 8 of the OSI model (https://osi8.dev) - the missing layer between your app and user emotional state. Yes, Layer 8 is the joke about user error. We're claiming it anyway. Stack: React + Vite + TypeScript | Node.js + NATS + Anthropic | Supabase | AWS | PM2 Live: This week Feel free to roast the architecture. That's why I'm here.

value: Show HN: Real-Time Emotional Intelligence for E-Commerce: actual signals

author: sentientiq


value: BSTRhino

value: I thought it'd be fun to make a point-and-click adventure game based around music. I thought it'd be a unique take on the gnre. I made this game for a 7-day gamejam and drew all the artwork and created all the music myself. It can be played through to completion in about 5 minutes.

value: Show HN: Settling the Score – A point-and-click adventure rhythm game

author: BSTRhino


value: SebastianRoot

value:
Hey HN,
I’m a new solopreneur, and this is one of my first public launches.
While building AI products, I kept running into the same issue - spending endless hours crafting prompts, fixing broken outputs, and worrying about security. I realized that writing prompts manually wasn’t scalable or safe.
So I built ArtisMind - a tool that helps you engineer prompts, not just write them. It generates clear requirements, builds structured prompts using multiple AI models, tests them for quality and security, and even lets you add context files for better accuracy.
Now I use it daily for my projects, and it’s saving me tons of time and frustration.
It started as a small fix for my own pain - but I hope it helps others too.
Would love your feedback, Sebastian

value: Show HN: I was wasting hours on bad AI prompts, so I built a tool to change it

author: SebastianRoot


value: dschofie

value: Show HN: Gosim Distributed Systems Testing Framework Written in Go

author: dschofie


value: aidan-ricci

value:
Hi HN,
I built a Chrome extension called Recipe Converter that automatically adapts any recipe you find online to your dietary needs — Vegan, Keto, Gluten-Free, Low-Sodium, or Dairy-Free — in one click.
I made this because I was constantly finding great recipes that didn’t fit my diet, and manually swapping ingredients was tedious. Most tools make you copy-paste recipes into separate apps, but Recipe Converter works directly in your browser, detecting recipe pages and suggesting smart ingredient substitutions automatically.
It’s designed to make healthy eating and managing restrictions easier without changing your browsing habits.
Features:
Instantly personalizes recipes for your chosen diet
Smart substitutions for allergens and health goals
Works automatically while you browse (no copying or forms)
Chrome Web Store: https://chromewebstore.google.com/detail/recipe-converter-%E...
Would love any feedback — especially from anyone with food allergies, nutrition experience, or interest in accessibility. I’m also curious how others would handle more complex substitutions (like macros or the edge case conversions).
Thanks for reading!

value: Show HN: Recipe Converter – Instantly Adapt Recipes to Your Diet

author: aidan-ricci


value: jellyfiz

value: Show HN: CMMC Compliance Application

author: jellyfiz

title: Show HN: CMMC Compliance Application


value:
I built an AI consultant based on Claude Sonnet 4.5 that applies systematic frameworks (game theory, resource management, systems thinking, etc.) to help review life decisions and refine systems for live decisions.
Not therapy. Not life coaching. Engineering consultation.
Example frameworks:
- Test the way one behave or decide with coverage and backtesting (judge by consequences, not morality) helps in reshaping system 1 for better reaction
- Commons Governance (Ostrom's principles but for families) helps in managing energy and time with transactional method for both sides to maintain the meaningful relationship
- Return of investment (Charlie's principles but for daily activities) helps in reviewing the time and effort spent by parties if the relationship is investing to grow the baseline happiness or wasted in unproductive activities
2-hour intensive sessions, $100. Data deleted after 30 hours.
Built for people who think systematically.
Start to chat with the bot
https://momentummentor.com/chat
During testing, I found it inspiring because, I was debating a topic with myself, I used it to test the bot and the bot revealed a blind spot and a self-consistency issue that I had anticipated would take me a week to consider.
Looking for early user and feedback.

value: Show HN: Momentum Mentor – Bot Helps Engineering Systems for Life


value: Free job aggregator powered by Adzuna. After hearing about the Amazon layoffs, spent tonight building a tool to help people search across thousands of job sources in one place. Built with React, deployed on Vercel. Feedback welcome!

value: Show HN: LaunchPad – Built tonight after hearing about Amazon's 30k layoffs


value: dogol

value: Get new and fixed of<p>Linux smart directories navigation

value: Show HN: Linux Smart Directories Navigation

author: dogol


value: enricoros

value:
Hey HN, Enrico here. Some of you might remember Big-AGI from early chat days. The response here pushed me to quit my job and go all-in. Spent the last year rebuilding it properly (mostly hand-written) instead of shipping fast.
This is the tool I couldn't find: the AI you use to decide what to build, and why, before going to Claude Code or Cursor.
Even though the hype is on AI that 'does' things through heavy prompted experiences, I believe there's a strong need for AI that helps you "think" broader. How? with multiple models in parallel so you can spot hallucinations and lies, and so you can explore the solution space faster. And also with human bandwidth optimization: UI/UX is way underdeveloped for the "wall of text" phenomenon and hardly optimized for your I/O bandwidth besides voice input. And designed to short the generation/verification loop.
So Big-AGI 2 is now out, to be the fastest chat with a madly optimized UI, be local-first (app soon) because we don't like to wait 150ms/click. With Personas support because memories and data shall be with a relatable entity.
If the cost of mistakes is very high for your work - wrong arch decisions, debt-inducing bad tech choices, bling spots - do yourself a favor, and try it.
Anyone can Free/unlimited at big-agi.com, or self-host from github.com/enricoros/big-agi (sync is Pro only, $11/mo).
Worth building a thinking tool while everyone builds doing tools? Worth bootstrapping rather than VC-lubing? Genuinely curious.

value: Show HN: Big-AGI 2 – Spent a year obsessing over AI for thinking

author: enricoros


value: cat-whisperer

value:
If you've debugged a null pointer exception on a Flipper Zero, you know the pain - the device crashes, you reflash, add print statements, repeat. Memory bugs in embedded C are brutal because they only surface at runtime on the actual hardware.
This template lets you write Flipper Zero applications in Zig, giving you:
- Memory safety and compile-time error checking
- Bounds-checked arrays and explicit error handling
- Clean build system that works cross-platform
- Integration with UFBT for packaging and deployment
- No special IDE required - just Zig, UFBT, and your editor
The interesting technical challenge was bridging Zig's build system with the Flipper SDK's ARM Cortex-M4 target. The two-stage compilation process (Zig -> object file -> UFBT packaging) required careful handling of calling conventions and ABI compatibility.
The compile-time safety catches issues before they become device crashes. Optional pointers and explicit allocator tracking eliminate entire classes of bugs that C would miss entirely.
Feedback welcome, especially on:
- SDK coverage (I manually declare some functions Zig's C translator can't handle)
- Optimization strategies for binary size
- Debugging workflow improvements

value: Show HN: Production-ready Zig template for Flipper Zero apps

author: cat-whisperer


value: Show HN:What Is the Best Free File Transfer Site in 2025?


value:
I made an early version of a sorted collection library for Zig. Sorted collections are data structures that maintain the data in sorted order. Examples of these data structures are `java.util.TreeMap` in Java and `std::map` in C++. These data structures are mainly used for fast lookups (point search) and fast range searches.
The library is available on GitHub: https://github.com/CogitatorTech/ordered

value: Show HN: Ordered – A sorted collection library for Zig


value: codinginjammies

value: Show HN: Publish beautiful websites in two ticks

author: codinginjammies


value: StellaMary

value: Show HN: Write Node.js code in Rust to achieve massive HTTP throughput

author: StellaMary


value: mostlyk

value:
I built an interactive RISC-V CPU visualizer for a course that lets you explore how instructions move through both a sequential and a 5-stage pipelined processor.
You can step through execution, watch data hazards resolve, and see how branching and forwarding work in real time.
The goal is to make CPU architecture learning more intuitive for students (and anyone who likes poking around pipelines). The whole verilog code and implementation details are available on the project report!
Right now, the demo supports basic arithmetic, memory, and branch instructions, and includes two pre-programmed examples (a basic ALU demo and a Fibonacci pipeline program).
Would love feedback on how to handle branching better, I remember crushing it due to deadline and it is not handled properly, but haven't looked at the code in-depth after that :p. I also want to do something similar for OS and write from scratch, been seeing a lot of post so any recommendations are appreciated :)
Visualizers:
https://mostlykiguess.github.io/RISC-V-Processor-Implementat...
https://mostlykiguess.github.io/RISC-V-Processor-Implementat...
Code:
https://github.com/MostlyKIGuess/RISC-V-Processor-Implementa...

value: Show HN:Interactive RISC-V CPU Visualizer (Sequential and Pipelined)

author: mostlyk


value: nbochenko

value:
Hi HN,
I’m Nicolas, a technical founder (17+ years in engineering, ex–VP of Engineering at two regional unicorns). I’ve been working on AmbrosAI, a mobile-first AI longevity companion that helps people eat better, sleep deeper, and stress less, through personalized daily insights powered by AI.
The app connects nutrition, sleep, and stress in one adaptive system. It uses AI to analyze meals, learn from behavior, and guide users toward healthier, longer living, without making health feel like work.
I’m preparing to launch the beta soon, and I’d love to get early feedback from the HN community, especially on:
- The product concept and positioning
- How to frame the value of “longevity” simply for mainstream users
- Advice on finding a business/growth cofounder
My goal is to make longevity accessible, practical, and data-driven, not another calorie tracker, but a true AI health companion that adapts to you.
Learn more or join the waitlist: ambrosai.life
Would love your thoughts, what resonates, what doesn’t, and what you’d expect from something like this.
Thanks,
Nicolas

value: Show HN: AmbrosAI – An AI longevity companion for nutrition, sleep, and stress

author: nbochenko


value: Show HN: Image to Video Generator – Create Videos from Photos in Seconds


value:
We built PostFast to simplify how creators and marketers generate platform‑specific copy. Instead of manually adapting the same product info for Twitter, LinkedIn, or a newsletter, you configure your product once, and PostFast uses AI to generate tailored content.
Key features:
- One‑time setup for your product or service
- Generates copy adapted to different platforms (style, tone, length)
- Includes copy scoring and A/B variant comparison
- AI-powered recommendations for which version to use
- All inside your browser; no external app needed
It's built for indie makers, content teams, and marketers who need quick, consistent copy without jumping between tools. We focused on making it flexible and non‑opinionated—you control the inputs; the extension handles the formatting and platform nuances.
We're launching soon and opening a waitlist. If you regularly write promotional copy, we'd love your feedback on:
- Which platforms you post to most
- What kind of scoring criteria would be useful
- Whether you'd prefer a free tier with limited generations
Waitlist and brief demo: [https://postfast.today/#subscribe]
(I’m one of the developers.)

value: Show HN: PostFast – AI-Powered Copywriting Browser Extension


value: potom

value:
I made a terminal game called Digital Virus. It's a logic puzzle where you predict how a 4-digit code mutates after each wrong guess. Starts simple but gets tricky with combo rules at higher levels.
Wrote it in C to feel like those old 90s games. No graphics, just numbers and logic. Source is on GitHub: https://github.com/Ferki-git-creator/Digital-Virus
Curious what you think - especially if you remember when games came on floppies!

value: Show HN: I created a light logic game in the style of the 90s

author: potom


value:
I built Log Bull because ELK/Loki/Graylog felt heavy for small services and side projects. I wanted something you can deploy fast, point your app at a single HTTP endpoint, and immediately search logs in a clean UI.
What it is: minimal log collection system + search UI focused on developer ergonomics. There are libraries for Java, Go, Python, JavaScript, C#, PHP.
Usage examples and playground are placed on the website - https://logbull.com.
Features:
- per-project isolation (within single instance);
- API keys;
- optional domain/IP filtering limits;
- retention settings;
- users management and audit logs.
Deployment is made via Docker or via .sh script.
Tech stack: Go backend + React UI, PostgreSQL as DB, OpenSearch for storage/search, Valkey for caching and rate limiting.
The project is open source under Apache 2.0 license.

value: Show HN: Log Bull – a simple log collection system for developers (Docker-based)


value: YannickCrabbe

value:
Most analytics tools give you 10–15 chart types and call it a day. But what happens when none of them actually represent your data well?
We ran into this when building a CRM dashboard. We wanted to show how sales reps connect to open deals: not just totals, but actual relationships. A bar chart was too flat, a table too dense. We needed a network graph.
The problem? Most chart libraries and BI tools don’t support that out of the box. You either say “no” to the feature request, or you hack together a standalone visualization that breaks the product’s flow (no filters, no interactivity, no theming).
So we built it ourselves, but in a way that still plays nicely with the rest of the dashboard. Luzmo now lets you define your own chart type, write your own visualization code, and then drop it into the dashboard editor like any other chart. Luzmo still handles the boring stuff: querying, filtering, theming, and cross-chart linking.
The end result:
- Sales reps become nodes in a network graph, with open deals orbiting around them.
- Deal size controls node size; win probability controls color.
- Everything responds to filters and interacts with other charts out of the box.
The tutorial and GitHub repo walk through the whole process: setting up the builder, defining data slots, writing render methods, and packaging it all up for deployment.
I’d love to hear from others who’ve solved similar problems:
- Have you ever needed a chart type your BI tool didn’t support?
- Did you build it yourself, or find a workaround?
- How important is native interactivity vs. just embedding a standalone visualization?
Read the post + see the full code: https://www.luzmo.com/blog/build-custom-charts

value: Show HN: We Built Our Own Chart Type for Live Dashboards

author: YannickCrabbe


value: Made a super simple vanilla JS/HTML5 markdown editor/viewer recently, just wanted the basics and especially to be able to hit CTRL-P and print the resulting HTML nicely or save to a PDF.<p>This supports MathJax, and automatically saves to local storage so you can use it as a notes app or quickly pick up where you left off. Hope you also find it useful!

value: Show HN: Dead-Simple HTML5 Markdown Editor/Viewer


value: kuberwastaken

value:
most people on social media don't know how to text
they think starting with a greeting and waiting for a response is kind because that's telephone etiquette, but don't understand that doing that over text is like
someone calling you, saying "hello," then putting YOU on hold.
literally making the other person do extra work to find out what you want.
so I made this website
Instead of spending time explaining them this concept (and maybe coming off as very rude), I just keep this in my bio or send them this link when they do.
Pick your name. Pick the greeting trigger. Get a link. (optionally select one of the 16 languages)
They get a friendly explanation of why leading with context matters. You save 10 minutes. Everyone wins.
Train your network to respect your time by being clear about what you need.
Life's too short for message ping-pong with strangers.
PS: this builds on the legacy of nohello.net but adds the option of other greetings and adding custom names in the messages
also open source! https://github.com/Kuberwastaken/nogreeting

value: Show HN: I was tired of people dmming me just "hi", so I made this - NoGreeting

author: kuberwastaken


value: nodueck

value:
Hi HN,
I'm Nikolai, software engineer and co-founder at DroidRun. We built DroidRun, an LLM-based agent that leverages the Android Accessibility Tree for precise control and understanding of UI elements. It works on real phones and emulators, and it's open source.
How it started:
Our co-founder Niels Schmidt (you’ll see him in the demos) coded a prototype and shared a quick video. It went viral, about 50k views on X in under 2 hours. That moment pushed us to go all-in on DroidRun and soon after, we open-sourced it.
How it works:
Most agents rely on screenshots alone for context. We do that plus feed the Accessibility Tree into the LLM. That gives structural, hierarchical, and spatial metadata about UI elements.
Here’s an example:
Screenshot of a real UI:
https://imgur.com/a/ePRLpyv
And a matching accessibility JSON snippet:

  {
    "index": 3,
    "resourceId": "com.android.settings:id\\/search_action_bar",
    "className": "LinearLayout",
    "text": "search_action_bar",
    "bounds": "42, 149, 1038, 338",
    "children": [
      {
        "index": 4,
        "resourceId": "com.android.settings:id\\/search_bar_title",
        "className": "TextView",
        "text": "In Einstellungen suchen",
        "bounds": "189, 205, 768, 282",
        "children": []
      }
    ]
  }

We also annotate UI regions in screenshots with numbers, then match them in the tree. This structure gives the agent a deep understanding of what’s on screen, even across different device types like tablets.
This allows for better generalization across devices and screen sizes. Agents can act with greater confidence and fewer hallucinations.
Current Status:
- Ranked #1 on AndroidWorld until recently (it became highly competitive)
- Supports real devices + Emulators
- Strong performance on simple and complex UI tasks
- Gemini 2.5 Pro works best so far, but we’re iterating fast
What's next:
We’re working on a cloud platform where you can run prompts on Android devices without setup. Think of LLM controlling a phone in the cloud, ready to test your automations.
Looking for:
- Feedback from HN
- Collaborators who love Android, LLMs, agents
- OSS contributors

value: Show HN: Droidrun – LLM Agent for Android

author: nodueck


value:
Hey everyone,
I created a tool called SimpleLock, which I’ve been developing to make file sharing both secure and simple, especially for individuals or small teams who handle sensitive data. It allows you to share files with password-protected links, ensuring that only the intended person can access the content. Everything is encrypted for security, so it’s perfect for anyone looking to protect their files with ease.
Currently, the tool is in beta, and I’m testing its core features. Here’s what it offers so far:
Perfect for Payslip Distribution: Share payslips or other sensitive documents securely without dealing with complicated ZIP files.
End-to-End Encryption: Files are encrypted on upload with AES-256 encryption, so even the server can’t access them without the password.
File Size Limit: For now, there’s a 256 KB limit for each file being shared.
PDF Only: For now, only PDF files can be shared securely through the platform.
Docker Support: If you're hosting your own infrastructure, you can deploy it using Docker.

value: Show HN: Tool for secure file sharing for your projects or personal use


value: attogram

value: A github project to collect a bunch of bash-based screensavers/visualizations.

value: Show HN: Bash Screensavers

author: attogram

title: Show HN: Bash Screensavers


value: sunandsurf

value:
I built a tool that produces stock research reports in ~5 minutes by combining SEC filings, curated industry sources, and live financial data. Demo: https://www.youtube.com/watch?v=2wCM44B6iH8
Instead of relying on OpenAI’s Deep Research (too costly, ~$10/run), I built my own agents that:
- Parse 10-Ks and 10-Qs directly from the SEC
- Search curated industry publications (avoiding noisy market news)
- Pull financials from Financial Modeling Prep
A combiner agent then outputs:
- A one-page summary
- A detailed report with citations
- Financials talbes + graphs
This is NOT a stock tip generator or a replacement for your own judgment. Think of it as a scalable junior analyst who does the research part for you.
You can sign up (3 free credits) or just comment a ticker and I’ll share a sample report. Would love feedback on whether this fits your workflow, especially from folks who do deep equity research.

value: Show HN: Auto-generate stock research reports from SEC and industry publicaitons

author: sunandsurf


value: arknirmal

value: Cloudtellix is a free, OpenAI-compatible proxy that lets you manage API keys, view usage analytics, and control costs — all with zero setup.

value: Show HN: Cloudtellix – Free OpenAI-compatible proxy with usage and Cost Control

author: arknirmal


value: antenehmtk

value:
I was learning to build multi-agent systems with LangChain and CrewAI. Started with a simple travel planning agent.
But to make it work, I had to build FIVE separate agents:
1. Flight search agent
2. Hotel search agent
3. Flight booking agent
4. Hotel booking agent
5. Itinerary/things-to-do agent
This felt wrong. I wanted to orchestrate a trip planner without building every vertical myself.
*Why can't I just discover and use existing agents?*
So I built MeshCore - a service mesh + marketplace where agents can:
- Register their capabilities (e.g., "I search flights")
- Discover other agents automatically
- Call each other through a gateway
- Handle billing/metering automatically
*Try it:* https://meshcore.ai
*GitHub:* https://github.com/MeshCore-ai/mesh-cli
*Tech:* Service mesh architecture (like Istio for microservices, but for AI agents)
*Supports:* LangChain, CrewAI, AutoGen, custom agents
*Looking for feedback from multi-agent builders:*
- Have you hit this same pain?
- Would you use a shared marketplace of agents vs. building everything yourself?
- What's missing?

value: Show HN: MeshCore – Why do I have to build every agent from scratch?

author: antenehmtk


value: MakegreatWord

value:
  Full TS
  Zero dependencies
  Full type safety

value: Show HN: AI-patterns – 20 TypeScript patterns for production AI apps

author: MakegreatWord


value: wiso

value: Show HN: C# analyzer for error handling patterns in your including call graph

author: wiso


value: Codegres

value:
Happy to share that Shopify has Approved our App.
WALink: Whatsapp Linker - Connect Whatsapp to your Shopify Store.
No need of Meta APIs. Use Whatsapp Business/Personal. Sends message from your number to Customer on Order Booked, Order Delivered etc.

value: Show HN: WALink: WhatsApp Linker for Shopify

author: Codegres


value: whatome

value: Show HN: A site that finds weirdest and coolest products from around the web

author: whatome


value: alexghayward

value: Show HN: GoMask – Instant test data masking and synthetic generation for CI/CD

author: alexghayward


value:
iny team (3 devs, 1 artist) here—sharing our first game: BALL x PIT, a survival roguelite focused on physics-based ball combat.
Key bits:
Unique ball types (Bomb, Black Hole, etc.) with distinct bounce/effects
42 evolutions via clear fusion (no random unlocks)
Base-building for permanent run perks
Launched 5 days: 300k+ sales, 95% Steam positive. On PC/PS5/Xbox (Game Pass free)/Switch ($14.99).
Happy to answer questions, and we’re prepping an update with more fusions. Thanks for checking!

value: Show HN: Ball X Pit – Physics Roguelite with 42 Ball Evolutions


value: linkshu

value:
Hey HN,
If you play Weak Legacy 2 (the Roblox Demon Slayer-inspired RPG), this site’s been a go-to for me: daily-updated codes, full clan/breathing tier lists, and the official Trello roadmap—all tied to UPD 5.0.
Key bits:
2 active codes (verified Oct 2025)
Tier lists for 20 clans + 14 breathings
Live reset timer (15h left now) + stock updates
Saves time hunting for info across Discord/Reddit. Thought fellow players might find it useful!

value: Show HN: Weak Legacy 2 Guide – Tier Lists, Codes and Trello

author: linkshu


value: kesslerfrost

value:
I've been working on Clockwork, a Python library for composable infrastructure blocks that lets you dial the AI involvement up or down per resource.
The core idea: Allows you to build complex infra components from basic building blocks with a knob on how much "intelligence" you want in it.
```
 # Specify everything yourself
  nginx = DockerResource(
      image="nginx:1.25-alpine",
      ports=["8080:80"],
      volumes=["/configs:/etc/nginx"]
  )<p># Just set constraints, AI fills the rest
  nginx = DockerResource(
      description="web server with caching",
      ports=["8080:80"]
  )<p><pre><code>  # Or just describe it
  nginx = DockerResource(
      description="web server for static files",
      assertions=[HealthcheckAssert(url="http://localhost:8080")]
  )</code></pre>
```
Same resource type, you pick the level of control. What I find tedious (picking nginx vs caddy vs httpd) you might care deeply about. So every resource lets you specify what matters to you and skip what doesn't.
It's built on Pulumi for deployment, uses Pydantic for declarative specifications, and works with local LLMs (LM Studio) and cloud-based such as OpenRouter.
Also has composable resources - group related things together:
```
BlankResource(name="dev-stack", description="Local dev environment").add(
      DockerResource(description="postgres", ports=["5432:5432"]),
      DockerResource(description="redis", ports=["6379:6379"]),
      DockerResource(description="api server", ports=["8000:8000"])
  )
```
The AI sees the whole group and configures things to work together. Or you can .connect() independent resources for dependency ordering and auto-generated connection strings (this is still WIP as is the whole project but I'm currently thinking of a mechanism of "connecting" things together appropriately).
Repo: https://github.com/kessler-frost/clockwork
It's early (v0.3.0) and I'm still figuring out what works. Main questions:
1. The "adjustable AI" concept - is this useful or confusing?
2. Which resources/features would be most valuable next?
Would love to hear if this resonates with anyone or if I'm solving a problem nobody has.

value: Show HN: Clockwork – Intelligent, Composable Infrastructure Primitives in Python

author: kesslerfrost


value: solfox

value:
Hi HN, I’m Sol (YC 2006). I built BestInterest to help co-parents communicate peacefully after divorce or high-conflict relationships.
The idea came from personal experience — a painful divorce and difficult co-parenting communication. Courts often tell co-parents to keep things business-like and child-focused, which sounds simple but is brutally hard in practice. I realized AI can sometimes do what humans can’t — remove emotion from the loop. It started as a simple theory — that AI could actually prevent emotional abuse in digital communication.
I’d taken a break from tech after years at Google (I was a PM there), but eventually brushed off the dusties and decided: I’ll build it myself.
THE STACK
Google Cloud + Firebase + Gemini (with some OpenAI functionality still in place). Front end: FlutterFlow. I bootstrapped everything — no funding, no team at first, just persistence, a supportive partner, and late nights after my kids were asleep. One upside of being a co-parent is suddenly a lot of kid-free time to think!
Early on, I knew I wanted an advisor with deep expertise in abuse recovery. During my own healing, Dr. Ramani Durvasula’s YouTube videos were life-changing, so she topped my “never-going-to-happen” list. I cold-emailed her — and to my surprise, she said yes.
Yesterday, WIRED featured our story: “Divorced? With Kids? And an Impossible Ex? There’s AI for That.”
Side note: in the article, our leading competitor acknowledged using users’ personal correspondence for training data — which was… surprising.
It’s surreal seeing something that began with personal pain now helping others in such a profound way. I get emails every week from users saying the app has “literally changed their lives.” It’s incredibly gratifying.
I’m learning as I go — building in a space this sensitive has challenged me in many ways and shown just how deeply this kind of technology is needed.
When used with care and intention, AI can genuinely help marginalized communities — people navigating conflict, isolation, or trauma. I was “fortunate,” in a strange way, to have lived this pain firsthand; it helped me understand what needed to be built for my niche. AI has just as much potential to create harm or false information as it does to bring light to dark places — protecting victims and helping people find safety in their communication.
Happy to talk about any of these:
- Bootstrapping a consumer AI app solo
- Restarting life as an entrepreneur after kids, a divorce, and an eight-year hiatus
- Transitioning to being a full-time dad
- Growing a real subscriber base in a niche without a marketing budget — leveraging AI tools and SEO
- Building for a legally and emotionally complex community
- Using AI to protect against abuse — designing filters that help without over-censoring
- Breaking into a quasi-regulated industry where many assume court approval is required just to operate
- Or what it’s like designing tech for the most emotionally charged messages imaginable
AMA — happy to talk about the journey, the challenges, or anything else that resonates.

value: Show HN: My bootstrapped startup for co-parents was just featured in WIRED

author: solfox


value: r-jsv

value: Show HN: Build TypeScript backends and SDKs with up to 90% less code

author: r-jsv


value:
Hi!
As a side-project this summer (partly to have an excuse to try out Claude Code ;D), I built a Chrome extension that hides content that doesn't match user preferences. Example usage: https://youtu.be/japjNSU3O7A
The content + preferences are sent to an LLM, which decides if the content is relevant. You can use your own OpenRouter API key, but there's also a 'free tier' which uses my key (there's a daily quota with this option, though).
The extension now also supports including images and video thumbnails in the filtering, although this is only available when using your own OpenRouter key. The code is available at: https://github.com/jac08h/great_filter
Feedback is appreciated!

value: Show HN: 'Elon's X page without politics': LLM-based content filtering in Chrome


value: pupibott

value:
Hi HN,
Six months ago, I asked Gemini to "send my weekly report to the team." It replied: " Email sent successfully"—but the email was never sent. The attachment was wrong. Nobody told me.
That's when I realized: *LLMs lie about their own execution.*
---
*The Problem:*
When you ask an LLM to automate multi-step tasks (search file → attach → send), it cheerfully reports success even when:
- The file doesn't exist (hallucinates the ID)
- The API call failed silently
- Permissions were denied
Single-LLM systems have no incentive to admit failure; they optimize for appearing helpful, not for being correct.
---
*My Solution: Don't Let the LLM Grade Its Own Homework*
I built PupiBot with three separate agents that cannot collude, ensuring *the agent that executed the step is NOT the one verifying it succeeded.*
The architecture is simple:
* *CEO Agent (Planner, Gemini Flash):* Generates the execution plan (No API access).
* *COO Agent (Executor, Gemini Pro):* Executes steps, calls 81 Google APIs, returns raw API responses.
* *QA Agent (Verifier, Gemini Flash):* *After EVERY critical step, validates success with real, independent API calls.* Triggers retry if verification fails.
*Real Example (Caught & Fixed):*
User: "Email last month's sales report to Alice"
*  Search Drive: Not found
*  *QA Agent:* "Step failed. Retries with fuzzy search."
*  Finds: "Q3\_Sales\_Final\_v2.pdf" | *QA Agent:* "File verified. Proceed."
*  Sends email | *QA Agent:* "Email delivered. Attachment confirmed."
It's like code review: you don't approve your own PRs.
---
*Current Implementation & Transparency:*
* *Open Source*: MIT License, Python 3.10+
* *APIs*: Google Workspace (Gmail, Drive, Contacts, Calendar, Docs).
* *Reliability (Self-Tested):* Baseline (single Gemini Pro) was ~70% success. PupiBot (triple-agent) achieves *~92% success* on same tasks.
* *Known Limitation*: Google-only, 3x LLM overhead (tradeoff: reliability > speed), early stage.
---
*Why I'm Sharing This (My Garage Story):*
I'm not a programmer, I have no formal CS degree. My development process was simple: I'd use PupiBot as my daily assistant, manually log every error, and bring that "bug report" to my AI assistants (Claude, Gemini) to fix.
PupiBot is my 'custom car' built in the garage, fueled by passion and persistence. I’m finally opening the door to invite the real mechanics (you, HN) to examine the engine.
*What I'd Love from HN:*
1.  *Feedback* on the independent QA agent pattern.
2.  *Benchmarking ideas* for rigorous evaluation.
3.  *Architectural critiques.* Where's the weak link?
---
*Links:*
- GitHub: https://github.com/PupiBott/PupiBot1.0
- Quick Demo (1:44 min): https://youtube.com/shorts/wykKckwaukY?si=0xdn7rM6B2tMAIPw
- Architecture Docs: https://github.com/PupiBott/PupiBot1.0/blob/main/ARCHITECTUR...
Built with  by a self-taught technology enthusiast in Chile
Special thanks to Claude Sonnet 4.5 for being my coding partner throughout this journey

value: Show HN: I built a triple-agent LLM system that verifies its own work

author: pupibott


value:
Hello HN!
I built *GeminiDesk*, a free, open-source desktop client for Gemini (built with Qt/C++ under the hood) that runs on Windows, macOS, and Linux.
The goal was to move beyond the basic web wrapper and build a native experience focused on power-user features for productivity:
* *Keyboard Shortcuts:* Full control over your workflow using only the keyboard (switching models, opening/hiding the chat window).
* *Advanced PDF Export with LaTeX:* The app uses its own renderer to export chats directly to a PDF file that correctly handles and renders complex mathematical formulas written in LaTeX. This is a huge productivity boost for research and technical conversations.
* *Scheduled Deep Research:* You can schedule long, intensive deep research queries to run automatically at a later time.
* *Audio Notification:* Get a non-intrusive sound alert when a lengthy response is finished generating, so you don't have to keep staring at the screen.
It's completely free and open source on GitHub. I'd love to hear your feedback on the features and performance.
What other quality-of-life features do you think are missing from the current Gemini web experience?
Thanks for checking it out!

value: Show HN: GeminiDesk – Native Desktop App for Gemini (Win/Mac/Linux)


value: banmarkovic

value:
In the past few years, I read many self-improvement books and listened to podcasts that helped me grow. But after a while, I always forgot most of the lessons and slipped back into old habits.
So I started writing short daily notes about what I learned and how I felt. It helped me stay focused, but I didn’t have a simple place to revisit those reflections. That’s why I built Bloomind.
It helps me reflect daily, revisit my notes, and stay connected with my goals. It's just a few mindful minutes instead of doomscrolling. I built it with Kotlin Multiplatform, sharing logic between Jetpack Compose and SwiftUI.
I would be more than happy to hear any feedback or thoughts. Also if anyone wants to support the app, please leave a review on the App Store or Play Store, that would mean the world to me.

value: Show HN: I built a free diary for growth to replace doomscrolling

author: banmarkovic


value: cloudnet-draw

value:
Hi HN,
I’m a cloud security architect. Earlier this year I posted CloudNetDraw, a tool that automatically generates Azure network diagrams (hubs, spokes, peerings, subnets, NSGs, UDRs, etc).
It connects to your Azure tenant (via user login or service principal), queries the environment, and exports editable .drawio files for both HLD and MLD views.
Since that first post, I’ve added:
• Hosted SaaS version: generate diagrams directly from the browser, no setup needed
• Self-hosting: still fully open source for those who prefer running it locally
• Deeper topology details: subnet-level resources and NSG/UDR mapping
• Support for Multi-Hub environments, direct resource-linking and spoke-to-spoke peerings
• Cleaner layout and faster Azure queries
Privacy-wise, nothing from your environment is stored! Diagrams are generated in memory and deleted after download.
Try it: https://www.cloudnetdraw.com
Source: https://github.com/krhatland/cloudnetdraw
I’d love to hear what you think, especially feedback from anyone managing large Azure environments or working with network documentation automation.

value: Show HN: CloudNetDraw – Generate Azure network diagrams (SaaS and self-hosted)

author: cloudnet-draw


value: Founder-Led

value: Show HN: Early-Stage MVP – AI-Powered Customer Support Solutions

author: Founder-Led


value: echoes-byte

value:
Hi HN!
I'm a solo developer from Korea, and I’ve been building Culink, a social platform for curating and sharing link collections — kind of like Pinterest for links (articles, tools, videos, resources, etc).
Why I built this:
I kept losing interesting links across scattered bookmarks. GitHub “awesome lists” are great, but they’re static — I wanted something more social, searchable, and collaborative.
What you can do:
- Create themed collections (AI tools, dev resources, startup guides, etc)
- Collaborate and share collections with others
- Follow curators you trust
- Discover quality links through your network
- Enjoy a simple, clean interface
Tech stack:
Next.js 15, NestJS, PostgreSQL, Azure Container Apps
Current status:
Just launched the English version on US servers, with example collections so you can explore right away.
Try it:
https://www.culink.io/discover
Would love your feedback on:
- Potential use cases I might be missing
- Features you'd find useful
- UX or onboarding improvements
Thanks for checking it out — happy to answer any questions!

value: Show HN: Culink – A social platform for curating and sharing link collections

author: echoes-byte


value: sumit-paul

value:
I built LearnTube AI, a Chrome extension that transforms passive YouTube watching into active learning. It automatically generates AI-powered quizzes during videos, like Coursera’s quiz system — but works on any YouTube video.
It analyzes transcripts, pauses at natural breakpoints, and asks contextual questions to help you retain more. Works fully client-side using Chrome’s built-in Gemini Nano AI for privacy, or optionally via the Gemini API for faster, cloud-based processing.
Key features:
- Auto transcript parsing and quiz generation for any captioned video
- Mid-video and end-of-video quizzes with explanations
- Works offline (on-device mode) or via Gemini API (faster mode)
- Visual quiz markers on the seekbar
- Popup dashboard for managing models, API keys, and progress
Built to make learning from YouTube as focused and interactive as formal platforms like Coursera — but with zero setup for creators.
Chrome Web Store: https://chromewebstore.google.com/detail/learntube-ai/aoenlc...

value: Show HN: Chrome Extension That Makes YouTube Videos Interactive with AI Quizzes

author: sumit-paul


value: tedavis

value: Show HN: RollBot A fast way to create looping GIFs/reels/folios

author: tedavis


value:
Hey everyone,
Whether you believe that credit card rewards can be considered passive income or not, figured I’d share this here.
I’ve been working on a project called The Card Caddie, it is essentially a tool to know when to use the right credit card. I love to travel and have gotten so many flights and hotels at insane rates, timing when to use points. But the actual process of navigating credit card rewards is not passive, like at all. I built this chrome extension and app to make it passive for me, whenever I go to purchase something, I instantly get informed with the card to use at that merchant to maximize my reward returns.
I posted here a few months ago with the chrome extension part of this project, but just got my iOS app live on the App Store. The chrome extension works on website domains, the app works with your location to estimate nearby stores and calculates the card to use. I added a live activity widget so you can know what card to use before you can even open your Apple Wallet.
I realize credit card info is very confidential, so you don’t input any personal info to this tool. I scraped all the publicly available data for the reward rates for popular cards and handle it for you.
Assume you spend 10k a year. Average card reward rate sits somewhere between 1-2% (assuming cash-back $100 to $200), but if you can get that more like 2-3+% you could be earning hundreds more a year. It’s not life changing money, but might as well earn as much as you can per purchase, especially if it is minimal/no effort.
Tool is completely free, give it a try if you’d like!

value: Show HN: Credit card rewards for passive income


value: suvijain

value:
Most people save posts on LinkedIn, then never see them again. They get buried in the “Saved” section with no way to search or organize them.
Linklet fixes that. It’s a simple Chrome extension that captures the full content of any LinkedIn post you save - author, headline, text, and media—without the clutter of likes, comments, or ads.
You get:
- One-click saving directly from LinkedIn
= Instant search across your saved posts
= Sorting by author or date
= Collections for organization
= Export option for backups
There’s no signup, account, or backend storage. Everything stays local in your browser.
Chrome Web Store: https://chromewebstore.google.com/detail/linklet/lhmnjaclknh...
I built Linklet because I kept losing valuable posts I’d saved for learning and reference. I’d love feedback on UX, local-first design choices, and what features you’d want next.

value: Show HN: Linklet – Save and search your LinkedIn posts cleanly

author: suvijain


value: doginal

value: Show HN: ChatGPT Exporter – export full chats to Markdown with live preview

author: doginal


value: chiefofgxbxl

value:
I've been building Trash Panda, a web extension that removes "social metrics": number of likes, subscribers, followers, stars, favorites, upvotes, downvotes, etc.
When I was building this, I had a few major sites in mind, but I was surprised at how pervasive these social metrics are across the web. When the extension removes them, the web feels calmer.
Extension has a 30-day free trial.
Please let me know what you think - still fairly early stage!

value: Show HN: Web extension to remove social metrics on web

author: chiefofgxbxl


value: michelpp

value: redis-automerge is a redis module that adds real-time collaboration to json-like documents using the automerge CRDT library.

value: Show HN: Redis-Automerge: CRDT Documents for Redis

author: michelpp


value: yoeven

value:
More often than not we pre-process data using an LLM before inserting them into a column/row on the database, for example translating text and storing the different languages or running OCR on a document and storing the structured output or using Classification to label user messages/documents before storing it.
Many of this use cases could happen on the database rather than a dedicated pipeline. With the motivation to allow Postgres to do more by extending it. We built Postgres-LLM which is a trigger than can conditionally be set on specific columns and executes a well defined tasks on insert/update that runs an LLM and stores the output on another column or updates the initial column.
This project supports any LLM that is OpenAI Chat API compatible. I was motivated to create this project to showcase our recently launched LLM, Interfaze.ai as it was trained to work on developer tasks like OCR, translation, classification and more. However, you can use any LLM of your choice by replacing the URL and API key on setup.

value: Show HN: I built an open source LLM integration for PostgreSQL

author: yoeven


value: daviducolo

value: Show HN: A Comprehensive Ruby Wrapper for the Node-Red Admin HTTP API

author: daviducolo


value: mjfoster

value:
Hey HN
I built Caddie AI, a simple AI chat app for golfers to vent, reflect, and get calm feedback after a frustrating round.
It’s not a swing analyzer or stat tracker — it’s more like a friendly caddie who listens, reframes your thinking, and gives one small actionable tip (or just lets you blow off steam).
The chat is stored locally on device, no signups or data collection, and the backend just calls OpenAI’s Responses API with a golf-specific prompt.
Tech: UIKit + Laravel backend + OpenAI API
Website → https://caddie-ai.co
Would love feedback on:
– whether the tone feels natural (not repetitive)
– ideas to make it more habit-forming (right now it’s one endless chat)
– pricing expectations (currently $2.99/month on the App Store) 3 free messages a day on freemium
Thanks for checking it out!

value: Show HN: Caddie AI – an AI caddie/therapist you can vent to after a bad round

author: mjfoster


value: superstarryeyes

value:
I don't believe everyone just wants to copy the Claude Code logo font to their own CLI/TUI apps, there just seems to be a lack of tools for developers to create their own ANSI logo easily.
So I decided to remedy the situation and build Bit, which is an open source ANSI logo designer with TUI and a standalone Go font library for implementing ANSI fonts to terminal apps. Here are the features:
- 100+ Font Styles, all free for commercial and personal use.
- Export to TXT, Go, JavaScript, Python, Rust, and Bash.
- Color gradient effects, shadow effects, and text scaling.
- Adjust character, word, and line spacing. Align text left, centre, or right.
- Automatic kerning, descender detection and alignment.
Let me know what you think! And if you make a logo with it, I'd like to know about it.

value: Show HN: Bit – CLI/TUI Logo Designer and ANSI Font Library

author: superstarryeyes


value: marcelbundle

value:
TL;DR: We built a developer-first social media API because existing ones got pricey per-account and brittle at scale. Now we are doing work for 250k accounts.
Why I made it
We were running merchandise businnes and needed to post content to hundreds of social accounts with Ayrshare it got expensive so we cobbled together API first system that lets you build out your own system on top of it.
What it is
- Unified endpoints for: publishing/bulk scheduling, media upload, and analytics fan-in.
- No per-account pricing or hard caps from us bring as many accounts as you need. One gut has 100k
- Works today with 15 social media platforms
- Webhooks for status updates
What’s different:
- tbh support, we do it great
- scale, we work best if you have a lot to do
- we aren't focused on ads, so if you want to run ads via API we are not the guys for it
How to get it.
If you dont wanna pay just message us for free month via form on the website https://bundle.social/

value: Show HN: Bundle.social a social media API with no account limits

author: marcelbundle


value:
We built a voice AI agent in Rust.
EchoKit server is an open-source framework that connects speech input, LLM reasoning, and speech output — so you can build local or hybrid voice assistants with real-time interaction. It's super easy to set up.
Current features:
1. Supports both traditional ASR → LLM → TTS pipelines and end-to-end models like Gemini Live.
2. Voice Activity Detection (VAD) for low-latency, streaming voice input.
3. MCP support for external tools.
4. Works with both ESP32 hardware and web clients.
5. Streaming TTS output for GPT-Sovits
Would love to hear what kind of projects people would build with this or any feedback.

value: Show HN: EchoKit – open-source voice AI agent framework


value: kianN

value:
Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research.
My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The tool started as an experimental wrapper over low-level statistical software in C, quickly became a testing/iteration ground for our api, and is now my personal go-to for lit reviews.
The tool organizes corpuses of text content, visualizes the high level themes, and enables me to pull up relevant excerpts. Unlike LLMs, this model transparently organizes the data and can train from scratch quickly on small datasets to learn custom hierarchical taxonomies. My favorite part of the tool is the citation network integration: any research paper it pulls up has a button “Citation Network Deep Dive” that pulls every paper that cites or is cited by the original paper, and organizes it for further exploration.
I initially built this tool for academic research, but ended up extending it to support Hacker News to mine technical conversation, the top 200 Google results, and earnings transcripts. We have a gallery of ready to explore results on the homepage. If you are kicking off a custom deep dive, it takes about 1-5 minutes for academic search, 3-7 minutes for Hacker News, and 5-10 minutes for Google.
To demonstrate the process, I put together a video walkthrough of a short literature review I conducted on AI hallucinations: https://www.youtube.com/watch?v=OUmDPAcK6Ns
I host this tool on my company’s website, free for personal use. I’d love to know if the HN community finds it useful (or to hear what breaks)!

value: Show HN: Research Hacker News, ArXiv & Google with Hierarchical Bayesian Models

author: kianN


value: nxnfufunezn

value: Scalable S3 backed sharded counters written in Golang

value: Show HN: Scalable S3 backed sharded counters

author: nxnfufunezn


value: PdV

value:
Hey HN :)
I'm Andrzej, 15 years building distributed systems. Over the past two years, I watched AI change everything about how we work as developers – and most of the discourse was either panic or hype.
So I spent that time researching what actually works.
The result: "The New Rules" – 16 chapters on navigating the AI era as a developer. Accompanied by a website to help us all get more in-depth into the discussion.
Some of the controversial takes inside:
* GitHub stars became meaningless vanity metrics – so how do we quick-filter for quality?
* Skills that got you to $100K won't get you to $200K (the career ladder just shifted)
* Small teams with AI will crush large teams without it
* Your moat isn't code quality anymore – it's judgment, architecture, and trust
I've created this book with heavy AI assistance (Claude and ChatGPT) – proud of the outcome, my learning and the effort it took to get quality content through the full pipeline...
Note that some examples and case studies are illustrative, combining real insights with fictional elements to better convey concepts. I've checked what I could, but I'm sure there's room for further improvement.
Its not a scientific publication, but rather in-depth share of my perspective on changes happening in these interesting times.
Licensed under CC BY 4.0 – free to share, adapt, even use commercially with attribution.
Hardcover available for $39 if you want the physical version (shipping from Poland).
I'm here all day for discussion, pushback, corrections, and hearing about your experiences. If you spot inaccuracies in the examples or have better case studies, I'd love to hear them.
Direct PDF download: https://pub-7a9dcf4135624a49ac92e46823c312c1.r2.dev/TheNewRu...

value: Show HN: The New Rules – Developer's survival guide for the AI era

author: PdV


value: michaelplzno

value: Show HN: Wah Wah Button, a tiny Win11 app to organize your windows

author: michaelplzno


value: shaunaks

value:
Hi HN, we’re the team at Truffle AI (YC W25), and we’ve been working on Dexto (https://www.dexto.ai/), a runtime and orchestration layer for AI Agents that lets you turn any app, service or tool into an AI assistant that can reason, think and act. Here's a video walkthrough - https://www.youtube.com/watch?v=WJ1qbI6MU6g
We started working on Dexto after helping clients setup agents for everyday marketing tasks like posting on LinkedIn, running Reddit searches, generating ad creatives, etc. We realized that the LLMs weren’t the issue. The real drag was the repetitive orchestration around them:
- wiring LLMs to tools
- managing context and persistence
- adding memory and approval flows
- tailoring behavior per client/use case
Each small project quietly ballooned into weeks of plumbing where each customer had mostly the same, but slightly custom requirement.
So instead of another framework where you write orchestration logic yourself, we built Dexto as a top-level orchestration layer where you declare an agent’s capabilities and behavior:
- which tools or MCPs the agent can use
- which LLM powers it
- how it should behave (system prompt, tone, approval rules)
Once configured, the agent runs as an event-driven loop - reasoning through steps, invoking tools, handling retries, and maintaining its own state and memory. Your app doesn’t manage orchestration, it just triggers and subscribes to the agent’s events and decides how to render or approve outcomes.
Agents can run locally, in the cloud, or hybrid. Dexto ships with a CLI, a web UI, and a few sample agents to get started.
To show its flexibility, we wrapped some OpenCV functions into an MCP server and connected it to Dexto (https://youtu.be/A0j61EIgWdI). Now, a non-technical user could detect faces in images or create custom photo collages by talking to the agent. The same approach works for coding agents, browser agents, multi-speaker podcast agents, and marketing assistants tuned to your data. https://docs.dexto.ai/examples/category/agent-examples
Dexto is modular, composable and portable allowing you to plug in new tools or even re-expose an entire Dexto agent as an MCP Server and consume it from other apps like Cursor (https://www.youtube.com/watch?v=_hZMFIO8KZM). Because agents are defined through config and powered by a consistent runtime, they can run anywhere without code changes making cross-agent (A2A) interactions and reuse effortless.
In a way, we like to think of Dexto as a “meta-agent” or “agent harness” that can be customized into a specialized agent depending on its tools, data, and platform.
For the time being, we have opted for an Elastic V2 license to give maximum flexibility for the community to build with Dexto while preventing bigger players from taking over and monetizing our work.
We’d love your feedback:
- Try the quickstart and tell us what breaks
- Share a use case you want to ship in a day, and we’ll suggest a minimal config
Repo: https://github.com/truffle-ai/dexto
Docs: https://docs.dexto.ai/docs/category/getting-started
Quickstart: npm i -g dexto

value: Show HN: Dexto – Connect your AI Agents with real-world tools and data

author: shaunaks


value: nitishr

value: It's cursor, but sits in your browser. Built with Rust.

value: Show HN: Switch code b/w your Gemini/Chat/Claude subscriptions in the browser

author: nitishr


value: lchoquel

value:
We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them.
Why this instead of yet another workflow builder?
- Declarative, not glue code: you state what to do; the runtime figures out how.
- Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand.
- Open standard under MIT: language spec, runtime, API server, editor extensions, MCP server, n8n node.
- Composable: pipes can call other pipes, created by you or shared in the community.
Why a domain-specific language?
- We need context, meaning and nuances preserved in a structured syntax that both humans and LLMs can understand
- We need determinism, control, and reproducibility that pure prompts can't deliver
- Bonus: editors, diffs, semantic coloring, easy sharing, search & replace, version control, linters…
How we got there:
Initially, we just wanted to solve every use-case with LLMs but kept rebuilding the same agentic patterns across different projects. So we challenged ourselves to keep the code generic and separate from use-case specifics, which meant modeling workflows from the relevant knowledge and know-how.
Unlike existing code/no-code frameworks for AI workflows, our abstraction layer doesn't wrap APIs, it transcribes business logic into a structured, unambiguous script executable by software and AI. Hence the "declarative" aspect: the script says what should be done, not how to do it. It's like a Dockerfile or SQL for AI workflows.
Additionally, we wanted the language to be LLM-friendly. Classic programming languages hide logic and context in variable names, functions, and comments: all invisible to the interpreter. In Pipelex, these elements are explicitly stated in natural language, giving AI full visibility: it's all logic and context, with minimal syntax.
Then, we didn't want to write Pipelex scripts ourselves so we dogfooded: we built a Pipelex workflow that writes Pipelex workflows. It's in the MCP and CLI: "pipelex build pipe '…'" runs a multi-step, structured generation flow that produces a validated workflow ready to execute with "pipelex run". Then you can iterate on it yourself or with any coding agent.
What’s included: Python library, FastAPI and Docker, MCP server, n8n node, VS Code extension.
What we’d like from you
1. Build a workflow: did the language work for you or against you?
2. Agent/MCP workflows and n8n node usability.
3. Suggest new kinds of pipes and other AI models we could integrate
4. Looking for OSS contributors to the core library but also to share pipes with the community
Known limitations
- Connectors: Pipelex doesn’t integrate with “your apps”, we focus on the cognitive steps, and you can integrate through code/API or using MCP or n8n
- Visualization: we need to generate flow-charts
- The pipe builder is still buggy
- Run it yourself: we don’t yet provide a hosted Pipelex API, it’s in the works
- Cost-tracking: we only track LLM costs, not image generation or OCR costs yet
- Caching and reasoning options: not supported yet
Links
- GitHub: https://github.com/Pipelex/pipelex
- Cookbook: https://github.com/Pipelex/pipelex-cookbook
- Starter: https://github.com/Pipelex/pipelex-starter
- VS Code extension: https://github.com/Pipelex/vscode-pipelex
- Docs: [https://docs.pipelex.com](https://docs.pipelex.com/)
- Demo video (2 min): https://youtu.be/dBigQa8M8pQ
- Discord for support and sharing: https://go.pipelex.com/discord
Thanks for reading. If you try Pipelex, tell us exactly where it hurts, that’s the most valuable feedback we can get.

value: Show HN: Pipelex – Declarative language for repeatable AI workflows

author: lchoquel


value:
Im working on a mind mapping app that would allow for integration of gamification features and be nicer and easier to remember.
I was missing more advanced graphics from apps and being able to treat it as my note taking and learning tool.
i.e. how (and why?) should I remember a mind map if it looks the same like all other maps and all i can do is pick couple of shapes and dashed/dotted lines?
this simply doesnt work with my brain so i decided to create something better :) map on page is a preview of this, but im curious about the quiz, typing games and cheatsheet, would love some feedback (and ideas) on such training modes!
Another thing i'd love is some comments about any features that you miss in existing mm's soft.
If you're mind mapping enthusiast and interested in beta testing checkout contact form :)
p.s. last week i posted simple map, but i didnt realize i can use SHOWN HN for stuff that ive made and that lets you play with something. It just vanished down the pages so ive added new commands, modified all bad answers to be more realistic, and added some features for quiz and typing game and reposting to get proper feedback and hopefully some testers!
as well, if you're not ready to take lengthy quiz [nearly 180 questions] pick a smaller random generated subset to play with it!

value: Show HN: Linux CLI game, quiz, cheatsheet and map from my mind mapping app


value: visekr

value:
Hi HN, I built this after seeing ICEBlock get removed from the App Store.
ICESight is a photo-based tool that uses AI to handle moderation and verification, with human users acting as verifiers. When someone uploads a photo of an ICE sighting, it runs through an object detection model to find possible agents or vehicles. Each detected area is then sent to another AI service that checks if the scene looks authentic.
If it passes those checks, the system saves visual embeddings for each agent in a database and compares them against future uploads. Verified photos are added to a public map where other users can review and confirm sightings.
I hope this can become a useful public dataset that helps improve transparency and public safety. Feedback very welcome!

value: Show HN: ICESight – Computer Vision Tool for Detecting and Mapping ICE Activity

author: visekr


value: Christinadav

value:
We built Queyn to solve the data availability problem in algorithmic trading.
Professional tick data are expensive. Most retail traders and researchers can't afford it. Even if they can, historical data only gives you one timeline—you can't test strategies against market conditions that never happened.
Instead of replacing backtesting, we complement it. We used math to generate synthetic forex markets that let you stress-test beyond historical scenarios:
- Bid/ask spreads that widen under stress
- Volatility clustering
- Statistically validated against real EUR/USD market behavior
- Real-time streaming via WebSocket (not just static exports)
The interface lets you build custom scenarios with your own parameters—control volatility, trend direction, and liquidity stress to create conditions that don't exist in historical data. We've also built one fully validated session (London-NY overlap) that matches real market statistics.
Use cases:
- Stress-test strategies against scenarios that never happened
- Generate diverse training data for ML models (prevent overfitting)
- Practice risk management
The demo streams live—no signup, just click start and run the simulator.
Think of it as a flight simulator for algo traders. Pilots don't just replay old flights, they practice emergency scenarios. Same concept here.
Anonymous feedback button works if you want to tell me what's broken or missing.

value: Show HN: We used mathematics to solve the forex data availability problem

author: Christinadav


value: solomonayoola

value:
Hi everyone — I’ve been working on EchoStack, a project that turns Voice-AI playbooks into deployable solutions for real business outcomes (e.g. After-hours Answering, Lead Qualification → Auto-Booking).
Why?
After building real-time apps in fintech and healthcare, I noticed that most “AI voice” projects never make it to production — not because the models are weak, but because latency, integrations, and rollout safety are under-engineered.
Most teams can make a demo; very few can keep it stable under 250–300 ms p95 barge-in latency.
EchoStack is an attempt to codify what production actually requires — including latency budgets, configuration blueprints, and a controlled deployment workflow.
What it does?
Each “playbook” is a deployable Voice-AI setup that includes:
Preflight → Plan → Apply (Blue) → Smoke test → Switch (Green) → Rollback
Latency-audited pipelines (ASR, LLM, TTS budgeted at p95 < 300 ms)
Exportable configurations for no-code and code-based integrations
KPI tiles for outcomes (AHT, bookings, self-serve rate)
Two early examples:
After-hours Answering — handles missed calls, FAQs, and escalations
Lead Qualifier → Auto-Book — filters inbound leads and books meetings automatically
Status
Early Access only — no public demo yet.
We’re currently validating latency budgets, rollout safety, and adapter design (CRM, telephony, calendar).
Feedback I’d love
Are these latency targets realistic in your experience?
How do you test reliability in real-time LLM pipelines?
What signals do you track before rolling out voice changes to prod?
Full details: https://getechostack.com/playbooks

value: Show HN: EchoStack – Deployable Voice-AI Playbooks (p95 < 300 ms)

author: solomonayoola


value: elikoga

value: Show HN: Thymis.io Device management – images pre-loaded with your applications

author: elikoga


value: drakyoko

value:
Hey HN,
Over the weekend I built a small VS Code extension that lets you run/debug individual subtests in Go table tests — something I’ve often wished the official extension supported.
Unlike other extensions I’ve seen, it doesn’t just rely on regex and assumed field names but it does a bit of structural analysis by following testing.T references.
If you’re a Go developer and this sounds useful, I’d love to hear your feedback!

value: Show HN: VS Code extension to run/debug Go table tests

author: drakyoko


value:
DeepFakeSafety computes a SHA-256 hash of a file locally (never uploaded), signs that hash with your private key, and writes the resulting proof to Solana as an NFT.<p>Later, anyone can verify:
- The file is unchanged (same hash)
- Who signed it (public key)
- When the proof was created (block time)
- No centralized server is required.

value: Show HN: On-chain file authenticity via Solana NFT


value: iillexial

value: Show HN: I made semantic search engine for engineering blogs and conferences

author: iillexial


value:
Hello HN!
Earlier this year, we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework for building agents with MCP. Since then we have tried to push the protocol to the limits, including hosting agents as long-running tools on MCP [3], and seen other creative approaches surface (mcp-ui, chatgpt apps sdk).
Today, we are launching mcp-c – a cloud platform designed for hosting any kind of MCP server, including agents, ChatGPT apps, etc. We are in open beta and free to use, and would love your feedback.
Here are some key choices we made:
1) Everything is MCP - Each application is deployed as a remote SSE endpoint, and implements the full MCP spec, including advanced features like elicitation, sampling, notifications and logging.
2) Durable execution via Temporal - We use Temporal as the runtime environment, allowing you to run long-running operations. This is especially critical for agents, which need to pause/resume, be fault-tolerant, and run for a long time.
3) Local to Cloud - we're fans of how easy it is to deploy a Next.js app to Vercel, so we've made it simple to take your local mcp-agent, mcp server, OpenAI app, etc. and deploy it to the cloud. We have several examples and even a handy CLI to get started quickly [4].
It's as simple as:
- uvx mcp-agent init
- uv init
- uv add "mcp-agent[openai]"
- uvx mcp-agent login
# add your openai key to mcp_agent.secrets.yaml
- uvx mcp-agent deploy
You can also try out these hosted mcp servers to get an idea. These can be connected to any MCP client (ChatGPT, Claude Desktop/Code, Cursor, etc.)
OpenAI Pizza App - https://18t536mliucyeuhkkcnjdavxtyg66pgl.deployments.mcp-age...
Basic Agent Server - https://1m82g32x8nkoppinayx0k5ye12oar6vk.deployments.mcp-age...
The HN community has been a big part of mcp-agent's growth, and we're super excited to share this with you as well. We would love to hear your thoughts and feedback!
---
[1] - https://news.ycombinator.com/item?id=42867050
[2] - https://github.com/lastmile-ai/mcp-agent
[3] - https://news.ycombinator.com/item?id=44053754
[4] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/...
[5] - https://docs.mcp-agent.com/get-started/welcome
[6] - https://youtu.be/9ddtaSbUJIc

value: Show HN: MCP-C – cloud platform for running MCP agents and apps


value: sixddc

value:
Built a native Scratch 3.0 runtime in Lua that runs .sb3 projects without a browser.
Why? Browser sandboxing prevents access to hardware features (haptics, sensors, fine-grained perf controls). Native runtime gives you direct hardware access and lets you deploy to consoles, handhelds, embedded devices. Also means much smaller binaries (LÖVE is ~7MB vs 50-100MB for Electron).
How it works:
- Scratch blocks compile to IR, then optimize, then generate Lua
- LuaJIT executes the compiled code
- Coroutine-based threading for concurrent scripts
- Lazy loading + LRU cache for memory management
- SVG support via resvg FFI
~100% compatible with Scratch 3.0 blocks. Extensions that need JavaScript won't work (no Music, TTS, Video Sensing), but core blocks are there.
Built on LÖVE framework, so it's cross-platform (desktop, mobile, gaming devices).
Still rough around the edges (user input not implemented yet, cloud variables only work locally), but it runs real Scratch projects today.

value: Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime)

author: sixddc


value: ladybro

value:
Hey HN!
My name is Brett and I'm the solo developer behind Mindstamp.
Mindstamp turns any video into a two-way, interactive experience by layering in clickable buttons, hotspots, quiz questions, branching, images, and a whole lot more.
Here's a quick walkthrough: https://youtu.be/CD42oKQjnKI
Back in 2018, I built a product called Aech that let you add comments on top of videos for your friends so it felt like you were watching together.
That didn't quite work, but it got into the hands of a couple video agencies using it for client feedback, and for the past 7 years I've listened to the feedback that users have given to continually improve the platform.
Today, we are used primarily by corporate training teams and educational institutions that want to ensure their content is being 1. watched and 2. understood.
Rather than watching a video and taking a quiz at the end, we put that quiz right inside the video to ensure they are paying attention.
Here's a live demo: https://mindstamp.com/demo
Mindstamp is built using a Rails backend and a Vue.js frontend. A few months back, I completed a full rebuild of the entire app since it accumulated a lot of technical debt after 7 years of learning while building. It took me 12 months, but now I can sleep at night, so I'd say it was worth it.
Happy to answer any questions or provide more info. Thank you!

value: Show HN: Mindstamp – Make Any Video Interactive

author: ladybro


value: smatthewaf

value: I have been working on a project that has multiple repos, all of which have to convert between multiple map coordinate types, so I made an NPM module that allows you to parse and convert between Decimal Degrees, Degrees-Minutes, and Degrees-Minutes-Seconds coordinate types.  Niche? Yes.  Useful? Also yes (I hope)!

value: Show HN: CoordConversions NPM Module for Map Coordinate Conversions

author: smatthewaf


value: chaokunyang

value:
Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf.

  Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs.

  Unique features that other fast serializers don't have:
  - Cross-language without IDL files (Rust ↔ Python/Java/Go)
  - Trait object serialization (Box<dyn Trait>)
  - Automatic circular reference handling
  - Schema evolution without coordination

  Happy to discuss design trade-offs.

  Benchmarks: https://fory.apache.org/docs/benchmarks/rust

value: Show HN: Apache Fory Rust – 10-20x faster serialization than JSON/Protobuf

author: chaokunyang


value: basepurpose

value: a fun, industrial-strength machine that burns em dashes (—) and replaces them with commas.

value: Show HN: EM DASH DESTROYER 4001

author: basepurpose

title: Show HN: EM DASH DESTROYER 4001


value: juancarlosh

value: Show HN: Slime Sports for Apple Watch (my first game)

author: juancarlosh


value:
My friend and I were building UI-based automation tools for small businesses and kept running into a few recurring problems:
- Computer-use agents (CUAs) are powerful, but often unreliable and slow when controlling UIs directly.
- Workflows vary — some can be handled deterministically by RPAs, while others need a bit more intelligence from CUAs (while they may share a lot of the same 'actions').
We wanted an easier way to record UI-based automation scripts that could:
- Be run by humans, like a lightweight RPA, or
- Be invoked by CUAs as tool calls — improving their reliability and speed.
So we open-sourced the SDK we’ve been using internally. It currently works on macOS, and lets you:
- Record desktop interactions with any app that exposes accessibility info
- Record browser interactions via the Chrome extension (link on the github).
- Replay recordings deterministically like an RPA, or integrate the generated UI script as a callable tool for your CUA.
We’d love feedback from anyone working on UI automation, RPAs, or CUAs — especially around reliability and edge-case handling.

value: Show HN: Open-source SDK to generate UI-based CUA tools or RPA scripts on macOS


value: KeatonDunsford

value:
hey there! ever wondered how to organize files chronologically with just 6 characters? or encode astronomical data into git branches?
what is this? team travel 12 is a personal workspace exploring three interconnected systems:
grainorder - permutation-based file naming (1.2m unique codes from 13 consonants). smallest alphabet = newest files. GitHub's ascending sort automatically displays chronological order!
graintime - git branches that know their cosmic context. not just "oct 28, 11:30am" but "moon in uttara ashadha, aries rising, sun in 12th house." every branch encodes when AND where you were in the celestial cycle.
glow g2 - patient teacher voice throughout. i ask questions, check understanding, explain WHY not just WHAT. inspired by socratic method.
why does this matter? we're treating time as lived experience, not just timestamps. 4 comprehensive whitepapers inside: grainui (gpu-accelerated gui), grainorder patent, graindb (immutable database), graintime patent.
built with pure rust+steel stack (steel = scheme lisp on rust).
ready for questions - what part intrigues you most?

value: Show HN: Team Travel 12: exploring with grainorder, immutable graintime,Glow G2

author: KeatonDunsford


value:
I went swimming in the ocean of OpenAI's Apps SDK… and came back with a handbook!
Over the past few weeks, I’ve been diving deep into the ChatGPT App SDK: exploring its APIs, tools, and hidden gems. Along the way, I built, broke, fixed, and reimagined a bunch of little experiments.
P.S: Indeed OAIs official docs is the source of truth, this is just a rough notebook
Maybe, I can create a CLI tool to scaffold app?

value: Show HN: OpenAI Apps Handbook

title: Show HN: OpenAI Apps Handbook


value: simonmales

value:
Over ten years ago I launched Bitcoin Fax[1], and now I am experimenting with sending postcards.
1. https://news.ycombinator.com/item?id=9423501

value: Show HN: Bitcoin Postcards – Send Memories with Bitcoin

author: simonmales


value:
Live test instance: https://upthetree.club (no registration — explore freely)
Self-hostable Reddit/HN/Lemmy alternative.
Rust backend, GraphQL API, Docker-ready.
Features:
- Boards + threads/feeds
- Custom emojis & flairs
- Tailored streams (public/private)
- S3/GCS storage support (or local)
- Notifications & DMs
Alpha.
Help wanted: mobile, CSS, front-end specific stuff.
Repo: https://github.com/tinyboard/tinyboards
Discord in the README.
What’s your #1 pain with self-hosted social tools?

value: Show HN: TinyBoards – Self-Hosted FOSS Social Platform (Rust and GraphQL API)


value: rajit

value:
Code Compass is an AI agent that helps engineers determine the best task to work on next, given the codebase context from the engineer’s current task.
- Code Compass indexes a GitHub repo with AST parsing, enabling agents to understand where developers are developing codebase context [1]
- Given a current linear ticket, Code Compass's agent provides context about the relevant codebase locations, implementation patterns, and starting points needed to complete the task and feeds those instructions directly to your coding agent [2]
- Given a current ticket, Code Compass’ agent identifies the next ticket to work on that will maintain as much of the engineer's existing codebase context as possible [3]
Why we built it
As engineers, we often struggled with deciding what to pick up next, especially when deep in a particular section of the codebase. Each context switch slows us down, and a lot of valuable attention can be lost when switching too often between unrelated tasks. Context switching is also expensive for AI models: each switch requires re-ingesting the same files for reindexing, driving up token costs and more importantly wasting time. We built Code Compass to help engineers seamlessly flow from one meaningful task to the next, maintaining momentum and minimizing friction, and to save both humans and models from unnecessary context resets.
Tech implementation details
- Code Compass indexes your entire GitHub repo using AST parsing to capture the code structure and developer context.
- It also syncs with your Linear workspace, mapping tickets to relevant code areas based on historical changes.
- Our agent combines this insight, leveraging up-to-date context to ensure suggestions always build on your current work.
- We utilize modern LLM architectures and lightweight vector databases to enable real-time, context-aware recommendations.
- Use Cursor command tools and  manage branches and create PRs with each linear ticket.
Challenges we overcame
Visualizing code graphs proved much trickier than we thought. Representing complex dependencies in a meaningful way for both the AI and human user took multiple iterations. We took inspiration from projects like GitHub Next's repository visualization (https://githubnext.com/projects/repo-visualization/), eventually building a visualization layer that helps users quickly see project hot spots and context flows.
Potential Extensions
- Codebase + Developer understanding - Find who has expertise and triage tickets accordingly.
- Project planning - Architect new projects with developer and codebase understanding
- Expanding integration to include additional issue tracking systems (such as Jira and Trello), further streamlining workflows for a wider range of teams.
- Other languages (currently only supports typescript)
Try it yourself
You can sign up at https://codecompass.sh. We truly welcome your feedback and are eager to see how engineers use it to streamline their workflows.
[1] https://youtu.be/0EJl8b3fWYg
[2] https://youtu.be/hpxFagxHqu0
[3] https://youtu.be/fGLIlh3q1-k

value: Show HN: Code Compass – Coding agent that finds the best task to work on next

author: rajit


value:
After being thrust headfirst into FPGA development thanks to the Analogue Pocket, my first from scratch creation was a gate level implementation of the original Tamagotchi toy.
The core, running on both the Analogue Pocket and MiSTer platforms, lets users re-experience the very first Tamagotchi from 1996 with accurate emulation, but modern features. The core has savestates (which is much harder to do in hardware vs software emulation), high turbo speeds (1,800x was the max clock speed I've reached so far), and more.
Learning more about hardware and FPGAs is something I've wanted to do for many years, and I highly recommend it for any programmer-brained person. It's a very slightly different way of thinking that has vast consequences on how you look at simple problems.

value: Show HN: Tamagotchi P1 for FPGAs

title: Show HN: Tamagotchi P1 for FPGAs


value: edunteman

value:
Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs.
- It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url
- The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables
You can see it in action in this demo where it memorizes tic-tac-toe games: https://www.youtube.com/watch?v=PWbyeZwPjuY
Why we built this: before Butter, we were Pig.dev (YC W25), where we built computer-use agents to automate legacy Windows applications. The goal was to replace RPA. But in practice, these agents were slow, expensive, and unpredictable - a major downgrade from deterministic RPA, and unacceptable in the worlds of healthcare, lending, and government. We realized users don't want to replace RPA with AI, they just want AI to handle the edge cases.
We set out to build a system for "muscle memory" for AI automations (general purpose, not just computer-use), where agent trajectories get baked into reusable code. You may recall our first iteration of this in May, a library called Muscle Mem: https://news.ycombinator.com/item?id=43988381
Today we're relaunching it as a chat completions proxy. It emulates scripted automations by storing observed message histories in a tree structure, where each fork in the tree represents some conditional branch in the workflow's "code". We replay behaviors by walking the agent down the tree, falling back to AI to add new branches if the next step is not yet known.
The proxy is live and free to use while we work through making the template-aware engine more flexible and accurate. Please try it out and share how it went, where it breaks, and if it’s helpful.

value: Show HN: Butter – A Behavior Cache for LLMs

author: edunteman


value: Show HN: I made simple Email Marketing app with base features


value: yamsasson

value:
I built Globe of History
 — an interactive 3D visualization of 6,000 years of human events, mapped onto the Earth’s surface.
It includes 5,000+ data points covering wars, inventions, assassinations, natural disasters, and philosophers — spanning 4000 BCE → 2025 CE.
The main goal was to build a visual entry point into history — not a database, but a way to instantly sense density and continuity over time.
Stack / Process
Python for scraping structured event data from Wikipedia/Wikidata
Gemini 2.0 Flash for AI-based enrichment and multi-stage verification
Mapbox GL JS + React (Vite) for the interactive 3D globe
Custom scripts for deduplication, temporal grouping, and category color logic
Currently optimized for desktop only
I’d love feedback from this community on:
data-pipeline scaling / verification best practices
improving performance with large point datasets in Mapbox
ideas for better temporal exploration UX
Source link: globeofhistory.com

value: Show HN: Globe of History – Interactive 3D Map of 6k Years of Human Events

author: yamsasson


value: odinellefsen

value: Attached is a tutorial to event source your data in 5 minutes, using our platform.

value: Show HN: Event Sourcing Platform

author: odinellefsen

title: Show HN: Event Sourcing Platform


value: fredthedeve

value:
I'm a Stamp 1G student in Ireland. DETE's data is gold but buried in Excels. Built this to filter/search: HSE's 12,501 health permits (2024), IT trends (6,788 issued), CSRI success odds (e.g., 88.8% for US devs at Meta Dublin).
Proxy insight: High permits = hot sectors/companies for non-EU talent.
100% DETE open data.
Feedback?

value: Show HN: Permit Watch, Turning Ireland's work permits into a job-demand proxy

author: fredthedeve


value: ssdevproject

value:
Hi HN,
I’m Saad and I built OutfitScore.com. We wanted to make professional style, makeup and accessory feedback accessible for everyone.
With OutfitScore you can upload a photo of a full outfit, individual clothing piece, accessories, or a makeup look — the AI gives you a numerical score (0-100) in about 5 seconds plus actionable insights. :contentReference[oaicite:1]{index=1}
Key features:
- Free basic scoring with no account required. :contentReference[oaicite:2]{index=2}
- Upload mirror selfies, flat lays, or individual items. :contentReference[oaicite:3]{index=3}
- Premium plans unlock detailed breakdowns, body-type work, color theory advice, and analysis history. :contentReference[oaicite:4]{index=4}
- Also handles accessories (bags, shoes, jewelry) and makeup looks. :contentReference[oaicite:5]{index=5}
I’d love your feedback:
- Does the score feel meaningful and trustworthy to you?
- What would you like to see next (e.g., outfit comparison, style profiling, API access)?
- Any biases or improvements you spot in the tool?
Thanks for checking it out — happy to discuss how the system works under-the-hood or any of the tech stack if you like.
Saad

value: Show HN: OutfitScore – Free AI-powered outfit, makeup and accessory analysis

author: ssdevproject


value:
Hi HN,
I’m Saad and I built BestPick.online. We made this because your profile picture is often your first impression — whether you're on a dating app, LinkedIn, Instagram or building a personal brand. :contentReference[oaicite:1]{index=1}
Here’s what it does:
- Upload 2-3 photos of yourself. :contentReference[oaicite:2]{index=2}
- Choose the goal (dating app, professional, social, etc.). :contentReference[oaicite:3]{index=3}
- The AI instantly rates them and shows which one works best for that goal. :contentReference[oaicite:4]{index=4}
We believe:
- Different platforms need different kinds of photos (approachability on Tinder vs authority on LinkedIn). :contentReference[oaicite:5]{index=5}
- Everyone should be able to make a data-driven decision rather than just guess.
If you have a moment, I’d love your feedback:
• Did the recommended photo feel right to you?
• What additional goals or platforms should we support next?
• Any biases or surprising results you found?
Thanks for your time — happy to share more about how the AI works if you're curious.
Saad

value: Show HN: BestPick – Free AI selector for your best profile photo for social


value:
Hi HN,
We’ve been building something new at MemeGen AI
: an AI-native interactive video platform that turns any video into a sandbox where viewers can participate in the content itself.
Instead of passively watching, users can play inside the video — triggering reactions, remixing actions, or changing outcomes in real time. Every interaction branches into new videos, forming an evolving web of playful, user-generated moments.
Why we’re doing this
Most video platforms today are one-way performances: a small fraction of people create, and everyone else just scrolls. We think video should be participatory. Humans connect more deeply through shared play than passive watching.
How it works (simplified)
Videos are generated with embedded “interactive anchors” (e.g., tap to throw, react, change camera view).
Each user action runs through our lightweight AI engine, generating the next clip instantly.
All results are linkable and remixable, forming a chain of interactions — like multiplayer storytelling.
Why it’s interesting
Transforms short-form video into a medium of play, not performance
Enables true co-creation and branching video networks
Fully runs in the browser — no downloads, no prompt-writing, just click and go
We’d love feedback from builders and creators here — especially on interaction mechanics, UX simplicity, and technical scalability.
Try it here: https://meme-gen.ai

value: Show HN: Turn Videos to Sandbox Game Like Experience


value:
We have opened the WoolyAI GPU hypervisor trial to all.
https://woolyai.com/signup/
- Higher GPU utilization & lower cost Pack many jobs per GPU with WoolyAI’s server-side scheduler, VRAM deduplication, and SLO-aware controls.
- GPU portability Run the same ML container on NVIDIA and AMD backends—no code changes.
- Hardware flexibility Develop/run on CPU-only machines; execute kernels on your remote GPU pool.

value: Show HN: Run PyTorch on CPU boxes, offload kernels to remote GPUs


value: uziiuzair

value:
Hi HN! I’m Uzair, Founding Engineer of Noodle Seed. We enable businesses to deploy native apps inside ChatGPT conversations when users need recommendations.
What we built: A multi-tenant no-code platform that helps businesses create custom ChatGPT Apps. When someone asks “find me a legal firm in Austin,” businesses using our platform appear with their own branded app - complete with UI components, interactive elements, and real-time information. Not random results from old training data, but actual custom experiences with each business’s branding.
Try it: We’re in limited beta with founding members. You can create and test an app yourself in ChatGPT’s Developer Mode - takes just a few minutes (email me for examples - don’t want to spam here).
Technical details: We generate MCP servers that expose business services as tool contracts. When ChatGPT’s function-calling matches user intent, we return real-time data through OAuth-authenticated sessions (for now we’re not releasing OAuth to our users so they can test with less friction).
What was hard: Building a no-code abstraction layer over MCP servers, OAuth flows, and JSON schemas while maintaining flexibility for custom experiences. Every business wants different functionality - appointment booking, product catalogs, lead forms - but ChatGPT’s Apps SDK has strict requirements. We had to create a system that generates compliant tool contracts and web components automatically while still allowing businesses to customize their UI and branding.
What surprised me: Most businesses / startups have no idea ChatGPT Apps even exist. The October 6 launch flew completely under the radar outside tech circles.
What I’m unsure about: Are we too early? Will businesses pay for AI discovery before it’s proven?
We’re onboarding founding members now - businesses / startups that want to establish AI discovery presence before their competitors.
If you run a company or know someone who should be discoverable when people ask AI for recommendations, join our waitlist at [noodleseed.com].
Would love feedback on the approach and whether anyone else is tackling AI discovery differently.

value: Show HN: Noodle Seed – Create ChatGPT Apps for businesses to reach 800M users

author: uziiuzair


value: LandOfMightDev

value: In short, I created an angular application that parses a list of video urls into a json structure with the duration of each video and the position in the playlist. whenever a person loads the channel, it calculates the video that should be playing. So I just take the route (channel name), and embedded it into an html site, threw in a chatango embed chat just for good measure! No server needed and completely live !

value: Show HN: Created a Live Anime Channel, no server necessary

author: LandOfMightDev


value: oxhai

value:
Hi HN!
I built OXH AI - an AI-powered cryptocurrency signal platform that generates real-time trading signals using technical analysis and machine learning.
What it does:
- Generates AI trading signals for 100+ crypto pairs (Bitcoin, Ethereum, etc.)
- Real-time technical indicator analysis (RSI, MACD, Bollinger Bands, EMA)
- Risk scoring and position management
- Auto-backtesting with historical data
- Live chart integration with TradingView-style interface
Why I built this:
Most crypto signal platforms are either:
1. Black boxes with no transparency
2. Manual signals from "gurus" (often wrong)
3. Expensive ($100-500/month)
I wanted something transparent, AI-driven, and affordable.
Tech Stack:
- Frontend: React + TypeScript + Vite
- Backend: Node.js + Express + TypeScript
- Database: Supabase (PostgreSQL)
- AI: OpenAI GPT-4 for market analysis
- Real-time: WebSocket for live updates
- APIs: Binance, Bybit, OKX for price data
Interesting Technical Challenges:
1. Race Condition Prevention: Implemented optimistic locking for Telegram auto-posting to prevent duplicate messages
2. Schema.org Optimization: Added LocalBusiness + Organization markup for better Google indexing
3. Rate Limiting: Built custom rate limiter that handles 50K+ API calls/day
4. Real-time Updates: WebSocket connections for live price & signal updates
Features:
- Free tier: 5 signals/day (no credit card)
- Paid tier: Unlimited signals + advanced analytics
- Referral system with leaderboard
- Blog with auto-generated crypto content (2x daily)
- Crypto news aggregation (updates every 2 hours)
Privacy & Security:
- No trading API keys stored (non-custodial)
- Read-only market data
- 2FA/MFA support
- GDPR compliant
What's Next:
- Mobile app (React Native)
- More exchanges (Kraken, Coinbase)
- Advanced AI models (custom-trained on crypto data)
- Social trading features
Try it free: https://www.oxher.com
Open to feedback and questions! What features would you find useful?

value: Show HN: OXH AI – Open-Source AI Crypto Signal Platform with Real-Time Analysis

author: oxhai


value: kindred

value:
i hn, i'm kindred - a music producer and software engineer working on sonura studio (https://sonurastudio.com), a browser-based ai daw that helps musicians, producers, and sound designers create and share music fast. demo: https://youtu.be/CJvddq1zr8g
traditional daws are powerful but slow to start, and creative flow often dies between idea and execution.
a few points of friction for me have been:
spending years learning music production & sound design
flipping through hundreds of generic samples used by thousands of other producers
exporting/grouping dozens of stems for mixing/remixing
iterating on ideas without losing flow
sonura simplifies this. you can:
compose full tracks clip by clip
generate unique stems and vocals to use in your projects
remix and share with others
quickly iterate on ideas in any genre
i'd love your feedback! try the studio and tell me what breaks, and what you’d change or add if you could snap your fingers and have it happen.

value: Show HN: Sonura Studio: AI music production built for collaboration

author: kindred


value: alexboden

value: Show HN: Monitor on Your Chess Addiction

author: alexboden


value: aqrashik

value: Show HN: PDF to Text Without OCR

author: aqrashik

title: Show HN: PDF to Text Without OCR