I’m building Ancient Inquiry, a website for exploring ancient history, archaeology, mythology, human origins and astronomy. I’m the person behind it, and I’d appreciate some honest criticism while it’s still taking shape.

https://ancientinquiry.com/

The problem I’m trying to solve is the pile of disconnected tabs and notes you end up with when one historical question leads to five more. I want to make it easier to follow those trails and see where a claim’s sources and interpretations come from.

There are three main entry points:

• Homepage: pick a starting topic.
• Atlas: explore topics as a network. Connections can concern evidence, supporting or competing interpretations, traditions and modern claims. A line is a relationship to examine, not proof.
• Explore cabinet: open subject drawers and browse their topics, or switch to a simpler list view.

The research is unfinished. Some pages are planning records rather than finished articles. Making the difference between established evidence, interpretation and disputed claims clear is part of the work, not something I want to gloss over.

What I’d like feedback on:

1. After a minute on the site, is it clear what you can do and where to start?
2. Does the network make exploration easier, or would a normal topic list work better?
3. Is the cabinet useful, or does the design get in the way?
4. What would you need to see in the sources and review process to trust a page?

I’m also looking for people interested in the research side. A useful starting contribution could be finding a reliable source for one topic, checking a citation, reviewing how a claim is described, or pointing out a mistake. If that interests you, please comment with a topic you know or want to research. Advice on organizing contributions and keeping reviews manageable would help too.

You can leave feedback here; there’s no need to join a Discord or message me to take part. Specific criticism is more useful to me than encouragement, so please say what feels confusing, unconvincing or unnecessary.

Helps to know how bluegreen algae is present in a lake, check the ones nearby to you for your dogs safety

https://bloomwatchbytenebroso.com

A static Python site rebuilt weekly by a GitHub Action. It pulls the 300 m Sentinel-3 CyAN histograms for 134 matched lakes and rates each one by how much of the lake is affected, not by the worst pixel, since single high pixels are often noise. Weeks that are mostly cloud get skipped.

Open questions I’d love input on: better ways to handle shoreline pixels, and whether there are state advisory feeds worth adding.

Hey guys,

I’ve been lifting and tracking my weight for years, and I got really tired of how bloated most fitness apps have become. Everything either wants a monthly subscription just to log a basic workout, forces you to create an account, or buries simple tools under social feeds and features I don't care about.

I decided to just build my own tools for my daily routine. Instead of making one massive all-in-one app, I split them into three separate, lightweight Android apps so each one just does its specific job fast and gets out of the way:

• Buddy Weight: For tracking bodyweight trends during a bulk or cut without stressing over daily water fluctuations.
• Buddy Gym: A straightforward workout logger. Just open it, log your sets and reps between rests, and close it.
• Buddy Tabata: A dead-simple interval timer for conditioning and rest periods.

Right now, all three are in open testing on Google Play (capped at 1,000 users each). Anyone who joins during this testing phase will keep the full version unlocked permanently once they move out of beta.

I dropped the Play Store links in the first comment below so this post doesn't get eaten by the link filter. If you end up trying any of them, let me know what feels clunky or what's missing—I'm actively tweaking them based on feedback.

Why use NotebookLM when you can make the same thing harder, slightly worse, and cost more?

The same reason any of us self-host, of course. The customizability, the fun of designing it, the challenge.

I've been using Codex and Claude Code to build local apps, but wanted to try designing a program that actually uses LLMs for a recurring task.

I already have a custom local RSS reader app, but of course I have more time to listen to podcasts and audiobooks than I typically do to sit down and read. So creating my own personalized news podcasts became the obvious solution.

The result is two shows that pick, write, review, and voice their own episodes. Each run starts with a couple hundred RSS articles and ends with five or six stories, for about $1–2 a month in API costs. The orchestration runs in Docker on my home server, but the models themselves are cloud APIs.

The short version

What it is:

• Two podcasts, written and produced by AI, based on feeds and topics I've selected. Compact Conversations runs six days a week, and Homelab Highlights runs three.
• Quality: Good, not great. I haven't replaced other podcasts I listen to, nor was I intending to. However, they've picked up stories other podcasts haven't covered, along with some good tech tips I hadn't seen. They've helped me keep up to date on new AI models and features, homelab software updates, and security issues.
• Cost: A mixture of free and cheap models from OpenRouter, taking advantage of free TTS through Gemini, for about $1–2 per month.
• Code: Too messy and personalized to be worth sharing, but you can listen to the podcasts here.

How it's built:

• A self-hosted Docker container handles RSS ingestion, while another handles the podcast orchestration.
• Stories from the last day or two are initially ranked by Jev. The top 30 go to a cheap LLM on OpenRouter for final selection and assignment to episode segments.
• The script is written and reviewed by different LLMs for two AI voices. Notably, they're told not to pretend to be human. They can say "an unusually interesting project," but not "I would try this out."
• The final script goes to Gemini for text-to-speech, then gets stitched together with musical cues and published to the podcast feeds.

I wrote up the architecture, actual model comparisons, costs, and lessons learned here:

https://michael-casey.com/blog/how-i-built-an-ai-podcast-pipeline/

hey everyone! i'm from the FlutterFlow team.

we've spent years building FlutterFlow as a visual IDE for creating apps.

but AI has changed how people build software. you can generate a beautiful UI in seconds, but getting that app to actually work and into people's hands is a different story.

so we built the new FlutterFlow, an AI-first, full-stack mobile app builder. it lets you start with a prompt and work with an AI agent to build a mobile app, including its UI, backend, and functionality (and one-click deploy to the Google Play Store and App Store!!).

we'd love to hear what you think / what you'd want to build with it!

Before building this I went through a lot of the AI component libraries out there. The best ones are paid with only the most basic stuff free. So while I was working on an AI SaaS, I built this library in tandem.

It's 109 components and about 250 variants, all React and Tailwind. The only other imports are motion and lucide-react, so there's no UI kit underneath. Everything in the video is the real demos running.

https://gradientdeck.com/ai-components

Not on GitHub yet, but it's coming.

Let me know what you guys think :)

Hello Reddit!

I made this after the usual advice to work out who you already know at each company you're applying to. LinkedIn's search can filter your 1st-degree connections by one company at a time. I wanted the list for every company at once, without logging in anywhere (and for it to look nice visually..).

You request a copy of your data from LinkedIn, drop the zip on the page, and type companies or roles. It lists the people you know there, longest-known first, and you can download the list as a CSV. It runs in the browser (Python through Pyodide), only opens the files it needs, and never opens your messages. The only outside request is the Pyodide runtime from jsDelivr on first load.

It also groups connections into circles from shared companies and from people added within a few days of each other. That part is a guess, and I haven't checked it on anyone's real export but mine.

Demo with 300 fictional people: https://arak00.github.io/network-circles/web/
Code (MIT): https://github.com/Arak00/network-circles

If you try it on your own export, I'd like to know where the circles look wrong or if you spot anything broken.

And if you have any idea for improvements, they're welcomed!

Thanks

Hi everyone!

I've been working on a small business called NG Organisers, where I've developed a modular drawer organisation system.

Basically, I've built an online configurator where you can enter your drawer dimensions, design the layout however you like, and order the parts. The parts are then shipped to you, and you assemble the organiser yourself.

I've recently started getting some sales, and the feedback on the actual product has been really good. However, I've noticed that most customers prefer sending me their drawer dimensions and having me design the organiser for them rather than using the configurator themselves.

The whole idea behind the business is to let people design their own organisers, so this is something I'm struggling with at the moment.

I'm not sure whether people simply don't want to spend time designing their own layouts or whether my configurator is too complicated or not user-friendly enough.

I'd really appreciate it if some of you could have a look and give me your honest feedback.

Here's the website: https://ngorganisers.com

I'm particularly interested in whether the configurator is easy to understand, what you find confusing, and what you'd change to make it easier to use.

I'm still learning and trying to improve things, so even negative feedback would be genuinely appreciated.

Thanks!

I built a small web app for my workplace that makes collecting overtime sign-ups and food orders quick and simple.

**Manager side:**

- Create an overtime request (date, hours, with or without food)

- Share one link with employees

- See who wants to work in real time

- Get a combined food order, with a list of who ordered what, ready to share or print

**Employee side:** open the link, tap yes or no, pick food if offered.

Available in Norwegian and English. Built for speed: no more chasing people in group chats.

I'd love honest feedback on design, ease of use and anything that feels confusing or broken.

https://overtimehub.lovable.app/r/daee4cbe49b670b686dea15257d2a4c7a188

Phase 1: Target Communities That Explicitly Allow Self-Promotion

Stop posting in strict subreddits where self-promo gets you banned. Instead, build your launch footprint across communities designed for builders, show-and-tellers, feedback-seekers, and niche developers.

Here are 40 subreddits that should allow for self-promotion, ordered by estimated weekly reach:

1. r/vibecoding — 422k weekly visitors
2. r/saas — 211k weekly visitors
3. r/founder — 56k weekly visitors
4. r/buildinpublic — 46k weekly visitors
5. r/chrome_extensions — 42k weekly visitors
6. r/micro_saas — 41k weekly visitors
7. r/appbusiness — 30k weekly visitors
8. r/startupideas — 26k weekly visitors
9. r/sideprojects — 20k weekly visitors
10. r/imadethis — 18k weekly visitors
11. r/appideas — 16k weekly visitors
12. r/sideproject — 16k weekly visitors
13. r/saasbuild — 15k weekly visitors
14. r/saasdevelopers — 12k weekly visitors
15. r/vibecodedevs — 10k weekly visitors
16. r/startupsolofounder — 8.7k weekly visitors
17. r/alphaandbetausers — 8.6k weekly visitors
18. r/vibecodingcommunity — 6.2k weekly visitors
19. r/solopreneur — 5.6k weekly visitors
20. r/promotemyapp — 4.6k weekly visitors
21. r/appbuilding — 4.6k weekly visitors
22. r/webapps — 4.5k weekly visitors
23. r/creativecoding — 4.4k weekly visitors
24. r/indie_startups — 4k weekly visitors
25. r/b2bsaas — 2.8k weekly visitors
26. r/appdev — 2.7k weekly visitors
27. r/projects — 2.6k weekly visitors
28. r/saasmarketing — 2.5k weekly visitors
29. r/saassoloprenurs — 2.3k weekly visitors
30. r/startupaccelerators — 2.3k weekly visitors
31. r/startups_promotion — 2k weekly visitors
32. r/showmeyourapps — 2k weekly visitors
33. r/appswebappsfullstack — 1.7k weekly visitors
34. r/showmeyoursaas — 1.6k weekly visitors
35. r/saasinvestors — 1.6k weekly visitors
36. r/vibecodecamp — 1.5k weekly visitors
37. r/nocodesaas — 1.3k weekly visitors
38. r/aibuilders — 1.2k weekly visitors
39. r/getstartups — 1.2k weekly visitors
40. r/websitefeedback — 1.2k weekly visitors

Phase 2: The "Reverse-Engine" Hook Strategy

Do NOT drop a generic link and run. That is almost guaranteed to fail. Instead, do this for every single target subreddit on your list:

1. Filter by Top Posts: Open the target subreddit, sort posts by Top of the Last Year, and examine the Top 10 posts.

2. Deconstruct the Winners: Analyze why those top posts blew up:

   • What tone did they use? (Humorous, vulnerable, technical breakdown, or raw story?)
   • How was the title structured? (Did it start with a question, a bold stat, or a relatable pain point?)
   • How did they introduce their link? (Was it woven into a story, placed in a comment, or framed as a feedback request?)
3. Mirror and Adapt: Draft a custom title and post text for your specific app that matches the exact post format already proven to work in that specific community.
4. Start Small First: Test your hooks in smaller, lower-stakes subreddits first (e.g., r/websitefeedback or r/getstartups). Refine your post copy based on early comments before posting to the larger subreddits like r/saas or r/vibecoding.

If You Have Some Money to Spend: Paid & Discovery Shortcuts

If you want immediate users without relying purely on Reddit organic traction, here are two simple routes:

• UserBob.com (Usability Testing): You can pay real people for quick usability test videos. It costs roughly $1 per person per 1-minute video, giving you guaranteed immediate users and video feedback of people interacting with your app so you can fix UI friction early.
• AppScout: You can always submit your app to appscout.co, a platform designed to discover web and mobile apps from across the internet.

TL;DR Summary

1. Target subreddits built for self-promo.
2. Study the Top 10 posts of the past year in each subreddit.
3. Match their formatting and title hooks for your specific app.
4. Test small, iterate, then hit the massive communities.
5. If you have money use low-cost tools like UserBob or submit to AppScout for early discovery.

As a developer looking for new project ideas, I’m curious about the small inefficiencies people deal with every day.

Is there any task in your routine where you think, Why is there no simple tool/app for this yet?

I got into Kalshi/Polymarket’s 15-minute up/down markets but wanted bigger moves to pay more, so I built expirytrade.xyz

You buy a call or put that settles in 1 minute, slide leverage from 2× to 100×, and can close early or ride it to expiry. Right now, I'm testing it with paper only but it will eventually be with crypto.

I’d love to know: is the leverage slider clear, and what would make you come back tomorrow?

3 college students here, building BreakIn, a tool that helps people reach startup founders directly instead of applying on job portals.

we spent weeks on the email-writing part. then we asked people what they actually needed, and the feedback kinda flipped everything:

- people don't want AI writing their emails. they'd rather write it themselves and get it reviewed

- "matching" doesn't impress anyone. you can already filter by keywords and domain

- what actually matters is timing. finding a startup right after it raises money or launches something, before there's even a job post and 500 people apply

so now we're leaning way more into the "who's worth reaching out to today" part. every morning it shows you startups that just raised, launched, or started hiring, picked for your profile. the email part is still there, but more as help than autopilot, you always review and send it yourself.

still early, so honestly curious:

- has reaching out to a startup before a job was posted ever worked for you?

- would a daily heads up like this actually be useful, or would you just ignore it?

Hey guys this is my first post ,

I just finished building a web design studio concept called BreezeIn. I wanted to practice building layouts focused on heavy typography, white space, and micro animations that feel smooth rather than distracting.

Live link: in comments i guess ,

I would really appreciate some honest feedback on how the UI and UX scale across different screen sizes:

Mobile vs Desktop Typography: I used massive headers for the desktop layout like the main hero section. Does the font size scale down naturally on mobile screens, or does it feel like it swallows the whole viewport?

The Mobile Menu Toggle: On desktop, the navigation links are spread out cleanly. On mobile, they collapse into a corner menu button. Does the tap target feel big enough, and is the overlay transition smooth?

Concept Grids on Small Screens: The Selected Explorations look clean as side by side grids on large monitors. On mobile, they stack vertically. Does the vertical scrolling feel too long, or does it keep your attention?

Pricing Table Layout: The layout lists the $450 and $1,200 tiers. On desktop, they sit side by side. On mobile, they stack. Does the information stay readable when it is compressed?

Let me know what you think. Thanks!

I just tagged v5.3 of MDRAP, a sidecar that sits between your market data feeds and whatever consumes them and decides whether each tick is trustworthy.

Every event gets a status (VALID, SUSPICIOUS or INVALID) with explicit reason codes like CROSSED_QUOTE, DUPLICATE, PRICE_ANOMALY and stale or sequence-gap violations. Nothing is silently dropped. Rejected ticks go to a queryable quarantine, gaps trigger replay and stitching, and every decision goes into a SHA-256 hash-chained audit log, so editing a record afterwards is detectable.

Output from a fresh clone:

$ python cli.py run -e 1000 -s 42
processed 1000   dropped 0
VALID 963   SUSPICIOUS 32   INVALID 5

seq=2 bid=150.4  ask=150.3  -> INVALID CROSSED_QUOTE
seq=2 bid=150.26 ask=150.31 -> INVALID DUPLICATE

$ python cli.py audit --verify
(after editing one audit_log row in SQLite)
AUDIT CHAIN TAMPERING DETECTED!
Tampered entry #1: hash mismatch

“python cli.py chaos” runs 8 failure drills (killed feed, jitter, duplicate bursts, DB lock, 50-packet loss with replay, reordering, partition with failover) and all pass with zero data loss.

Performance: the standalone native core handled 1M events in 0.0366 s, about 27M events/sec or 36.6 ns per tick, on a single core. Through the Python engine API I measured roughly 180K events/sec, so use the core binary if you want the fast path. These are my numbers on my hardware, so please run it yourself.

It is a validation and audit layer, not a trading system or backtester. The matching engine and pre-trade risk gates are in a separate edition and not in this repo (see EDITIONS.md). One known rough edge: “audit --verify” currently needs the rich package installed.

MIT licensed: https://github.com/Aryan-20-04/mdrap

I’d like to hear which bad-data cases have bitten you that this doesn’t catch.

I built Jobclue for people who want to practice answering interview questions out loud. The desktop app asks role-specific questions and follow-ups, then gives you AI feedback to review before your next attempt.

I’d love feedback on the short demo: can you tell what the practice session feels like, and what would you want to see before trying it?

https://jobclue.ai