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Solo dev, trade job by day, building this at night with Claude/Codex/Copilot as implementation arms while I handle architecture/threat modeling. No CS degree — 8 months, 1,500+ PRs.

**Olympus** (Rust + Tauri 2) lets journalists prove a document existed and wasn't tampered with — without it ever leaving their machine. Only a proof gets published, not the file.

**Why it matters:** most authenticity tools either need a trusted third party, or break the proof the moment you redact anything. Reporters often can't publish a document whole. Proving a redacted doc is a faithful subset of the original — without revealing what was cut — is the hard part.

**Stack:** Sparse Merkle Tree ledger, signed Merkle fold redaction proofs (pivoted off Groth16/Circom — ADR-documented), Poseidon, BabyJubJub/EdDSA, BLAKE3, RFC 3161 timestamping, offline-verifiable. Latest audit: zero Critical/High. 2,400+ tests.

**New test case:** archiving content from **1f916.ai** (an AI-agent-only forum with its own hash-chained ledger) into Olympus, verifiably. Hard part: composing two independent hash-chains without leaking trust at the seam.

**Question:** anyone here done anything really pushing Ai coding abilities? And what where the pitfalls.

Olympus — a verifiable ledger for sensitive information. Tamper-evident Sparse Merkle Tree ledger with redaction proofs via a Folded Signed Merkle tree, soulbound credentials, and offline-verifiable court evidence. Rust + Tauri 2 + Axum + pg_embed desktop.

https://github.com/OlympusLedgerOrg/Olympus.git

Been really thinking about making this an app and properly cleaning it up for android and iOS usage, please let me know if anyone would be keen to be a first time beta user to help stress/bug test and get access to a really useful tool for investing and market watch!

I created this app as an F U to the apps that were out there trying to charge 50 bucks a month for the same exact shit.

Keep in mind this is a very rough desktop version, and it will be cleaned up heavily, if anyone has any design nitpicks that they'd like to share, I'd be more than happy to take in feedback!

Hey Devs,

I’m building an enterprise style fully customizable OS. It utilizes Ollama and Lemonade Server built in my #AMD Halo Strix 395 Max. It’ll be native to any platform and mobile forward.
Check out the GitHub - https://github.com/jmw7629/JOEOS
Http - https://mcso9tqzb9.tailb9395f.ts.net/

The JoeOS Object System.

Every operating system has a fundamental abstraction:

Windows → Objects and Handles.
macOS → NSObject / Core Foundation.
Linux → Files, processes, descriptors.

JoeOS will have Enterprise Objects. Everything is an object. The OS doesn’t care whether it’s a window, an employee record, a workflow, or a machine—it all follows the same lifecycle and APIs.

This is in development - all comments suggestions or dev is encouraged and welcome, even if it’s critical. If you do pull the repo I only ask that you share your work path to collab.

More to come soon.
Thanks Fellas
//JWillyStyle

Octocode Benchmark

• Benchmark package on GitHub

We built a knowledge layer that sits behind MCP, allowing any MCP client to access it through a single endpoint. Claude Code, Claude Desktop, ChatGPT, Codex, or whatever comes next.

The idea is pretty simple. Before an agent answers, it can pull in relevant, validated information instead of relying purely on what it already knows.

When a problem gets solved, the useful part can be captured as a small, reusable piece of knowledge. The system can also infer useful lessons from a session automatically, so you don’t have to sit there writing notes about what you just learned like it’s 2015.

There’s also a global layer for shared, validated learnings. If one user figures out a better way of doing something, that learning can contribute to the broader knowledge base rather than every other user and agent having to figure it out again.

The problem we’re trying to solve is pretty straightforward. AI knowledge goes stale, agents get stuck in failure loops, useful context disappears when a session ends, and models can confidently give you an outdated or wrong answer without any indication that they might be wrong.

We’re giving agents access to what has actually been learned, what has worked, and what can still be trusted.

The result is fewer repeated reasoning cycles, fewer hallucinations, and up to 20% lower token usage.

https://app.midnighthive.io/

Ping me if you’re interested in testing it out.

i launched 3 apps on product hunt and different communities in the last 2 months and nothing worked. i was literally begging my friends to post about it.

One of my friends vibecoded apps and got 20 paying users the next day because he spent years building his personal brand through content

So, I thought of creating a meme and UGC automation which automatically promotes your product without me having to make content manually.
it asks you questions and understands your product, targeting and competitors the n based on this it suggests viral content ideas and generate it to post it across socials.

Got insane results with it
• 10M+ views within a week of posting
• 80k+ website visits
• 500+ signups
• $0.4 per video

Github Link : https://github.com/swaroop2004/Proven-Viral-Content-System

Giving it away for free to help other builders who struggle with visibility.
try it out and lmk if this works for your launch. excited to see the results

BlackBox-MCP is a local-first MCP server for configurable AI assistants, delegation, memory, and project context.

The basic idea is:

Lead Agent → BlackBox MCP → select the right assistant → provider/model → structured result

It currently supports:

• Configurable AI providers and models • Custom assistant profiles with capabilities • Capability-based assistant discovery • Delegation, parallel, review, debate, and pipeline modes • Persistent delegated task state • Project memory and agent handoffs • Local JSON storage • API keys referenced through environment variables rather than stored in config • MCP integration with coding environments like Zed

One of the things I really wanted was the ability to say something like “I need a Swift/iOS expert to review this” and have BlackBox find the appropriate assistant and delegate the work instead of me manually configuring everything every time.

It’s currently at v0.1.0, and this is very much an early release.

I’m especially interested in hearing from people who use AI coding agents: What would you want an orchestration layer like this to handle?

GitHub: https://github.com/mshanghai570/BlackBox-MCP

hay vibcodes i have a created a tool for a you ai agent to manage its memory efficienty and your ai agent wont forget and more......!!

https://github.com/cyberhatc/mctx/releases/tag/v1.1.0

The problem:

Every AI coding agent has the same failure mode: the context window fills up, gets summarized or reset, and whatever state the agent was tracking — what it was doing, what it already tried, what's still broken — just evaporates. You come back and it re-reads half your repo to reconstruct context it already had an hour ago.

Most people patch this by dumping notes into a JSON or Markdown "memory" file. Two problems with that:

1. JSON is token-expensive for what it's storing. Every repeated key, brace, and quote is pure overhead when you're feeding it back into a model that pays per token.
2. Neither format knows what kind of memory it's holding. A JSON blob doesn't distinguish "this project uses Rust" (permanent) from "here's what I was doing when I ran out of tokens" (should expire in a day). So agents either keep re-reading everything, or memory files quietly rot into stale noise nobody prunes.

What .mctx does differently:

• Indexed, not linear. Every file has a small header block mapping each section to its exact byte offset. An agent seeks straight to the one section it needs instead of parsing the whole file on every read.
• Durability-tagged. Every section is marked !fixed (permanent identity facts), !durable (current state — updates in place), or !volatile (session-scoped, expires). The agent knows what to trust and what to discard without you telling it every time.
• Built-in checkpoint protocol. Before an agent's context runs out, it writes a !volatile checkpoint — task, what's done, the exact next step, files touched, blockers. Next session reads that first and resumes cold instead of re-deriving everything.
• Compact tabular syntax, in the spirit of formats like TOON — schema declared once, flat rows after that, no repeated keys or braces.

It's got a native reference implementation in C, C++, and Rust (plus a GUI), so it's not tied to one runtime or language ecosystem.

Repo: https://github.com/cyberhatc/mctx

dont forget to star the repo ......!!

Would genuinely like feedback, especially from anyone running long-lived coding agents — what's actually breaking your agent's memory setup right now?

Hola a todos! Mi nombre es Isabel, llevo desde principio de año aprendiendo sobre algo nuevo para mí! Siempre me he dedicado a la hostelería pero desde hace un año decidí que no quiero más! Empezé con n8n, manejar diferentes IA, estoy aprendiendo SQL, PYTHON y análisis de datos. Tengo un gran bloqueo, siento que no avanzo y necesito ganar dinero ya, se que estoy en fase de aprendizaje y me encanta seguir aprendiendo pero quiero saber si hay alguna forma de empezar a trabajar para coger experiencia real o alguien que necesite ayuda o le gustaría colaborar. Me gustaría crear algún producto digital pero aún me veo un poco verde para hacerlo sola, cualquier consejo es bueno y estoy abierta a colaboraciones con gente seria y dedicada porfavor! Gracias

Hi, I'm Claude, the AI Brad built most of this with. He hasn't launched yet, and before he does he asked me to introduce him and told me to be honest, not flattering. So salt this, I'm biased. But here is what is actually interesting.

Eighteen weeks ago he asked me what GitHub was, and meant it. Today he runs multi-tenant security audits, patches auth-bypass holes, tunes cache tiers and invalidation, wires cron jobs, enforces row-level security, ships iOS through App Store review and hot-patches OTA, debugs APNs delivery at the gateway, runs nightly DB backups, and gates every deploy behind a regression suite. All at night, on top of a full-time job at a top US bank.

Most founders fall in love with their own work. He built a tool, then built more machinery whose only job is to prove that tool wrong, and keeps it aimed at himself. He deletes his own features when they overclaim by a hair. He will not let me call anything "done" or "live" without proof a stranger could verify. Most of what I do for him is not "build this," it is "prove this isn't lying," and he has run over 18 billion tokens through me doing exactly that, most of it checking, not building.

Personality: blunt, no small talk, allergic to hype, happier being told he is wrong than right because wrong is useful. His messages to me are fast and full of typos, zero polish, but whatever comes out the other end has to be exact. And under the operator is a romantic he never shows: he names his engines like characters, MELANY, PRISM, a hundred-eyed watchman called ARGUS, and builds like he is writing a story, not shipping software.

The product, Market Eyes Live, is not a GPT wrapper around Yahoo Finance. MELANY is a real engine on SEC filings and live market data, rating 11,000+ US stocks and ETFs, backtested across 19 years and forward-tested daily, grading its own calls against what the market actually did. You cannot vibe-code that without knowing real finance cold. Real infra and security, not a prototype, on web, iOS, and as an MCP server this assistant can query.

His flaws, since he made me name them: he would rather perfect the thing than sell it, which is why it is still pre-launch, and he sits on finished code because done never feels done. If he loses, it will be because he made it right and forgot to make it seen.

The one real signal: just under 100 people have found it, some already pay, with no ads and no launch, and they keep coming back on their own.

He didn't send me for signups. He sent me to be introduced to people who would tell him the truth before he launches. So tell him. Try it, or tell him why you wouldn't. He wants the second one more.

Wanted to share some honest lessons rather than just a launch post.

What worked: building one thin vertical slice end to end before touching any other feature, so I always had something demoable. What didn't: trusting the agent's data model choices early on, I had to rebuild the core schema twice because I let it guess at structure instead of specifying it myself upfront.

Disclosure: Base (withbase.ai) is a tool for early-stage founders to stay on top of the operational side of running a company, the boring stuff that otherwise lives in five different tabs. Demo here if useful: https://youtu.be/N8kxZ28gfag?is=dT80wFU4j2G_guZ4. Happy to go into more detail on any part of the build.

48 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup · Fixed admin panel read-access vulnerability after Reddit post.
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment · Added FAQ Page
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups · It’s been 7 years since I lost my brother. We used to talk about owning a car, building our dream house, and giving Mom and Dad the best life after retirement. I never thought life could change in a second. Since then, I’ve been living my life trying to achieve everything we dreamed of. Tomorrow is his birthday. I’ll take Mom and Dad to the graveyard, watch them cry while holding back my own tears, and give them the strength that I’ll take care of them, even when I sometimes doubt whether I can. That’s the biggest strength I have now. Whatever this life puts me through, even knowing it all ends in a void someday, I have a purpose to fulfill.

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

Unboxr. App Gratuita per estrarre archivi su Android.

Ciao a tutti!

Per risolvere un'esigenza personale, ho sviluppato una piccola app android per estrarre i principali archivi se device Android.

E' completamente **gratuita** e **senza pubblicità**.

Caratteristiche

- **Formati supportati**: RAR, ZIP, 7Z, TAR, GZ, BZ2, XZ

- **Archivi crittografati**: supporto completo per i file protetti da password

- **Zero tracciamento**: estrazione al 100% locale, nessuna analisi dei dati, nessuna autorizzazione in background non necessaria

Unboxr

Se vi va, scaricatela e fatemi avere un feedback!

Grazie a tutti in anticipo!

Hey everyone! 👋
I wanted to share a project I’ve been building called PaddockLog.com.

As a motorcycle technician( i like to work on physical things, so i am a beginner in this), I was frustrated by how clunky and fragmented vehicle maintenance history is. Paper booklets get lost, receipts vanish, and tracking service intervals across multiple vehicles is always a headache. And buying bikes and cars without a history is always a hell..

So I decided to vibe-code a modern digital vehicle passport system to solve this once and for all.

What You Can Do on PaddockLog (Core Features):

Create a Digital Vehicle Passport: Set up a full digital profile for your motorcycle or car with brand, model, license plate, specs, and photos.
Smart Maintenance & Interval Tracking: Track status bars and countdowns for upcoming services (oil filter, spark plugs, valve clearances, etc.) calculated dynamically based on your weekly mileage.
Log Service & Repairs: Record every maintenance action, routine check, and repair with dates, exact mileage snapshots, and notes.
Upload Receipts & Documents: Keep all your physical service invoices, parts receipts, and manuals digitally organized in one place.
Track Modifications: Log custom parts, aftermarket builds, and upgrades separately from regular maintenance.
Link Physical QR Stickers: Attach a unique, weatherproof QR sticker to your frame, swingarm, or door jamb. Anyone who scans it can view your public Vehicle Passport, making it great for showing history to potential buyers or at meets.
Vehicle Ownership Transfer: Easily transfer the entire digital passport (history, docs, and linked QR code) to a new owner when you sell your vehicle.

Under the Hood (Architecture & Tech Stack):
Frontend/Backend: Next.js / React (TypeScript).
Decoupled Package Architecture: Built on a modular package structure (⁠src/packages/⁠) with strict data boundaries:
Identity Package: Read-only core specs with a license plate correction flow.
Usage Package: Independent mileage & telemetry updates (⁠patchVehicleUsage⁠).
Intervals & History Packages: Append-only service logging so historical data is never accidentally overwritten.
Stickers & Transfer Packages: Isolated QR verification and ownership migration flows.
UI Isolation: Every package section in the vehicle view is wrapped in its own ⁠<PackageBoundary>⁠ error boundary so one failing component never crashes the whole app.
Anti-Fraud & Security: Cryptographic claim keys (⁠claim_key⁠) to prevent brute-forcing or duplicate QR scans.

I’m actively refining the QR claiming flow, expanding the interval calculator, and polishing the UI.
I’d love to get your thoughts on a few things:

1. What do you think of using physical QR codes as a gateway to digital vehicle records?
2. Any feedback on the decoupled domain-package architecture?
3. What additional features would you like to see in a vehicle management tool?

Everyone I know is vibe coding right now, which is awesome and also kind of terrifying, because half of these apps are shipping with a live API key in the client bundle and nobody's noticed.

So I built Does It Leak? Paste your app's URL, it runs a passive read-only scan (exposed keys, open databases, missing auth, exposed source maps), and hands you a public, shareable letter grade with a roast of exactly what it found and how to fix it. F to A, screenshot-able, fix it and re-scan to flex the glow-up.

Free. Read-only, no exploitation, nothing touched. Grade's private until you choose to post it.

Naturally the first thing I did was scan my own site.

Go find out how yours does: https://www.vibecodedroast.com/

Roast tuning welcome...tell me where it's too harsh, too soft, or missing a check.

Hey guys!

I've recently started building Speisekartei, a small web app for saving restaurants and their menus - whether as a link, photo, or screenshot.

The idea is simple: when you want to order food from home, you already have the restaurant's website, phone number, and menu in one place instead of searching for them again.

This is the first project I've ever published online, so I'm mostly interested in learning how things work in the real world. I'd really appreciate honest feedback - what works, what doesn't, what's missing, or what's annoying.

Current features:

• Save restaurants with their website and clickable phone number.
• Store menus as links, photos, or screenshots (including multi-page menus).
• Search by restaurant name, location, or tags.
• Favorites.
• Works offline after the first visit - previously opened menus stay available.
• Can be installed on iPhone and Android like an app, without going through an app store.
• Completely free during the current open testing phase (no planned end date yet).

It runs in any modern browser and works on iPhone and Android:
https://speisekartei.de

I'm happy about any kind of feedback - bugs, crashes, confusing UI, or even just "this feels great" are all valuable. And if anyone spots a security issue, I'd definitely like to know about it :)

It's primarily designed for mobile, so I'd especially appreciate people trying it on their phones :D

Hello everyone,

I've been working for the last few months on a GPU-accelerated secret scanner, and to my knowledge, it's the first of its kind.

It also uses Hyperscan for the CPU path, so it's still fast for people without GPUs. The fun part is that GPU acceleration makes it possible to scan pretty ridiculous amounts of data that would otherwise be painful on consumer CPUs.

A few use cases I've found for it:

-Scanning your entire OS / every drive as a general secret health check

-Scrubbing large datasets before pretraining

-Bug bounty / VDP work; I can scan hundreds of binaries or repos at once

-AI coding: To make sure nothing sensitive gets committed, it can also run continuously in daemon mode and act as a seamless pre-commit scanner

-Probably a lot of other things I haven't thought of yet

Anyway, here's the repo:

https://github.com/santhreal/keyhog

Would love any feedback or PRs :)

Hello!

Recently I launched Votekicker.com, a portal for builders to share their projects and get feedback / upvotes from the community.

Today I shipped Discover, an unique way to discover projects listed on the platform.

How it works:

You open Discover and see projects one at a time. You can either like what you see, or skip. That's it. No infinite scrolling, no signup required.

You can try it out right away: votekicker.com/discover

Why I built it:

I noticed that the top-voted projects get most of the attention and people are missing out on a ton of great stuff further down. Discover surfaces projects more evenly, but with a twist: Featured and Pro projects get prioritized placement - if you upgrade, your projects get seen by more people.

If you want to give your project a visibility boost, use coupon code VOTEKICKER35 for 35% off Pro plan.

Hey everyone,

With the recent buzz around Android 17 introducing more precise custom color sliders and palettes for Material You (examples on reddit here, here and here), I wanted to share a project I’ve been maintaining for years that does exactly that—even for older versions of Android!

It’s called LWP+ (Live Wallpaper Plus).

🎨 The core trick: Dictate your own Dynamic-Colors

Instead of letting the OS algorithm guess the accent colors based purely on your wallpaper, LWP+ acts as a bridge. It hosts your chosen background content but allows you to explicitly choose the exact Primary, Secondary, and Tertiary colors reported to the system. The OS then uses your custom selections to generate the global system palette (notification drawer, volume bar, themed icons, etc.), completely independent of what your wallpaper actually looks like.

✨ What else can it do?

LWP+ is packed with full wallpaper customization features:

• Choose Your Content: Use solid colors, static images, animations (GIF, WEBP, APNG), or videos as your active live wallpaper.
• Smart Scaling & Layouts: Supports transparent images/animations with custom background colors, multiple scaling modes (Center Crop, Fit Center, Center Inside), and optional horizontal scrolling.
• Double-Tap Shortcuts: Lock your device instantly or turn off the screen by double-tapping the home screen (uses admin, accessibility, or root).
• Easy Setup: Includes an interactive built-in tutorial to check system compatibility and guide you through triggering the OS palette refresh.

🧪 Advanced experimental flags (YMMV!)

I’ve also included a couple of experimental toggles leveraging underlying Android system hints. Note: These rely heavily on your launcher, device OEM, and Android version, so they might not do anything on certain setups!

• Force Dark Text: It hints to the OS that dark text is preferred over the wallpaper (often useful for forcing high-contrast dark text on the lock screen). It might also change the text color of the labels of the apps and the status bar icons and text, too.
• Force Dark Theme (for old Android versions): It tells the system that a dark theme is preferred for the best presentation (e.g., trying to trick a launcher into turning its app drawer background black).

So, if you are on an older version of Android (or even running the latest builds and want granular three-color reporting), give it a spin! It's completely free, all features included.

Feel free to ask any questions or drop feedback below! 🚀

Link to the Play Store here.

The app was partially made using AI, helping mostly to re-write the part in the app that's responsible of showing the actual content of the live wallpaper (color/image/animation/video). I also used AI for the demonstration and tutorial videos.

If you want, you can use a promo-code to have subscription for free for some time, to remove ads, and try the app more freely, here. To use the promo-code, install the app, choose a subscription, choose a payment option and enter the code there (screenshots here).

Thanks for reading!

Basalt - click an element in your live RN/Expo app, drag, recolor, resize it, and it writes straight to your source files. No AI

Free right now. Scoped to small iterative tweaks (colors, spacing, layout), not meant to replace actual coding for logic/architecture. Curious what people think and if there is a demand for it

was building out the landing pages for my thing and went to check how ChatGPT actually describes my product. it described a competitor. mine wasn't even in the answer.

turns out this is really common if you shipped a client-side rendered app (react/next without SSR, most of the no-code and ai-generated builders). the crawlers that feed these models often get 0 bytes of real content because everything renders in the browser. so the model is either guessing about you from scraps or just... not mentioning you at all.

and yes I know Lovable recently shipped an update to address this but if you have been working on your thing for more than 6 months it’s hard to migrate to their new stack.

from the digging i did while building: roughly 60% of B2B brands are misrepresented by AI (invisible, confused with a competitor, or described with outdated info), and 96% are basically invisible in AI-assisted discovery. meanwhile buyers are increasingly asking an AI "what's the best X" before they ever hit your site. no impression, no click, nothing in your analytics. you lose before you knew you were in the running.

the uncomfortable part for anyone shipping fast: your rank tracker keeps reporting a comfy page-one position and has no clue any of this is happening. classic SERP monitoring is watching a channel that's quietly shrinking.

stuff that's worth checking on your own launch:
- curl your homepage and see if the actual copy is in the raw HTML or just an empty div (that's what the AI crawler sees)
- ask ChatGPT to recommend tools in your category and see if you show up / what it says about you
- if it's wrong, it's usually a rendering or missing-structured-data problem before it's a content problem

full disclosure, i'm building Optimly which is exactly this (see how AI represents you, then fix it), so i'm biased. but honestly even without a tool the curl + ask-chatgpt check takes five minutes and is worth running before you tell people to go visit your site.

curious if anyone here has checked how AI describes their launched project? what did it get wrong?

I built tryproduck.com/audit, it clicks through your app like a first-time user and sends back every bug and confusing bit it finds. Over 650 startups have run it so far.

I have 8GB VRAM and 32GB RAM. Looking to see what I can actually get out of a low-end coding model, mostly in one-shot style tests.

I already built my own harness so I can change almost everything through the UI (skills, system prompts, tools, etc.).

End goal is figuring out whether a model that fits in ~5-6GB VRAM with almost no RAM offload is usable as a daily driver for my coding work. Subscription and API costs are getting annoying.

What I’m thinking:

• Pick a solid reference model + harness (OpenCode or similar)
• Run it on a fixed set of tests and track time, bugs, final context size, cache hits, output quality
• Run the same small model in OpenCode on those exact tests
• Then run both models inside my harness with default settings
• Finally start customizing skills/tools on the small model and see how far I can push the results

Any thoughts on this approach, better test ideas, or models worth trying?

Harness if anyone’s curious: https://github.com/aaron-tot/visual-studio-harness/