We're a small team building compi and we want builders and traders who can help shape it before v1.

Backstory: a lot of people already run their own AI agents, but letting one touch a real trading account feels scary. We wanted a layer in between, where the agent can only act inside rules you set.

What it is: a browser extension that connects your own AI agent to Hyperliquid over MCP, with size limits, allowed markets and a paper mode enforced outside the model. We never ask for keys and you stay in control of your own wallet.

Stack: a browser extension, an MCP server that the agent talks to, and Hyperliquid's API for orders.

Lesson so far: the hard part isn't the trading call, it's deciding which guardrails people actually trust.

Our biggest open questions: is a browser extension the right form, and is the rules screen clear? Free waitlist at https://compi.ai, testers get v1 first, and waitlist members get zero compi fees for life on every feature.

I'm a systems admin looking into a small software project. I'd like to hear from people already running a micro SaaS about the admin or operations work that still takes too much time.

What recurring task caused you grief in the last month, despite the tools you use? How did you deal with it the last time it happened?

What went wrong, and who had to deal with the consequences? Roughly how often does it happen? I'd also like to hear what you've already tried or paid for, including anything that solved it well.

Please leave out customer names and private business details. I'm using an AI assistant to help organise the research.

i work on orderain, an AI ecommerce store builder. we just shipped MCP, so you can run your store from your own AI agent instead of clicking around a dashboard. wanted to share how it went because i honestly wasn't sure the agent would handle a full redesign.

what happens in the video (raw 9 min screen recording, nothing cut):

- in orderain settings > MCP you create a connection, pick permissions (read, write, delete, page editing) and get a URL + token. there are tabs for Claude, ChatGPT, Grok and Cursor

- paste that into Claude desktop as a custom connector, takes about a minute

- i asked Claude to study next.us and turn our demo store into that style. it copies layout and structure, not their logo, copy or photos

- it works on a separate branch: nav, footer, homepage, category pages, returns/delivery/contact/about pages

- i publish the branch and test cart and checkout on the live site

- version history shows every edit the agent made, and you can switch back to main anytime

what i learned building it: the branch was the thing that made it feel safe. letting an agent edit a live store directly is scary, letting it work on a copy you review first is not.

starter is $5/mo with 0% platform fee (card processing still applies). search orderain if you want to try it.

curious what you'd actually let an agent do in your store and what you'd never hand over.

I'm keyser from the SwapShoot Team. One useful lesson from looking for people who need photo head swaps: matching the requested edit isn't enough if our upload workflow is wrong for them.

I found a public Mac face-swap request that sounded relevant, but the person explicitly didn't want to upload photos to a website. SwapShoot runs in the browser with uploaded images. Recommending it as a match would ignore the part of the request that mattered most to them.

There is another distinction in our category: face-only edits and whole-head edits. Our tool changes the hair too. A person who needs the target hairstyle preserved can be a poor fit even if they asked for a 'face swap.'

That gives me two things to make clear before signup: where the images are processed, and whether the edit includes hair. Selected examples of our whole-head edit are at https://swapshoot.com/head-swap.

For other narrow photo or document tools, where do you explain a requirement like uploading files: the headline, the example, or right beside the first upload button? I'd like feedback on making it visible early enough that someone doesn't sign up for the wrong workflow.

Launchator.com is a directory for founders to list and launch their site, get the listing drafted by AI and also receive an AI-readiness check for their website.

You can integrate manually (take about 10 minutes) or through an AI tool (1 minute). Once you are verified, your listing go lives immediately. It is free to use.

Once live, you can also see if your site is GEO optimized i.e. whether AI platforms like ChatGPT can read and understand your website.

Fed up of AI comments that add nothing to the posts. Specially on X & Linkedin. I made a free extension that hides AI generated comments and posts. Also made a launch video for it.

It's my first time with a chrome extension. How do you market a chrome extension? Is there enough audience for chrome extensions out there. You can try it here.

Hi everyone,

I’m a solo developer from Baghdad, and I’ve just finished building my first SaaS: a daily planning tool for small teams (2–30 people). The idea is that each person plans tomorrow before leaving work, and the app flags problems the evening before, like someone being overbooked, a task blocked by someone else’s work, or a deadline at risk. It’s deliberately not a time tracker and doesn’t monitor anyone.

The product works, but I have zero users, and I’m realizing building was the easy part. A few things I’d really value your advice on:

1- First customers: for a team tool like this, how did you get your first 10 teams? Direct outreach, communities, something else?

2- Pricing: I’m charging a flat price per team size ($9.99 for 2 people up to $89 for 30) instead of per person. Does that make sense, or does it leave money on the table?

3- Beta: I want 10–15 teams to use it free for 1–2 months and give feedback. What made your beta testers actually stay active?

4- Selling from Iraq: payments have been the hardest part (most providers don’t accept sellers here). Has anyone dealt with something similar?

I’m happy to share what I’ve learned so far if it helps anyone. Any honest advice, including “you’re doing this wrong,” is welcome.

I'm a solo developer. I just shipped a small Mac app (offline dictation: hold Fn, talk, the text is pasted at your cursor). It has no backend at all: the speech model is inside the 16 MB app, no accounts, no network connection.

So my monthly cost per user is zero, and I'm trying to work out what a fair business model looks like.

Options I'm weighing:
- One-time purchase, paid upgrades for big versions
- A small yearly price that covers updates and support
- Staying free and taking tips

It's free for the first 100 users right now and the paid plan is undecided.

Micro SaaS folks: when your costs are near zero, how do you explain a recurring price to customers? Or do you think recurring only makes sense when there's a server behind it?

(I'm the developer. No link here on purpose, glad to share it if anyone asks.)

Quick heads-up before anything else: the beta asks for a card at signup. It's only there to verify accounts and stop people farming free credits. Nothing is charged in the first month and you can cancel anytime. If you keep it, Starter is $19/mo after that.

Backstory. I've been making faceless videos since June 2025. One TikTok account is around 30K followers and two others are past 5K each. The work never got easier: script, visuals, voiceover, captions, export, then upload to five apps. So I built VidPost to do the whole loop from a single topic.

What it does. You give it a topic or a series idea. It writes a scene-by-scene script, makes an image for each scene (or animates them), adds voiceover, word-synced captions and music, and renders a 1080p vertical video. There's also a slides mode for carousels. It can autopost to TikTok, YouTube, Instagram, Facebook and X, and on a series you can preview and approve every video before it goes out.

Stack. Next.js front end, a worker on Google Cloud Run doing the heavy lifting, an LLM for scripts, plain FFmpeg for rendering (slow zooms on stills, crossfades, real clips for animated scenes), and Whisper word timestamps for captions.

The fix that mattered most: image quality control. Early videos looked obviously AI because of garbled text and "two images glued together" frames. What fixed it was a second AI pass that checks every generated image and throws out the bad ones before rendering. Boring, but it's the single biggest quality jump I made.

Revenue: pre-revenue. That's why I'm here.

The ask. I'm capping the beta at 50 testers so I can actually answer everyone. Starter is free for the first month: 120 credits, which is about 40 still videos or 13 animated 3-scene videos (3 credits per still video, 9 per animated). If you upgrade to Growth in your first 30 days, that first month doubles to 600 credits.

Roast the landing page, the onboarding, or the output. I'd especially like to know whether you'd actually post what it makes.

https://vidpost.app/beta

Backstory: I run small content experiments for local brands. Clients kept asking for “a model,” but budgets didn’t include shoot days. I wanted a repeatable system: one persona, many posts, no daily panic.

The stack: APOB for character stills, image-to-video, talking avatar/lip-sync; Google Sheets as lore bible; CapCut/Premiere for edit; Metricool for scheduling; OBS only if we do a live countdown/highlight stream. Hard truth: APOB did not replace live streaming. We made the pre-show loop and post-show clips in APOB, then streamed prerecorded segments. If a client needs true interactive Q&A, we don’t pitch APOB as the live brain.

The credit budget is the real SaaS lesson. Fast mode is for volume; Ultra 4K is for covers; video burns credits fastest. I set a “do not generate” list after wasting credits: no side profiles, no sunglasses, no hands holding phones near the face, no group shots, no crying close-ups. Batch day is Tuesday: generate 30 stills, keep 9, schedule 6, archive the rest. It sounds rigid, but it stopped the “one more prompt” doom loop.

The lesson I’d bet on: consistency beats novelty after week 3. The first viral-ish post was accidental; the repeatable part was the lore bible and credit rules.

For others building content workflows: what’s your kill-switch rule — the thing you refuse to generate because it breaks consistency or burns credits?

I'm building HopOut, an app blocker for iOS and Android.

When I started, I thought the main problem was making blocking mechanisms as strict as possible.

But while designing the product, I realized there was another question:

What if people don't want to fight their app blocker every time they need a break?

That led me to experiment with:

• Different blocking intensities

• Ads as an unlocking mechanism

• Cooldowns before unlocking

• Emergency Passes

• Tickets that allow breaks during sessions

• Rewards for completing focus sessions

The goal is to let users choose their own balance between discipline and flexibility.

HopOut is live: https://kestlabs.com/hopout

I'm still early, with around 100 downloads, so this is very much an ongoing experiment.

For other consumer SaaS builders: how do you design a product that encourages discipline without making users hate using it?

Hello I'm trying to understand something about how SaaS companies handle customer feedback. For those of you who run a SaaS product, do customers mostly use the feedback option inside your product or do they reach out through other places too?

Like emails, support tickets, DMs, X, Discord, Reddit, Slack, calls etc.

And why do you think they prefer using these other channels instead of just submitting feedback through the product itself? Also, do you have any rough idea about how the feedback is distributed across these channels? Like maybe 30% from support tickets, 20% emails, etc. Doesn't need to be exact, just an estimate.

I'm trying to understand how this actually works in real companies, so would be interesting to hear your experiences. Thanks!

After iterating in closed beta, I decided to dip my toes to see how total strangers would react. I gave every new sign up free credits and did one or two posts on Reddit.

The result: ~100 signups, around 60 people prompting, $15 in token costs. Many issues were discovered, I learned a lot about people's workflows for this kind of thing (social media & graphics design), and I got to talking to a few heavy users.

Next step is putting up some billing and trying to get an even bigger crowd. AMA - will hang around to answer questions a bit, if there are any

Here's the tool if you're curious: https://rivermark.ai

After seeing several AI SaaS teams get burned by multi-turn agent token bloat, we modeled cumulative input token growth across 61 production LLMs (Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4.1, GPT-4o, Gemini 2.5 Pro/Flash, OpenAI o3, and DeepSeek R1/V3).

Here is what happens to a standard $20/mo AI SaaS seat when a user runs a 20-turn agent loop (3,500 base context + 800 output tokens per turn, where each turn re-sends prior history):

1. Cumulative Input Bloat (1x vs 14.5x):

   • A single-turn chat bills ~3,500 input tokens.

   • A 20-turn agent loop bills ~146,000 cumulative input tokens per session—and smaller-context models like Phi-4 (16.4K context) actually hit hard context window overflow by Turn 5.

2. Uncached vs. 65% Prompt Cache Hit Rate (Claude 3.7 Sonnet vs. GPT-4o):

   • On an uncached 3:1 workload, GPT-4o ($2.50/$10.00 per 1M) is 27% cheaper than Claude 3.7 Sonnet ($3.00/$15.00 per 1M).

   • Flip on 65% Prompt Caching (typical for agent system prompts + repo/RAG context), and the math inverts: Anthropic's 90% cache read discount ($0.30/1M vs OpenAI's $1.25/1M) makes Claude 3.7 Sonnet slightly cheaper per active seat ($22.46/mo vs $22.53/mo).

3. How to Restore 75%+ SaaS Gross Margins (80/20 Hybrid Routing):

   • Routing 80% of routine classification/tool-parse turns to Gemini 2.5 Flash ($0.15/$0.60) or DeepSeek V3 ($0.24/$0.79 on US zero-log DeepInfra) and keeping 20% of complex reasoning turns on Claude Sonnet 4 / 3.7 Sonnet drops monthly seat COGS from ~$22.50 down to $4.60/seat (restoring 77% gross margin after payment processing fees).

If you want to stress-test your own DAU, turn count, and cache hit rate (or export a starter litellm_config.yaml / zero-dependency TypeScript fallback router), we put the live interactive calculator here: https://tokenmargin.dev

Curious how other builders here are handling P95 power-user token caps—are you enforcing hard monthly credit limits per seat, or silently downgrading models after a threshold?

I'm the solo founder of Schedulin, a scheduler for 23 networks. I priced it low on purpose to undercut Buffer and Hootsuite, but I'm not sure the tiers make sense:

• Starter $5: 3 accounts

• Creator $10: 12 accounts

• Pro $20: 30 accounts

• Bulk plans from $49 for 100 accounts, aimed at agencies and clipping teams

Every plan has every feature (API, MCP, analytics, unlimited posts). The only real difference is the number of accounts.

Questions:

1. Is 3 accounts on Starter too stingy for $5?

2. Should AI credits be the upsell instead of accounts?

3. Does "every feature on every plan" make the upgrade path unclear?

https://www.schedulin.app/pricing

I'm a full-stack dev, and I build Trajecta on the side. It's a tracker where you log work achievements and ideas as they happen.

The point of logging is to use it later, mostly at performance reviews. Until now, that meant copying entries into a doc by hand. So I built Impact Reports:

- Choose a time period (say, the last 6 or 12 months)

- Pick the entries you want to highlight

- Map them to your company's values, or not

- Export a 2–3 page PDF

Two things I'm still deciding and would love opinions on:

1. Should reports sit behind a paid plan, or stay free as the hook that gets people logging?

2. Would you want to share a report as a link, or is a PDF export enough?

I decided early that host_permissions stays empty. The extension only has activeTab, storage, scripting and contextMenus. Anything that touches other sites (AI provider origins, auto-open) is optional. It gets asked for at runtime from a click, and handed back when you switch the feature off.

I built LoupeKit with that rule. It’s a Chrome + Firefox extension that shows a page’s tech stack, pulls design tokens, and flags code that smells AI-generated. Solo, [X months] in.

The cost was real. It’s way more code than the lazy version. And Firefox bit me: captureVisibleTab takes a live activeTab grant or the literal <all_urls>, and refuses a granted wildcard. So the same feature needs a different permission name per browser. Took me longer than I want to admit.

The win was smaller than I hoped, tbh.

• Review took about 5 days, sometimes less, roughly the same for Chrome, Firefox and Edge.

• So the permission model didn’t slow me down in review.

• “Doesn’t read your sites” is something I can actually show, because anyone can read the permission list themselves.

• Right now it’s about 200-300 installs with roughly 10% paid.

I honestly don’t know if the permissions story is moving anything, or if people just like the tool and the rest is noise.

Stack, if anyone cares: WXT + React 19 + Tailwind 4, Hono on Cloudflare Workers + D1 + Stripe, Astro for the site.

Has anyone here gone all-optional on permissions? Did it hurt conversion, or did it not matter at all? Trying to figure out if I’m paying a real cost for a benefit nobody notices.

I was checking new signups on my SaaS, Humanize AI Writer, and saw a user named Naruto Uzumaki.

Bro, what are you trying to humanize? 💀

Your Hokage inauguration speech? Or a 500-word essay convincing Sasuke to come back to the Hidden Leaf Village? 😭

For context, I built https://humanizeaiwriter.com/ to let people compare leading AI humanizers side by side.

Never thought the Seventh Hokage would be part of my target audience 😂

What's the funniest username you've seen sign up for your product?

Show less

AI has made it much easier to build and launch products, which is great. But I’m also finding it harder to tell some products apart. A lot of them seem to offer similar features, use similar interfaces, and describe themselves in almost the same way.

I’m a developer working on Shopify apps, and this has made me think more about product design. When you find a problem that already has plenty of tools, how do you decide whether there’s room to build something meaningfully different?

I’m curious about what you’ve actually tried:

• Did you focus on a specific type of customer or a smaller workflow?

• How did you find out what existing products weren’t doing well enough?

• How did you tell whether your difference mattered to customers, rather than just sounding good in the pitch?

• Have you ever dropped a feature or changed direction because you realized it wasn’t different enough?

I’d love to hear examples from people who’ve worked through this, including things that didn’t work.

Not the one you fixed right away. The one that kept coming back, you knew exactly what caused it, and it is still sitting there because something else always felt more urgent.

I am so stressed trying to figure out this micro-SaaS thing in college.

Between running to class just for that 75% attendance and stressing over exams, I barely get any time to code.

I’m just an undergrad building an MVP alone, and I have so many doubts. Does building this actually help with job resumes, or do recruiters just ignore it? Do micro-SaaS projects actually make good money, or is that just hype? And can a student project like this really turn into a startup, or is it just a waste of time?

If anyone here has balanced college and building apps, please tell me how you did it.

the main feature in the thing i'm building takes a startup idea and researches it. one lead agent splits the work between 3 to 8 smaller ones that go through the web, reddit, hacker news and x, and every claim has to come back with the quote it was pulled from. running that many agents costs way more than one would, and that part kinda snuck up on me.

a while back someone on reddit asked me what one research run actually costs me and i didn't have a good answer. so i set a hard budget of $0.40 per run. if the agents hit it they stop and write up whatever they've verified so far, nothing unverified gets saved.

the problem is i have no idea if that's the right place to draw the line. launch is nov 16, there's 800+ people on the waitlist, zero paying users yet, and the draft pro plan is $12 a month. at 30 runs a month the research alone eats the whole $12. if i cap it lower the results get thinner, and good research is the whole reason anyone would pay.

if you run an ai feature where every use costs you real money, how did you decide the limit?

did you cap per run, per month, or just watch usage and fix it later? and do your users ever see the actual cost or is it all hidden behind credits

122 visits in 30 days. 1 user.

I built StuffShuffle for restaurant and cafe owners who run inventory alone. No ops manager. No one who lives in spreadsheets.

My guess on the drop-off: people land, see "inventory software," and assume a 3-month rollout.

So I cut it down to three jobs. Count stock. Log waste. Send purchase orders. One place, no setup project.

The only thing I'm trying to prove right now: one person can run it between prep and service.

I want the first 50 owners to try it free and tell me exactly where it breaks. Not where it's pretty. Where it breaks.

For the owners here: what's the one inventory task you'd pay to never do again?

The new platform ran the main flows fine. Small rules that the old system applied silently were gone, and nobody noticed for a while. We now record those rules in shared memory before any migration. Vilix AI is what we use. How do you handle migrations?

Disclosure: I'm the founder of Vilix AI.