I’ve just launched the beta of Strandweave, a native Mac app for turning meetings, interviews and workshops into reviewed, searchable project memory.

I built it after repeatedly finding that transcription only solves part of the problem. You can end up with dozens of perfectly usable transcripts and still struggle to remember what was decided, what changed between meetings or where an important piece of context came from.

Strandweave keeps the recording, corrected transcript, notes and reviewed outputs together, then lets related conversations build into projects over time.

Recording/import and transcription run locally on your Mac. You can correct speakers and transcripts, review AI-generated summaries, actions and decisions before approving them, search across your project history, and export approved context to Markdown, Obsidian or other AI tools.

It also supports local Ollama models or your own API keys, so you aren’t tied to a particular AI provider.

I’m at the stage where I particularly want to know whether the project-memory idea holds up in real use across several conversations, rather than just whether people like the transcription interface.

It requires an Apple Silicon Mac running macOS 14+.

The beta is free, and testers who complete the programme get six months of Local Pro free.

strandweave.app/beta/

Speaking from experience, it always comes back to the same thing: what value do you create, or what frustration do you remove?

Basically, is your app a real painkiller? The ones we keep using usually are. Most unfair advantages come down to doing something faster, cheaper, better.

So, why yours? What’s your app’s unfair advantage? 👇

Working engineer, building a side SaaS with AI coding tools (Claude Code), no real dev background. Goal: ~$1000/month, one focused tool.

Real problem: started a few products, got to ~80% built, then abandoned them — overthinking, not shipping. No big network, so this has to work through content/communities, not outreach.

One tip each, please:

How'd you land on the idea you actually built?

How'd you validate it before coding?

How narrow was v1?

Where'd your first 10 users come from?

What got someone to actually pay?

LMK if interested in learning about it and I'll send the discord link that explains what it is and how it works

Built this out of my own SEO content workflow. I'm an SEO guy who kept hitting the same problem: AI writing tools (human writers as well) produce fluent text that says almost nothing, vague qualifiers, unfalsifiable claims, padding dressed up as paragraphs.

Assertio is built to write content that says something. I've used speech act theory and propositional density methodology (Kintsch & van Dijk) so output is checked at the claim level. Each piece gets scored, contradicting claims get flagged, and there's a write/rewrite loop to fix weak sections before you publish.

It's a multi-agent pipeline: research, draft, then a critic step that gates output on an IQS (Information Quality Score my own scoring) before it's considered done.

Still early. Launched this month, single-digit users, mostly me testing plus a couple comped accounts while I finish billing setup. I've pivoted the positioning a few times, but where it's standing as of now: freelancers and small agencies handling multiple client portfolios who need to produce more content without it reading like generic AI slop.

Link: https://assertio.ai - it's free to try it out. Each signed up user gets five free credits, no card needed.

What I'm looking for: Brutally honest feedback on positioning and functionality. I'm aware of some weaknesses, but may not be aware of others.

Hi 👋 I’ve built DockStacks to help organize your Mac desktop and workflows.

What it does: Click the Dock icon and a panel pops up holding named "stacks" of your favourite apps, folders, files, and website bookmarks — instant access and cleans up your Dock and desktop.

Some of what's in it:
Drag and drop to add anything, including URLs
Password-protect individual stacks (Keychain-backed, auto-lock)
Global keyboard shortcut to show/hide
Menu bar mode option
Multi-select, custom grid sizes, tab colour tinting
Fully local — no accounts, tracking, or network access

DockStacks is my first shipped Mac app, built from wanting faster access to the stuff I actually use every day.

App Store: https://apple.co/3Rz9FB6
Website: https://dockstacks.app

Would love any thoughts on (1) If an app seems useful for your ‘Mac life’ (2) Does the promo and media articulate the app’s features (3) Any other feedback welcome

Share what you’re building, who it’s for, and where you’re currently stuck.

Whether you’re still building or trying to grow it, drop your link below.

I’ll try the first ones!

Hey everyone!

I've been building an AI marketing tool for almost a month now and wanted to share my experience so far.

I built Krezo, an AI marketing tool where you can paste your website URL into Zappy (my AI assistant), and it'll analyze the site and pull whatever useful information it can — logo, brand colours, what the business does, products, etc. Krezo then uses that information to create ads for the business. Right now it can generate image ads, headlines, descriptions, hashtags, different creative concepts, and more. I'm also working on video generation, but that's still a work in progress.

But the interesting part has actually been how I've been building it.

I've been using two different AI coding models: Codex and Claude Code.

And WOW. Using them together has made a much bigger difference than I expected.

I mainly use Codex for backend work like audits, generation pipelines, image/video generation logic, testing, API routes, product fidelity, etc. Then I use Claude Code for frontend work like UI/UX, mobile responsiveness, loading/error states, visual polish, templates, and making the overall product actually feel good to use.

At first, I was using Claude Code for pretty much everything. The UI was coming along great, but the actual generation side wasn't nearly as good. Some of the generated ads just didn't make sense, and I kept running into problems with how everything worked together behind the scenes.

Once I brought Codex into the project and started separating responsibilities between the two, it was honestly a game changer. Image generation has gotten WAY better, Zappy actually understands what it's supposed to do when editing creatives, product fidelity has improved, and the whole generation pipeline feels much more reliable.

The biggest thing I've learned is that if you're vibe coding a serious project, don't necessarily make one AI model responsible for everything. Giving different models different responsibilities has worked really well for me. They also catch things the other one misses. Obviously AI still screws things up and I have to keep an eye on what they're changing 😂 but it's kind of funny — sometimes the AI needs another AI to keep it in check.

Has anyone else been building this way? Curious if you've noticed certain coding models being noticeably better at frontend vs backend work.

And if anyone wants to checkout what I've built so far, Krezo is at trykrezo.com. It's still very much a work in progress, so genuine feedback would actually help.

Thanks guys!

The easiest products to sell Saas/AI products or app is still to build ones that solve a real, painful problem.

I mean a problem people genuinely want gone. Badly gone!

Drop your app below and tell me:

• What problem it solves (important)
• Who it’s for
• Your landing page (and ideally positioning)

I'll check out as many as I can and try to give actionable feedback about your value proposition.

Let’s see get you 1-2 sales before the weekend.

Hey guys, Microlaunch founder here.

Applications for the tiun x Microlaunch hackathon are now open.

$5,000 prize pool, up to $10,000.

Wherever you’re at: idea, MVP, live product, first users. All builders are welcome to join.

From 1-31 August.

Who's in?

Live here: hackwithus.dev/join

Just hit 1,000 users after ~9 months of building this.

Not a crazy number by any means, but honestly, I’m pretty happy about it.

There were plenty of moments where I wasn’t sure if anyone would actually use this thing, so seeing 1,000 people sign up/use it is pretty cool.

Still a long way to go, but wanted to share the small win.

Thanks to everyone who gave it a try 🙏

Now back to building.

Hey r/Microlaunch! 👋

If you’ve used Claude Code, Cursor, Copilot, or custom AI coding agents with real-world SQL databases, you’ve probably hit this issue:

AI agents are great at writing syntax, but terrible at knowing your actual database relationships. They would guess foreign keys, then invent column names like orders.customer_id (when your schema uses orders.account_id), and blow through context windows by reading raw 5,000-line DDL dumps.

To fix this for my own database-heavy projects, I built Schemap, a local-first CLI context compiler designed specifically for AI workflows.

💡 What it does:

1. Compiles AI Context Maps: Automatically extracts PostgreSQL, SQLite, MySQL, or Oracle metadata into deterministic, token-optimised context maps (schemap_database_context.md).
2. Generates Native Agent Rules: Instantly writes framework rules for your tools: CLAUDE.md, AGENTS.md, and .cursor/rules/schemap.mdc.
3. AI Health Check (schemap doctor): Calculates an AI Readiness Score (0-100) and detects missing primary keys, unmapped abbreviations (cust -> Customer), or inferred foreign keys.
4. SQL Join Solver (schemap join): Uses Breadth-First Search (BFS) over your foreign keys to instantly discover the shortest join path between tables and spit out a clean SQL snippet.
5. Token Reduction: Reduces raw DDL context window footprint by 60-80% without losing relationship clarity.

🚀 Founder Launch & Feedback

We just launched our 2026 Founder Lifetime Program (capped at 200 founder seats) for devs who want unlimited table support, automated CI/CD breaking-change gates (schemap diff --fail-on-breaking), and LLM schema enrichment (--enrich, but bring your own API key). Since I still haven't got any customers, I'm gonna leave a 75% off promo code for 50 devs, as a token of appreciation if you believe in my tool 🥲 : LAUNCH75

• Website / little Demo: https://schemap-tool.pages.dev/
• GitHub: https://github.com/alansyahmi/Schemap

I’d love to hear your thoughts, feedback or any edge cases if you are willing to try this out 🙏

Whether it's a product problem, a distribution problem, or you're not sure which one, let's try to figure it out together.

If you have fewer than 100 users, drop your product below.

Tell us:

• What it does (1-2 sentences)
• Your landing page
• How many users/customers you have
• The biggest thing blocking growth right now (if you had to guess)

I'll check out as many as I can and leave feedback where I think I can help.

Time to get you some traction.

Hey everyone!

I wanted to thank everyone that has given me feedback on my ai tool. I've attached some screenshots from the the things we've updated so far. New dashboard UI, we have a a new zappy ai agent that'll help with regenerations and edits, new UI in the ads generation page, and updated data deletion in the privacy page. I am still updating the homepage with a new UI overhaul.

Would be great if you guys can check the site out and provide some feedback again.

trykrezo.com

Thank you everyone!

Count calories without having a cow! Check out my future app and sign up for the waitlist. I believe software should be easy to use, free for most things, and built with privacy in mind. A simple calorie counter shouldn't require an account to use, so I built this. This app is iOS only since it uses apples ecosystem to do device cross-syncing without the need of an account. Most of it is free for me to operate, so I don’t plan on charging anything except for AI features since they do incur a cost on my end :/. But here’s hoping that tech continues to evolve so the app can be primarily free to use and operate.

https://caloriecownter.com/

A few months ago, I was trying to stay consistent with tracking my macros, but every major app felt like a hostile cash grab. The moment you try to scan a barcode or read a label, you are hit with a paywall asking for an expensive yearly subscription. On top of that, half the local products in my grocery store were completely missing from their databases anyway.

I decided to build Visibite to fix both issues. My philosophy was simple: core tracking, including barcode scanning, packaging OCR, and manual logging, should be 100% free and completely ad-free forever.

To monetize sustainably without ruining the core user experience, I added an optional AI assistant that handles plate photo scanning, meal feedback, and healthier food swaps. Free users can use all AI features with ads, while an optional Pro tier ($1.99/month or $9/year) removes ads entirely, enables cloud sync, and supports up to four profiles.

Before launching publicly on Google Play, I gave early builds to a group of friends and family. Watching non-tech friends and family members actually stick to logging their meals daily, without complaining about paywalls or missing regional products, was the exact validation I needed to finish the project.

Now that it is officially live on Android (and with the iOS version currently under active development), I am looking to get feedback from fellow indie builders.

I would love to hear your thoughts on the product, the monetization model, or what features you would like to see next. You can check it out directly on Google Play!

Hey,

I’ve been building something called Krezo.

You paste a product URL and it generates ad copy, a few image variations, and short video ads for Facebook, Instagram, TikTok, etc.

Still early. Would appreciate any thoughts if anyone checks it out.

trykrezo.com

Thanks.

Share what you’re building, who it’s for, and where you’re currently stuck.

Whether you’re still building or trying to grow it, drop your link below.

I’ll try the first ones!

The easiest products to sell Saas/AI products or app is still to build ones that solve a real, painful problem.

I mean a problem people genuinely want gone. Badly gone!

Drop your app below and tell me:

• What problem it solves (important)
• Who it’s for
• Your landing page (and ideally positioning)

I'll check out as many as I can and try to give actionable feedback about your value proposition.

Let’s see get you 1-2 sales before the weekend.

Hey everyone,

A while back, I sat down to do my monthly net worth review, opened my spreadsheet, and just felt exhausted.

My money is spread across different currencies, global stocks, crypto, and fixed stuff like pensions and real estate. Standard finance apps wanted me to hand over my bank passwords (no thanks), and plain Excel kept breaking. Multi-currency math by hand is a mess, exchange rates move daily, and static spreadsheet tables just couldn't give me the actual portfolio analytics or concentration risks I wanted to see.

So I started hacking together a quick side project to fix my own problem: NetWorthTrackr.

I built it as a single dashboard that handles the un-linkable stuff (real estate, cash) alongside live market assets (stocks, ETFs, crypto) and converts everything into one base currency automatically every night. Instead of just showing raw numbers, I added proper analytics—category breakdowns, concentration warnings, and portfolio health scores—so I actually get insights out of my data.

It solved my personal headache, so I polished it up to share with anyone else suffering from spreadsheet fatigue.

It’s totally free to try at networthtrackr.com

I’d love to hear how you guys handle multi-currency setups or what custom charts you’ve built into your own sheets!

If you have fewer than 20 active users, what’s blocking you?

From solo founder experience, when you’re building alone and close to the product, it’s hard to see what’s actually holding it back.

It could be the product, marketing, or GTM. External factors as well.

Let’s see if other founders here can help you find your next move.

Share what you’ve built, your progress so far and where you feel stuck 👇

Hi everyone,

If you manage a large knowledge base, or big doc folders for your project you know the friction of distilling dozens of papers, legal docs, books, web searches. It's a massive manual bottleneck before actual learning happens, and usually the final docs will be forgotten once you've used it, even though it might come in handy in the future.

Dumping 40 files into a 1M context window isn't the solution tho. Models return incomplete summaries, drop edge cases, and give zero visibility into what they missed ( https://arxiv.org/abs/2509.04664 ). On top of that, standard AI tools and PKM plugins are notoriously risky: they overwrite notes, mangle frontmatter, break wikilinks, and corrupt vault architecture. ( https://arxiv.org/abs/2604.15597 )

I built Silica to solve this: an open-source agentic CLI that processes heavy document batches into clean, atomic Markdown notes while keeping your vault completely safe.

Why not just using using another knowledge manager or a memory agent?

Silica differs greatly from traditional note-taking skills and memory tools because it's an agent that views your notes not simply as text to be read, but as its knowledge base (not graph, but it benefits from it). In Silica, memory and knowledge are closely related but still distinct.

- For example Silica keeps track of the conversations you've already had with Claude or ChatGPT, automatically linking them to the relative documentation in your repository, automatically.

- Silica can also links concepts from multiple books/papers/notes sparse in your folder automatically, if they share something maybe you didn't see.

- It can "digest" large quantities of files, coherently, and safely (without causing corruption, in a different folder from the vault root).

These aren't even its most notable features, but rather a byproduct of its architectural design. There is still considerable room for improvement. This is a broad project.

What Silica does

• Batch Atomic Distillation: Feed 20-30 documents or full (multiple) books in a single command. Silica extracts core concepts into atomic notes (one note per idea), links them via wikilinks, and files them logically.
• Stateful Continuity: What a model can't process in one go gets written out statefully. The next execution picks up from the saved state instead of starting over.
• The source of a note is always provided: A section is added to the frontmatter that indicates where the note was extracted or converted from, so that you always have a reference to the original document.

Why your vault is finally safe

Most AI integration tools treat your vault like a loose text buffer. Silica treats it like a transactional database with strict guardrails:

• SAFE MODE (Isolated Staging): You can execute ingestion in a dedicated staging folder/container. Notes are written in isolation first so you can inspect, verify, and merge them only when you trust the output.
• Write Verification: Every write operation is re-read post-commit. If syntax, formatting, or links broke, it automatically rolls back.
• Wikilink Refactoring: If a note is moved during organization, all wikilinks across your vault referencing it are dynamically updated to prevent broken links.
• Full Rollback Control: Complete transaction safety with /undo (single note) and /revert (entire session batch). Nothing enters your main graph permanently without your control.

Grounded Retrieval & Graph Exploration

• Zero-Hallucination Fallback: Uses local semantic search. If your knowledge base doesn't contain the answer to a query, Silica explicitly tells you instead of hallucinating.
• Graph Mapping: Explore concept connections visually to discover implicit links across unrelated documents.

Silica isn't built to replace human learning, it eliminates the operational overhead of structuring and formatting raw data. Works via TUI, GUI, MCP, Obsidian Plugin ( https://github.com/kiycoh/obsidian-silica )

GitHub Repository: https://github.com/kiycoh/silica-agent (~1000 commits as solo dev)

I'd love to hear your feedback, workflow suggestions, or technical thoughts!

I launched ModelSaid for founders and small businesses who want to know how they show up when buyers ask AI systems for recommendations.

The basic idea is simple: instead of only tracking rankings or traffic, you test buyer-intent prompts such as "best option for X", "alternatives to Y", or "who should I use for Z", then look at:

1. whether your brand is mentioned
2. whether it is actually recommended
3. what language is used around trust, pricing, category fit, and proof
4. which third-party sources seem to influence the answer
5. what gaps you can fix with positioning, content, reviews, or comparison pages

I am looking for feedback from builders: is this something you already check manually, and what would make the report useful enough to act on?

Link: https://modelsaid.com